WO2024020343A1 - Procédés et systèmes pour déterminer l'état d'un gène diagnostique - Google Patents

Procédés et systèmes pour déterminer l'état d'un gène diagnostique Download PDF

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WO2024020343A1
WO2024020343A1 PCT/US2023/070329 US2023070329W WO2024020343A1 WO 2024020343 A1 WO2024020343 A1 WO 2024020343A1 US 2023070329 W US2023070329 W US 2023070329W WO 2024020343 A1 WO2024020343 A1 WO 2024020343A1
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cancer
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chromosome
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Karthikeyan Murugesan
Ethan S. SOKOL
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Foundation Medicine, Inc.
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present disclosure relates generally to methods and systems for analyzing genomic profiling data, and more specifically to methods and systems for determining a diagnostic gene status in a sample.
  • the diagnostic gene status (e.g., a hormone receptor status or receptor gene status) of a patient can be useful for informing the prognosis and treatment options for patients.
  • breast cancer cells that express estrogen or progesterone female hormone receptors e.g., on the cell surface or inside the cell
  • estrogen or progesterone female hormone receptors e.g., on the cell surface or inside the cell
  • hormone receptor positivity estrogen receptor (ER) positive status
  • PR progesterone receptor
  • Hormone receptor positive patients may respond well to treatment with hormone therapy in both the adjuvant and the neo-adjuvant setting.
  • hormone therapies may be used to block the interaction between the hormone and a respective receptor (e.g., to block the interaction between estrogen and estrogen receptors) and/or to reduce hormone levels (e.g., to reduce the estrogen levels).
  • diagnostic gene status may refer to a receptor status or hormone status corresponding to a gene that has a known pathogenic or likely pathogenic effect.
  • the receptor status of breast cancer patients may be determined using an immunohistochemistry (IHC) test based on a tissue biopsy.
  • IHC immunohistochemistry
  • the IHC test can provide a semi-quantitative measurement of ER/PR positive tumor nuclei in stained histologic tissue sections.
  • obtaining tissue samples is an invasive and painful process, and in some situations, it may not be possible to obtain a tissue sample. Accordingly, there is a need to provide an accurate system to determine a receptor status that can be run on both solid and liquid biopsy samples.
  • a diagnostic gene status e.g., a receptor status
  • the diagnostic gene status can be useful for informing the treatment options for an individual.
  • embodiments of the methods disclosed herein may be used to predict a hormone receptor status (e.g., estrogen receptor (ER) status, progesterone receptor (PR) status, androgen receptor status) or a status of a gene (e.g., human epidermal growth factor receptor 2 (HER2) status).
  • the prediction method may rely on evaluating a predetermined set of features (e.g., genomic features) to predict the receptor status.
  • the methods of the present disclosure may be used to assess the presence or absence other genomic alterations and complex biomarker signatures in addition to predicting receptor status to get a holistic view of the genomic landscape driving the growth tumor, as opposed to tests that may provide information only for a single receptor biomarker.
  • the genomic features to be evaluated may provide granularity with respect to the presence of specific genomic alterations that allow specific and meaningful insights from such data to be applied to the determination of receptor status.
  • the breadth of features included in the statistical model may enhance the accuracy of the predictions.
  • embodiments of the present disclosure may be used to predict the receptor status through genomic sequencing data obtained from either a solid biopsy specimen or a liquid biopsy specimen.
  • IHC immunohistochemistry
  • embodiments of the present disclosure may rely on genomic profile testing, and in this manner are able to detect receptor status in both solid and liquid tumor (e.g., blood-based) specimens.
  • the ability to predict a receptor status via either solid or liquid samples permits a prediction of a receptor status when solid samples are inaccessible.
  • traditional solid biopsies may suffer from tissue inaccessibility due to the anatomical site of the tumor, poor quality of the tissue sample, and/or an insufficient amount of the tissue sample.
  • liquid biopsies are quick, less invasive, have a high throughput, are convenient to the patient, potentially less expensive, can be run multiple times for disease and/or treatment monitoring, and can provide real-time, tissue-site agnostic holistic information about the tumor.
  • methods comprise providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of reads; receiving, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data; obtaining, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining
  • the receptor gene status corresponds to an estrogen receptor status, a progesterone receptor status, an androgen receptor status, or a human epidermal growth factor receptor 2 status.
  • the receptor gene status comprises a hormone receptor status.
  • the method further comprises applying the trained statistical model to the values for the one or more input features to obtain an output indicative of the receptor gene status.
  • the sample type is indicative of a solid sample and the set of input features comprises genomic alteration features, complex mutational signatures, chromosomal instability features, clinicopathological features, clinical features, additional features, or a combination thereof.
  • the genomic alteration features comprise one or more genomic alterations and a type of alteration associated with the one or more genomic alterations.
  • the sample type is indicative of a liquid sample and the set of input features comprises genomic alteration features, clinicopathological features, clinical features, tumor fraction features, fragmentomic features, additional features, or a combination thereof.
  • the genomic alteration features comprise one or more genomic alterations and a type of alteration associated with the one or more genomic alterations.
  • sequence read data derived from a liquid sample is associated with a first set of one or more input features and sequence read data derived from a solid sample is associated with a second set of one or more input features.
  • the subject is suspected of having or is determined to have cancer.
  • the cancer comprises breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), and prostate cancer.
  • the method further comprises treating the subject with an anti-cancer therapy.
  • the anti-cancer therapy comprises a targeted anti-cancer therapy.
  • the targeted anti-cancer therapy comprises alpelisib (Piqray), CDK4/6 inhibitors, or any combination thereof.
  • the method further comprises obtaining the sample from the subject.
  • the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the set of features differs between a tissue biopsy sample and a liquid biopsy.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs).
  • the sample is a liquid biopsy sample and comprises cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample
  • the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non- tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • cfDNA non- tumor, cell-free DNA
  • the one or more adapters comprise amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences.
  • the captured nucleic acid molecules are captured from the amplified nucleic acid molecules by hybridization to one or more bait molecules.
  • the one or more bait molecules comprise one or more nucleic acid molecules, each comprising a region that is complementary to a region of a captured nucleic acid molecule.
  • amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique.
  • the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.
  • the sequencing comprises massively parallel sequencing
  • the massively parallel sequencing technique comprises next generation sequencing (NGS).
  • the sequencer comprises a next generation sequencer.
  • one or more of the plurality of sequencing reads overlap one or more gene loci within one or more subgenomic intervals in the sample.
  • the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and
  • the one or more gene loci comprise ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C,
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB 1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
  • the method further comprises generating, by the one or more processors, a report indicating a receptor gene status of the sample. In one or more examples of this disclosure, the method further comprises transmitting the report to a healthcare provider. In one or more examples of this disclosure, the report is transmitted via a computer network or a peer-to-peer connection.
  • methods comprise receiving, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data; obtaining, using the one or more processors, a set of input features associated with the receptor gene status and a sample type, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receiving, using one or more processors, sequence read data associated with a sample from an individual; determining, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more input features into the trained statistical model; and predicting, using the one or more processors, the receptor gene status of
  • the receptor gene status corresponds to an estrogen receptor status, a progesterone receptor status, an androgen receptor status, or a human epidermal growth factor receptor 2 status.
  • the method further comprises applying the trained machine learning model to the values for the one or more input features to obtain an output indicative of the receptor gene status.
  • sequence read data derived from a liquid sample is associated with a first set of one or more input features and sequence read data derived from a solid sample is associated with a second set of one or more input features.
  • the sequence read data for the individual is derived from a solid sample. In one or more examples of this disclosure, the sequence read data for the individual is derived from a biopsy sample. In one or more examples of this disclosure, the set of input features comprises genomic alteration features, complex mutational signatures, chromosomal instability features, clinicopathological features, clinical features, additional features, or a combination thereof.
  • the one or more input features are associated with one or more genomic alteration features.
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a copy number alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the predetermined short variant comprises a point mutation, an insertion, or a deletion.
  • the one or more genomic alteration features comprise CCNE1, EGFR, ERBB2, ERBB2 Amplification, ESRI, FGF3, FGFR1, GATA3, MAP2K4, MYC, PIK3CA, PTEN, RBI, TP53, ZNF703, or a combination thereof.
  • the one or more genomic alteration features comprise BRCA1, TP53, MYC, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, PIK3CA, ESRI, TBX3, CDH1, ZNF703, GATA3, FGF3, CCNE1, RBI, FGFR1, PTEN, EGFR, MAP2K4, or a combination thereof.
  • the one or more genomic alteration features comprise AKT1, AKT2, AKT3, APC, AR, ARFRP1, ARID1A, ATM, AURKA, BAP1, BCL2L1, BCL2L2, BRAF, BRCA1, BRCA2, BRD4, CBFB, CCND3, CCNE1, CDH1, CDK12, CDK4, CDK6, CDKN2A, CDKN2B, CHEK2, CREBBP, CTNNA1, DNMT3A, EGFR, EMSY, EP300, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, ERBB3, ESRI, FANCA, FAS, FBXW7, FGF10, FGF12, FGF3, FGF6, FGFR1, FGFR2, FGFR4, GATA3, GNAS, HGF, IGF1R, IKBKE, IRS2, JAK2, KDM5A, KDM6A, KDR, KEL, KIT, KMT2D
  • the one or more input features are associated with one or more complex mutational signatures.
  • the one or more complex mutational signatures comprise a genome- wide loss of heterozygosity (gLOH) quantification, trinucleotide signatures, insertion signatures, deletion signatures, copy number signatures, or a combination thereof.
  • gLOH genome- wide loss of heterozygosity
  • the one or more input features are associated with one or more chromosomal instability features.
  • the one or more chromosomal instability features is indicative of aneuploidy.
  • the one or more chromosomal instability features is indicative of chromosome gain, chromosome not gain, chromosome loss, chromosome not loss, or a combination thereof.
  • the one or more chromosomal instability features comprises a total aneuploidy count.
  • the one or more chromosomal instability features comprise chromosome Iq loss status, chromosome Ip loss status, chromosome 3p loss status, chromosome 4p loss status, chromosome 5q loss status, chromosome 9p loss status, chromosome lOq loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome 14q loss status, chromosome 15q loss status, chromosome 16p loss status, chromosome 16q loss status, chromosome 17q loss status, chromosome 19p loss status, chromosome 21q loss status, chromosome 22q loss status, chromosome Iq gain status, chromosome 2p gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6q gain status, chromosome 7q gain status, chromosome 9p gain status, chromosome lOp gain
  • the one or more chromosomal instability features comprise chromosome Ip gain status, chromosome Iq gain status, chromosome2p gain status, chromosome 2q gain status, chromosome 3p gain status, chromosome 3q gain status, chromosome 4p gain status, chromosome 4q gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6p gain status, chromosome 6q gain status, chromosome 7p gain status, chromosome 7q gain status, chromosome 8p gain status, chromosome 8q gain status, chromosome 9p gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome lOp gain status, chromosome lOq gain status, chromosome l ip gain status,
  • the one or more input features are associated with one or more clinicopathological features.
  • the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor status of the individual, a tumor type, an anatomical sub-classification of a tumor, or a combination thereof.
  • the one or more input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more input features are associated with a tumor mutational burden.
  • the one or more input features are associated with a germline status.
  • the one or more input features are associated with homologous repair deficiency (HRD) signature.
  • HRD homologous repair deficiency
  • the one or more input features are associated with a methylation signature, a mRNA expression level, a miRNA expression level, proteomics, cosmic mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, or a combination thereof.
  • the sequence read data for the individual is derived from a liquid sample. In one or more of the examples of this disclosure, the sequence read data for the individual is derived from a liquid biopsy sample. In one or more of the examples of this disclosure, the set of input features comprises genomic alteration features, clinicopathological features, clinical features, tumor fraction features, fragmentomic features, additional features, or a combination thereof. [0035] In one or more of the examples of this disclosure, the one or more input features are associated with one or more genomic alteration features. In one or more of the examples of this disclosure, the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a rearrangement alteration, an insertion, a deletion, or a combination thereof.
  • the one or more genomic alteration features comprise TP53, ESRI, PIK3CA, or a combination thereof. In one or more of the examples of this disclosure, the one or more genomic alteration features comprise CDH1, BRCA1, AKT1, BRCA2, CDKN2A, ERBB2, KRAS, NF1, PTEN, or a combination thereof.
  • the one or more input features are associated with one or more clinicopathological features.
  • the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor type, or a combination thereof.
  • the one or more input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more input features are associated with a tumor mutational burden. In one or more of the examples of this disclosure, the one or more input features are associated with trinucleotide signatures, insertion signatures, deletion signatures, copy number signatures, or a combination thereof. In one or more of the examples of this disclosure, the one or more input features are associated with the HRD signature.
  • the one or more input features are associated with one or more fragmentomic features, the one or more fragmentomic features associated with a plurality of reads obtained from the sequence read data.
  • the one or more fragmentomic features comprise lengths of the plurality reads, regions associated with the plurality of reads, or a combination thereof.
  • the one or more fragmentomic features comprise an amount of a fragment having a specified length, a mean fragment length of the plurality of reads, a median fragment length of the plurality of reads, an interquartile range of fragment lengths of the plurality of reads, a distribution of fragment lengths of the plurality of reads, one or more peaks of fragment lengths for the plurality of reads, a fragment length for the plurality of reads, a start position of a fragment for the plurality of reads, and an end position of a fragment for the plurality of reads.
  • the one or more input features are associated with an estimated tumor fraction.
  • the one or more input features are associated with a methylation signature, a mRNA expression level, a miRNA expression level, proteomics, cosmic mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, or a combination thereof.
  • the output of the trained statistical model is indicative of a receptor status. In one or more of the examples of this disclosure, the output of the trained statistical model comprises a first score indicative of a probability of a positive receptor gene status. In one or more of the examples of this disclosure, the output of the trained statistical model comprises a second score indicative of a probability of a negative receptor gene status.
  • the training set of input features associated with the training values for the input features is different from the values for one or more input features input into the trained statistical model.
  • the training data further comprises receptor gene statuses corresponding to each of the plurality of training samples.
  • the method further comprises determining, using the one or more processors, weights associated with the training values for the plurality of training input features based on the training; and filtering, using the one or more processors, the plurality of training input features based on the weights; wherein filtering the plurality of training input features comprises removing low prevalence training values, highly correlated training values, or a combination thereof.
  • the method further comprises weighting, using the one or more processors, the training values for the one or more input features based on the weights, wherein predicting the receptor gene status is based on the weighted values for the one or more input features.
  • the receptor gene status comprises a hormone receptor status.
  • the trained statistical model is a trained machine learning model, a part of a machine learning process, or a combination thereof.
  • the trained statistical model includes an artificial intelligence learning model.
  • the trained statistical model comprises a random forest model.
  • the trained statistical model comprises at least one of an extreme gradient boosting model, a logistic regression model, an elastic net model, a ridge regression model, a support vector machine model, a k- nearest neighbor model, a Bayesian model, a naive-based model, a Gaussian naive-based model, a clustering model, a limited expectation maximization model, a gradient boosting ensemble model, an adaboost model, a bagging model, a neural network model, a backpropagation model, a stochastic gradient descent model, a linear regression model, a non-linear regression model, a multivariate regression model, a robust machine learning model, and a proportional hazards model.
  • the method further comprises: inputting, using the one or more processors, a diagnostic image taken of the individual into a second statistical model; and determining, using the one or more processors, a third score indicative of a tumor classification based on the diagnostic image, wherein predicting the receptor gene status is further based on the third score.
  • the second statistical model is at least one of a deep learning model, a convolutional neural network model, a recurrent neural network model, and an auto-encoder model.
  • the method further comprises training the second statistical model, wherein the second statistical model is trained by: receiving, using the one or more processors, training data including a plurality of training diagnostic images; and training, using the one or more processors, the second statistical model based on the training data.
  • the diagnostic image comprises at least one of a histopathological image, a radiological image, a magnetic resonance image, an ultrasound image, an X-ray image, a bone scan, a CT scan, a PET scan, or a combination thereof.
  • the method further comprises assigning, using the one or more processors, a therapy for the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises determining, using the one or more processors, a treatment decision for the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises administering, using the one or more processors, a treatment to the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises monitoring, using the one or more processors, a prognosis of the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises predicting, using the one or more processors, one or more clinical outcomes based on the predicted receptor gene status.
  • methods of the present disclosure are directed to methods for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a receptor gene status for a sample from the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods for selecting an anti-cancer therapy, the method comprising: responsive to determining a receptor gene status for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods of treating a cancer in a subject, comprising: responsive to determining a receptor gene status for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first receptor gene status in a first sample obtained from the subject at a first time point according to the method of any one of the methods described above; determining a second receptor gene status in a second sample obtained from the subject at a second time point; and comparing the first receptor gene status to the second receptor gene status, thereby monitoring the cancer progression or recurrence.
  • the second receptor gene status for the second sample is determined according to the method of any one of the methods described above.
  • the method further comprises selecting an anti-cancer therapy for the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises administering an anti-cancer therapy to the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises adjusting an anti-cancer therapy for the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises administering the adjusted anti-cancer therapy to the subject.
  • the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.
  • the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
  • the cancer is a solid tumor. In one or more of the examples of this disclosure, the cancer is a breast cancer. In one or more of the examples of this disclosure, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery. [0056] In any of the examples of this disclosure, the method further comprises determining, identifying, or applying the receptor gene status for the sample as a diagnostic value associated with the sample. In any of the examples of this disclosure, the method further comprises generating a genomic profile for the subject based on the determination of the receptor gene status.
  • the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • the method further comprises selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
  • the determination of receptor gene status for the sample is used in making suggested treatment decisions for the subject. In any of the examples of this disclosure, the determination of the receptor gene status for the sample is used in applying or administering a treatment to the subject.
  • systems comprise one or more processors and a memory communicatively coupled to the one or more processors and configured to store instructions.
  • the instructions when executed by the one or more processors, cause the system to: receive, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; train, using the one or more processors, a statistical model based on the training data; obtain, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receive, using one or more processors, sequence read data associated with a sample from an individual; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one
  • non-transitory computer-readable storage mediums can store one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; train, using the one or more processors, a statistical model based on the training data; obtain, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receive, using one or more processors, sequence read data associated with a sample from an individual; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a
  • methods for predicting a receptor gene status of a sample from an individual.
  • the method can comprise receiving, using one or more processors, sequence read data associated with a sample from an individual; obtaining a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determining, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more input features into a statistical model; and predicting, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • the receptor gene status corresponds to an estrogen receptor status, a progesterone receptor status, an androgen receptor status, or a human epidermal growth factor receptor 2 status.
  • the method further comprises applying the trained machine learning model to the values for the one or more input features to obtain an output indicative of the receptor gene status.
  • sequence read data derived from a liquid sample is associated with a first set of one or more input features and sequence read data derived from a solid sample is associated with a second set of one or more input features.
  • the sequence read data for the individual is derived from a solid sample. In one or more examples of this disclosure, the sequence read data for the individual is derived from a biopsy sample. In one or more examples of this disclosure, the set of input features comprises genomic alteration features, complex mutational signatures, chromosomal instability features, clinicopathological features, clinical features, additional features, or a combination thereof.
  • the one or more input features are associated with one or more genomic alteration features.
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a copy number alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the predetermined short variant comprises a point mutation, an insertion, a deletion.
  • the one or more genomic alteration features comprise CCNE1, EGFR, ERBB2, ERBB2 Amplification, ESRI, FGF3, FGFR1, GATA3, MAP2K4, MYC, PIK3CA, PTEN, RBI, TP53, ZNF703, or a combination thereof.
  • the one or more genomic alteration features comprise BRCA1, TP53, MYC, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, PIK3CA, ESRI, TBX3, CDH1, ZNF703, GATA3, FGF3, CCNE1, RBI, FGFR1, PTEN, EGFR, MAP2K4, or a combination thereof.
  • the one or more genomic alteration features comprise AKT1, AKT2, AKT3, APC, AR, ARFRP1, ARID1A, ATM, AURKA, BAP1, BCL2L1, BCL2L2, BRAF, BRCA1, BRCA2, BRD4, CBFB, CCND3, CCNE1, CDH1, CDK12, CDK4, CDK6, CDKN2A, CDKN2B, CHEK2, CREBBP, CTNNA1, DNMT3A, EGFR, EMSY, EP300, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, ERBB3, ESRI, FANCA, FAS, FBXW7, FGF10, FGF12, FGF3, FGF6, FGFR1, FGFR2, FGFR4, GATA3, GNAS, HGF, IGF1R, IKBKE, IRS2, JAK2, KDM5A, KDM6A, KDR, KEL, KIT, KMT2D
  • the one or more input features are associated with one or more complex mutational signatures.
  • the one or more complex mutational signatures comprise a genome- wide loss of heterozygosity (gLOH) quantification, trinucleotide signatures, insertion signatures, deletion signatures, copy number signatures, or a combination thereof.
  • gLOH genome- wide loss of heterozygosity
  • the one or more input features are associated with one or more chromosomal instability features.
  • the one or more chromosomal instability features is indicative of aneuploidy.
  • the one or more chromosomal instability features is indicative of chromosome gain, chromosome not gain, chromosome loss, chromosome not loss, or a combination thereof.
  • the one or more chromosomal instability features comprises a total aneuploidy count.
  • the one or more chromosomal instability features comprise chromosome Iq loss status, chromosome Ip loss status, chromosome 3p loss status, chromosome 4p loss status, chromosome 5q loss status, chromosome 9p loss status, chromosome lOq loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome 14q loss status, chromosome 15q loss status, chromosome 16p loss status, chromosome 16q loss status, chromosome 17q loss status, chromosome 19p loss status, chromosome 21q loss status, chromosome 22q loss status, chromosome Iq gain status, chromosome 2p gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6q gain status, chromosome 7q gain status, chromosome 9p gain status, chromosome lOp gain
  • the one or more chromosomal instability features comprise chromosome Ip gain status, chromosome Iq gain status, chromosome2p gain status, chromosome 2q gain status, chromosome 3p gain status, chromosome 3q gain status, chromosome 4p gain status, chromosome 4q gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6p gain status, chromosome 6q gain status, chromosome 7p gain status, chromosome 7q gain status, chromosome 8p gain status, chromosome 8q gain status, chromosome 9p gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome lOp gain status, chromosome lOq gain status, chromosome l ip gain status, chromosome l lq gain status, chromosome 12p gain status, chromosome 12q gain
  • the one or more input features are associated with one or more clinicopathological features.
  • the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor status of the individual, a tumor type, an anatomical sub-classification of a tumor, or a combination thereof.
  • the one or more input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more input features are associated with a tumor mutational burden.
  • the one or more input features are associated with a germline status.
  • the one or more input features are associated with homologous repair deficiency (HRD) signature.
  • HRD homologous repair deficiency
  • the one or more input features are associated with a methylation signature, a mRNA expression level, a miRNA expression level, proteomics, cosmic mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, or a combination thereof.
  • the sequence read data for the individual is derived from a liquid sample. In one or more of the examples of this disclosure, the sequence read data for the individual is derived from a liquid biopsy sample. In one or more of the examples of this disclosure, the set of input features comprises genomic alteration features, clinicopathological features, clinical features, tumor fraction features, fragmentomic features, additional features, or a combination thereof. [0074] In one or more of the examples of this disclosure, the one or more input features are associated with one or more genomic alteration features. In one or more of the examples of this disclosure, the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a rearrangement alteration, an insertion, a deletion, or a combination thereof.
  • the one or more genomic alteration features comprise TP53, ESRI, PIK3CA, or a combination thereof. In one or more of the examples of this disclosure, the one or more genomic alteration features comprise CDH1, BRCA1, AKT1, BRCA2, CDKN2A, ERBB2, KRAS, NF1, PTEN, or a combination thereof.
  • the one or more input features are associated with one or more clinicopathological features.
  • the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor type, or a combination thereof.
  • the one or more input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more input features are associated with a tumor mutational burden. In one or more of the examples of this disclosure, the one or more input features are associated with trinucleotide signatures, insertion signatures, deletion signatures, copy number signatures, or a combination thereof. In one or more of the examples of this disclosure, the one or more input features are associated with the HRD signature.
  • the one or more input features are associated with one or more fragmentomic features, the one or more fragmentomic features associated with a plurality of reads obtained from the sequence read data.
