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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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  • 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
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
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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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  • C12Q1/6869—Methods for sequencing
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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
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
  • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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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
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
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  • C40—COMBINATORIAL TECHNOLOGY
  • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
  • C40B40/00—Libraries per se, e.g. arrays, mixtures
  • C40B40/04—Libraries containing only organic compounds
  • C40B40/06—Libraries containing nucleotides or polynucleotides, or derivatives thereof
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/154—Methylation markers

Definitions

• the present disclosure generally relates to methods for detecting cellular states in bodily fluids or nucleic acid mixtures.
• the method comprises providing or having been provided a sample comprising DNA or RNA and generating a methylation profile for the DNA or RNA in the sample or providing or having been provided a methylation profile of the DNA or RNA in the sample.
• the methylation profile comprises co-associated CpG methylation patterns and methylation haplotype blocks (MHBs) (tightly coupled CpG sites) of the DNA.
• the method comprises detecting cell type or cell state comprising counting co-associated CpG methylation patterns in the DNA, wherein co-associated CpG methylation patterns comprises two or more CpGs in the DNA or counting MHBs.
• the method comprises assigning the DNA to a cell type or cell state based on reference CpG values or reference MHB values, wherein reference CpG values or reference MHB values are determined from reference cell types or reference cell states.
• the method comprises counting DNA molecules assigned to each reference CpG value or reference MHB value, wherein each reference CpG value or reference MHB value corresponds to a cell type or a cell state.
• the method further comprises counting known single CpG methylation profiles to increase sensitivity.
• the sample is a blood sample.
• reference values are differentially methylated CpGs derived from DNA originating from known cell types and known cell states, optionally of bacterial, viral, fungal, or eukaryotic parasitic origin.
• the sample is a plasma, tissue, or biopsy sample.
• the sample comprises a bodily fluid.
• the bodily fluid is selected from whole blood, plasma, urine, saliva, or stool.
• the sample does not comprise a solid tissue biopsy.
• the DNA or RNA is cell-free DNA or RNA and is plasma-derived.
• the method comprises determining cell state-specific signatures by the method of claim 1 or providing or having been provided cell state-specific signatures of the sample.
• the DNA or RNA is cell-free and a rare cell type circulating DNA or RNA.
• the sample comprises cell-free DNA (cfDNA) or cell- free RNA (cfRNA); and the sample is collected from a tumor microenvironment.
• the tumor microenvironment comprises tumor infiltrating leukocytes.
• the DNA is cell-free tumor ctDNA.
• the subject has been administered immunotherapy prior to providing a sample.
• the cell state measured is from DNA from a circulating, cell-free tumor infiltrating leukocyte (TIL) from a tumor microenvironment (TME).
• the method comprises profiling TILs according to methylation signatures; and/or determining the proportions of distinct TIL subsets from a cell type-specific methylation profile identified in the cell-free DNA.
• DNA is classified as originating from a normal leukocyte cell, a tumor-associated cell, or a tumor infiltrating leukocyte.
• the method comprises administering a cancer treatment to the subject (e.g., immunotherapy, chemotherapy, radiation) and measuring cell type and cell state in a sample as an indication of treatment response.
• the subject is determined to be at risk for being a non-responder to immunotherapy.
• the sample comprises cell-free DNA (cfDNA); and the sample is blood from a subject having, suspected of having, or at risk for having sepsis.
• the sample is a blood sample from a subject having, suspected of having, or at risk for having sepsis.
• exhausted lymphocyte cell states are measured.
• exhausted T cells are measured.
• organ-specific cell states or organ-specific cell types are measured.
• the DNA originates from an organ, a damaged organ, a T cell, exhausted T cells, an immune cell, a microbe, septic tissue, or a secondary infection site.
• cfDNA analysis detects DNA originating from a microbial pathogen, the subject is diagnosed with an infection or sepsis.
• cfDNA analysis detects reduced cfDNA originating from a microbial pathogen compared to the cfDNA originating from a microbial pathogen, and the subject is administered a treatment (e.g., antibiotic), the subject is determined to be responding to treatment.
• a treatment e.g., antibiotic
• cfDNA analysis detects reduced cfDNA from a microbial pathogen compared to the cfDNA analysis measured at an earlier time, it is determined that the subject is responding to a treatment or an infection is improving.