  • the one or more fragmentomic features comprise lengths of the plurality reads, regions associated with the plurality of reads, or a combination thereof.
  • the one or more fragmentomic features comprise an amount of a fragment having a specified length, a mean fragment length of the plurality of reads, a median fragment length of the plurality of reads, an interquartile range of fragment lengths of the plurality of reads, a distribution of fragment lengths of the plurality of reads, one or more peaks of fragment lengths for the plurality of reads, a fragment length for the plurality of reads, a start position of a fragment for the plurality of reads, and an end position of a fragment for the plurality of reads.
  • the one or more input features are associated with an estimated tumor fraction.
  • the one or more input features are associated with a methylation signature, a mRNA expression level, a miRNA expression level, proteomics, cosmic mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, or a combination thereof.
  • the output of the trained statistical model is indicative of a receptor status. In one or more of the examples of this disclosure, the output of the trained statistical model comprises a first score indicative of a probability of a positive receptor gene status. In one or more of the examples of this disclosure, the output of the trained statistical model comprises a second score indicative of a probability of a negative receptor gene status.
  • the training set of input features associated with the training values for the input features is different from the values for one or more input features input into the trained statistical model.
  • the training data further comprises receptor gene statuses corresponding to each of the plurality of training samples.
  • the method further comprises determining, using the one or more processors, weights associated with the training values for the plurality of training input features based on the training; and filtering, using the one or more processors, the plurality of training input features based on the weights; wherein filtering the plurality of training input features comprises removing low prevalence training values, highly correlated training values, or a combination thereof.
  • the method further comprises weighting, using the one or more processors, the training values for the one or more input features based on the weights, wherein predicting the receptor gene status is based on the weighted values for the one or more input features.
  • the receptor gene status comprises a hormone receptor status.
  • the trained statistical model is a trained machine learning model, a part of a machine learning process, or a combination thereof.
  • the trained statistical model includes an artificial intelligence learning model.
  • the trained statistical model comprises a random forest model.
  • the trained statistical model comprises at least one of an extreme gradient boosting model, a logistic regression model, an elastic net model, a ridge regression model, a support vector machine model, a k- nearest neighbor model, a Bayesian model, a naive-based model, a Gaussian naive-based model, a clustering model, a limited expectation maximization model, a gradient boosting ensemble model, an adaboost model, a bagging model, a neural network model, a backpropagation model, a stochastic gradient descent model, a linear regression model, a non-linear regression model, a multivariate regression model, a robust machine learning model, and a proportional hazards model.
  • the method further comprises: inputting, using the one or more processors, a diagnostic image taken of the individual into a second statistical model; and determining, using the one or more processors, a third score indicative of a tumor classification based on the diagnostic image, wherein predicting the receptor gene status is further based on the third score.
  • the second statistical model is at least one of a deep learning model, a convolutional neural network model, a recurrent neural network model, and an auto-encoder model.
  • the method further comprises training the second statistical model, wherein the second statistical model is trained by: receiving, using the one or more processors, training data including a plurality of training diagnostic images; and training, using the one or more processors, the second statistical model based on the training data.
  • the diagnostic image comprises at least one of a histopathological image, a radiological image, a magnetic resonance image, an ultrasound image, an X-ray image, a bone scan, a CT scan, a PET scan, or a combination thereof.
  • the method further comprises assigning, using the one or more processors, a therapy for the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises determining, using the one or more processors, a treatment decision for the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises administering, using the one or more processors, a treatment to the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises monitoring, using the one or more processors, a prognosis of the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises predicting, using the one or more processors, one or more clinical outcomes based on the predicted receptor gene status.
  • methods of the present disclosure are directed to methods for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a receptor gene status for a sample from the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods for selecting an anti-cancer therapy, the method comprising: responsive to determining a receptor gene status for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods of treating a cancer in a subject, comprising: responsive to determining a receptor gene status for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first receptor gene status in a first sample obtained from the subject at a first time point according to the method of any one of the methods described above; determining a second receptor gene status in a second sample obtained from the subject at a second time point; and comparing the first receptor gene status to the second receptor gene status, thereby monitoring the cancer progression or recurrence.
  • the second receptor gene status for the second sample is determined according to the method of any one of the methods described above.
  • the method further comprises selecting an anti-cancer therapy for the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises administering an anti-cancer therapy to the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises adjusting an anti-cancer therapy for the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises administering the adjusted anti-cancer therapy to the subject.
  • the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.
  • the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
  • the cancer is a solid tumor. In one or more of the examples of this disclosure, the cancer is a breast cancer. In one or more of the examples of this disclosure, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery. [0095] In any of the examples of this disclosure, the method further comprises determining, identifying, or applying the receptor gene status for the sample as a diagnostic value associated with the sample. In any of the examples of this disclosure, the method further comprises generating a genomic profile for the subject based on the determination of the receptor gene status.
  • the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • the method further comprises selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
  • the determination of receptor gene status for the sample is used in making suggested treatment decisions for the subject. In any of the examples of this disclosure, the determination of the receptor gene status for the sample is used in applying or administering a treatment to the subject.
  • systems comprise one or more processors and a memory communicatively coupled to the one or more processors and configured to store instructions.
  • the instructions when executed by the one or more processors, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more input features into a statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • non-transitory computer-readable storage mediums can store one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more input features into a statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • the methods described can comprise receiving, using one or more processors, sequence read data associated with a sample from an individual; obtaining a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determining, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more expression input features into the statistical model; and predicting, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • the receptor gene status corresponds to an estrogen receptor status, a progesterone receptor status, an androgen receptor status, or a human epidermal growth factor receptor 2 status.
  • the method further comprises applying the trained machine learning model to the values for the one or more expression input features to obtain an output indicative of the receptor gene status.
  • sequence read data derived from a liquid sample is associated with a first set of one or more input features and sequence read data derived from a solid sample is associated with a second set of one or more input features.
  • the sequence read data for the individual is derived from a solid sample. In one or more examples of this disclosure, the sequence read data for the individual is derived from a biopsy sample. In one or more examples of this disclosure, the set of expression input features comprises genomic alteration features, complex mutational signatures, chromosomal instability features, clinicopathological features, clinical features, additional features, or a combination thereof.
  • the one or more expression input features are associated with one or more genomic alteration features.
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a copy number alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the predetermined short variant comprises a point mutation, an insertion, a deletion.
  • the one or more genomic alteration features comprise CCNE1, EGFR, ERBB2, ERBB2 Amplification, ESRI, FGF3, FGFR1, GATA3, MAP2K4, MYC, PIK3CA, PTEN, RBI, TP53, ZNF703, or a combination thereof.
  • the one or more genomic alteration features comprise BRCA1, TP53, MYC, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, PIK3CA, ESRI, TBX3, CDH1, ZNF703, GATA3, FGF3, CCNE1, RBI, FGFR1, PTEN, EGFR, MAP2K4, or a combination thereof.
  • the one or more genomic alteration features comprise AKT1, AKT2, AKT3, APC, AR, ARFRP1, ARID1A, ATM, AURKA, BAP1, BCL2L1, BCL2L2, BRAF, BRCA1, BRCA2, BRD4, CBFB, CCND3, CCNE1, CDH1, CDK12, CDK4, CDK6, CDKN2A, CDKN2B, CHEK2, CREBBP, CTNNA1, DNMT3A, EGFR, EMSY, EP300, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, ERBB3, ESRI, FANCA, FAS, FBXW7, FGF10, FGF12, FGF3, FGF6, FGFR1, FGFR2, FGFR4, GATA3, GNAS, HGF, IGF1R, IKBKE, IRS2, JAK2, KDM5A, KDM6A, KDR, KEF, KIT, KMT2D
  • the one or more expression input features are associated with one or more complex mutational signatures.
  • the one or more complex mutational signatures comprise a genome-wide loss of heterozygosity (gLOH) quantification, trinucleotide signatures, insertion signatures, deletion signatures, copy number signatures, or a combination thereof.
  • gLOH genome-wide loss of heterozygosity
  • the one or more expression input features are associated with one or more chromosomal instability features.
  • the one or more chromosomal instability features is indicative of aneuploidy.
  • the one or more chromosomal instability features is indicative of chromosome gain, chromosome not gain, chromosome loss, chromosome not loss, or a combination thereof.
  • the one or more chromosomal instability features comprises a total aneuploidy count.
  • the one or more chromosomal instability features comprise chromosome Iq loss status, chromosome Ip loss status, chromosome 3p loss status, chromosome 4p loss status, chromosome 5q loss status, chromosome 9p loss status, chromosome lOq loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome 14q loss status, chromosome 15q loss status, chromosome 16p loss status, chromosome 16q loss status, chromosome 17q loss status, chromosome 19p loss status, chromosome 21q loss status, chromosome 22q loss status, chromosome Iq gain status, chromosome 2p gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6q gain status, chromosome 7q gain status, chromosome 9p gain status, chromosome lOp gain
  • the one or more chromosomal instability features comprise chromosome Ip gain status, chromosome Iq gain status, chromosome2p gain status, chromosome 2q gain status, chromosome 3p gain status, chromosome 3q gain status, chromosome 4p gain status, chromosome 4q gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6p gain status, chromosome 6q gain status, chromosome 7p gain status, chromosome 7q gain status, chromosome 8p gain status, chromosome 8q gain status, chromosome 9p gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome lOp gain status, chromosome lOq gain status, chromosome l ip gain status, chromosome l lq gain status, chromosome 12p gain status, chromosome 12q gain
  • the one or more expression input features are associated with one or more clinicopathological features.
  • the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor status of the individual, a tumor type, an anatomical sub-classification of a tumor, or a combination thereof.
  • the one or more expression input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more expression input features are associated with a tumor mutational burden. In one or more examples of this disclosure, the one or more expression input features are associated with a germline status. In one or more examples of this disclosure, the one or more expression input features are associated with homologous repair deficiency (HRD) signature. In one or more examples of this disclosure, the one or more expression input features are associated with a methylation signature, a mRNA expression level, a miRNA expression level, proteomics, cosmic mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, or a combination thereof.
  • HRD homologous repair deficiency
  • the sequence read data for the individual is derived from a liquid sample. In one or more of the examples of this disclosure, the sequence read data for the individual is derived from a liquid biopsy sample. In one or more of the examples of this disclosure, the set of expression input features comprises genomic alteration features, clinicopathological features, clinical features, tumor fraction features, fragmentomic features, additional features, or a combination thereof.
  • the one or more expression input features are associated with one or more genomic alteration features.
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a rearrangement alteration, an insertion, a deletion, or a combination thereof.
  • the one or more genomic alteration features comprise TP53, ESRI, PIK3CA, or a combination thereof.
  • the one or more genomic alteration features comprise CDH1, BRCA1, AKT1, BRCA2, CDKN2A, ERBB2, KRAS, NF1, PTEN, or a combination thereof.
  • the one or more expression input features are associated with one or more clinicopathological features.
  • the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor type, or a combination thereof.
  • the one or more expression input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more expression input features are associated with a tumor mutational burden. In one or more of the examples of this disclosure, the one or more expression input features are associated with trinucleotide signatures, insertion signatures, deletion signatures, copy number signatures, or a combination thereof. In one or more of the examples of this disclosure, the one or more expression input features are associated with the HRD signature.
  • the one or more expression input features are associated with one or more fragmentomic features, the one or more fragmentomic features associated with a plurality of reads obtained from the sequence read data.
  • the one or more fragmentomic features comprise lengths of the plurality reads, regions associated with the plurality of reads, or a combination thereof.
  • the one or more fragmentomic features comprise an amount of a fragment having a specified length, a mean fragment length of the plurality of reads, a median fragment length of the plurality of reads, an interquartile range of fragment lengths of the plurality of reads, a distribution of fragment lengths of the plurality of reads, one or more peaks of fragment lengths for the plurality of reads, a fragment length for the plurality of reads, a start position of a fragment for the plurality of reads, and an end position of a fragment for the plurality of reads.
  • the one or more expression input features are associated with an estimated tumor fraction. In one or more of the examples of this disclosure, the one or more expression input features are associated with a methylation signature, an mRNA expression level, a miRNA expression level, proteomics, cosmic mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, or a combination thereof.
  • the output of the trained statistical model is indicative of a receptor status. In one or more of the examples of this disclosure, the output of the trained statistical model comprises a first score indicative of a probability of a positive receptor gene status. In one or more of the examples of this disclosure, the output of the trained statistical model comprises a second score indicative of a probability of a negative receptor gene status.
  • the training set of expression input features associated with the training values for the expression input features is different from the values for one or more expression input features input into the trained statistical model.
  • the training data further comprises receptor gene statuses corresponding to each of the plurality of training samples.
  • the method further comprises determining, using the one or more processors, weights associated with the training values for the plurality of training expression input features based on the training; and filtering, using the one or more processors, the plurality of training expression input features based on the weights; wherein filtering the plurality of training expression input features comprises removing low prevalence training values, highly correlated training values, or a combination thereof.
  • the method further comprises weighting, using the one or more processors, the training values for the one or more expression input features based on the weights, wherein predicting the receptor gene status is based on the weighted values for the one or more expression input features.
  • the receptor gene status comprises a hormone receptor status.
  • the trained statistical model is a trained machine learning model, a part of a machine learning process, or a combination thereof.
  • the trained statistical model includes an artificial intelligence learning model.
  • the trained statistical model comprises a random forest model.
  • the trained statistical model comprises at least one of an extreme gradient boosting model, a logistic regression model, an elastic net model, a ridge regression model, a support vector machine model, a k- nearest neighbor model, a Bayesian model, a naive-based model, a Gaussian naive-based model, a clustering model, a limited expectation maximization model, a gradient boosting ensemble model, an adaboost model, a bagging model, a neural network model, a backpropagation model, a stochastic gradient descent model, a linear regression model, a non-linear regression model, a multivariate regression model, a robust machine learning model, and a proportional hazards model.
  • the method further comprises: inputting, using the one or more processors, a diagnostic image taken of the individual into a second statistical model; and determining, using the one or more processors, a third score indicative of a tumor classification based on the diagnostic image, wherein predicting the receptor gene status is further based on the third score.
  • the second statistical model is at least one of a deep learning model, a convolutional neural network model, a recurrent neural network model, and an auto-encoder model.
  • the method further comprises training the second statistical model, wherein the second statistical model is trained by: receiving, using the one or more processors, training data including a plurality of training diagnostic images; and training, using the one or more processors, the second statistical model based on the training data.
  • the diagnostic image comprises at least one of a histopathological image, a radiological image, a magnetic resonance image, an ultrasound image, an X-ray image, a bone scan, a CT scan, a PET scan, or a combination thereof.
  • the method further comprises assigning, using the one or more processors, a therapy for the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises determining, using the one or more processors, a treatment decision for the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises administering, using the one or more processors, a treatment to the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises monitoring, using the one or more processors, a prognosis of the individual based on the predicted receptor gene status. In any of the examples of this disclosure, the method further comprises predicting, using the one or more processors, one or more clinical outcomes based on the predicted receptor gene status.
  • methods of the present disclosure are directed to methods for diagnosing a disease, the method comprising: diagnosing that a subject has the disease based on a determination of a receptor gene status for a sample from the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods for selecting an anti-cancer therapy, the method comprising: responsive to determining a receptor gene status for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods of treating a cancer in a subject, comprising: responsive to determining a receptor gene status for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the receptor gene status is determined according to the method of any one of the methods described above.
  • methods of the present disclosure are directed to methods for monitoring cancer progression or recurrence in a subject, the method comprising: determining a first receptor gene status in a first sample obtained from the subject at a first time point according to the method of any one of the methods described above; determining a second receptor gene status in a second sample obtained from the subject at a second time point; and comparing the first receptor gene status to the second receptor gene status, thereby monitoring the cancer progression or recurrence.
  • the second receptor gene status for the second sample is determined according to the method of any one of the methods described above.
  • the method further comprises selecting an anti-cancer therapy for the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises administering an anti-cancer therapy to the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises adjusting an anti-cancer therapy for the subject in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression. In one or more of the examples of this disclosure, the method further comprises administering the adjusted anti-cancer therapy to the subject.
  • the first time point is before the subject has been administered an anti-cancer therapy, and wherein the second time point is after the subject has been administered the anti-cancer therapy.
  • the subject has a cancer, is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
  • the cancer is a solid tumor. In one or more of the examples of this disclosure, the cancer is a breast cancer. In one or more of the examples of this disclosure, the anti-cancer therapy comprises chemotherapy, radiation therapy, immunotherapy, a targeted therapy, or surgery.
  • the method further comprises determining, identifying, or applying the receptor gene status for the sample as a diagnostic value associated with the sample. In any of the examples of this disclosure, the method further comprises generating a genomic profile for the subject based on the determination of the receptor gene status. In one or more of the examples of this disclosure, the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof. In one or more of the examples of this disclosure, the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test. In one or more of the examples of this disclosure, the method further comprises selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
  • CGP genomic profiling
  • the method further comprises selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying
  • the determination of receptor gene status for the sample is used in making suggested treatment decisions for the subject. In any of the examples of this disclosure, the determination of the receptor gene status for the sample is used in applying or administering a treatment to the subject.
  • systems comprise one or more processors and a memory communicatively coupled to the one or more processors and configured to store instructions.
  • the instructions when executed by the one or more processors, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more expression input features into the statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • non-transitory computer-readable storage mediums can store one or more programs, comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more expression input features into the statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • the methods described can comprise receiving, using one or more processors, training data comprising values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data, wherein the trained statistical model is configured to predict a receptor gene status of an individual sample; determining, using the one or more processors, weights associated with the training values for the plurality of training input features based on the training; filtering, using the one or more processors, the one or more training input features based on the weights; determining a set of input features associated with the receptor gene status based on the filtered training input features and a sample type of a sample from an individual, wherein filtering the one or more training input features comprises removing training input features associated with low prevalence training values and highly correlated training values, or a combination thereof; and obtaining a trained statistical model configured to receive a set of input feature based on a sample from an individual to output a prediction of
  • FIG. 1 provides a non-limiting example of an exemplary process for predicting a receptor status of a sample from an individual, according to embodiments of the present disclosure.
  • FIG. 2A provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2B provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2C provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2D provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2E provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2F provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2G provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2H provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 21 provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2J provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2K provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 2L provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3A provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3B provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3C provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3D provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3E provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3F provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3G provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 3H provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 31 provides a non-limiting example of input features for a statistical model according to embodiments of the present disclosure.
  • FIG. 4 provides a non-limiting example of using a statistical model to predict receptor status according to embodiments of the present disclosure.
  • FIG. 5 provides a non-limiting example of an exemplary process for predicting a receptor status of a sample from an individual, according to embodiments of the present disclosure.
  • FIG. 6 provides a non-limiting example of a process for training a statistical model predicting a receptor status of a sample from an individual, according to embodiments of the present disclosure
  • FIG. 7 depicts an exemplary computing device or system, according to embodiments of the present disclosure.
  • FIG. 8 depicts an exemplary computer system or computer network, according to embodiments of the present disclosure.
  • FIG. 9 depicts an exemplary process for training a statistical model, according to embodiments of the present disclosure.
  • FIG. 10A provides a non-limiting example of cross validation metrics of an exemplary model using a training dataset, according to embodiments of the present disclosure.
  • FIG. 10B provides a non-limiting example of performance metrics of an exemplary model using a test dataset, according to embodiments of the present disclosure.
  • FIG. 10C provides a non-limiting example of performance metrics of an exemplary model using a validation dataset, according to embodiments of the present disclosure.
  • FIG. 10D provides a non-limiting example of a plot illustrating relative feature importance of input features for an exemplary model, according to embodiments of the present disclosure.
  • FIG. 11A provides a non-limiting example of cross validation metrics of an exemplary model using a training dataset, according to embodiments of the present disclosure.
  • FIG. 11B provides a non-limiting example of performance metrics of an exemplary model using a test dataset, according to embodiments of the present disclosure.
  • FIG. 11C provides a non-limiting example of performance metrics of an exemplary model using a validation dataset, according to embodiments of the present disclosure.
  • FIG. HD provides a non-limiting example of performance metrics of an exemplary model using a validation dataset, according to embodiments of the present disclosure.
  • FIG. HE provides a non-limiting example of a plot illustrating relative feature importance of input features for an exemplary model, according to embodiments of the present disclosure.
  • ER estrogen receptor
  • PR progesterone receptor
  • HER2 human epidermal growth factor receptor 2
  • ER estrogen receptor
  • PR progesterone receptor
  • HER2 human epidermal growth factor receptor 2
  • Determining the receptor status of a patient can be useful for informing the prognosis and treatment options for the patient.
  • breast cancer cells that express estrogen or progesterone hormone receptors e.g., on the cell surface or inside the cell
  • hormone receptor positivity ER positive status and/or PR positive status
  • Hormone receptor positive individuals may respond well to treatment with hormone therapy in both the adjuvant and the neoadjuvant setting.
  • hormone therapies may can be used to block the interaction between the hormone and a respective receptor (e.g., by blocking the interaction between estrogen and estrogen receptors) and/or to reduce hormone levels (e.g., by reducing estrogen levels).
  • determining the diagnostic gene status or receptor status of breast cancer patients can inform the prognosis and treatment options for patients.
  • the receptor status of breast cancer patients can be determined using an immunohistochemistry (IHC) test based on a tissue biopsy.
  • IHC test can provide a semi-quantitative measurement of ER/PR positive tumor nuclei in stained histologic tissue sections.
  • tissue samples can be an invasive and painful process.
  • the biopsy site may be inaccessible such that it is not possible to obtain an adequate tissue sample. Accordingly, there is a need to provide a system to determine a receptor status that can be used for both solid and liquid samples.
  • methods comprise providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of reads; receiving, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data; obtaining, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining
  • methods comprise receiving, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data; obtaining, using the one or more processors, a set of input features associated with the receptor gene status and a sample type, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receiving, using one or more processors, sequence read data associated with a sample from an individual; determining, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more input features into the trained statistical model; and predicting, using the one or more processors, the receptor gene status of
  • systems comprise one or more processors and a memory communicatively coupled to the one or more processors and configured to store instructions.
  • the instructions when executed by the one or more processors, cause the system to: receive, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; train, using the one or more processors, a statistical model based on the training data; obtain, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receive, using one or more processors, sequence read data associated with a sample from an individual; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one
  • non-transitory computer-readable storage mediums can store one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; train, using the one or more processors, a statistical model based on the training data; obtain, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receive, using one or more processors, sequence read data associated with a sample from an individual; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a
  • methods for predicting a receptor gene status of a sample from an individual.
  • the method can comprise receiving, using one or more processors, sequence read data associated with a sample from an individual; obtaining a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determining, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more input features into a statistical model; and predicting, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • systems comprise one or more processors and a memory communicatively coupled to the one or more processors and configured to store instructions.
  • the instructions when executed by the one or more processors, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more input features into a statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • non-transitory computer-readable storage mediums can store one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more input features into a statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • the methods described can comprise receiving, using one or more processors, sequence read data associated with a sample from an individual; obtaining a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determining, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more expression input features into the statistical model; and predicting, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • systems comprise one or more processors and a memory communicatively coupled to the one or more processors and configured to store instructions.
  • the instructions when executed by the one or more processors, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more expression input features into the statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • non-transitory computer-readable storage mediums can store one or more programs, comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more expression input features into the statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • the methods described can comprise receiving, using one or more processors, training data comprising values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data, wherein the trained statistical model is configured to predict a receptor gene status of an individual sample; determining, using the one or more processors, weights associated with the training values for the plurality of training input features based on the training; filtering, using the one or more processors, the one or more training input features based on the weights; determining a set of input features associated with the receptor gene status based on the filtered training input features and a sample type of a sample from an individual, wherein filtering the one or more training input features comprises removing training input features associated with low prevalence training values and highly correlated training values, or a combination thereof; and obtaining a trained statistical model configured to receive a set of input feature based on a sample from an individual to output a prediction of
  • the disclosed methods and systems can be used to determine a diagnostic gene status or receptor status of an individual based on a sample, e.g., a liquid or solid sample.
  • Embodiments of the present disclosure can further be used to inform treatment decisions to improve the outcomes for individuals.
  • “About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values.
  • the terms “comprising” (and any form or variant of comprising, such as “comprise” and “comprises”), “having” (and any form or variant of having, such as “have” and “has”), “including” (and any form or variant of including, such as “includes” and “include”), or “containing” (and any form or variant of containing, such as “contains” and “contain”), are inclusive or open-ended and do not exclude additional, un-recited additives, components, integers, elements, or method steps.
  • the terms “individual,” “patient,” or “subject” are used interchangeably and refer to any single animal, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired.
  • a mammal including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates
  • the individual, patient, or subject herein is a human.
  • cancer and “tumor” are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include a solid tumor, a soft tissue tumor, or a metastatic lesion. As used herein, the term “cancer” includes premalignant, as well as malignant cancers.
  • treatment refers to clinical intervention (e.g., administration of an anti-cancer agent or anticancer therapy) in an attempt to alter the natural course of the individual being treated, and can be performed either for prophylaxis or during the course of clinical pathology.
  • Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing metastasis, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.
  • genomic interval refers to a portion of a genomic sequence.
  • the term “subject interval” refers to a subgenomic interval or an expressed subgenomic interval (e.g., the transcribed sequence of a subgenomic interval).
  • the terms “variant sequence” or “variant” are used interchangeably and refer to a modified nucleic acid sequence relative to a corresponding “normal” or “wild-type” sequence.
  • a variant sequence may be a “short variant sequence” (or “short variant”), i.e., a variant sequence of less than about 50 base pairs in length.
  • allele frequency and “allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular allele relative to the total number of sequence reads for a genomic locus.
  • variant allele frequency and “variant allele fraction” are used interchangeably herein and refer to the fraction of sequence reads corresponding to a particular variant allele relative to the total number of sequence reads for a genomic locus.
  • diagnosis gene status and “receptor status” may refer to a status of a receptor corresponding to a gene or hormone that has a known pathogenic or likely pathogenic effect.
  • the diagnostic gene status of a patient can be useful for informing the prognosis and treatment options for patients.
  • hormone receptors e.g., estrogen receptors (ER), progesterone receptors (PR), androgen receptors
  • gene receptors e.g., human epidermal growth factor receptors 2 (HER2)
  • prescribing receptor-targeted therapies can be an effective way to block the interaction between the hormone or gene and a respective receptor (e.g., by blocking the interaction between estrogen and estrogen receptors) and/or to reduce the hormone levels (e.g., by reducing the estrogen levels near the estrogen receptors) to reduce stimulation and growth of the cancer cells.
  • therapies may be based on one or more of the receptor gene statuses.
  • tumors that are ER positive and HER2 positive may be treated as HER2 tumors.
  • the diagnostic gene status may include, but is not limited to, an estrogen receptor (ER), a progesterone receptor (PR), an androgen receptor, or a human epidermal growth factor receptor 2 (HER2).
  • ER estrogen receptor
  • PR progesterone receptor
  • HER2 human epidermal growth factor receptor 2
  • One or more embodiments of the present disclosure may be used to determine the receptor status of a tumor in an individual based on a solid biopsy sample.
  • One or more embodiments of the present disclosure may be used to determine the receptor status of a tumor in an individual based on a liquid biopsy sample.
  • FIG. 1 provides a non-limiting example of a process 100 for determining a receptor status of a sample from an individual.
  • Process 100 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 100 is performed using a client-server system, and the blocks of process 100 are divided up in any manner between the server and a client device.
  • the blocks of process 100 are divided up between the server and multiple client devices.
  • portions of process 100 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 100 is not so limited.
  • process 100 is performed using only a client device or only multiple client devices.
  • process 100 some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • the system can receive sequence read data associated with a sample from an individual.
  • the sequence read data may be derived from single region sequencing (e.g., sequencing of a single tissue biopsy sample collected from the patient tumor).
  • the genomic data comprising sequence read data may be derived from multi-region sequencing (e.g., sequencing of multiple tissue biopsy samples collected from the patient tumor).
  • the genomic data comprising sequence read data may be derived from single cell sequencing data as opposed to bulk tumor sequencing.
  • the genomic data comprising sequence read data may be derived from sequencing the circulating tumor DNA in a liquid biopsy sample.
  • the genomic data comprising sequence read data may be derived from targeted sequencing, e.g., targeted exome sequencing.
  • the genomic data comprising sequence read data may be derived from, e.g., whole genome or whole exome sequencing, as opposed to targeted exome sequencing to increase the number of genomic features (e.g., the number of short variants) detected.
  • the sequence read data may be received by the system as a BAM file.
  • the sequence read data may be indicative of a presence or absence of one or more short variants (SVs) in a patient sample.
  • the sequence read data may also be indicative of the presence or absence of other genomic features in addition to short variants, such as copy number alterations, rearrangements, insertions, deletions, fusions, chromosomal aneuploidy, whole genome doubling, Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures, or any combination thereof.
  • COSMIC Somatic Mutations in Cancer
  • the system can determine a set of input features associated with a receptor status based on a sample type associated with the type of sample and based on an expected prevalence and correlation of the features to the sequence read data. For example, input features that are expected to have a low prevalence and high correlation to the sequence read data may be omitted.
  • the set of input features may differ based on the type of sample, e.g., liquid sample or solid sample.
  • the input features for a model to predict a receptor status based on a solid tissue sample may be associated with a first set of features, while the input features for a model to predict a receptor status based on a liquid sample may be associated with a second set of features.
  • one or more features of the first set of features may overlap with one or more features of the second set of features.
  • one or more features of the first set of features may differ from one or more features of the second set of features.
  • the input features expected to have a low prevalence and be highly correlated with the sequence read data may be omitted from the set of input features.
  • the system may improve the reliability of the model. For example, an input feature that is found in less than one percent of tumors may not provide valuable information to predict a receptor status and may potentially skew a receptor status prediction based on such features.
  • input features with a prevalence of less than a predetermined prevalence threshold may be determined to have a low prevalence.
  • the predetermined prevalence threshold may be in a range of 0.1%-5%.
  • input features with a correlation greater than a predetermined correlation threshold e.g., 90%
  • the predetermined correlation threshold may be in a range of approximately 50%-100%, or approximately 75%-100%.
  • FIG. 2A illustrates exemplary input features 210A for a solid model in accordance with one or more embodiments of this disclosure.
  • the set of input features 210A based on a solid sample can include one or more genomic alteration features, one or more complex mutational signatures, one or more chromosomal instability features, one or more clinicopathological features, one or more clinical features, and one or more additional features.
  • genomic alteration features one or more complex mutational signatures
  • chromosomal instability features one or more chromosomal instability features
  • clinicopathological features one or more clinical features
  • additional features one or less input features can be included or omitted without departing from the scope of this disclosure.
  • FIGs. 2B-2E illustrates exemplary genomic alteration features 210B-210E for a solid model in accordance with one or more embodiments of this disclosure.
  • these genomic alteration features may correspond to alteration features expected to have a low prevalence and high correlation to the sequence read data.
  • the genomic alterations can correspond to one or more known pathogenic alterations and/or one or more likely pathogenic alterations.
  • the known pathogenic alterations and the likely pathogenic alterations may correspond to genomic alterations that are associated with a biologically activating alteration and/or an alteration that causes a change in a biological process that is known or likely to have an impact on a patient’s disease status.
  • the genomic alteration features for the solid model may include, at least, TP53, ESRI, PIK3CA, ZNF703, and GATA3. As shown in FIG.
  • the genomic alteration features for the solid model can include, at least, CCNE1, EGFR, ERBB2, ERBB2 Amplification, ESRI, FGF3, FGFR1, GATA3, MAP2K4, MYC, PIK3CA, PTEN, RB I, TP53, and ZNF703.
  • the genomic alteration features for the solid model can include, at least, AKT1, AKT2 , AKT3, APC, AR, ARFRP1, ARID1A, ATM, AURKA, BAP1, BCL2L1, BCL2L2, BRA, BRCA1, BRCA2, BRD4, CBFB, CCND3, CCNE1, CDH1, CDK12, CDK4, CDK6, CDKN2A, CDKN2B, CHEK2, CREBBP, CTNNA1, DNMT3A, EGFR, EMSY, EP300, ERBB2, ERBB2-Amplification, ERBB2 Short-Variant, ERBB3, ESRI, FANCA, FAS, FBXW7, FGF10 FGF12, FGF3, FGF6, FGFR1, FGFR2, FGFR4, GATA3, GNAS, HGF, IGF1R, IKBKE, IRS2, JAK2, KDM5A, KDM6A
  • determining whether a genomic alteration is present in the sequence read data may be helpful in determining a receptor status because particular genomic alterations may be associated with a known receptor status.
  • genomic alterations may be associated with a known receptor status.
  • PIK3CA short variant alterations may be associated with ER positive tumors.
  • TP53 short variant alterations may be associated with ER negative tumors.
  • high level ERBB2 amplifications may be associated with HER2 positive tumors.
  • the particular genomic alteration as well as the specific type of alteration may be included as an alteration feature.
  • the genomic alteration features can include, for each genomic alteration, the presence of a predetermined short variant, the absence of a predetermined short variant, a copy number alteration, a zygosity of the genomic alteration (e.g., homozygous or heterozygous), a somatic status of the genomic alteration, a germline status of the genomic alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the genomic alterations 210E may be associated with a specific gene.
  • This level of granularity in determining the type of alteration may be important for determining the biological processes that are relevant to disease.
  • HER2 short variants may be associated with ER positive subtypes while HER2 amplifications are not.
  • an input feature corresponding to a presence of a HER2 variant without further specifying the type of alteration may not be as relevant to determining a receptor status. In this manner, distinguishing between the types of alterations improve the accuracy of the model for predicting the receptor status.
  • the genomic alteration features may be obtained via a computational pipeline for analyzing sequence read data.
  • the tumor fraction feature may be determined by the system based on information provided by the computational pipeline.
  • the input features for the solid statistical model can include one or more complex mutational signature features.
  • FIG. 2F illustrates exemplary complex mutational signature features 210F for a solid model, according to embodiments of the present disclosure.
  • the complex mutational signature features can include genome-wide loss of heterozygosity (gLOH), trinucleotide signatures, insertion signatures, deletion signatures, and copy number signatures.
  • the genome- wide loss of heterozygosity may be associated with a loss of heterozygosity of the genome, typically caused by the loss of a gene associated with DNA repair.
  • the trinucleotide signatures may be associated with one or more trinucleotide alterations in the sample.
  • the insertion signatures may be associated with one or more insertion alterations in the sample.
  • the deletion signatures may be associated with one or more deletion alterations in the sample.
  • the copy number signatures may be associated with one or more copy number alterations in the sample.
  • the input features for the solid statistical model can include one or more chromosomal instability features.
  • FIGs. 2G-2I illustrate exemplary chromosomal instability features 210G-210I for a solid model. In one or more examples, these chromosomal instability features may correspond to features expected to have a low prevalence and high correlation to the sequence read data.
  • FIG. 2G exemplary chromosomal instability features 210G which may include chromosome gain, chromosome not gain, chromosome loss, chromosome not loss.
  • the chromosomal instability features can include a total aneuploidy count. As shown in FIG.
  • the chromosomal instability features can include at least, chromosome 5q loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome lOp gain status, chromosome 16p gain status.
  • the chromosomal instability features can include at least, chromosome Iq loss status, chromosome Ip loss status, chromosome 3p loss status, chromosome 4p loss status, chromosome 5q loss status, chromosome 9p loss status, chromosome lOq loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome 14q loss status, chromosome 15q loss status, chromosome 16p loss status, chromosome 16q loss status, chromosome 17q loss status, chromosome 19p loss status, chromosome 21q loss status, chromosome 22q loss status, chromosome Iq gain status, chromosome 2p gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6q gain status, chromosome 7q gain status, chromosome 9p gain status, chromosome lOp gain status, chromosome
  • the chromosomal instability features can include at least, chromosome Ip gain status, chromosome Iq gain status, chromosome2p gain status, chromosome 2q gain status, chromosome 3p gain status, chromosome 3q gain status, chromosome 4p gain status, chromosome 4q gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6p gain status, chromosome 6q gain status, chromosome 7p gain status, chromosome 7q gain status, chromosome 8p gain status, chromosome 8q gain status, chromosome 9p gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome lOp gain status, chromosome lOq gain status, chromos
  • the input features for the solid statistical model can include one or more clinicopathological features.
  • FIG. 2J illustrates exemplary clinicopathological features 210J for a solid model.
  • the clinicopathological features can include an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor status of the individual, an anatomical sub-classification of a tumor, or a combination thereof. In one or more examples, these features can be obtained via a patient’s medical records, and/or based on laboratory test results.
  • the age of the individual can be based on an integer value of corresponding to the age in years of the individual.
  • the sex of the individual can be associated with the biological sex of the individual.
  • a disease diagnosis can be associated with a disease ontology for a particular disease, for example, a breast cancer diagnosis.
  • the disease diagnosis can be based on the International Classification of Diseases (ICD) codes (e.g., ICD-9 code or ICD-10 code).
  • ICD International Classification of Diseases
  • a tumor status of the individual may associated with the local or metastatic status of the sample.
  • an anatomical sub-classification of a tumor may be associated with breast cancer histology including, for example, but not limited to invasive ductal carcinoma, invasive lobular carcinoma, mixed ductal and lobular carcinoma, breast carcinoma, breast metaplastic carcinoma, breast myoepithelial carcinoma, breast carcinosarcoma, breast inflammatory carcinoma, breast mucinous carcinoma, breast papillary carcinoma, breast adenomyoepithelioma, breast phyllodes tumor etc.
  • Evaluating the clinicopathological features can provide further insight into the receptor status of an individual.
  • male breast cancer may be generally associated with a positive ER status.
  • disease occurrence in younger patients may be associated with a negative ER status.
  • invasive lobular breast cancer ILC may be associated with a positive ER status.
  • the input features for the solid statistical model can include one or more clinical features.
  • FIG. 2K illustrates exemplary clinical features 210K for a solid model.
  • clinical features may correspond to input features expected to have a low prevalence and high correlation to the sequence read data.
  • the clinical features 210K can include an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, and an indication of body vitamin levels. In one or more examples, these features can be obtained via a patient’s medical records, and/or based on laboratory test results.
  • Evaluating the clinical features can provide further insight into the receptor status of an individual.
  • different ancestry groups may be associated with a different preponderances of disease.
  • breast cancer occurring in individuals with African ancestry is associated with a negative ER status.
  • a stage of the disease may be associated with a particular receptor status.
  • some diseases may present at higher stage at diagnosis (e.g., ILC may be associated with a positive ER status at diagnosis).
  • lifestyle habits can play a role in developing certain cancers, e.g., a history of smoking or exercise. Obesity for example increases the risk of ER positive disease, e.g., breast cancer.
  • the number of live births performed by an individual may influence the types of breast cancers that can develop.
  • the input features for the solid statistical model can include one or more additional features not described above.
  • FIG. 2L illustrates exemplary additional input features for a solid model 210L.
  • these additional input features may correspond to features expected to have a low prevalence and high correlation to the sequence read data.
  • the exemplary additional input features for a solid model 210L can include, but not be limited to a tumor mutational burden (TMB) value, a germline status, methylation signatures, a mRNA expression level, a miRNA expression level, proteomics, COSMIC mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, and diagnostic images.
  • TMB tumor mutational burden
  • the one or more input features may be associated with a germline status of one or more genes. Evaluating these additional features can provide further insight into the receptor status of an individual.
  • the germline status for certain genes may be associated with the incidence of disease.
  • germline BRCA1 alterations may be associated with developing triple negative breast cancer (TNBC), and further associated with a negative ER status, a negative HER2 status, and a negative PR status.
  • TNBC triple negative breast cancer
  • the TMB value can correspond to a measure of genome-wide mutation frequency.
  • individuals with in invasive lobular carcinoma (ILC) may present with high TMB and may be ER positive. Accordingly, a high TMB value may be indicative of an ER positive status.
  • ER negative tumors are maybe associated with intermediate TMB values.
  • germline status may be indicative of whether an alteration is familial. In some examples, whether an alteration is germline or somatic may impact what type of disease the patient might develop. For example patients with germline BRCA1 tumors are frequently ER negative. Patients with germline CDH1 alterations are frequently ER positive.
  • methylation signatures can correspond to patterns of methylation across the genome that can be indicative of chromatin state and gene expression. These patterns may differ across tumor types and can be informative as to the underlying cell state of tumor and the receptor status (e.g., ER positive versus ER negative).
  • RNA signatures can be used to identify characteristics of a tumor. For example, in breast cancer multiple RNA signatures (e.g., PAM50) and custom signatures may be used.
  • the system can cluster similar states, and identify if a tumor is ‘basal like,’ for example, which can be indicative of an ER negative status.
  • the level of ESRI expression or ERBB2 (HER2) expression can be informative for the receptor status.
  • the miRNA expression level can be associated with regulatory RNAs.
  • the presence or absence of miRNA could indicate a state of the tumor cells (e.g., positive or negative receptor status).
  • proteomics measures the levels of different proteins in the cell.
  • the specific expression levels of certain proteins can help indicate cell state. For example, tumor cells with a lot of ER protein may be indicative of an ER positive status.
  • COSMIC mutation signatures correspond to signatures of underlying mutational processes that can be measured by looking at the context of mutations. There are a number of COSMIC signatures including those derived from point mutations, indels, and dinucleotide substitutions.
  • COSMIC signatures include those derived from point mutations, indels, and dinucleotide substitutions.
  • APOBEC apolipoprotein B mRNA editing catalytic polypeptide
  • HRD homologous repair deficiency
  • immunohistochemical markers can measure protein expression and localization in tissue slides.
  • the presence or absence of other markers can be indicative of a diagnostic receptor gene status. For example, tumors that have lost membrane- localized E-cadherin may be more likely to have an ER positive status.
  • genetic predispositions can include disorders or family histories that predispose an individual to cancer.
  • the type of cancer developed by individuals may have a bias in the receptor status. For example, an individual may be more likely to have an ER negative status if there is a family history of ER-negative breast cancer.
  • cell adhesion biomarkers can correspond to cell surface molecules like E-cadherin or cytokeratins (e.g., CK8/18).
  • cytokeratins can indicate an epithelial and/or mesenchymal state which can correlate with ER status.
  • saliva based biomarkers and enzyme based biomarkers can indicate possible predispositions to certain disease types. For example, individuals with biomarkers indicative of obesity or diabetes may be predisposed to certain types of breast cancers, which can be indicative of receptor status.
  • the exemplary additional features may be obtained via a computational pipeline for analyzing sequence read data.
  • sequence read data from the computational pipeline may be used, but is not limited to determining the tumor mutational burden, germline status, COSMIC mutation signatures, etc.
  • the exemplary additional features may be obtained via tests administered by a clinician, e.g., diagnostic images, saliva based biomarkers, urinalysis, etc.
  • urinalysis can detect features such as red blood cell (RBC) count, sugar content, circulating tumor cells, etc.
  • FIG. 3A illustrates exemplary input features 310A for a liquid model in accordance with one or more embodiments of this disclosure.
  • the set of input features 310A for a liquid sample can include one or more genomic alteration features, one or more clinicopathological features, one or more clinical features, one or more tumor fraction features, one or more fragmentomic features, and one or more additional features.
  • the exemplary input features 310A for a liquid model may differ from the exemplary input features 210A for a solid model. Some of these differences may be because of the differences in tumor purity in liquid biopsy samples and solid biopsy samples.
  • Liquid samples typically have less shed so calling amplifications and deletions may be more difficult than with solid samples. Additionally, for liquid samples comprising blood, the sample may integrate all sites that shed and different patterns of resistance alterations may be observed (e.g., the system may observe polyclonal alterations or alterations from multiple resistance pathways). Additionally, solid and liquid samples may be associated with different patterns of baiting (e.g., with respect to genes baited in an assay and/or the level of coverage).
  • FIGs. 3B-3E illustrates exemplary genomic alteration features 210B-210E for a liquid model in accordance with one or more embodiments of this disclosure.
  • these genomic alteration features may correspond to alteration features expected to have a low prevalence and high correlation to the sequence read data.
  • the genomic alterations can correspond to one or more known pathogenic alterations and/or one or more likely pathogenic alterations.
  • the known pathogenic alterations and the likely pathogenic alterations may correspond to genomic alterations that are associated with a biologically activating alteration that is known or likely to have an impact on a patient’s disease status.
  • the genomic alteration features for the liquid model may include, at least, TP53, ESRI, PIK3CA, CDH1, and BRCA1. As shown in FIG.
  • the genomic alteration features for the liquid model can include, at least, AKT1, BRCA1, BRCA2, CDH1, CDKN2A, ERBB2, ESRI, KRAS, NF1, PIK3CA, PTEN, and TP53.
  • the genomic alteration as well as the specific type of alteration may be included as an alteration feature.
  • the genomic alteration features 310D for a liquid model can include, for each genomic alteration, the presence of a predetermined short variant, the absence of a predetermined short variant, a copy number alteration, a zygosity of the genomic alteration (e.g., homozygous or heterozygous), a somatic status of the genomic alteration, a germline status of the genomic alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the genomic alterations 310D may be associated with a specific gene. As discussed above, this level of granularity in determining the type of alteration may be important for determining the biological processes that are relevant to disease.
  • the genomic alteration features may be obtained via a computational pipeline for analyzing sequence read data.
  • the tumor fraction feature may be determined by the system based on information provided by the computational pipeline.
  • the input features for the liquid model can include one or more clinicopathological features.
  • FIG. 3E illustrates exemplary clinicopathological features 310E for a liquid model.
  • the clinicopathological features can include an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor status of the individual, a tumor type, an anatomical sub-classification of a tumor, or a combination thereof. As discussed above with respect to the solid model, in one or more examples, these features can be obtained via a patient’s medical records, and/or based on laboratory test results.
  • the input features for the liquid model can include one or more clinical features.
  • FIG. 3F illustrates exemplary clinical features 310F for a liquid model.
  • clinical features may correspond to input features expected to have a low prevalence and high correlation to the sequence read data.
  • the clinical features 310F can include an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, and an indication of body vitamin levels.
  • these features can be obtained via a patient’s medical records, and/or based on laboratory test results.
  • the input features for the liquid model can include one or more tumor fraction features.
  • FIG. 3G illustrates an exemplary tumor fraction feature 310G for a liquid model.
  • the exemplary tumor fraction feature can include an estimated tumor fraction of the sample, an estimated degree of polyclonality, or a combination thereof.
  • the estimated tumor fraction may be based on a level of shed of a tumor, which can be indicative of a receptor status.
  • the estimated tumor fraction can be expressed as a percentage of circulating cell-free DNA.
  • the tumor fraction feature may be obtained via a computational pipeline for analyzing sequence read data.
  • the tumor fraction feature may be determined, directly or indirectly using multiple computational processes (e.g., using fragmentomic length, variant allele fraction, and flow cytometry), by the system based on information provided by the computational pipeline.
  • the input features for the liquid model can include one or more fragmentomic features.
  • FIG. 3H illustrates exemplary fragmentomic features 310H for a liquid model.
  • the exemplary fragmentomic features 310H can include one or more of an amount of the selected plurality of reads having a specified length, a mean fragment length of the selected plurality of reads, a median fragment length of the selected plurality of reads, an interquartile range of fragment lengths of the selected plurality of reads, a distribution of fragment lengths of the selected plurality of reads, one or more peaks of fragment lengths for the selected plurality of reads, a fragment length for at least one of the selected plurality of reads, a start position of a fragment for the selected plurality of reads, and an end position of a fragment for the selected plurality of reads.
  • the provided examples of fragmentomic characteristics 310A is not exhaustive and a skilled artisan would understand that additional fragmentomic characteristics could be determined for the plurality of reads
  • the amount of a fragment having a specified length can correspond to a total amount (z.e., count) of fragments at the specified length. For example, for a particular sample, the total number of fragments that have a length below lOObp, a length of lOObp, a length of lOlbp, a length of 102 bp, . . ., a length of 550bp, and a length greater than 550bp.
  • the specified length can correspond to a specific number of base pairs, a range of number of base pairs, or a combination thereof.
  • the amount of a fragment having a specified length can correspond to a relative amount of fragments of a selected plurality of reads (e.g., reads overlapping with the alteration or gene of interest) corresponding to a specified length.