• cfDNA analysis detects elevated cfDNA from an organ tissue, an infection source is determined to be the organ tissue with elevated detected cfDNA.
• cfDNA analysis detects elevated cfDNA from an organ tissue suspected of being damaged compared to a control, the organ is determined to be damaged.
• cfDNA analysis detects reduced cfDNA from a damaged organ tissue compared to the cfDNA analysis measured at an earlier time, it is determined that the organ damage is improving. In some embodiments, if cfDNA analysis detects elevated cfDNA from an organ tissue suspected of being damaged compared to a control, the organ is determined to be damaged. In some embodiments, if cfDNA analysis detects elevated cfDNA from multiple organ systems compared to a control, the subject is determined to be at risk for multi organ failure. In some embodiments, if cfDNA analysis detects elevated cfDNA from exhausted T cells or an opportunistic pathogen compared to a control, the subject is determined to be at risk for a secondary infection. In some embodiments, the DNA is cell-free DNA. In some embodiments, instead of DNA, the method uses RNA.
• the method comprises providing a plurality of reads, each read comprising a sequence of the DNA and associated methylation status.
• the method comprises providing a CpG library comprising a plurality of entries, each entry comprising a CpG site and a corresponding cell identity, each CpG site comprising a co-associated CpG site, and each corresponding cell identity comprising a cell type or a cell state.
• the method comprises transforming, using a computing device, the plurality of reads into a plurality of read assignments according to at least one assignment rule, each read assignment comprising one of a cell identity, a cell-related identity, and an unrelated identity.
• the method comprises transforming, using the computing device, the plurality of read assignments into the at least one abundance, each abundance corresponding to one cell identity, each abundance comprising a total number of read assignments comprising the one cell identity.
• At least one assignment rule comprises at least one of: transforming, using the computing device, the read into the cell-related identity if the read comprises no more than one CpG site from the plurality of entries of the CpG library; transforming, using the computing device, the read into the cell identity if the read comprises at least two CpG sites from the plurality of entries of the CpG library with the same corresponding cell identity; and/or transforming, using the computing device, the read into the unrelated identity if the read does not comprise any CpG site from the plurality of entries of the CpG library.
• the method comprises transforming, using the computing device, each abundance into at least one of a relative abundance and an absolute abundance.
• each relative abundance comprises the abundance of one cell identity normalized by the total of all abundances of all cell identities; and/or each absolute abundance comprises the abundance of one cell identity normalized by a sum of the abundance and the total number of read assignments.
• providing the plurality of reads further comprises performing bisulfite sequencing or microarray methylation profiling on the DNA.
• each CpG site is differentially methylated within cells of one cell identity and each co-associated CpG site comprises a sequence position proximal to at least one additional CpG site with the same corresponding cell identity.
• providing the CpG library further comprises providing a plurality of isolated DNA corresponding to one cell identity; performing bisulfite sequencing or microarray methylation profiling on the plurality of isolated cfDNA to obtain a plurality of isolated reads, each isolated read comprising an isolated sequence of an isolated DNA and associated methylation status; performing differential methylated region analysis on the plurality of isolated reads to identify a plurality of candidate CpG sites; and/or assigning a candidate CpG site as an entry of the CpG library for the one cell identity if the candidate CpG site comprises a sequence position proximal to at least one additional candidate CpG site.
• the biological sample comprises a bodily fluid.
• the bodily fluid is selected from whole blood, plasma, urine, saliva, or stool.
• the biological sample does not comprise a solid tissue biopsy.
• the DNA is cell-free DNA. In some embodiments, instead of DNA, the method uses RNA.