  • the amount of a fragment having a specified length can comprise a fraction.
  • the amount of a fragment having a specified length can be determined based on the number of fragments with a specified length (e.g., a length below 50bp, a length of 50pb, a length of 51bp, . .
  • the amount can correspond to a selected plurality of reads that include an alteration and/or a selected plurality of reads that include a wild type gene.
  • the mean fragment length of the selected plurality of reads can correspond to an average fragment length of the selected plurality of reads.
  • the median fragment length of the selected plurality of reads can correspond to the middle fragment length value of a sorted list of the fragment lengths of the selected plurality of reads.
  • the interquartile range of fragment lengths of the plurality of reads can correspond to a first fragment length value associated with the 25th percentile of the fragment lengths of the selected plurality of reads and a second fragment length value associated with the 75th percentile of the fragment lengths of the selected plurality of reads.
  • the peak fragment length can correspond to the mode or the fragment length value that appears most frequently in the length characteristics for the selected plurality of reads.
  • the system can determine more than one peak fragment length.
  • the distribution of the fragment length can correspond to a summary statistics characterizing the distribution, e.g., maximum value, minimum value, standard deviation, shape, etc.
  • the fragmentomic features may be obtained via a computational pipeline for analyzing sequence read data. In one or more examples, the fragmentomic features may be determined by the system based on information provided by the computational pipeline.
  • the input features for the solid statistical model can include one or more additional features not described above.
  • FIG. 31 illustrates exemplary additional input features 3101 for a liquid model.
  • these additional input features may correspond to features expected to have a low prevalence and high correlation to the sequence read data.
  • the exemplary additional input features 3101 for a liquid model can include, but not be limited to a tumor mutational burden, germline status, methylation signatures, a mRNA expression level, a miRNA expression level, proteomics, COSMIC mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, and a germline status for certain genes.
  • the exemplary additional features may be obtained via a computational pipeline for analyzing sequence read data, e.g., to estimate the tumor mutational burden, germline status, COSMIC mutation signatures, etc.
  • the exemplary additional features may be obtained via tests administered by a clinician, e.g., diagnostic images, saliva based biomarkers, etc.
  • a clinician e.g., diagnostic images, saliva based biomarkers, etc.
  • the system can determine values for one or more input features corresponding to the set of input features based on the sequence read data.
  • the values e.g., input feature values
  • the system can determine values for one or more input features based on the sequence read data. For example, the values (e.g., input feature values) for the one or more input features can be determined based on the presence of each of one or more of the input features of the set of input features in the sequence read data.
  • the system may determine corresponding input feature values.
  • the genomic alteration features e.g., 210B-210E
  • the input feature values corresponding to one or more genomic alteration features 31OB-31OD for the liquid model may also be associated with binary values indicative of a presence of a particular genomic alteration (e.g., TP53, ESRI, etc.) and/or indicative of a particular type of alteration (e.g., short variant, copy-number alteration, rearrangement, etc.).
  • a particular genomic alteration e.g., TP53, ESRI, etc.
  • a particular type of alteration e.g., short variant, copy-number alteration, rearrangement, etc.
  • the system may determine corresponding input feature values.
  • the complex mutational signature features may be determined using the methods of Macintyre et al. incorporated herein by reference. (See, e.g., G Macintyre et al.-. Copy number signatures and mutational processes in ovarian carcinoma. Nat Genet 2018, 50(9): 1262- 1270, hereby incorporated in its entirety).
  • a gLOH quantification may be associated with a binary value indicative of the presence of high gLOH or a float point number indicative of a measure of the gLOH.
  • the gLOH quantification can correspond to a measure of focal genome-wide loss of heterozygosity, a biomarker for HRD and genomic instability.
  • a high gLOH value may be indicative of an ER negative status.
  • trinucleotide signatures, indel signatures, and copy number signatures may be associated with a binary value indicative of the presence or absence of the respective signature.
  • copy number features can be extracted using the methods of Macintyre et al.
  • the trinucleotide signatures and indel signatures may be based on the COSMIC signatures described above.
  • the system may determine corresponding input feature values.
  • the genomic alteration features e.g., 210B-210E
  • the genomic alteration features may be associated with binary values indicative of a presence of a particular chromosomal instability feature (e.g., chromosome gain, chromosome not gain, chromosome loss, chromosome not loss, total aneuploidy count, etc.) and/or indicative of a gain or loss status of a particular chromosome (e.g., chromosome 5q loss status, chromosome l lq loss status, chromosome 12q loss status, etc.).
  • the system may determine corresponding input feature values.
  • age may be associated with any positive value.
  • sex may be associated with a binary value.
  • a disease diagnosis, a tumor status, a tumor type, and an anatomical sub-classification of the tumor may be associated with a categorical string.
  • the system may determine corresponding input feature values.
  • ancestry may be associated with a categorical string (e.g., EAS, SAS, AFR, AMR, EUR, etc.) or float value for admixture in each ancestry group.
  • the number and types of ancestry groups is not intended to limit the scope of the disclosure.
  • the stage of the disease can be associated with a categorical string (e.g., IA, II, etc.).
  • lifestyle habits may be associated with a series of binary values (e.g., overweight: yes/no, smoking: yes/no, live birth: (yes/no)) or one or more floating point values or integers (e.g., number of live births: integer, number of pack years smoked: integer or floating point value, etc.) indicative of the lifestyle habits of the individual.
  • an obesity status may be associated with a binary value (e.g., yes, no) or a floating point value (e.g., BMI).
  • a body vitamin level may be associated with a floating point value or a categorical string (e.g., high) indicative of the amount of body vitamin level in the individual.
  • family history may be associated with a categorical string or a binary value.
  • the tumor fraction feature 310G may be associated with a percentage or a floating point value indicative of an estimated tumor fraction in the sample.
  • the fragmentomic features may be associated with an integer and or floating point value or fraction, as discussed above.
  • the system may determine corresponding input feature values.
  • a particular type of value e.g., binary, non-binary, integer, floating point, categorical, etc.
  • methylation signatures may be associated with a binary value indicative of a presence of a methylation signature, for example the methylation status of the BRCA1 promoter. In one or more examples, the methylation signatures may be associated with a non-binary value and/or any positive value.
  • an mRNA expression level may be associated with an integer or floating point value indicative of the mRNA expression level, for example a PAM50 classification.
  • a miRNA expression level may be associated with an integer or floating point value indicative of the miRNA expression level.
  • proteomics may be associated with a high or low expression of a protein indicative of pathway activity.
  • COSMIC mutation signatures may be associated with a probability value indicative of the presence of a respective mutation signature, for example the presence of an APOBEC trinucleotide signature or a HRD indel signature.
  • immunohistochemical markers may be associated with a binary value, a percentage, or a category (e.g., negative, low, high) indicative of the presence of a respective immunohistochemical marker (e.g., E-cadherin membrane staining).
  • genetic predispositions may be associated with a binary value indicative of one or more genetic predispositions (e.g., a family history of triple negative breast cancer or carrier status for germline CDH1 that can predispose to ER+ disease).
  • cell adhesion biomarkers may be associated with a binary value, a floating point value, and the like, indicative of the presence of a respective cell adhesion biomarker (e.g., cytokeratin or cadherin statuses).
  • saliva based biomarkers may be associated with a binary value indicative of the presence of a respective saliva based biomarker.
  • enzyme based biomarkers may be associated with a binary value indicative of the presence of a respective enzyme based biomarkers.
  • the input value may be associated with an image file (e.g., JPEG, TIFF, raw image format, BMP, etc.) of the diagnostic image.
  • the system can input the one or more input feature values into the statistical model.
  • the statistical model can be a trained machine learning model.
  • the system can input one or more of the one or more input feature values into a trained machine learning model.
  • the trained machine learning model may be a random forest model.
  • the statistical model may be part of a machine learning process.
  • the machine learning model can include an artificial intelligence (“Al”) learning model.
  • Al artificial intelligence
  • the machine learning model can be at least one of a supervised model or an unsupervised model.
  • the machine learning model can include one or more machine learning models, such as regression-based models (e.g., including but not limited to logistic regression, nearest neighbor regression, proportional hazards regression etc.), regularization-based models (e.g., including but not limited to elastic net, ridge regression etc.), instance-based models (e.g., including but not limited to support vector machines, k-nearest neighbor etc.), Bayesian-based models (e.g., including but not limited to naive-based, Gaussian naive-based etc.), clustering -based models (e.g., including but not limited to expectation maximization), ensemble-based models (e.g., including but not limited to adaboost, bagging, gradient boosting machines etc.), and neural network-based models (e.g., including but not limited to backpropagation, stochastic gradient descent etc.).
  • regression-based models e.g., including but not limited to logistic regression, nearest neighbor regression, proportional hazards regression etc.
  • regularization-based models
  • the model can be trained to predict the receptor status of an individual based on an output of the statistical model.
  • the output of the statistical model may be indicative of a receptor status.
  • the output of the statistical model may include a score indicative of a probability of a positive receptor status.
  • the output of the statistical model may include a score indicative of a probability of a negative receptor status.
  • the system can predict the receptor status of the individual based on an output of the trained statistical model.
  • the system can predict the receptor status of the individual by comparing a score (e.g., probability score) output by the statistical model to one or more predefined thresholds.
  • a score e.g., probability score
  • the system can compare the score to one or more predefined thresholds and determine whether the sample has a positive receptor status or a negative receptor status.
  • the predefined threshold may be 0.5. In one or more examples, the predefined threshold may be in a range of 0.05 to 0.95.
  • the thresholds may be configured to maximize different measures of prediction accuracy in a binary classification problem (e.g., the Fl score, F2 score, Matthew's correlation coefficient, Youden index, Cohen’s kappa).
  • the thresholds can correspond to a value between zero and one.
  • a first threshold of the one or more thresholds can be determined such that if the score is above the threshold, then the sequence read data is predicted to have a positive receptor status. In such examples, if the score is below the threshold, then the system can predict that the sequence read data of the sample has a non-positive receptor status.
  • the one or more predetermined thresholds can be determined by maximizing or minimizing a function of sensitivity and specificity (such as the sum) For example, a loss function associated with performance metrics (e.g., whether the score corresponds to an accurate prediction) can be maximized or minimized.
  • the threshold can be set to maximize sensitivity and specificity.
  • the one or more predetermined thresholds can be determined based on the area under the prediction function’s receiver operating characteristic (ROC) curve.
  • the area under a receiver operating characteristic curve can be used in statistics to measure the prediction accuracy of a binary classifier system.
  • the thresholds can be determined using one or more statistical techniques combined with predetermined confidence levels.
  • FIG. 4 is a diagram illustrating a process of predicting a receptor status using a statistical model, according to embodiments of the present disclosure.
  • input data 410 corresponding to one or more input feature values (e.g., associated with solid model genomic alteration features 210B-210E, complex mutational signature features 210F, chromosomal instability features 210G-210I, clinicopathological features 210J, clinical features 210K, and additional features 210L; or liquid model genomic alteration features 31OB-31OD, clinicopathological features 310E, clinical features 310F, tumor fraction features 310G, fragmentomic features 31 OH, and additional features 3101) can be input into model 420.
  • the input data 410 can be associated with the input feature values described above with respect to step 106.
  • the model 420 can be a statistical model, such as a trained machine learning model configured to predict a receptor status of a sample.
  • the model 420 can then output 430 one or more scores indicative of a receptor status.
  • the output 430 of the model can include one or more scores associated with, for example, an indication of a positive receptor status and an indication of a negative receptor status.
  • the model 420 may output a single score (e.g., a score indicative of a positive receptor status or a score indicative of a negative receptor status).
  • model 420 can be associated with process 100.
  • FIG. 5 provides a non-limiting example of a process 500 for predicting a receptor status of a sample from an individual.
  • the process 500 includes one or more steps related to training a statistical model to predict the receptor status.
  • Process 500 can be performed, for example, using one or more electronic devices implementing a software platform.
  • process 500 is performed using a clientserver system, and the blocks of process 500 are divided up in any manner between the server and a client device.
  • the blocks of process 500 are divided up between the server and multiple client devices.
  • process 500 is performed using only a client device or only multiple client devices.
  • some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted.
  • additional steps may be performed in combination with the process 500. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.
  • the system can receive training data including a plurality of training input feature values corresponding to a plurality of training samples.
  • the training data can further include a receptor status of the respective training sample.
  • the plurality of input training feature values may be determined based on a plurality of training samples.
  • the types of training input features may be based on a sample type, e.g., solid sample or liquid sample.
  • the input training feature values may be associated with one or more genomic alteration features, complex mutational signature features, chromosomal instability features, clinicopathological features, clinical features, or additional features.
  • the input training feature values may be associated with one or more genomic alteration features, clinicopathological features, clinical features, tumor fraction features, fragmentomic features, or additional features.
  • the plurality of input training feature values may be determined based on via a computational pipeline for analyzing sequence read data, e.g., to determine the genomic alteration feature values, complex mutation signature features, chromosomal instability features, and/or additional features.
  • one or more of the clinicopathological features, clinical features, and additional features may be obtained via a patient’s medical records.
  • one or more additional features may be determined via tests administered by a clinician, e.g., diagnostic images, saliva based biomarkers, etc.
  • the system can train a statistical model based on the training data.
  • the model can be trained to predict a score indicative of a receptor status.
  • the model can be trained to determine one or more scores associated with an indication of a positive receptor status and an indication of a negative receptor status.
  • the model e.g., model 420, may output a single score (e.g., a score indicative of a positive receptor status or a score indicative of a negative receptor status).
  • the model may also be configured to output an indication of a relevance of the one or more training input features. For example, for each of the training input features, the model can output a respective score that indicates the relative importance of the training input feature in determining the score indicative of the receptor status. In one or more examples, the indication of the relevance of the one or more training input features may be used to adjust the weights of the model associated with the training input features.
  • models may be separately trained based on the receptor status and the sample type. For example, a first model may be trained to determine a PR status for liquid biopsy samples and a second model may be trained to determine a PR status for solid biopsy samples. As another example, a third model may be trained to determine an ER status for liquid biopsy samples and a fourth model may be trained to determine an ER status for solid biopsy samples. As another example, a fifth model may be trained to determine a HER2 status for liquid biopsy samples and a sixth model may be trained to determine a HER2 status for solid biopsy samples. As another example, a seventh model may be trained to determine an androgen receptor status for liquid biopsy samples and an eighth model may be trained to determine an androgen receptor status for solid biopsy samples.
  • each of the models may be trained separately. In some instances the models may be trained simultaneously via, for example, a multi-task learning structure.
  • a skilled artisan will understand that the models enumerated above are exemplary and additional or less models may be trained to determine a receptor status of various hormones according to embodiments of the present disclosure.
  • embodiments of the present disclosure can further include fine tuning a machine learning tumor type classification by employing a statistical model (e.g., a deep learning model, including but not limited to convolutional neural networks, recurrent neural networks, auto-encoders etc.).
  • a statistical model e.g., a deep learning model, including but not limited to convolutional neural networks, recurrent neural networks, auto-encoders etc.
  • models may be trained on breast tumor diagnostic images such as histopathological images, radiological images, magnetic resonance imaging, ultrasound imaging, X-ray imaging (mammogram), bone scans, CT scans, PET scans, etc.
  • modifying or fine-tuning the classification model may include adding human interpretable features (HIFs) and phenotypes relevant to the receptor status of the tumor.
  • HIFs may be extracted from imaging data and used as additional features in the model for predicting the receptor status of a patient.
  • the HIFs can be extracted from histopathological images using machine learning methods, such as, deep learning machine learning methods.
  • the system may input a diagnostic image associated with an individual into a second statistical model (e.g., trained on diagnostic images) and determine a score indicative of a tumor classification based on the diagnostic image.
  • the tumor classification can be used as an input to the model for predicting a receptor status (e.g., models 420 and 620).
  • FIG. 6 illustrates a non-limiting example of a diagram for a process 600 for training a model 620 to predict a receptor status, according to embodiments of this disclosure.
  • process 600 can correspond to Step 504 of process 500.
  • the training at step 504 can be applied to train model 420 described with respect to FIG. 4.
  • training data 602 can be input into model 620.
  • the training data 602 can include one or more data sets corresponding to a plurality of samples (e.g., samples from individuals or patients). Each data set can include training values associated with a plurality of training input features and a corresponding label indicative of a receptor status of the sample.
  • the training data may be associated with a sample type, e.g., solid biopsy sample type or liquid biopsy sample type.
  • the training data 602 may include the receptor status of a particular receptor, e.g., ER, PR, HER2, androgen receptor.
  • the model 620 may be configured to predict the receptor status for a particular receptor for a particular sample type.
  • a statistical model can be built using a training data associated with a first sample type (e.g., solid training samples) and the model can be validated using training data associated with a second sample type (e.g., liquid training samples). In one or more examples, the statistical model can be trained and validated using the same sample type (e.g., solid training samples).
  • a first sample type e.g., solid training samples
  • a second sample type e.g., liquid training samples.
  • the statistical model can be trained and validated using the same sample type (e.g., solid training samples).
  • the score of the model e.g., model 420, 620
  • the score of the model can be determined based on a weighted evaluation of the training data 602 (e.g., training values for genomic features associated with a receptor status). For example, the training can assign weights to the different training input feature values.
  • the system can obtain a set of input features associated with a receptor status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model. For example, based on the indication of the relevance of the one or more training input features, the system can determine which input features are associated with a low prevalence, but nonetheless have a high correlation to the score indicative of the receptor status. Such input features may be excluded from the statistical model in order to improve the accuracy of the prediction as discussed above.
  • the input features expected to have a low prevalence and/or high correlation to the statistical model may be omitted from the set of input features to be used in the trained model.
  • the system may improve the reliability of the model. For example, an input feature that is found in less than one percent of tumors may not provide valuable information to predict a receptor status and may potentially skew the data.
  • omitting the low prevalence and highly correlated training input features may reduce the number of input features by about 40%.
  • omitting the low prevalence and highly correlated training input features may reduce the number of input features by about 25%, 30%, 35%, 45%, 50%, 55%, 60%, 65% 70%, 75%, 80%, 85%, 90%, or 95%.
  • training input features with a prevalence less than a predetermined prevalence threshold may be determined to have a low prevalence.
  • the predetermined prevalence threshold may be in a range of 0.1%-5.0%.
  • training input features with a correlation greater than a predetermined correlation threshold e.g., 90%
  • the predetermined correlation threshold may be in a range of 50%-95%.
  • the system can determine one or more input feature values corresponding to the set of input features associated based on the selected plurality of reads and based on a sample type associated with of the sample.
  • step 510 can correspond to step 106 of process 100 described above.
  • step 512 in FIG. 5 the system can input the one or more input feature values into the trained statistical model.
  • step 512 can correspond to step 108 of process 100 described above.
  • step 514 in FIG. 5 the system can predict the receptor status of the individual based on an output of the trained statistical model.
  • step 514 can correspond to step 110 of process 100 described above.
  • the sequence read data (e.g., obtained in step 102 of process 100 and/or step 506 of process 500) may be obtained from a gene panel.
  • the gene panel may comprise at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, or more than 40 genes.
  • the disclosed methods may be used to determine a receptor status of an individual by assessing one or more input features associated with at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, or more than 40 gene loci.
  • the disclosed methods may be used to identify variants in the ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, APC, AR, ARAF, ARFRP1, ARID1A, ASXL1, ATM, ATR, ATRX, AURKA, AURKB, AXIN1, AXL, BAP1, BARD1, BCL2, BCL2L1, BCL2L2, BCL6, BCOR, BCORL1, BCR, BRAF, BRCA1, BRCA2, BRD4, BRIP1, BTG1, BTG2, BTK, CALR, CARD11, CASP8, CBFB, CBL, CCND1, CCND2, CCND3, CCNE1, CD22, CD274, CD70, CD74, CD79A, CD79B, CDC73, CDH1, CDK12, CDK4, CDK6, CDK8, CDKN1A, CDKN1B, CDKN2A, CDKN2B, CDKN2C, CE
  • the disclosed methods may be used to identify variants in the ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HDAC, HER1, HER2, HR, IDH2, IL-ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRP, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, or VEGFB gene locus, or any combination thereof
  • the disclosed methods may further comprise one or more of the steps of: (i) obtaining the sample from the subject (e.g., a subject suspected of having or determined to have cancer), (ii) extracting nucleic acid molecules (e.g., a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules) from the sample, (iii) ligating one or more adapters to the nucleic acid molecules extracted from the sample (e.g., one or more amplification primers, flow cell adaptor sequences, substrate adapter sequences, or sample index sequences), (iv) amplifying the nucleic acid molecules (e.g., using a polymerase chain reaction (PCR) amplification technique, a non-PCR amplification technique, or an isothermal amplification technique), (v) capturing nucleic acid molecules from the amplified nucleic acid molecules (e.g., by hybridization to one or more bait molecules, where the bait molecules each comprise one or more nucleic acid
  • PCR polymerase
  • the report comprises output from the methods described herein. In some instances, all or a portion of the report may be displayed in the graphical user interface of an online or web-based healthcare portal. In some instances, the report is transmitted via a computer network or peer-to-peer connection.
  • the disclosed methods may be used with any of a variety of samples.
  • the sample may comprise a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the sample may be a liquid biopsy sample and may comprise blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • the sample may be a liquid biopsy sample and may comprise cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the nucleic acid molecules extracted from a sample may comprise a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules may be derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules may be derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample may comprise a liquid biopsy sample, and the tumor nucleic acid molecules may be derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample while the non-tumor nucleic acid molecules may be derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • the disclosed methods for determining a receptor status of an individual may be used to diagnose (or as part of a diagnosis of) the presence of disease or other condition (e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease) in a subject (e.g., a patient).
  • disease or other condition e.g., cancer, genetic disorders (such as Down Syndrome and Fragile X), neurological disorders, or any other disease type where detection of variants, e.g., copy number alternations, are relevant to diagnosing, treating, or predicting said disease
  • a subject e.g., a patient
  • the disclosed methods may be applicable to diagnosis of any of a variety of cancers as described elsewhere herein.
  • the disclosed methods for determining a receptor status of an individual may be used to predict genetic disorders in fetal DNA. (e.g., for invasive or non- invasive prenatal testing). For example, sequence read data obtained by sequencing fetal DNA extracted from samples obtained using invasive amniocentesis, chorionic villus sampling (cVS), or fetal umbilical cord sampling techniques, or obtained using non-invasive sampling of cell-free DNA (cfDNA) samples (which comprises a mix of maternal cfDNA and fetal cfDNA), may be processed according to the disclosed methods to identify variants, e.g., copy number alterations, associated with, e.g., Down Syndrome (trisomy 21), trisomy 18, trisomy 13, and extra or missing copies of the X and Y chromosomes.
  • the disclosed methods for determining a receptor status of an individual may be used to select a subject (e.g., a patient) for a clinical trial based on the score indicative of a receptor status based on alterations present at one or more gene loci.
  • patient selection for clinical trials based on, e.g., identification of a receptor status may accelerate the development of targeted therapies and improve the healthcare outcomes for treatment decisions.
  • the disclosed methods for determining a receptor status of an individual may be used to select an appropriate therapy or treatment (e.g., an anti-cancer therapy or anti-cancer treatment) for a subject.
  • an appropriate therapy or treatment e.g., an anti-cancer therapy or anti-cancer treatment
  • the anti-cancer therapy or treatment may comprise use of a poly (ADP-ribose) polymerase inhibitor (PARPi), a platinum compound, chemotherapy, radiation therapy, a targeted therapy (e.g., immunotherapy), surgery, or any combination thereof.
  • PARPi poly (ADP-ribose) polymerase inhibitor
  • the targeted therapy may comprise therapies that target the relevant receptor (e.g., AR, PR, ER, HER2 receptor) and/or therapies that target hormone production, (e.g., hormone therapies).
  • the therapies may include alpelisib as well as CDK4/6 inhibitors.
  • therapies may be target HER2 and include, for example, antibody drug conjugates (ADC).
  • the targeted therapy may comprise abemaciclib (Verzenio), abiraterone acetate (Zytiga), acalabrutinib (Calquence), ado-trastuzumab emtansine (Kadcyla), afatinib dimaleate (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), alitretinoin (Panretin), alpelisib (Piqray), amivantamab-vmjw (Rybrevant), anastrozole (Arimidex), apalutamide (Erleada), asciminib hydrochloride (Scemblix), atezolizumab (Tecentriq), avapritinib (Ayvakit), avelumab (Bavencio), axicabtagene c
  • the disclosed methods for determining a receptor status of an individual may be used in treating a disease (e.g., a cancer) in a subject.
  • a disease e.g., a cancer
  • an effective amount of an anti-cancer therapy or anti-cancer treatment may be administered to the subject.
  • only one anti-cancer therapy or anti-cancer treatment may be administered to the subject, while in other instances, one or more anti-cancer therapy or anti-cancer treatment may be administered to the subject.
  • the disclosed methods for determining a receptor status of an individual may be used for monitoring disease progression or recurrence (e.g., cancer or tumor progression or recurrence) in a subject.
  • the methods may be used to determine a receptor status of an individual based on a first sample obtained from the subject at a first time point, and used to determine a receptor status of the individual based on a second sample obtained from the subject at a second time point, where comparison of the first determination of the receptor status and the second determination of the receptor status allows one to monitor disease progression or recurrence.
  • the first time point is chosen before the subject has been administered a therapy or treatment
  • the second time point is chosen after the subject has been administered the therapy or treatment.
  • the disclosed methods may be used for adjusting a therapy or treatment (e.g., an anti-cancer treatment or anti-cancer therapy) for a subject, e.g., by adjusting a treatment dose and/or selecting a different treatment in response to a change in the determination of the receptor status.
  • a therapy or treatment e.g., an anti-cancer treatment or anti-cancer therapy
  • the value of determining a receptor status of an individual using the disclosed methods may be used as a prognostic or diagnostic indicator associated with the sample.
  • the prognostic or diagnostic indicator may comprise an indicator of the presence of a disease (e.g., cancer) in the sample, an indicator of the probability that a disease (e.g., cancer) is present in the sample, an indicator of the probability that the subject from which the sample was derived will develop a disease (e.g., cancer) (i.e., a risk factor), or an indicator of the likelihood that the subject from which the sample was derived will respond to a particular therapy or treatment.
  • a disease e.g., cancer
  • an indicator of the probability that a disease e.g., cancer
  • an indicator of the probability that the subject from which the sample was derived will develop a disease e.g., cancer
  • a risk factor i.e., a risk factor
  • the disclosed methods for determining a receptor status of an individual may be implemented as part of a genomic profiling process that comprises identification of the presence of variant sequences at one or more gene loci in a sample derived from a subject as part of detecting, monitoring, predicting a risk factor, or selecting a treatment for a particular disease, e.g., cancer.
  • the variant panel selected for genomic profiling may comprise the detection of variant sequences at a selected set of gene loci.
  • the variant panel selected for genomic profiling may comprise detection of variant sequences at a number of gene loci through comprehensive genomic profiling (CGP), which is a next-generation sequencing (NGS) approach used to assess hundreds of genes (including relevant cancer biomarkers) in a single assay.
  • CGP comprehensive genomic profiling
  • NGS next-generation sequencing
  • Inclusion of the disclosed methods for determining a receptor status of an individual as part of a genomic profiling process can improve the validity of, e.g., disease detection calls and treatment decisions, made on the basis of the genomic profile by, for example, independently confirming the presence of a receptor status in a given patient sample.
  • a genomic profile may comprise information on the presence of genes (or variant sequences thereof), copy number variations, epigenetic traits, proteins (or modifications thereof), and/or other biomarkers in an individual’s genome and/or proteome, as well as information on the individual’s corresponding phenotypic traits and the interaction between genetic or genomic traits, phenotypic traits, and environmental factors.
  • a genomic profile for the subject may comprise results from a comprehensive genomic profiling (CGP) test, a nucleic acid sequencing-based test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the method can further include administering or applying a treatment or therapy (e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy) to the subject based on the generated genomic profile.
  • a treatment or therapy e.g., an anti-cancer agent, anti-cancer treatment, or anti-cancer therapy
  • An anti-cancer agent or anti-cancer treatment may refer to a compound that is effective in the treatment of cancer cells.
  • anti-cancer agents or anti-cancer therapies include, but not limited to, alkylating agents, antimetabolites, natural products, hormones, chemotherapy, radiation therapy, immunotherapy, surgery, or a therapy configured to target a defect in a specific cell signaling pathway, e.g., a defect in a DNA mismatch repair (MMR) pathway.
  • MMR DNA mismatch repair
  • the disclosed methods and systems may be used with any of a variety of samples (also referred to herein as specimens) comprising nucleic acids (e.g., DNA or RNA) that are collected from a subject (e.g., a patient).
  • samples also referred to herein as specimens
  • nucleic acids e.g., DNA or RNA
  • a sample examples include, but are not limited to, a tumor sample, a tissue sample, a biopsy sample (e.g., a tissue biopsy, a liquid biopsy, or both), a blood sample (e.g., a peripheral whole blood sample), a blood plasma sample, a blood serum sample, a lymph sample, a saliva sample, a sputum sample, a urine sample, a gynecological fluid sample, a circulating tumor cell (CTC) sample, a cerebral spinal fluid (CSF) sample, a pericardial fluid sample, a pleural fluid sample, an ascites (peritoneal fluid) sample, a feces (or stool) sample, or other body fluid, secretion, and/or excretion sample (or cell sample derived therefrom).
  • the sample may be frozen sample or a formalin-fixed paraffin-embedded (FFPE) sample.
  • FFPE formalin-fixed paraffin-embedded
  • the sample may be collected by tissue resection (e.g., surgical resection), needle biopsy, bone marrow biopsy, bone marrow aspiration, skin biopsy, endoscopic biopsy, fine needle aspiration, oral swab, nasal swab, vaginal swab or a cytology smear, scrapings, washings or lavages (such as a ductal lavage or bronchoalveolar lavage), etc.
  • the sample is a liquid biopsy sample, and may comprise, e.g., whole blood, blood plasma, blood serum, urine, stool, sputum, saliva, or cerebrospinal fluid.
  • the sample may be a liquid biopsy sample and may comprise circulating tumor cells (CTCs).
  • CTCs circulating tumor cells
  • the sample may be a liquid biopsy sample and may comprise cell- free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • the sample may comprise one or more premalignant or malignant cells.
  • Premalignant refers to a cell or tissue that is not yet malignant but is poised to become malignant.
  • the sample may be acquired from a solid tumor, a soft tissue tumor, or a metastatic lesion.
  • the sample may be acquired from a hematologic malignancy or pre-malignancy.
  • the sample may comprise a tissue or cells from a surgical margin.
  • the sample may comprise tumor-infiltrating lymphocytes.
  • the sample may comprise one or more non- malignant cells.
  • the sample may be, or is part of, a primary tumor or a metastasis (e.g., a metastasis biopsy sample).
  • the sample may be obtained from a site (e.g., a tumor site) with the highest percentage of tumor (e.g., tumor cells) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the sample may be obtained from a site (e.g., a tumor site) with the largest tumor focus (e.g., the largest number of tumor cells as visualized under a microscope) as compared to adjacent sites (e.g., sites adjacent to the tumor).
  • the disclosed methods may further comprise analyzing a primary control (e.g., a normal tissue sample). In some instances, the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control. In some instances, the sample may comprise any normal control (e.g., a normal adjacent tissue (NAT)) if no primary control is available. In some instances, the sample may be or may comprise histologically normal tissue. In some instances, the method includes evaluating a sample, e.g., a histologically normal sample (e.g., from a surgical tissue margin) using the methods described herein.
  • a primary control e.g., a normal tissue sample.
  • the disclosed methods may further comprise determining if a primary control is available and, if so, isolating a control nucleic acid (e.g., DNA) from said primary control.
  • the sample may comprise any normal control (e.g.,
  • the disclosed methods may further comprise acquiring a sub-sample enriched for non-tumor cells, e.g., by macro-dissecting non-tumor tissue from said NAT in a sample not accompanied by a primary control. In some instances, the disclosed methods may further comprise determining that no primary control and no NAT is available, and marking said sample for analysis without a matched control.
  • samples obtained from histologically normal tissues may still comprise a genetic alteration such as a variant sequence as described herein.
  • the methods may thus further comprise re-classifying a sample based on the presence of the detected genetic alteration.
  • multiple samples e.g., from different subjects are processed simultaneously.
  • tissue samples e.g., solid tissue samples, soft tissue samples, metastatic lesions, or liquid biopsy samples.
  • tissues include, but are not limited to, connective tissue, muscle tissue, nervous tissue, epithelial tissue, and blood.
  • Tissue samples may be collected from any of the organs within an animal or human body.
  • human organs include, but are not limited to, the brain, heart, lungs, liver, kidneys, pancreas, spleen, thyroid, mammary glands, uterus, prostate, large intestine, small intestine, bladder, bone, skin, etc.
  • the nucleic acids extracted from the sample may comprise deoxyribonucleic acid (DNA) molecules.
  • DNA DNA that may be suitable for analysis by the disclosed methods include, but are not limited to, genomic DNA or fragments thereof, mitochondrial DNA or fragments thereof, cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA).
  • Cell-free DNA (cfDNA) is comprised of fragments of DNA that are released from normal and/or cancerous cells during apoptosis and necrosis, and circulate in the blood stream and/or accumulate in other bodily fluids.
  • Circulating tumor DNA ctDNA is comprised of fragments of DNA that are released from cancerous cells and tumors that circulate in the blood stream and/or accumulate in other bodily fluids.
  • DNA is extracted from nucleated cells from the sample.
  • a sample may have a low nucleated cellularity, e.g., when the sample is comprised mainly of erythrocytes, lesional cells that contain excessive cytoplasm, or tissue with fibrosis.
  • a sample with low nucleated cellularity may require more, e.g., greater, tissue volume for DNA extraction.
  • the nucleic acids extracted from the sample may comprise ribonucleic acid (RNA) molecules.
  • RNA examples include, but are not limited to, total cellular RNA, total cellular RNA after depletion of certain abundant RNA sequences (e.g., ribosomal RNAs), cell-free RNA (cfRNA), messenger RNA (mRNA) or fragments thereof, the poly(A)-tailed mRNA fraction of the total RNA, ribosomal RNA (rRNA) or fragments thereof, transfer RNA (tRNA) or fragments thereof, and mitochondrial RNA or fragments thereof.
  • RNA may be extracted from the sample and converted to complementary DNA (cDNA) using, e.g., a reverse transcription reaction.
  • the cDNA is produced by random-primed cDNA synthesis methods. In other instances, the cDNA synthesis is initiated at the poly(A) tail of mature mRNAs by priming with oligo(dT)-containing oligonucleotides. Methods for depletion, poly(A) enrichment, and cDNA synthesis are well known to those of skill in the art.
  • the sample may comprise a tumor content (e.g., comprising tumor cells or tumor cell nuclei), or a non-tumor content (e.g., immune cells, fibroblasts, and other nontumor cells).
  • the tumor content of the sample may constitute a sample metric.
  • the sample may comprise a tumor content of at least 5-50%, 10-40%, 15-25%, or 20-30% tumor cell nuclei.
  • the sample may comprise a tumor content of at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, or at least 50% tumor cell nuclei.
  • the percent tumor cell nuclei (e.g., sample fraction) is determined (e.g., calculated) by dividing the number of tumor cells in the sample by the total number of all cells within the sample that have nuclei.
  • a different tumor content calculation may be required due to the presence of hepatocytes having nuclei with twice, or more than twice, the DNA content of other, e.g., non-hepatocyte, somatic cell nuclei.
  • the sensitivity of detection of a genetic alteration e.g., a variant sequence, or a determination of, e.g., micro satellite instability, may depend on the tumor content of the sample. For example, a sample having a lower tumor content can result in lower sensitivity of detection for a given size sample.
  • the sample comprises nucleic acid (e.g., DNA, RNA (or a cDNA derived from the RNA), or both), e.g., from a tumor or from normal tissue.
  • nucleic acid e.g., DNA, RNA (or a cDNA derived from the RNA), or both
  • the sample may further comprise a non-nucleic acid component, e.g., cells, protein, carbohydrate, or lipid, e.g., from the tumor or normal tissue.
  • the sample is obtained (e.g., collected) from a subject (e.g., patient) with a condition or disease (e.g., a hyperproliferative disease or a non-cancer indication) or suspected of having the condition or disease.
  • a condition or disease e.g., a hyperproliferative disease or a non-cancer indication
  • the hyperproliferative disease is a cancer.
  • the cancer is a solid tumor or a metastatic form thereof.
  • the cancer is a breast cancer.
  • the subject has a cancer or is at risk of having a cancer.
  • the subject has a genetic predisposition to a cancer (e.g., having a genetic mutation that increases his or her baseline risk for developing a cancer).
  • the subject has been exposed to an environmental perturbation (e.g., radiation or a chemical) that increases his or her risk for developing a cancer.
  • the subject is in need of being monitored for development of a cancer.
  • the subject is in need of being monitored for cancer progression or regression, e.g., after being treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject is in need of being monitored for relapse of cancer.
  • the subject is in need of being monitored for minimum residual disease (MRD).
  • the subject has been, or is being treated, for cancer.
  • the subject has not been treated with an anti-cancer therapy (or anti-cancer treatment).
  • the subject e.g., a patient
  • a post-targeted therapy sample e.g., specimen
  • the post-targeted therapy sample is a sample obtained after the completion of the targeted therapy.
  • the patient has not been previously treated with a targeted therapy.
  • the sample comprises a resection, e.g., an original resection, or a resection following recurrence (e.g., following a disease recurrence post-therapy).
  • the sample is acquired from a subject having a cancer.
  • exemplary cancers include, but are not limited to, hormone driven cancers such as breast cancer and prostate cancer.
  • the cancers may include, but are not limited to, B cell cancer (e.g., multiple myeloma), melanomas, breast cancer, lung cancer (such as non-small cell lung carcinoma or NSCLC), bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, adenocarcinomas, inflammatory myofibro
  • the cancer comprises acute lymphoblastic leukemia (Philadelphia chromosome positive), acute lymphoblastic leukemia (precursor B-cell), acute myeloid leukemia (FLT3+), acute myeloid leukemia (with an IDH2 mutation), anaplastic large cell lymphoma, basal cell carcinoma, B-cell chronic lymphocytic leukemia, bladder cancer, breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), cervical cancer, cholangiocarcinoma, chronic lymphocytic leukemia, chronic lymphocytic leukemia (with 17p deletion), chronic myelogenous leukemia, chronic myelogenous leukemia (Philadelphia chromosome positive), classical Hodgkin lymphoma, colorectal cancer, colorectal cancer (dMMR and MSI-H), colorectal cancer (KRAS wild type), cryopyrin-associated periodic syndrome,
  • the cancer is a hematologic malignancy (or premaligancy).
  • a hematologic malignancy refers to a tumor of the hematopoietic or lymphoid tissues, e.g., a tumor that affects blood, bone marrow, or lymph nodes.
  • Exemplary hematologic malignancies include, but are not limited to, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, acute monocytic leukemia (AMoL), chronic myelomonocytic leukemia (CMML), juvenile myelomonocytic leukemia (JMML), or large granular lymphocytic leukemia), lymphoma (e.g., AIDS-related lymphoma, cutaneous T-cell lymphoma, Hodgkin lymphoma (e.g., classical Hodgkin lymphoma or nodular lymphocyte- predominant Hodgkin lymphoma), mycosis fungoides, non-Hodgkin lymphoma (e.g., B-cell non-Hodgkin lymphoma (e.g.
  • DNA or RNA may be extracted from tissue samples, biopsy samples, blood samples, or other bodily fluid samples using any of a variety of techniques known to those of skill in the art (see, e.g., Example 1 of International Patent Application Publication No. WO 2012/092426; Tan, et al. (2009), “DNA, RNA, and Protein Extraction: The Past and The Present”, J. Biomed. Biotech. 2009:574398; the technical literature for the Maxwell® 16 LEV Blood DNA Kit (Promega Corporation, Madison, WI); and the Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual (Promega Literature #TM333, January 1, 2011, Promega Corporation, Madison, WI)). Protocols for RNA isolation are disclosed in, e.g., the Maxwell® 16 Total RNA Purification Kit Technical Bulletin (Promega Literature #TB351, August 2009, Promega Corporation, Madison, WI).
  • a typical DNA extraction procedure for example, comprises (i) collection of the fluid sample, cell sample, or tissue sample from which DNA is to be extracted, (ii) disruption of cell membranes (i.e., cell lysis), if necessary, to release DNA and other cytoplasmic components, (iii) treatment of the fluid sample or lysed sample with a concentrated salt solution to precipitate proteins, lipids, and RNA, followed by centrifugation to separate out the precipitated proteins, lipids, and RNA, and (iv) purification of DNA from the supernatant to remove detergents, proteins, salts, or other reagents used during the cell membrane lysis step.
  • Disruption of cell membranes may be performed using a variety of mechanical shear (e.g., by passing through a French press or fine needle) or ultrasonic disruption techniques.
  • the cell lysis step often comprises the use of detergents and surfactants to solubilize lipids the cellular and nuclear membranes.
  • the lysis step may further comprise use of proteases to break down protein, and/or the use of an RNase for digestion of RNA in the sample.
  • Examples of suitable techniques for DNA purification include, but are not limited to, (i) precipitation in ice-cold ethanol or isopropanol, followed by centrifugation (precipitation of DNA may be enhanced by increasing ionic strength, e.g., by addition of sodium acetate), (ii) phenol-chloroform extraction, followed by centrifugation to separate the aqueous phase containing the nucleic acid from the organic phase containing denatured protein, and (iii) solid phase chromatography where the nucleic acids adsorb to the solid phase (e.g., silica or other) depending on the pH and salt concentration of the buffer.
  • the solid phase e.g., silica or other
  • cellular and histone proteins bound to the DNA may be removed either by adding a protease or by having precipitated the proteins with sodium or ammonium acetate, or through extraction with a phenol-chloroform mixture prior to a DNA precipitation step.
  • DNA may be extracted using any of a variety of suitable commercial DNA extraction and purification kits. Examples include, but are not limited to, the QIAamp (for isolation of genomic DNA from human samples) and DNAeasy (for isolation of genomic DNA from animal or plant samples) kits from Qiagen (Germantown, MD) or the Maxwell® and ReliaPrepTM series of kits from Promega (Madison, WI).
  • the sample may comprise a formalin-fixed (also known as formaldehyde-fixed, or paraformaldehyde-fixed), paraffin-embedded (FFPE) tissue preparation.
  • FFPE formalin-fixed
  • the FFPE sample may be a tissue sample embedded in a matrix, e.g., an FFPE block.
  • Methods to isolate nucleic acids (e.g., DNA) from formaldehyde- or paraformaldehyde-fixed, paraffin-embedded (FFPE) tissues are disclosed in, e.g., Cronin, et al., (2004) Am J Pathol.
  • the Maxwell® 16 FFPE Plus LEV DNA Purification Kit is used with the Maxwell® 16 Instrument for purification of genomic DNA from 1 to 10 pm sections of FFPE tissue. DNA is purified using silica-clad paramagnetic particles (PMPs), and eluted in low elution volume.
  • PMPs silica-clad paramagnetic particles
  • the E.Z.N.A.® FFPE DNA Kit uses a spin column and buffer system for isolation of genomic DNA.
  • QIAamp® DNA FFPE Tissue Kit uses QIAamp® DNA Micro technology for purification of genomic and mitochondrial DNA.
  • the disclosed methods may further comprise determining or acquiring a yield value for the nucleic acid extracted from the sample and comparing the determined value to a reference value. For example, if the determined or acquired value is less than the reference value, the nucleic acids may be amplified prior to proceeding with library construction. In some instances, the disclosed methods may further comprise determining or acquiring a value for the size (or average size) of nucleic acid fragments in the sample, and comparing the determined or acquired value to a reference value, e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • a reference value e.g., a size (or average size) of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 base pairs (bps).
  • the nucleic acids are typically dissolved in a slightly alkaline buffer, e.g., Tris-EDTA (TE) buffer, or in ultra-pure water.
  • the isolated nucleic acids e.g., genomic DNA
  • genomic DNA can be fragmented by physical shearing methods, enzymatic cleavage methods, chemical cleavage methods, and other methods known to those of skill in the art. Methods for DNA shearing are described in Example 4 in International Patent Application Publication No. WO 2012/092426. In some instances, alternatives to DNA shearing methods can be used to avoid a ligation step during library preparation.
  • the nucleic acids isolated from the sample may be used to construct a library (e.g., a nucleic acid library as described herein).
  • the nucleic acids are fragmented using any of the methods described above, optionally subjected to repair of chain end damage, and optionally ligated to synthetic adapters, primers, and/or barcodes (e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences), size-selected (e.g., by preparative gel electrophoresis), and/or amplified (e.g., using PCR, a non-PCR amplification technique, or an isothermal amplification technique).
  • synthetic adapters, primers, and/or barcodes e.g., amplification primers, sequencing adapters, flow cell adapters, substrate adapters, sample barcodes or indexes, and/or unique molecular identifier sequences
  • the fragmented and adapter-ligated group of nucleic acids is used without explicit size selection or amplification prior to hybridization-based selection of target sequences.
  • the nucleic acid is amplified by any of a variety of specific or non-specific nucleic acid amplification methods known to those of skill in the art.
  • the nucleic acids are amplified, e.g., by a whole-genome amplification method such as random-primed strand-displacement amplification. Examples of nucleic acid library preparation techniques for next-generation sequencing are described in, e.g., van Dijk, et al. (2014), Exp. Cell Research 322: 12 - 20, and Illumina’s genomic DNA sample preparation kit.
  • the resulting nucleic acid library may contain all or substantially all of the complexity of the genome.
  • the term “substantially all” in this context refers to the possibility that there can in practice be some unwanted loss of genome complexity during the initial steps of the procedure.
  • the methods described herein also are useful in cases where the nucleic acid library comprises a portion of the genome, e.g., where the complexity of the genome is reduced by design. In some instances, any selected portion of the genome can be used with a method described herein. For example, in certain embodiments, the entire exome or a subset thereof is isolated.
  • the library may include at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the library may consist of cDNA copies of genomic DNA that includes copies of at least 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%, 10%, or 5% of the genomic DNA.
  • the amount of nucleic acid used to generate the nucleic acid library may be less than 5 micrograms, less than 1 microgram, less than 500 ng, less than 200 ng, less than 100 ng, less than 50 ng, less than 10 ng, less than 5 ng, or less than 1 ng.
  • a library (e.g., a nucleic acid library) includes a collection of nucleic acid molecules.
  • the nucleic acid molecules of the library can include a target nucleic acid molecule (e.g., a tumor nucleic acid molecule, a reference nucleic acid molecule and/or a control nucleic acid molecule; also referred to herein as a first, second and/or third nucleic acid molecule, respectively).
  • the nucleic acid molecules of the library can be from a single subject or individual.
  • a library can comprise nucleic acid molecules derived from more than one subject (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30 or more subjects).
  • two or more libraries from different subjects can be combined to form a library having nucleic acid molecules from more than one subject (where the nucleic acid molecules derived from each subject are optionally ligated to a unique sample barcode corresponding to a specific subject).
  • the subject is a human having, or at risk of having, a cancer or tumor.
  • the library may comprise one or more subgenomic intervals.
  • a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position for which a variant at the position is associated (positively or negatively) with a tumor phenotype.
  • a subgenomic interval comprises more than one nucleotide position. Such instances include sequences of at least 2, 5, 10, 50, 100, 150, 250, or more than 250 nucleotide positions in length.
  • Subgenomic intervals can comprise, e.g., one or more entire genes (or portions thereof), one or more exons or coding sequences (or portions thereof), one or more introns (or portion thereof), one or more microsatellite region (or portions thereof), or any combination thereof.
  • a subgenomic interval can comprise all or a part of a fragment of a naturally occurring nucleic acid molecule, e.g., a genomic DNA molecule.
  • a subgenomic interval can correspond to a fragment of genomic DNA which is subjected to a sequencing reaction.
  • a subgenomic interval is a continuous sequence from a genomic source.
  • a subgenomic interval includes sequences that are not contiguous in the genome, e.g., subgenomic intervals in cDNA can include exonexon junctions formed as a result of splicing.