• Yet another aspect of the present disclosure provides for a computing device configured to detect at least one abundance of at least one cell identity in a biological sample, the sample comprising DNA, the computing device comprising at least one processor and a non-volatile computer-readable media, the non-volatile computer-readable media containing instructions executable on the at least one processor to: receive a plurality of reads, each read comprising a sequence of the DNA and associated methylation status; provide a CpG library comprising a plurality of entries, each entry comprising a CpG site and a corresponding cell identity, each CpG site comprising a co-associated CpG site, and each corresponding cell identity comprising a cell type or a cell state; transform the plurality of reads into a plurality of read assignments according to at least one assignment rule, each read assignment comprising one of a cell identity, a cell-related identity, and an unrelated identity; and/or transform the plurality of read assignments into the at least one abundance, each abundance corresponding to one cell identity, each abundance
• the at least one assignment rule comprises at least one of transforming, using the computing device, the read into the cell-related identity if the read comprises no more than one CpG site from the plurality of entries of the CpG library; transforming, using the computing device, the read into the cell identity if the read comprises at least two CpG sites from the plurality of entries of the CpG library with the same corresponding cell identity; and/or transforming, using the computing device, the read into the unrelated identity if the read does not comprise any CpG site from the plurality of entries of the CpG library.
• the non volatile computer-readable media further contains instructions executable on the at least one processor to transform each abundance into at least one of a relative abundance and an absolute abundance, wherein: each relative abundance comprises the abundance of one cell identity normalized by the total of all abundances of all cell identities; and/or each absolute abundance comprises the abundance of one cell identity normalized by a sum of the abundance and the total number of read assignments.
• each CpG site is differentially methylated within cells of one cell identity and each co-associated CpG site comprises a sequence position proximal to at least one additional CpG site with the same corresponding cell identity.
• the biological sample comprises a bodily fluid.
• the bodily fluid is selected from whole blood, plasma, urine, saliva, or stool.
• the biological sample does not comprise a solid tissue biopsy.
• the DNA is cell-free DNA.
• the device instead of DNA, the device detects RNA.
• Yet another aspect of the present disclosure provides for a computer-aided method for detecting at least one abundance of at least one cell identity in a biological sample, the sample comprising DNA, the method comprising: providing a plurality of reads, each read comprising a sequence of the DNA and associated methylation status; providing a Methylation Haplotype Block (MHB) library comprising a plurality of entries, each entry comprising an MHB and a corresponding cell identity, each MHB comprising at least two co-associated CpG sites, and each corresponding cell identity comprising a cell type or a cell state; transforming, using a computing device, the plurality of reads into a plurality of read assignments according to at least one assignment rule, each read assignment comprising one of a cell identity, a cell-related identity, and an unrelated identity; and/or transforming, using the computing device, the plurality of read assignments into at least one abundance, each abundance corresponding to one cell identity, each abundance comprising a total number of read assignments comprising the one cell identity.
• At least one assignment rule comprises transforming, using the computing device, the read into the cell identity if the read comprises at least one MHB from the plurality of entries of the MHB library with the corresponding cell identity.
• the method comprises transforming, using the computing device, each abundance into a relative abundance, wherein each relative abundance comprises the abundance of one cell identity normalized by the total of all abundances of all cell identities.
• providing the plurality of reads further comprises performing bisulfite sequencing or microarray methylation profiling on the DNA.
• each MHB site comprises at least two differentially methylated CpG sites in proximity to one another within cells of one cell identity.
• providing the MHB library further comprises: providing a plurality of isolated DNA corresponding to one cell identity; performing bisulfite sequencing or microarray methylation profiling on the plurality of isolated DNA to obtain a plurality of isolated reads, each isolated read comprising an isolated sequence of the isolated DNA and associated methylation status; performing differential methylated region analysis on the plurality of isolated reads to identify a plurality of candidate CpG sites; and/or assigning each sequence including at least two candidate CpG sites near one another as an MHB corresponding to the one cell identity in the MHB library for the one cell identity.
• the biological sample comprises a bodily fluid.
• the bodily fluid is selected from whole blood, plasma, urine, saliva, or stool.
• the biological sample does not comprise a solid tissue biopsy.
• the DNA is cell-free DNA.
• the method uses RNA.
• a computing device configured to detect at least one abundance of at least one cell identity in a biological sample, the sample comprising DNA
• the computing device comprising at least one processor and a non-volatile computer-readable media, the non-volatile computer-readable media containing instructions executable on the at least one processor to: receive a plurality of reads, each read comprising a sequence of the DNA and associated methylation status; receive a Methylation Haplotype Block (MHB) library comprising a plurality of entries, each entry comprising an MHB and a corresponding cell identity, each MHB comprising at least two co-associated CpG sites, and each corresponding cell identity comprising a cell type or a cell state; transform, using a computing device, the plurality of reads into a plurality of read assignments according to at least one assignment rule, each read assignment comprising one of a cell identity, a cell-related identity, and an unrelated identity; and/or transform, using the computing device, the plurality of read assignments into
• MHB Methy
• At least one assignment rule comprises transforming, using the computing device, the read into the cell identity if the read comprises at least one MHB from the plurality of entries of the MHB library with the corresponding cell identity.