  • the subgenomic interval comprises a tumor nucleic acid molecule.
  • the subgenomic interval comprises a non-tumor nucleic acid molecule.
  • the methods described herein can be used in combination with, or as part of, a method for evaluating a plurality or set of subject intervals (e.g., target sequences), e.g., from a set of genomic loci (e.g., gene loci or fragments thereof), as described herein.
  • a plurality or set of subject intervals e.g., target sequences
  • genomic loci e.g., gene loci or fragments thereof
  • the set of genomic loci evaluated by the disclosed methods comprises a plurality of, e.g., genes, which in mutant form, are associated with an effect on cell division, growth or survival, or are associated with a cancer, e.g., a cancer described herein.
  • the set of gene loci evaluated by the disclosed methods comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, or more than 100 gene loci.
  • the selected gene loci may include subject intervals comprising non-coding sequences, coding sequences, intragenic regions, or intergenic regions of the subject genome.
  • the subject intervals can include a non-coding sequence or fragment thereof (e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof), a coding sequence of fragment thereof, an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
  • a non-coding sequence or fragment thereof e.g., a promoter sequence, enhancer sequence, 5’ untranslated region (5’ UTR), 3’ untranslated region (3’ UTR), or a fragment thereof
  • a coding sequence of fragment thereof e.g., an exon sequence or fragment thereof, an intron sequence or a fragment thereof.
  • the methods described herein may comprise contacting a nucleic acid library with a plurality of target capture reagents in order to select and capture a plurality of specific target sequences (e.g., gene sequences or fragments thereof) for analysis.
  • a target capture reagent i.e., a molecule which can bind to and thereby allow capture of a target molecule
  • a target capture reagent is used to select the subject intervals to be analyzed.
  • a target capture reagent can be a bait molecule, e.g., a nucleic acid molecule (e.g., a DNA molecule or RNA molecule) which can hybridize to (i.e., is complementary to) a target molecule, and thereby allows capture of the target nucleic acid.
  • the target capture reagent e.g., a bait molecule (or bait sequence)
  • the target nucleic acid is a genomic DNA molecule, an RNA molecule, a cDNA molecule derived from an RNA molecule, a microsatellite DNA sequence, and the like.
  • the target capture reagent is suitable for solution-phase hybridization to the target. In some instances, the target capture reagent is suitable for solid-phase hybridization to the target. In some instances, the target capture reagent is suitable for both solution-phase and solid-phase hybridization to the target.
  • the design and construction of target capture reagents is described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the methods described herein provide for optimized sequencing of a large number of genomic loci (e.g., genes or gene products (e.g., mRNA), micro satellite loci, etc.) from samples (e.g., cancerous tissue specimens, liquid biopsy samples, and the like) from one or more subjects by the appropriate selection of target capture reagents to select the target nucleic acid molecules to be sequenced.
  • a target capture reagent may hybridize to a specific target locus, e.g., a specific target gene locus or fragment thereof.
  • a target capture reagent may hybridize to a specific group of target loci, e.g., a specific group of gene loci or fragments thereof.
  • a plurality of target capture reagents comprising a mix of target- specific and/or group- specific target capture reagents may be used.
  • the number of target capture reagents (e.g., bait molecules) in the plurality of target capture reagents (e.g., a bait set) contacted with a nucleic acid library to capture a plurality of target sequences for nucleic acid sequencing is greater than 10, greater than 50, greater than 100, greater than 200, greater than 300, greater than 400, greater than 500, greater than 600, greater than 700, greater than 800, greater than 900, greater than 1,000, greater than 1,250, greater than 1,500, greater than 1,750, greater than 2,000, greater than 3,000, greater than 4,000, greater than 5,000, greater than 10,000, greater than 25,000, or greater than 50,000.
  • the overall length of the target capture reagent sequence can be between about 70 nucleotides and 1000 nucleotides. In one instance, the target capture reagent length is between about 100 and 300 nucleotides, 110 and 200 nucleotides, or 120 and 170 nucleotides, in length. In addition to those mentioned above, intermediate oligonucleotide lengths of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length can be used in the methods described herein. In some embodiments, oligonucleotides of about 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, or 230 bases can be used.
  • each target capture reagent sequence can include: (i) a target-specific capture sequence (e.g., a gene locus or micro satellite locus-specific complementary sequence), (ii) an adapter, primer, barcode, and/or unique molecular identifier sequence, and (iii) universal tails on one or both ends.
  • a target-specific capture sequence e.g., a gene locus or micro satellite locus-specific complementary sequence
  • an adapter, primer, barcode, and/or unique molecular identifier sequence e.g., a target-specific capture sequence
  • universal tails e.g., a target-specific capture sequence
  • target capture reagent can refer to the targetspecific target capture sequence or to the entire target capture reagent oligonucleotide including the target- specific target capture sequence.
  • the target-specific capture sequences in the target capture reagents are between about 40 nucleotides and 1000 nucleotides in length. In some instances, the targetspecific capture sequence is between about 70 nucleotides and 300 nucleotides in length. In some instances, the target- specific sequence is between about 100 nucleotides and 200 nucleotides in length. In yet other instances, the target- specific sequence is between about 120 nucleotides and 170 nucleotides in length, typically 120 nucleotides in length.
  • target-specific sequences of about 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 300, 400, 500, 600, 700, 800, and 900 nucleotides in length, as well as target- specific sequences of lengths between the above-mentioned lengths.
  • the target capture reagent may be designed to select a subject interval containing one or more rearrangements, e.g., an intron containing a genomic rearrangement.
  • the target capture reagent is designed such that repetitive sequences are masked to increase the selection efficiency.
  • complementary target capture reagents can be designed to recognize the juncture sequence to increase the selection efficiency.
  • the disclosed methods may comprise the use of target capture reagents designed to capture two or more different target categories, each category having a different target capture reagent design strategy.
  • the hybridization-based capture methods and target capture reagent compositions disclosed herein may provide for the capture and homogeneous coverage of a set of target sequences, while minimizing coverage of genomic sequences outside of the targeted set of sequences.
  • the target sequences may include the entire exome of genomic DNA or a selected subset thereof.
  • the target sequences may include, e.g., a large chromosomal region (e.g., a whole chromosome arm).
  • the methods and compositions disclosed herein provide different target capture reagents for achieving different sequencing depths and patterns of coverage for complex sets of target nucleic acid sequences.
  • DNA molecules are used as target capture reagent sequences, although RNA molecules can also be used.
  • a DNA molecule target capture reagent can be single stranded DNA (ssDNA) or double- stranded DNA (dsDNA).
  • ssDNA single stranded DNA
  • dsDNA double- stranded DNA
  • an RNA- DNA duplex is more stable than a DNA-DNA duplex and therefore provides for potentially better capture of nucleic acids.
  • the disclosed methods comprise providing a selected set of nucleic acid molecules (e.g., a library catch) captured from one or more nucleic acid libraries.
  • the method may comprise: providing one or a plurality of nucleic acid libraries, each comprising a plurality of nucleic acid molecules (e.g., a plurality of target nucleic acid molecules and/or reference nucleic acid molecules) extracted from one or more samples from one or more subjects; contacting the one or a plurality of libraries (e.g., in a solution-based hybridization reaction) with one, two, three, four, five, or more than five pluralities of target capture reagents (e.g., oligonucleotide target capture reagents) to form a hybridization mixture comprising a plurality of target capture reagent/nucleic acid molecule hybrids; separating the plurality of target capture reagent/nucleic acid molecule hybrids from said hybridization mixture, e.g., by
  • the disclosed methods may further comprise amplifying the library catch (e.g., by performing PCR). In other instances, the library catch is not amplified.
  • the target capture reagents can be part of a kit which can optionally comprise instructions, standards, buffers or enzymes or other reagents.
  • the methods disclosed herein may include the step of contacting the library (e.g., the nucleic acid library) with a plurality of target capture reagents to provide a selected library target nucleic acid sequences (i.e., the library catch).
  • the contacting step can be effected in, e.g., solution-based hybridization.
  • the method includes repeating the hybridization step for one or more additional rounds of solution-based hybridization.
  • the method further includes subjecting the library catch to one or more additional rounds of solution-based hybridization with the same or a different collection of target capture reagents.
  • the contacting step is effected using a solid support, e.g., an array.
  • a solid support e.g., an array.
  • suitable solid supports for hybridization are described in, e.g., Albert, T.J. et al. (2007) Nat. Methods 4(11):903-5; Hodges, E. et al. (2007) Nat. Genet. 39(12): 1522-7; and Okou, D.T. et al. (2007) Nat. Methods 4(11 ):907-9, the contents of which are incorporated herein by reference in their entireties.
  • Hybridization methods that can be adapted for use in the methods herein are described in the art, e.g., as described in International Patent Application Publication No. WO 2012/092426. Methods for hybridizing target capture reagents to a plurality of target nucleic acids are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the methods and systems disclosed herein can be used in combination with, or as part of, a method or system for sequencing nucleic acids (e.g., a next-generation sequencing system) to generate a plurality of sequence reads that overlap one or more gene loci within a subgenomic interval in the sample and thereby determine, e.g., gene allele sequences at a plurality of gene loci.
  • a method or system for sequencing nucleic acids e.g., a next-generation sequencing system
  • next-generation sequencing (or “NGS”) as used herein may also be referred to as “massively parallel sequencing” (or “MPS”), and refers to any sequencing method that determines the nucleotide sequence of either individual nucleic acid molecules (e.g., as in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a high throughput fashion (e.g., wherein greater than 10 3 , 10 4 , 10 5 or more than 10 5 molecules are sequenced simultaneously).
  • next-generation sequencing methods are known in the art, and are described in, e.g., Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.
  • Other examples of sequencing methods suitable for use when implementing the methods and systems disclosed herein are described in, e.g., International Patent Application Publication No. WO 2012/092426.
  • the sequencing may comprise, for example, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, or direct sequencing.
  • GGS whole genome sequencing
  • sequencing may be performed using, e.g., Sanger sequencing.
  • the sequencing may comprise a paired-end sequencing technique that allows both ends of a fragment to be sequenced and generates high-quality, alignable sequence data for detection of, e.g., genomic rearrangements, repetitive sequence elements, gene fusions, and novel transcripts.
  • sequencing may comprise Illumina MiSeq sequencing.
  • sequencing may comprise Illumina HiSeq sequencing.
  • sequencing may comprise Illumina NovaSeq sequencing. Optimized methods for sequencing a large number of target genomic loci in nucleic acids extracted from a sample are described in more detail in, e.g., International Patent Application Publication No. WO 2020/236941, the entire content of which is incorporated herein by reference.
  • the disclosed methods comprise one or more of the steps of: (a) acquiring a library comprising a plurality of normal and/or tumor nucleic acid molecules from a sample; (b) simultaneously or sequentially contacting the library with one, two, three, four, five, or more than five pluralities of target capture reagents under conditions that allow hybridization of the target capture reagents to the target nucleic acid molecules, thereby providing a selected set of captured normal and/or tumor nucleic acid molecules (i.e., a library catch); (c) separating the selected subset of the nucleic acid molecules (e.g., the library catch) from the hybridization mixture, e.g., by contacting the hybridization mixture with a binding entity that allows for separation of the target capture reagent/nucleic acid molecule hybrids from the hybridization mixture, (d) sequencing the library catch to acquiring a plurality of reads (e.g., sequence reads) that overlap one or more subject intervals (e.g.
  • acquiring sequence reads for one or more subject intervals may comprise sequencing at least 1, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 750, at least 800, at least 850, at least 900, at least 950, at least 1,000, at least 1,250, at least 1,500, at least 1,750, at least 2,000, at least 2,250, at least 2,500, at least 2,750, at least 3,000, at least 3,500, at least 4,000, at least 4,500, or at least 5,000 loci, e.g., genomic loci, gene loci, microsatellite loci, etc.
  • loci e.g., genomic loci, gene loci, microsatellite loci, etc.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing a subject interval for any number of loci within the range described in this paragraph, e.g., for at least 2,850 gene loci.
  • acquiring a sequence read for one or more subject intervals comprises sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of at least 20 bases, at least 30 bases, at least 40 bases, at least 50 bases, at least 60 bases, at least 70 bases, at least 80 bases, at least 90 bases, at least 100 bases, at least 120 bases, at least 140 bases, at least 160 bases, at least 180 bases, at least 200 bases, at least 220 bases, at least 240 bases, at least 260 bases, at least 280 bases, at least 300 bases, at least 320 bases, at least 340 bases, at least 360 bases, at least 380 bases, or at least 400 bases.
  • acquiring a sequence read for the one or more subject intervals may comprise sequencing a subject interval with a sequencing method that provides a sequence read length (or average sequence read length) of any number of bases within the range described in this paragraph, e.g., a sequence read length (or average sequence read length) of 56 bases.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with at least lOOx, at least 150x, at least 200x, at least 250x, at least 500x, at least 750x, at least l,000x, at least 1,500 x, at least 2,000x, at least 2,500x, at least 3,000x, at least 3,500x, at least 4,000x, at least 4,500x, at least 5,000x, at least 5,500x, or at least 6,000x or more coverage (or depth) on average.
  • acquiring a sequence read for one or more subject intervals may comprise sequencing with an average coverage (or depth) having any value within the range of values described in this paragraph, e.g., at least 160x.
  • acquiring a read for the one or more subject intervals comprises sequencing with an average sequencing depth having any value ranging from at least lOOx to at least 6,000x for greater than about 90%, 92%, 94%, 95%, 96%, 97%, 98%, or 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 125x for at least 99% of the gene loci sequenced.
  • acquiring a read for the subject interval comprises sequencing with an average sequencing depth of at least 4,100x for at least 95% of the gene loci sequenced.
  • the relative abundance of a nucleic acid species in the library can be estimated by counting the relative number of occurrences of their cognate sequences (e.g., the number of sequence reads for a given cognate sequence) in the data generated by the sequencing experiment.
  • the disclosed methods and systems provide nucleotide sequences for a set of subject intervals (e.g., gene loci), as described herein.
  • the sequences are provided without using a method that includes a matched normal control (e.g., a wild-type control) and/or a matched tumor control (e.g., primary versus metastatic).
  • the level of sequencing depth as used herein refers to the number of reads (e.g., unique reads) obtained after detection and removal of duplicate reads (e.g., PCR duplicate reads).
  • duplicate reads are evaluated, e.g., to support detection of copy number alteration (CNAs).
  • Alignment is the process of matching a read with a location, e.g., a genomic location or locus.
  • NGS reads may be aligned to a known reference sequence (e.g., a wild-type sequence).
  • NGS reads may be assembled de novo. Methods of sequence alignment for NGS reads are described in, e.g., Trapnell, C. and Salzberg, S.L. Nature Biotech., 2009, 27:455-457. Examples of de novo sequence assemblies are described in, e.g., Warren R., et al., Bioinformatics, 2007, 23:500-501; Butler, J.
  • Misalignment e.g., the placement of base-pairs from a short read at incorrect locations in the genome
  • misalignment of reads due to sequence context can lead to reduction in sensitivity of mutation detection
  • sequence context e.g., the presence of repetitive sequence
  • Other examples of sequence context that may cause misalignment include short-tandem repeats, interspersed repeats, low complexity regions, insertions - deletions (indels), and paralogs.
  • misalignment may introduce artifactual reads of “mutated” alleles by placing reads of actual reference genome base sequences at the wrong location. Because mutation-calling algorithms for multigene analysis should be sensitive to even low-abundance mutations, sequence misalignments may increase false positive discovery rates and/or reduce specificity.
  • the methods and systems disclosed herein may integrate the use of multiple, individually-tuned, alignment methods or algorithms to optimize base-calling performance in sequencing methods, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci.
  • the disclosed methods and systems may comprise the use of one or more global alignment algorithms.
  • the disclosed methods and systems may comprise the use of one or more local alignment algorithms. Examples of alignment algorithms that may be used include, but are not limited to, the Burrows-Wheeler Alignment (BWA) software bundle (see, e.g., Li, et al.
  • BWA Burrows-Wheeler Alignment
  • the methods and systems disclosed herein may also comprise the use of a sequence assembly algorithm, e.g., the Arachne sequence assembly algorithm (see, e.g., Batzoglou, et al. (2002), “ARACHNE: A Whole-Genome Shotgun Assembler”, Genome Res. 12: 177-189).
  • the alignment method used to analyze sequence reads is not individually customized or tuned for detection of different variants (e.g., point mutations, insertions, deletions, and the like) at different genomic loci.
  • different alignment methods are used to analyze reads that are individually customized or tuned for detection of at least a subset of the different variants detected at different genomic loci.
  • different alignment methods are used to analyze reads that are individually customized or tuned to detect each different variant at different genomic loci.
  • tuning can be a function of one or more of: (i) the genetic locus (e.g., gene loci, micro satellite locus, or other subject interval) being sequenced, (ii) the tumor type associated with the sample, (iii) the variant being sequenced, or (iv) a characteristic of the sample or the subject.
  • the selection or use of alignment conditions that are individually tuned to a number of specific subject intervals to be sequenced allows optimization of speed, sensitivity, and specificity.
  • the method is particularly effective when the alignment of reads for a relatively large number of diverse subject intervals are optimized.
  • the method includes the use of an alignment method optimized for rearrangements in combination with other alignment methods optimized for subject intervals not associated with rearrangements.
  • the methods disclosed herein allow for the rapid and efficient alignment of troublesome reads, e.g., a read having a rearrangement.
  • a read for a subject interval comprises a nucleotide position with a rearrangement, e.g., a translocation
  • the method can comprise using an alignment method that is appropriately tuned and that includes: (i) selecting a rearrangement reference sequence for alignment with a read, wherein said rearrangement reference sequence aligns with a rearrangement (in some instances, the reference sequence is not identical to the genomic rearrangement); and (ii) comparing, e.g., aligning, a read with said rearrangement reference sequence.
  • a method of analyzing a sample can comprise: (i) performing a comparison (e.g., an alignment comparison) of a read using a first set of parameters (e.g., using a first mapping algorithm, or by comparison with a first reference sequence), and determining if said read meets a first alignment criterion (e.g., the read can be aligned with said first reference sequence, e.g., with less than a specific number of mismatches); (ii) if said read fails to meet the first alignment criterion, performing a second alignment comparison using a second set of parameters, (e.g., using a second mapping algorithm, or by comparison with a second reference sequence); and (iii) optionally, determining if said read meets said second criterion (e.g., the read can be
  • the alignment of sequence reads in the disclosed methods may be combined with a mutation calling method as described elsewhere herein.
  • reduced sensitivity for detecting actual mutations may be addressed by evaluating the quality of alignments (manually or in an automated fashion) around expected mutation sites in the genes or genomic loci (e.g., gene loci) being analyzed.
  • the sites to be evaluated can be obtained from databases of the human genome (e.g., the HG19 human reference genome) or cancer mutations (e.g., COSMIC).
  • Regions that are identified as problematic can be remedied with the use of an algorithm selected to give better performance in the relevant sequence context, e.g., by alignment optimization (or re-alignment) using slower, but more accurate alignment algorithms such as Smith- Waterman alignment.
  • customized alignment approaches may be created by, e.g., adjustment of maximum difference mismatch penalty parameters for genes with a high likelihood of containing substitutions; adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain tumor types (e.g. C- T in melanoma); or adjusting specific mismatch penalty parameters based on specific mutation types that are common in certain sample types e.g. substitutions that are common in FFPE).
  • Reduced specificity (increased false positive rate) in the evaluated subject intervals due to misalignment can be assessed by manual or automated examination of all mutation calls in the sequencing data. Those regions found to be prone to spurious mutation calls due to misalignment can be subjected to alignment remedies as discussed above. In cases where no algorithmic remedy is found possible, “mutations” from the problem regions can be classified or screened out from the panel of targeted loci.
  • Base calling refers to the raw output of a sequencing device, e.g., the determined sequence of nucleotides in an oligonucleotide molecule.
  • Mutation calling refers to the process of selecting a nucleotide value, e.g., A, G, T, or C, for a given nucleotide position being sequenced. Typically, the sequence reads (or base calling) for a position will provide more than one value, e.g., some reads will indicate a T and some will indicate a G.
  • Mutation calling is the process of assigning a correct nucleotide value, e.g., one of those values, to the sequence.
  • mutant calling it can be applied to assign a nucleotide value to any nucleotide position, e.g., positions corresponding to mutant alleles, wild-type alleles, alleles that have not been characterized as either mutant or wild-type, or to positions not characterized by variability.
  • the disclosed methods may comprise the use of customized or tuned mutation calling algorithms or parameters thereof to optimize performance when applied to sequencing data, particularly in methods that rely on massively parallel sequencing (MPS) of a large number of diverse genetic events at a large number of diverse genomic loci (e.g., gene loci, micro satellite regions, etc.) in samples, e.g., samples from a subject having cancer.
  • MPS massively parallel sequencing
  • optimization of mutation calling is described in the art, e.g., as set out in International Patent Application Publication No. WO 2012/092426.
  • Methods for mutation calling can include one or more of the following: making independent calls based on the information at each position in the reference sequence (e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)); removing false positives (e.g., using depth thresholds to reject SNPs with read depth much lower or higher than expected; local realignment to remove false positives due to small indels); and performing linkage disequilibrium (LD)/imputation- based analysis to refine the calls.
  • making independent calls based on the information at each position in the reference sequence e.g., examining the sequence reads; examining the base calls and quality scores; calculating the probability of observed bases and quality scores given a potential genotype; and assigning genotypes (e.g., using Bayes’ rule)
  • removing false positives e.g., using depth thresholds to reject SNP
  • Equations used to calculate the genotype likelihood associated with a specific genotype and position are described in, e.g., Li, H. and Durbin, R. Bioinformatics, 2010; 26(5): 589-95.
  • the prior expectation for a particular mutation in a certain cancer type can be used when evaluating samples from that cancer type.
  • Such likelihood can be derived from public databases of cancer mutations, e.g., Catalogue of Somatic Mutation in Cancer (COSMIC), HGMD (Human Gene Mutation Database), The SNP Consortium, Breast Cancer Mutation Data Base (BIC), and Breast Cancer Gene Database (BCGD).
  • Examples of LD/imputation based analysis are described in, e.g., Browning, B.L. and Yu, Z. Am. J. Hum. Genet. 2009, 85(6):847-61.
  • Examples of low-coverage SNP calling methods are described in, e.g., Li, Y., et al., Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.
  • detection of substitutions can be performed using a mutation calling method (e.g., a Bayesian mutation calling method) which is applied to each base in each of the subject intervals, e.g., exons of a gene or other locus to be evaluated, where presence of alternate alleles is observed.
  • a mutation calling method e.g., a Bayesian mutation calling method
  • This method will compare the probability of observing the read data in the presence of a mutation with the probability of observing the read data in the presence of basecalling error alone. Mutations can be called if this comparison is sufficiently strongly supportive of the presence of a mutation.
  • An advantage of a Bayesian mutation detection approach is that the comparison of the probability of the presence of a mutation with the probability of base-calling error alone can be weighted by a prior expectation of the presence of a mutation at the site. If some reads of an alternate allele are observed at a frequently mutated site for the given cancer type, then presence of a mutation may be confidently called even if the amount of evidence of mutation does not meet the usual thresholds. This flexibility can then be used to increase detection sensitivity for even rarer mutations/lower purity samples, or to make the test more robust to decreases in read coverage.
  • the likelihood of a random base-pair in the genome being mutated in cancer is ⁇ le-6.
  • the likelihood of specific mutations occurring at many sites in, for example, a typical multigenic cancer genome panel can be orders of magnitude higher. These likelihoods can be derived from public databases of cancer mutations (e.g., COSMIC).
  • Indel calling is a process of finding bases in the sequencing data that differ from the reference sequence by insertion or deletion, typically including an associated confidence score or statistical evidence metric.
  • Methods of indel calling can include the steps of identifying candidate indels, calculating genotype likelihood through local re-alignment, and performing LD-based genotype inference and calling.
  • a Bayesian approach is used to obtain potential indel candidates, and then these candidates are tested together with the reference sequence in a Bayesian framework.
  • Methods for generating indel calls and individual-level genotype likelihoods include, e.g., the Dindel algorithm (Albers, C.A., et al., Genome Res. 2011; 21(6):961-73).
  • the Bayesian EM algorithm can be used to analyze the reads, make initial indel calls, and generate genotype likelihoods for each candidate indel, followed by imputation of genotypes using, e.g., QCALL (Le S.Q. and Durbin R. Genome Res. 2011;21(6):952-60).
  • Parameters, such as prior expectations of observing the indel can be adjusted ⁇ e.g., increased or decreased), based on the size or location of the indels.
  • the mutation calling method used to analyze sequence reads is not individually customized or fine-tuned for detection of different mutations at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for at least a subset of the different mutations detected at different genomic loci.
  • different mutation calling methods are used that are individually customized or fine-tuned for each different mutant detected at each different genomic loci.
  • the customization or tuning can be based on one or more of the factors described herein, e.g., the type of cancer in a sample, the gene or locus in which the subject interval to be sequenced is located, or the variant to be sequenced. This selection or use of mutation calling methods individually customized or fine-tuned for a number of subject intervals to be sequenced allows for optimization of speed, sensitivity and specificity of mutation calling.
  • a nucleotide value is assigned for a nucleotide position in each of X unique subject intervals using a unique mutation calling method, and X is at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 1000, at least 1500, at least 2000, at least 2500, at least 3000, at least 3500, at least 4000, at least 4500, at least 5000, or greater.
  • the calling methods can differ, and thereby be unique, e.g., by relying on different Bayesian prior values.
  • assigning said nucleotide value is a function of a value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • the method comprises assigning a nucleotide value (e.g., calling a mutation) for at least 10, 20, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1,000 nucleotide positions, wherein each assignment is a function of a unique value (as opposed to the value for the other assignments) which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type.
  • a nucleotide value e.g., calling a mutation
  • assigning said nucleotide value is a function of a set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a specified frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone).
  • the mutation calling methods described herein can include the following: (a) acquiring, for a nucleotide position in each of said X subject intervals: (i) a first value which is or represents the prior (e.g., literature) expectation of observing a read showing a variant, e.g., a mutation, at said nucleotide position in a tumor of type X; and (ii) a second set of values which represent the probabilities of observing a read showing said variant at said nucleotide position if the variant is present in the sample at a frequency (e.g., 1%, 5%, 10%, etc.) and/or if the variant is absent (e.g., observed in the reads due to base-calling error alone); and (b) responsive to said values, assigning a nucleotide value (e.g., calling a mutation) from said reads for each of said nucleotide positions by weighing, e.g., by a Bay
  • the systems may comprise, e.g., one or more processors, and a memory unit communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; train, using the one or more processors, a statistical model based on the training data; obtain, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receive, using one or more processors, sequence read data associated with a sample from an individual; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data
  • the disclosed systems may further comprise a sequencer, e.g., a next generation sequencer (also referred to as a massively parallel sequencer).
  • a sequencer e.g., a next generation sequencer (also referred to as a massively parallel sequencer).
  • next generation (or massively parallel) sequencing platforms include, but are not limited to, Roche/454’s Genome Sequencer (GS) FLX system, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 sequencing systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, ThermoFisher Scientific’s Ion Torrent Genexus system, or Pacific Biosciences’ PacBio® RS system.
  • GS Genome
  • the disclosed systems may be used for determining a receptor status of an individual in any of a variety of samples as described herein (e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject).
  • samples e.g., a tissue sample, biopsy sample, hematological sample, or liquid biopsy sample derived from the subject.
  • the plurality of gene loci for which sequencing data is processed to determine a receptor status may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10 gene loci.
  • the nucleic acid sequence data is acquired using a next generation sequencing technique (also referred to as a massively parallel sequencing technique) having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • a next generation sequencing technique also referred to as a massively parallel sequencing technique having a read-length of less than 400 bases, less than 300 bases, less than 200 bases, less than 150 bases, less than 100 bases, less than 90 bases, less than 80 bases, less than 70 bases, less than 60 bases, less than 50 bases, less than 40 bases, or less than 30 bases.
  • the determination of a receptor status is used to select, initiate, adjust, or terminate a treatment for cancer in the subject (e.g., a patient) from which the sample was derived, as described elsewhere herein.
  • the disclosed systems may further comprise sample processing and library preparation workstations, microplate-handling robotics, fluid dispensing systems, temperature control modules, environmental control chambers, additional data storage modules, data communication modules (e.g., Bluetooth®, WiFi, intranet, or internet communication hardware and associated software), display modules, one or more local and/or cloud-based software packages (e.g., instrument / system control software packages, sequencing data analysis software packages), etc., or any combination thereof.
  • the systems may comprise, or be part of, a computer system or computer network as described elsewhere herein.
  • FIG. 7 illustrates an example of a computing device or system in accordance with one embodiment.
  • Device 700 can be a host computer connected to a network.
  • Device 700 can be a client computer or a server.
  • device 700 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.
  • the device can include, for example, one or more processor(s) 710, input devices 720, output devices 730, memory or storage devices 740, communication devices 760, and nucleic acid sequencers 770.
  • Software 750 residing in memory or storage device 740 may comprise, e.g., an operating system as well as software for executing the methods described herein.
  • Input device 720 and output device 730 can generally correspond to those described herein, and can either be connectable or integrated with the computer.
  • Input device 720 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 730 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 740 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk).
  • Communication device 760 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device.
  • the components of the computer can be connected in any suitable manner, such as via a wired media (e.g., a physical system bus 780, Ethernet connection, or any other wire transfer technology) or wirelessly (e.g., Bluetooth®, Wi-Fi®, or any other wireless technology).
  • Software module 750 which can be stored as executable instructions in storage 740 and executed by processor(s) 710, can include, for example, an operating system and/or the processes that embody the functionality of the methods of the present disclosure (e.g., as embodied in the devices as described herein).
  • Software module 750 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a computer-readable storage medium can be any medium, such as storage 740, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit.
  • various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above. Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that the above processes may be routines or modules within other processes.
  • Software module 750 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device.
  • the transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
  • Device 700 may be connected to a network (e.g., network 804, as shown in FIG. 8 and/or described below), which can be any suitable type of interconnected communication system.
  • the network can implement any suitable communications protocol and can be secured by any suitable security protocol.
  • the network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.
  • Device 700 can be implemented using any operating system, e.g., an operating system suitable for operating on the network.
  • Software module 750 can be written in any suitable programming language, such as C, C++, Java or Python.
  • application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example.
  • the operating system is executed by one or more processors, e.g., processor(s) 710.
  • Device 700 can further include a sequencer 770, which can be any suitable nucleic acid sequencing instrument.
  • FIG. 8 illustrates an example of a computing system in accordance with one embodiment.
  • device 700 e.g., as described above and illustrated in FIG. 7
  • network 804 which is also connected to device 806.
  • device 806 is a sequencer.
  • Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq® 2500, HiSeq® 3000, HiSeq® 4000 and NovaSeq® 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio® RS system.
  • Devices 700 and 806 may communicate, e.g., using suitable communication interfaces via network 804, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet.
  • network 804 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network.
  • Devices 700 and 806 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.1 lb wireless, or the like. Additionally, devices 700 and 806 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network.
  • Communication between devices 700 and 806 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like.
  • Devices 700 and 806 can communicate directly (instead of, or in addition to, communicating via network 804), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like.
  • devices 700 and 806 communicate via communications 808, which can be a direct connection or can occur via a network (e.g., network 804).
  • One or all of devices 700 and 806 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 804 according to various examples described herein.
  • logic e.g., http web server logic
  • devices 700 and 806 are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 804 according to various examples described herein.
  • This section provides a non-limiting example of training a machine learning model in accordance with embodiments of the present disclosure.
  • embodiments of the present disclosure may be used to train a statistical model to determine a receptor status.
  • FIG. 9 illustrates an exemplary process for training a statistical model in accordance with embodiments of the present disclosure.
  • a statistical model for determining an ER status for an individual was trained and validated using 2,030 solid biopsy samples.
  • This cohort of 2,030 samples was derived from solid biopsy specimens of cancer patients, which were previously sequenced in an accredited laboratory. To create a stringent high-quality cohort of cases, the 2,030 solid biopsy samples were confirmed to pass tumor purity, sample quality, and copy number noise quality control criteria.
  • the resulting quality- controlled dataset of 2,030 cases underwent an 80:20 class-weighted random split to yield 1,624 cases for the training cohort and 406 cases for the testing cohort as shown in the figure.
  • the statistical model was trained using a random forest-based machine learning algorithm based on a cohort of 1,624 breast cancer solid tumor cases (e.g., 1,010 ER positive samples and 614 ER negative samples) using one or more training input feature values, e.g., one or more genomic alteration features, one or more complex mutation signature features, one or more chromosomal instability features, one or more clinicopathological features, one or more clinical features, one or more additional features, or a combination thereof.
  • the trained statistical model was tested on an independent cohort of 406 cases (e.g., 253 ER positive samples and 153 ER negative samples).
  • a binary classifier was built using the random forest algorithm, on a training cohort of 1,624 ER positive and 614 ER negative cases. Separate models were built for to determine the ER status for solid biopsy samples and for liquid biopsy samples. Each of these models included different features. For example, the model for solid samples included 194 features while the liquid model included 15 features.
  • the model parameters, of the classifier models, including the number of trees grown and size of the random feature subset considered at each split, were tuned by a Cartesian hyperparameter grid search, to maximize AUC (ROC), with a scalable machine learning platform (e.g., H20.ai v3.28.0.4, in R v4.0.3).
  • a stratified sampling methodology was used and an equal number of cases were sampled from the ER positive cases and ER negative cases, equal to 60% of the total ER negative cases in the training cohort.
  • Prediction performance of the model was estimated on the training cohort by 10-fold cross validation and an independent test cohort of 406 solid samples (e.g., 253 ER positive samples and 153 ER negative samples) were also used to evaluate the performance of classifier model.
  • the solid and liquid models were then correspondingly applied to the validation cohorts of 130 solid biopsy cases (e.g., 97 ER positive samples and 33 ER negative samples) and 693 liquid biopsy cases, respectively.
  • the ER statuses were determined based on paired solid biopsy specimens.
  • a validation cohort of 130 solid biopsy samples (e.g., 97 ER positive samples and 33 ER negative samples) was used to validate the solid model. Based on this example, the system achieved an accuracy of 83% with respect to determining the ER status of the solid biopsy validation samples.
  • a validation cohort of 693 liquid biopsy samples was used to validate the liquid model.
  • the ER status information was determined for the cohort of 693 liquid biopsy samples based on a paired solid biopsy specimen (e.g., 490 ER positive samples and 203 ER negative samples). For example, the ER status of the paired solid biopsy sample was analyzed to determine the ER receptor status for validation.
  • the liquid samples were further analyzed based on the circulating tumor fraction (cTF) of the sample.
  • cTF circulating tumor fraction
  • 445 liquid biopsy samples had a cTF above 1% (e.g., 312 ER positive samples and 133 ER negative samples). Based on this example, the system achieved an accuracy of 77% based on the liquid biopsy validation samples with a cTF above 1%.
  • 248 liquid biopsy samples had a cTF below 1% (e.g., 178 ER positive samples and 70 ER negative samples). Based on this example, the system achieved an accuracy of 69% based on liquid biopsy validation samples with a cTF below 1%.
  • examples in accordance with embodiments of this disclosure can provide clinically meaningful receptor predictions for both solid and liquid samples. Further in the case for liquid samples, examples in accordance with embodiments of this disclosure can provide clinically meaningful receptor predictions for samples with low levels of cTF.
  • a binary classifier using a random forest algorithm on a training cohort of 1,010 ER+ and 614 ER- cases was built to determine the ER receptor status for solid tissue samples.
  • the binary classifier parameters including number of trees grown and size of the random feature subset were considered at each split, were tuned by a cartesian hyperparameter grid search, to maximize AUC (ROC), with a scalable machine learning platform.
  • ROC AUC
  • a stratified sampling methodology was used and an equal number of cases were sampled from the ER+ cases and ER- cases, equal to 60% of the total ER- cases in the training cohort.
  • Prediction performance of the model was estimated on the training cohort by 10-fold cross validation and an independent test cohort of 253 ER+ cases and 153 ER- cases was also used to evaluate the performance of classification. Performance metrics are described in FIGs. 10A-10C.
  • FIG. 10A illustrates the 10-fold cross validation metrics of the random forest solid model on the solid training dataset.
  • FIG. 10B illustrates an exemplary performance metrics of the random forest solid model on the solid test dataset. As shown in the figure, the accuracy of the model on the test dataset is about 80%.
  • FIG. 10C illustrates an exemplary performance metrics of the random forest solid model on the solid validation dataset. As shown in the figure, the accuracy of the model on the test dataset is about 83%.
  • FIG. 10D illustrates the relative feature importance of fifty input features out of an input feature set comprising 194 features. The features included genomic features, complex mutational signatures, chromosomal instability, and clinicopathological features, as described above. In some examples, after fitting the model and studying feature importance, the system can retrospectively select which features to keep and estimate the percentage of features to be kept and removed.
  • FIG. HA illustrates the 10-fold cross validation metrics of the random forest liquid model on the liquid training dataset.
  • FIG. 11B illustrates exemplary performance metrics of the random forest liquid model on the liquid test dataset. As shown in the figure, the accuracy of the model on the test dataset is about 75%.
  • FIG. 11C illustrates exemplary performance metrics of the random forest liquid model on the liquid validation dataset with a cTF greater than one percent. As shown in the figure, the accuracy of the model on the test dataset is about 77%.
  • FIG. 11D illustrates exemplary performance metrics of the random forest liquid model on the liquid validation dataset with a cTF less than one percent. As shown in the figure, the accuracy of the model on the validation dataset is about 69%.
  • FIG. HE illustrates the relative feature importance of the fifteen input features corresponding to the set of input features for the exemplary liquid model.
  • a method comprising: providing a plurality of nucleic acid molecules obtained from a sample from a subject; ligating one or more adapters onto one or more nucleic acid molecules from the plurality of nucleic acid molecules; amplifying the one or more ligated nucleic acid molecules from the plurality of nucleic acid molecules; capturing amplified nucleic acid molecules from the amplified nucleic acid molecules; sequencing, by a sequencer, the captured nucleic acid molecules to obtain a plurality of reads that represent the captured nucleic acid molecules; receiving, at one or more processors, sequence read data for the plurality of reads; receiving, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data; obtaining, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting
  • the receptor gene status corresponds to an estrogen receptor status, a progesterone receptor status, an androgen receptor status, or a human epidermal growth factor receptor 2 status.
  • the method of any of clauses 1 to 2 wherein the receptor gene status comprises a hormone receptor status.
  • the method of any of clauses 1 to 3 further comprising applying the trained statistical model to the values for the one or more input features to obtain an output indicative of the receptor gene status.
  • the method of any of clauses 1 to 4 wherein the sample type is indicative of a solid sample and the set of input features comprises genomic alteration features, complex mutational signatures, chromosomal instability features, clinicopathological features, clinical features, additional features, or a combination thereof.
  • the genomic alteration features comprise one or more genomic alterations and a type of alteration associated with the one or more genomic alterations.
  • the method of clause 5, wherein the genomic alteration features comprise one or more genomic alterations and a type of alteration associated with the one or more genomic alterations.
  • the cancer is a B cell cancer (multiple myeloma), a melanoma, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain cancer, central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine cancer, endometrial cancer, cancer of an oral cavity, cancer of a pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel cancer, appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, a cancer of hematological tissue, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), colon cancer, multiple myeloma (MM), myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD
  • the cancer comprises breast cancer (HER2 overexpressed/amplified), breast cancer (HER2+), breast cancer (HR+, HER2-), and prostate cancer.
  • the anti-cancer therapy comprises a targeted anticancer therapy.
  • the targeted anti-cancer therapy comprises alpelisib (Piqray), CDK4/6 inhibitors, or any combination thereof.
  • the method of any of clauses 1 to 14 further comprising obtaining the sample from the subject.
  • the method of any of clauses 1 to 15, wherein the sample comprises a tissue biopsy sample, a liquid biopsy sample, or a normal control.
  • the set of features differs between a tissue biopsy sample and a liquid biopsy.
  • the sample is a liquid biopsy sample and comprises blood, plasma, cerebrospinal fluid, sputum, stool, urine, or saliva.
  • the sample is a liquid biopsy sample and comprises circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), or any combination thereof.
  • CTCs tumor cells
  • cfDNA cell-free DNA
  • ctDNA circulating tumor DNA
  • the method of any one of clauses 1 to 19, wherein the plurality of nucleic acid molecules comprises a mixture of tumor nucleic acid molecules and non-tumor nucleic acid molecules.
  • the tumor nucleic acid molecules are derived from a tumor portion of a heterogeneous tissue biopsy sample, and the non-tumor nucleic acid molecules are derived from a normal portion of the heterogeneous tissue biopsy sample.
  • the sample comprises a liquid biopsy sample, and wherein the tumor nucleic acid molecules are derived from a circulating tumor DNA (ctDNA) fraction of the liquid biopsy sample, and the non-tumor nucleic acid molecules are derived from a non-tumor, cell-free DNA (cfDNA) fraction of the liquid biopsy sample.
  • ctDNA circulating tumor DNA
  • cfDNA non-tumor, cell-free DNA
  • amplifying nucleic acid molecules comprises performing a polymerase chain reaction (PCR) amplification technique, a non- PCR amplification technique, or an isothermal amplification technique.
  • PCR polymerase chain reaction
  • the sequencing comprises use of a massively parallel sequencing (MPS) technique, whole genome sequencing (WGS), whole exome sequencing, targeted sequencing, direct sequencing, or Sanger sequencing technique.
  • MPS massively parallel sequencing
  • WGS whole genome sequencing
  • whole exome sequencing targeted sequencing
  • direct sequencing direct sequencing
  • Sanger sequencing technique sequencing technique
  • the method of clause 27 wherein the sequencing comprises massively parallel sequencing, and the massively parallel sequencing technique comprises next generation sequencing (NGS).
  • NGS next generation sequencing
  • the method of clause 30, wherein the one or more gene loci comprises between 10 and 20 loci, between 10 and 40 loci, between 10 and 60 loci, between 10 and 80 loci, between 10 and 100 loci, between 10 and 150 loci, between 10 and 200 loci, between 10 and 250 loci, between 10 and 300 loci, between 10 and 350 loci, between 10 and 400 loci, between 10 and 450 loci, between 10 and 500 loci, between 20 and 40 loci, between 20 and 60 loci, between 20 and 80 loci, between 20 and 100 loci, between 20 and 150 loci, between 20 and 200 loci, between 20 and 250 loci, between 20 and 300 loci, between 20 and 350 loci, between 20 and 400 loci, between 20 and 500 loci, between 40 and 60 loci, between 40 and 80 loci, between 40 and 100 loci, between 40 and
  • the one or more gene loci comprise ABL, ALK, ALL, B4GALNT1, BAFF, BCL2, BRAF, BRCA, BTK, CD19, CD20, CD3, CD30, CD319, CD38, CD52, CDK4, CDK6, CML, CRACC, CS 1, CTLA-4, dMMR, EGFR, ERBB1, ERBB2, FGFR1-3, FLT3, GD2, HD AC, HER1, HER2, HR, IDH2, IL- ip, IL-6, IL-6R, JAK1, JAK2, JAK3, KIT, KRAS, MEK, MET, MSLH, mTOR, PARP, PD-1, PDGFR, PDGFRa, PDGFRp, PD-L1, PI3K5, PIGF, PTCH, RAF, RANKL, RET, ROS1, SLAMF7, VEGF, VEGFA, VEGFB, or any combination thereof.
  • a method comprising: receiving, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data; obtaining, using the one or more processors, a set of input features associated with the receptor gene status and a sample type, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receiving, using one or more processors, sequence read data associated with a sample from an individual; determining, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more input features into the trained statistical model; and predicting, using the one or more processors, the receptor gene status of the individual based on an output of the training
  • the set of input features comprises genomic alteration features, complex mutational signatures, chromosomal instability features, clinicopathological features, clinical features, additional features, or a combination thereof.
  • the method of clause 42, wherein the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a copy number alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the predetermined short variant comprises a point mutation, an insertion, or a deletion.
  • the one or more genomic alteration features comprise CCNE1, EGFR, ERBB2, ERBB2 Amplification, ESRI, FGF3, FGFR1, GATA3, MAP2K4, MYC, PIK3CA, PTEN, RBI, TP53, ZNF703, or a combination thereof.
  • the one or more genomic alteration features comprise BRCA1, TP53, MYC, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, PIK3CA, ESRI, TBX3, CDH1, ZNF703, GAT A3, FGF3, CCNE1, RBI, FGFR1, PTEN, EGFR, MAP2K4, or a combination thereof.
  • the one or more genomic alteration features comprise AKT1, AKT2, AKT3, APC, AR, ARFRP1, ARID1A, ATM, AURKA, BAP1, BCL2L1, BCL2L2, BRAF, BRCA1, BRCA2, BRD4, CBFB, CCND3, CCNE1, CDH1, CDK12, CDK4, CDK6, CDKN2A, CDKN2B, CHEK2, CREBBP, CTNNA1, DNMT3A, EGFR, EMSY, EP300, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, ERBB3, ESRI, FANCA, FAS, FBXW7, FGF10, FGF12, FGF3, FGF6, FGFR1, FGFR2, FGFR4, GATA3, GNAS, HGF, IGF1R, IKBKE, IRS2, JAK2, KDM5A, KDM6A, KDR, KEL, KIT, KMT2D
  • the method of any of clauses 39 to 49, wherein the one or more input features are associated with one or more chromosomal instability features.
  • the method of clause 50, wherein the one or more chromosomal instability features is indicative of aneuploidy.
  • the one or more chromosomal instability features is indicative of chromosome gain, chromosome not gain, chromosome loss, chromosome not loss, or a combination thereof.
  • the method of clause 50, wherein the one or more chromosomal instability features comprises a total aneuploidy count.
  • the one or more chromosomal instability features comprise chromosome Iq loss status, chromosome Ip loss status, chromosome 3p loss status, chromosome 4p loss status, chromosome 5q loss status, chromosome 9p loss status, chromosome lOq loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome 14q loss status, chromosome 15q loss status, chromosome 16p loss status, chromosome 16q loss status, chromosome 17q loss status, chromosome 19p loss status, chromosome 21q loss status, chromosome 22q loss status, chromosome Iq gain status, chromosome 2p gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6q gain status, chromosome 7q gain status, chromosome 9p gain status, chromosome lOp gain status, chromosome
  • the one or more chromosomal instability features comprise chromosome Ip gain status, chromosome Iq gain status, chromosome2p gain status, chromosome 2q gain status, chromosome 3p gain status, chromosome 3q gain status, chromosome 4p gain status, chromosome 4q gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6p gain status, chromosome 6q gain status, chromosome 7p gain status, chromosome 7q gain status, chromosome 8p gain status, chromosome 8q gain status, chromosome 9p gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome lOp gain status, chromosome lOq gain status, chromosome l ip gain status, chromosome 1 Iq gain status, chromosome 12p gain status, chromosome 12q gain status,
  • any of clauses 39 to 55 wherein the one or more input features are associated with one or more clinicopathological features.
  • the method of clause 56 wherein the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor status of the individual, a tumor type, an anatomical sub-classification of a tumor, or a combination thereof.
  • the method of any of clauses 39 to 57, wherein the one or more input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the method of any of clauses 39 to 60, wherein the one or more input features are associated with a germline status.
  • any of clauses 39 to 61 wherein the one or more input features are associated with a methylation signature, a mRNA expression level, a miRNA expression level, proteomics, COSMIC mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, a homologous repair deficiency (HRD) signature, or a combination thereof.
  • sequence read data derived from a liquid sample is associated with a first set of one or more input features and sequence read data derived from a solid sample is associated with a second set of one or more input features.
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a rearrangement alteration, an insertion, a deletion, or a combination thereof.
  • the method of any of clauses 67 to 68, wherein the one or more genomic alteration features comprise TP53, ESRI, PIK3CA, or a combination thereof.
  • the method of clause 67 to 68, wherein the one or more genomic alteration features comprise CDH1, BRCA1, AKT1, BRCA2, CDKN2A, ERBB2, KRAS, NF1, PTEN, or a combination thereof.
  • any of clauses 64 to 70 wherein the one or more input features are associated with one or more clinicopathological features.
  • the method of clause 71, wherein the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor type, or a combination thereof.
  • the method of any of clauses 64 to 72, wherein the one or more input features are associated with one or more clinical features.
  • the method of clause 73, wherein the one or more clinical features comprise an ancestry of the individual, a stage of the disease, one or more laboratory -based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • any of clauses 64 to 74 wherein the one or more input features are associated with a tumor mutational burden.
  • the method of any of clauses 64 to 77, wherein the one or more input features are associated with one or more fragmentomic features, the one or more fragmentomic features associated with a plurality of reads obtained from the sequence read data.
  • the one or more fragmentomic features comprise lengths of the plurality reads, regions associated with the plurality of reads, or a combination thereof.
  • the method of clause 78 to 79, wherein the one or more fragmentomic features comprise an amount of a fragment having a specified length, a mean fragment length of the plurality of reads, a median fragment length of the plurality of reads, an interquartile range of fragment lengths of the plurality of reads, a distribution of fragment lengths of the plurality of reads, one or more peaks of fragment lengths for the plurality of reads, a fragment length for the plurality of reads, a start position of a fragment for the plurality of reads, and an end position of a fragment for the plurality of reads.
  • the method of clause 83, wherein the output of the trained statistical model comprises a first score indicative of a probability of a positive receptor gene status. 85.
  • training data further comprises receptor gene statuses corresponding to each of the plurality of training samples.
  • the trained statistical model comprises at least one of an extreme gradient boosting model, a logistic regression model, an elastic net model, a ridge regression model, a support vector machine model, a k- nearest neighbor model, a Bayesian model, a naive-based model, a Gaussian naive-based model, a clustering model, a limited expectation maximization model, a gradient boosting ensemble model, an adaboost model, a bagging model, a neural network model, a backpropagation model, a stochastic gradient descent model, a linear regression model, a non-linear regression model, a multivariate regression model, a robust machine learning model, and a proportional hazards model.
  • the second statistical model is at least one of a deep learning model, a convolutional neural network model, a recurrent neural network model, and an auto-encoder model.
  • the diagnostic image comprises at least one of a histopathological image, a radiological image, a magnetic resonance image, an ultrasound image, a X-ray image, a bone scan, a CT scan, a PET scan, or a combination thereof.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on a determination of a receptor gene status for a sample from the subject, wherein the receptor gene status is determined according to the method of any one of clauses 36 to 103.
  • a method of selecting an anti-cancer therapy comprising: responsive to determining a receptor gene status for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the receptor gene status is determined according to the method of any one of clauses 36 to 103.
  • a method of treating a cancer in a subject comprising: responsive to determining a receptor gene status for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the receptor gene status is determined according to the method of any one of clauses 36 to 103.
  • a method for monitoring cancer progression or recurrence in a subject comprising: determining a first receptor gene status in a first sample obtained from the subject at a first time point according to the method of any one of clauses 36 to 103; determining a second receptor gene status in a second sample obtained from the subject at a second time point; and comparing the first receptor gene status to the second receptor gene status, thereby monitoring the cancer progression or recurrence.
  • clause 107 wherein the second receptor gene status for the second sample is determined according to the method of any one of clauses 36 to 103.
  • the method of any of clauses 107 to 108 further comprising selecting an anti-cancer therapy for the subject in response to the cancer progression.
  • the method of clauses 107 to 108 further comprising administering an anti-cancer therapy to the subject in response to the cancer progression.
  • the method of clause 107 to 108 further comprising adjusting an anti-cancer therapy for the subject in response to the cancer progression.
  • the method of any one of clauses 109 to 111 further comprising adjusting a dosage of the anti-cancer therapy or selecting a different anti-cancer therapy in response to the cancer progression.
  • the method of any one of clauses 107 to 115, wherein the cancer is a breast cancer.
  • the method of clause 120, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the method of any of clauses 120 to 121 wherein the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • the method of any of clauses 120 to 122 further comprising selecting an anti-cancer therapy, administering an anti-cancer therapy, or applying an anti-cancer therapy to the subject based on the generated genomic profile.
  • the method of any one of clauses 36 to 103 wherein the determination of receptor gene status for the sample is used in making suggested treatment decisions for the subject.
  • the method of any one of clauses 36 to 103, wherein the determination of the receptor gene status for the sample is used in applying or administering a treatment to the subject.
  • a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; train, using the one or more processors, a statistical model based on the training data; obtain, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receive, using one or more processors, sequence read data associated with a sample from an individual; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or
  • a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, training data comprising training values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; train, using the one or more processors, a statistical model based on the training data; obtain, using the one or more processors, a set of input features associated with the receptor gene status, wherein obtaining the set of input features comprises omitting input features with a low prevalence and high correlation to the statistical model; receive, using one or more processors, sequence read data associated with a sample from an individual; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more
  • a method for predicting a receptor gene status of a sample from an individual comprising: receiving, using one or more processors, sequence read data associated with a sample from an individual; obtaining a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determining, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more input features into a statistical model; and predicting, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • the method of clause 131 wherein the set of input features comprises genomic alteration features, complex mutational signatures, chromosomal instability features, clinicopathological features, clinical features, additional features, or a combination thereof.
  • the method any of clauses 131 to 133, wherein the one or more input features are associated with one or more genomic alteration features.
  • the method of clause 134, wherein the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a copy number alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the method of clause 135, wherein the predetermined short variant comprises a point mutation, an insertion, or a deletion.
  • the one or more genomic alteration features comprise CCNE1, EGFR, ERBB2, ERBB2 Amplification, ESRI, FGF3, FGFR1, GATA3, MAP2K4, MYC, PIK3CA, PTEN, RBI, TP53, ZNF703, or a combination thereof.
  • the one or more genomic alteration features comprise BRCA1, TP53, MYC, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, PIK3CA, ESRI, TBX3, CDH1, ZNF703, GAT A3, FGF3, CCNE1, RBI, FGFR1, PTEN, EGFR, MAP2K4, or a combination thereof.
  • the one or more genomic alteration features comprise AKT1, AKT2, AKT3, APC, AR, ARFRP1, ARID1A, ATM, AURKA, BAP1, BCL2L1, BCL2L2, BRAF, BRCA1, BRCA2, BRD4, CBFB, CCND3, CCNE1, CDH1, CDK12, CDK4, CDK6, CDKN2A, CDKN2B, CHEK2, CREBBP, CTNNA1, DNMT3A, EGFR, EMSY, EP300, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, ERBB3, ESRI, FANCA, FAS, FBXW7, FGF10, FGF12, FGF3, FGF6, FGFR1, FGFR2, FGFR4, GATA3, GNAS, HGF, IGF1R, IKBKE, IRS2, JAK2, KDM5A, KDM6A, KDR, KEL, KIT, KMT
  • the method of any of clauses 131 to 141, wherein the one or more input features are associated with one or more chromosomal instability features.
  • the method of clause 142, wherein the one or more chromosomal instability features is indicative of aneuploidy.
  • the one or more chromosomal instability features is indicative of chromosome gain, chromosome not gain, chromosome loss, chromosome not loss, or a combination thereof.
  • the method of clause 142, wherein the one or more chromosomal instability features comprises a total aneuploidy count.
  • the one or more chromosomal instability features comprise chromosome Iq loss status, chromosome Ip loss status, chromosome 3p loss status, chromosome 4p loss status, chromosome 5q loss status, chromosome 9p loss status, chromosome lOq loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome 14q loss status, chromosome 15q loss status, chromosome 16p loss status, chromosome 16q loss status, chromosome 17q loss status, chromosome 19p loss status, chromosome 21q loss status, chromosome 22q loss status, chromosome Iq gain status, chromosome 2p gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6q gain status, chromosome 7q gain status, chromosome 9p gain status, chromosome lOp gain status, chromosome
  • the one or more chromosomal instability features comprise chromosome Ip gain status, chromosome Iq gain status, chromosome2p gain status, chromosome 2q gain status, chromosome 3p gain status, chromosome 3q gain status, chromosome 4p gain status, chromosome 4q gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6p gain status, chromosome 6q gain status, chromosome 7p gain status, chromosome 7q gain status, chromosome 8p gain status, chromosome 8q gain status, chromosome 9p gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome lOp gain status, chromosome lOq gain status, chromosome l ip gain status, chromosome 1 Iq gain status, chromosome 12p gain status, chromosome 12q gain status, chromosome 13q gain status,
  • the method of any of clauses 131 to 149, wherein the one or more input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the method of any of clauses 131 to 152, wherein the one or more input features are associated with a germline status.
  • any of clauses 131 to 153 wherein the one or more input features are associated with a methylation signature, a mRNA expression level, a miRNA expression level, proteomics, COSMIC mutation signatures, immunohistochemical markers, genetic predispositions, cell adhesion biomarkers, saliva based biomarkers, enzyme based biomarkers, a diagnostic image, a homologous repair deficiency (HRD) signature, or a combination thereof.
  • HRD homologous repair deficiency
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a rearrangement alteration, an insertion, a deletion, or a combination thereof.
  • the method of any of clauses 159 to 160, wherein the one or more genomic alteration features comprise TP53, ESRI, PIK3CA, or a combination thereof.
  • the method of clause 159 to 161, wherein the one or more genomic alteration features comprise CDH1, BRCA1, AKT1, BRCA2, CDKN2A, ERBB2, KRAS, NF1, PTEN, or a combination thereof.
  • any of clauses 159 to 162 wherein the one or more input features are associated with one or more clinicopathological features.
  • the method of clause 163, wherein the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor type, or a combination thereof.
  • the method of any of clauses 159 to 164, wherein the one or more input features are associated with one or more clinical features.
  • the method of clause 165 wherein the one or more clinical features comprise an ancestry of the individual, a stage of the disease, one or more laboratory -based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more fragmentomic features comprise lengths of the plurality reads, regions associated with the plurality of reads, or a combination thereof.
  • the statistical model is a trained machine learning model and trained by: receiving, using the one or more processors, training data including a plurality of training values for a plurality of input features corresponding to a plurality of training samples; and training, using the one or more processors, the machine learning model based on the training data.
  • training data further comprises receptor gene statuses corresponding to each of the plurality of training samples.
  • the statistical model comprises at least one of an extreme gradient boosting model, a logistic regression model, an elastic net model, a ridge regression model, a support vector machine model, a k-nearest neighbor model, a Bayesian model, a naive-based model, a Gaussian naive-based model, a clustering model, a limited expectation maximization model, a gradient boosting ensemble model, an adaboost model, a bagging model, a neural network model, a backpropagation model, a stochastic gradient descent model, a linear regression model, a non-linear regression model, a multivariate regression model, a robust machine learning model, and a proportional hazards model.
  • the second statistical model is at least one of a deep learning model, a convolutional neural network model, a recurrent neural network model, and an auto-encoder model.
  • the second statistical model is trained by: receiving, using the one or more processors, training data including a plurality of training diagnostic images; and training, using the one or more processors, the second statistical model based on the training data.
  • the diagnostic image comprises at least one of a histopathological image, a radiological image, a magnetic resonance image, an ultrasound image, a X-ray image, a bone scan, a CT scan, a PET scan, or a combination thereof.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on a determination of a receptor gene status for a sample from the subject, wherein the receptor gene status is determined according to the method of any one of clauses 128 to 195.
  • a method of selecting an anti-cancer therapy comprising: responsive to determining a receptor gene status for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the receptor gene status is determined according to the method of any one of clauses 128 to 195.
  • a method of treating a cancer in a subject comprising: responsive to determining a receptor gene status for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the receptor gene status is determined according to the method of any one of clauses 128 to 195.
  • a method for monitoring cancer progression or recurrence in a subject comprising: determining a first receptor gene status in a first sample obtained from the subject at a first time point according to the method of any one of clauses 128 to 195; determining a second receptor gene status in a second sample obtained from the subject at a second time point; and comparing the first receptor gene status to the second receptor gene status, thereby monitoring the cancer progression or recurrence.
  • the method of clause 204 further comprising administering the adjusted anti-cancer therapy to the subject.
  • the method of any one of clauses 199 to 206, wherein the subject has a cancer is at risk of having a cancer, is being routine tested for cancer, or is suspected of having a cancer.
  • the method of any one of clauses 199 to 207 wherein the cancer is a solid tumor.
  • the method of any one of clauses 199 to 207, wherein the cancer is a breast cancer.
  • the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the genomic profile for the subject further comprises results from a nucleic acid sequencing-based test.
  • a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more input features into a statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of input features associated with the receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more input features associated with the receptor gene status based on the sequence read data, wherein the one or more input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more input features into a statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model.
  • a method comprising: receiving, using one or more processors, sequence read data associated with a sample from an individual; obtaining a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determining, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; inputting, using the one or more processors, the values for the one or more expression input features into the statistical model; and predicting, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a copy number alteration, a rearrangement alteration, an amplification, a deletion, or a combination thereof.
  • the method of any of clauses 226 to 228, wherein the one or more genomic alteration features comprise CCNE1, EGFR, ERBB2, ERBB2 Amplification, ESRI, FGF3, FGFR1, GATA3, MAP2K4, MYC, PIK3CA, PTEN, RBI, TP53, ZNF703, or a combination thereof.
  • the one or more genomic alteration features comprise BRCA1, TP53, MYC, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, PIK3CA, ESRI, TBX3, CDH1, ZNF703, GAT A3, FGF3, CCNE1, RBI, FGFR1, PTEN, EGFR, MAP2K4, or a combination thereof.
  • the one or more genomic alteration features comprise AKT1, AKT2, AKT3, APC, AR, ARFRP1, ARID1A, ATM, AURKA, BAP1, BCL2L1, BCL2L2, BRAF, BRCA1, BRCA2, BRD4, CBFB, CCND3, CCNE1, CDH1, CDK12, CDK4, CDK6, CDKN2A, CDKN2B, CHEK2, CREBBP, CTNNA1, DNMT3A, EGFR, EMSY, EP300, ERBB2, ERBB2 Amplification, ERBB2 Short Variant, ERBB3, ESRI, FANCA, FAS, FBXW7, FGF10, FGF12, FGF3, FGF6, FGFR1, FGFR2, FGFR4, GATA3, GNAS, HGF, IGF1R, IKBKE, IRS2, JAK2, KDM5A, KDM6A, KDR, KEL, KIT, KMT2
  • any of clauses 223 to 231 wherein the one or more expression input features are associated with one or more complex mutational signatures.
  • the method of clause 232, wherein the one or more complex mutational signatures comprise a genome- wide loss of heterozygosity (gLOH) quantification, trinucleotide signatures, insertion signatures, deletion signatures, copy number signatures, or a combination thereof.
  • the method of any of clauses 223 to 233, wherein the one or more expression input features are associated with one or more chromosomal instability features.
  • the method of clause 234, wherein the one or more chromosomal instability features is indicative of aneuploidy.
  • the one or more chromosomal instability features is indicative of chromosome gain, chromosome not gain, chromosome loss, chromosome not loss, or a combination thereof.
  • the method of clause 235, wherein the one or more chromosomal instability features comprises a total aneuploidy count.
  • the one or more chromosomal instability features comprise chromosome Iq loss status, chromosome Ip loss status, chromosome 3p loss status, chromosome 4p loss status, chromosome 5q loss status, chromosome 9p loss status, chromosome lOq loss status, chromosome l lq loss status, chromosome 12q loss status, chromosome 14q loss status, chromosome 15q loss status, chromosome 16p loss status, chromosome 16q loss status, chromosome 17q loss status, chromosome 19p loss status, chromosome 21q loss status, chromosome 22q loss status, chromosome Iq gain status, chromosome 2p gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6q gain status, chromosome 7q gain status, chromosome 9p gain status, chromosome lOp gain status, chromosome
  • the one or more chromosomal instability features comprise chromosome Ip gain status, chromosome Iq gain status, chromosome2p gain status, chromosome 2q gain status, chromosome 3p gain status, chromosome 3q gain status, chromosome 4p gain status, chromosome 4q gain status, chromosome 5p gain status, chromosome 5q gain status, chromosome 6p gain status, chromosome 6q gain status, chromosome 7p gain status, chromosome 7q gain status, chromosome 8p gain status, chromosome 8q gain status, chromosome 9p gain status, chromosome 9q gain status, chromosome 9q gain status, chromosome lOp gain status, chromosome lOq gain status, chromosome l ip gain status, chromosome l lq gain status, chromosome 12p gain status, chromosome 12q gain status, chromosome 13q gain
  • the one or more expression input features are associated with one or more clinicopathological features.
  • the method of clause 240, wherein the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor status of the individual, a tumor type, an anatomical sub-classification of a tumor, or a combination thereof.
  • the method of any of clauses 223 to 241, wherein the one or more expression input features are associated with one or more clinical features.
  • the one or more clinical features comprise an ancestry of the individual, a family history of the individual, a stage of the disease, one or more laboratory-based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the method of any of clauses 223 to 244, wherein the one or more expression input features are associated with a germline status.
  • sequence read data derived from a liquid sample is associated with a first set of one or more input features and sequence read data derived from a solid sample is associated with a second set of one or more input features.
  • the one or more genomic alteration features comprise a presence of a predetermined short variant, an absence of a predetermined short variant, a rearrangement alteration, an insertion, a deletion, or a combination thereof.
  • the method of any of clauses 251 to 252, wherein the one or more genomic alteration features comprise TP53, ESRI, PIK3CA, or a combination thereof.
  • the method of clause 251 to 252, wherein the one or more genomic alteration features comprise CDH1, BRCA1, AKT1, BRCA2, CDKN2A, ERBB2, KRAS, NF1, PTEN, or a combination thereof.
  • any of clauses 248 to 254, wherein the one or more expression input features are associated with one or more clinicopathological features.
  • the method of clause 255, wherein the one or more clinicopathological features comprise an age of the individual, a sex of the individual, a disease diagnosis of the individual, a tumor type, or a combination thereof.
  • the method of any of clauses 248 to 256, wherein the one or more expression input features are associated with one or more clinical features.
  • the method of clause 257, wherein the one or more clinical features comprise an ancestry of the individual, a stage of the disease, one or more laboratory -based test results, one or more lifestyle habits, a diabetes status, an obesity status, an indication of body vitamin levels, or a combination thereof.
  • the one or more fragmentomic features comprise lengths of the plurality reads, regions associated with the plurality of reads, or a combination thereof.
  • the output of the statistical model comprises a first score indicative of a probability of a positive receptor gene status.
  • training data further comprises receptor gene statuses corresponding to each of the plurality of training samples.
  • the statistical model comprises at least one of an extreme gradient boosting model, a logistic regression model, an elastic net model, a ridge regression model, a support vector machine model, a k-nearest neighbor model, a Bayesian model, a naive-based model, a Gaussian naive-based model, a clustering model, a limited expectation maximization model, a gradient boosting ensemble model, an adaboost model, a bagging model, a neural network model, a backpropagation model, a stochastic gradient descent model, a linear regression model, a non-linear regression model, a multivariate regression model, a robust machine learning model, and a proportional hazards model.
  • the second statistical model is at least one of a deep learning model, a convolutional neural network model, a recurrent neural network model, and an auto-encoder model.
  • the diagnostic image comprises at least one of a histopathological image, a radiological image, a magnetic resonance image, an ultrasound image, a X-ray image, a bone scan, a CT scan, a PET scan, or a combination thereof.
  • a method for diagnosing a disease comprising: diagnosing that a subject has the disease based on a determination of a receptor gene status for a sample from the subject, wherein the receptor gene status is determined according to the method of any one of clauses 220 to 287.
  • a method of selecting an anti-cancer therapy comprising: responsive to determining a receptor gene status for a sample from a subject, selecting an anti-cancer therapy for the subject, wherein the receptor gene status is determined according to the method of any one of clauses 220 to 287.
  • a method of treating a cancer in a subject comprising: responsive to determining a receptor gene status for a sample from the subject, administering an effective amount of an anti-cancer therapy to the subject, wherein the receptor gene status is determined according to the method of any one of clauses 220 to 287.
  • a method for monitoring cancer progression or recurrence in a subject comprising: determining a first receptor gene status in a first sample obtained from the subject at a first time point according to the method of any one of clauses 220 to 287; determining a second receptor gene status in a second sample obtained from the subject at a second time point; and comparing the first receptor gene status to the second receptor gene status, thereby monitoring the cancer progression or recurrence.
  • the method of clause 304, wherein the genomic profile for the subject further comprises results from a comprehensive genomic profiling (CGP) test, a gene expression profiling test, a cancer hotspot panel test, a DNA methylation test, a DNA fragmentation test, an RNA fragmentation test, or any combination thereof.
  • CGP genomic profiling
  • the method of any one of clauses 220 to 287, wherein the determination of receptor gene status for the sample is used in making suggested treatment decisions for the subject.
  • the method of any one of clauses 220 to 287, wherein the determination of the receptor gene status for the sample is used in applying or administering a treatment to the subject.
  • a system comprising: one or more processors; and a memory communicatively coupled to the one or more processors and configured to store instructions that, when executed by the one or more processors, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more expression input features into the statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • a non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of a system, cause the system to: receive, using one or more processors, sequence read data associated with a sample from an individual; obtain a set of expression input features associated with a receptor gene status based on an expected prevalence and correlation to the sequence read data and on a sample type associated with the type of sample; determine, using the one or more processors, values for one or more expression input features associated with the receptor gene status based on the sequence read data, wherein the expression input features to be evaluated are specified based on a sample type of the sample; input, using the one or more processors, the values for the one or more expression input features into the statistical model; and predict, using the one or more processors, the receptor gene status of the individual based on an output of the statistical model associated with the one or more expression input features.
  • a method comprising: receiving, using one or more processors, training data comprising values for a plurality of training input features associated with a receptor gene status in a plurality of training samples; training, using the one or more processors, a statistical model based on the training data, wherein the trained statistical model is configured to predict a receptor gene status of an individual sample; determining, using the one or more processors, weights associated with the training values for the plurality of training input features based on the training; filtering, using the one or more processors, the one or more training input features based on the weights; determining a set of input features associated with the receptor gene status based on the filtered training input features and a sample type of a sample from an individual, wherein filtering the one or more training input features comprises removing training input features associated with low prevalence training values and highly correlated training values, or a combination thereof; and obtaining a trained statistical model configured to receive a set of input feature based on a sample from an individual to output a prediction of a receptor gene status of the training

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Abstract

Des modes de réalisation de la présente divulgation concernent des systèmes et des procédés comprenant la réception de données d'entraînement comprenant des valeurs d'entraînement pour une pluralité de caractéristiques en entrée associées à un statut de récepteur de gène dans une pluralité d'échantillons d'entraînement, l'entraînement d'un modèle statistique fondé sur les données d'entraînement, l'obtention d'un ensemble de caractéristiques en entrée associées au statut de récepteur de gène et à un type d'échantillon, la réception de données de lecture de séquence associées à un échantillon provenant d'un individu, la détermination de valeurs pour une ou plusieurs caractéristiques en entrée associées au statut de récepteur de gène fondées sur les données de lecture de séquence, une ou plusieurs caractéristiques en entrée à évaluer étant spécifiées en fonction d'un type d'échantillon de l'échantillon en question, en introduisant les valeurs pour une ou plusieurs caractéristiques en entrée dans le modèle statistique entraîné et en prédisant l'état du récepteur de gène de l'individu en se fondant sur un résultat du modèle statistique entraîné.
PCT/US2023/070329 2022-07-22 2023-07-17 Procédés et systèmes pour déterminer l'état d'un gène diagnostique WO2024020343A1 (fr)

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US20180233227A1 (en) * 2017-02-10 2018-08-16 Alivecor, Inc. Systems and methods of analyte measurement analysis
US20210090694A1 (en) * 2019-09-19 2021-03-25 Tempus Labs Data based cancer research and treatment systems and methods
US20220042109A1 (en) * 2020-08-06 2022-02-10 Agendia NV Methods of assessing breast cancer using circulating hormone receptor transcripts

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180233227A1 (en) * 2017-02-10 2018-08-16 Alivecor, Inc. Systems and methods of analyte measurement analysis
US20210090694A1 (en) * 2019-09-19 2021-03-25 Tempus Labs Data based cancer research and treatment systems and methods
US20220042109A1 (en) * 2020-08-06 2022-02-10 Agendia NV Methods of assessing breast cancer using circulating hormone receptor transcripts

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