• the non-volatile computer- readable media further contains instructions executable on the at least one processor to transform each abundance into a relative abundance, wherein each relative abundance comprises the abundance of one cell identity normalized by the total of all abundances of all cell identities.
• each MHB site comprises at least two differentially methylated CpG sites in proximity to each other within cells of one cell identity.
• the biological sample comprises a bodily fluid.
• the bodily fluid is selected from whole blood, plasma, urine, saliva, or stool.
• the biological sample does not comprise a solid tissue biopsy.
• the DNA is cell-free DNA. In some embodiments, instead of DNA, the device detects RNA.
• Yet another aspect of the present disclosure provides for a computer-aided method for detecting at least one abundance of at least two cell identities in a biological sample, the sample comprising DNA, the method comprising: providing a plurality of reads, each read comprising a sequence of the DNA and associated methylation status; providing a signature matrix comprising at least two pluralities of differentially methylated CpG sites, each portion corresponding to each cell identity of the at least two cell identities; and/or deconvolving, using a computing device, the plurality of reads into at least two relative abundances, each relative abundance comprising a portion of one cell identity within the biological sample.
• the DNA is cell-free DNA.
• the method uses RNA.
• Yet another aspect of the present disclosure provides for a computing device configured to detect at least one abundance of at least two cell identities in a biological sample, the sample comprising DNA, the computing device comprising at least one processor and a non-volatile computer-readable media, the non-volatile computer-readable media containing instructions executable on the at least one processor to receive a plurality of reads, each read comprising a sequence of the DNA and associated methylation status; receive a signature matrix comprising at least two pluralities of differentially methylated CpG sites, each portion corresponding to each cell identity of the at least two cell identities; and deconvolve the plurality of reads into at least two relative abundances, each relative abundance comprising a portion of one cell identity within the biological sample.
• the DNA is cell-free DNA.
• the method uses RNA.
• FIG. 1 Methylation profiling reveals a consistent TIL-specific signature across colorectal cancer patients, but distinct from peripheral blood leukocytes and tumor epithelial cells from colorectal cancer.
• Heatmap indicates whole genome bisulfite (WGBS) data of sorted tumor (turn), tumor infiltrating leukocyte (TIL), and peripheral blood leukocyte (PBL) populations from different colorectal cancer patients (columns) followed by differential methylated region (DMR) analysis.
• LiquidTME detects TIL signal in colorectal cancer blood plasma.
• Whole genome bisulfite sequencing (WGBS) was applied to plasma cell-free DNA from 13 colorectal cancer (CRC) patients. Sequencing results were deconvolved by CIBERSORTx using the methylation signatures derived from the FIG. 1 analysis.
• This analytical method is referred to as LiquidTME.
• FIG. 3 LiquidTME validation of TIL detection from blood plasma in colorectal cancer. Shown are plasma cfDNA (LiquidTME) results vs. tumor ground-truth for the 9 colorectal cancer (CRC) patients in FIG. 2 with detectable plasma TIL signal.
• X- axis indicates the fraction of cell-free DNA coming from the specified population (tumor cell vs. TIL vs. PBL), while the Y-axis indicates ground truth proportions from tumor measurement and sequencing (CIBERSORTx deconvolution result multiplied by the sum of longest tumor diameters (SLD)). Data was analyzed in both rank space (shown here with Spearman p) and in non-rank space (Pearson r shown).
• FIG. 4 LiquidTME measurement of TIL signal from blood plasma correlates strongly with immunotherapy response in melanoma.
• Plasma cell-free DNA obtained within 4 weeks of immunotherapy start from 12 patients was analyzed by whole genome bisulfite sequencing (WGBS) followed by CIBERSORTx deconvolution using our custom methylation signature matrix (see FIG. 1). Eight of 12 (67%) samples were detectable and are shown here.
• ctilDNA refers to the percentage of cell-free DNA arising from TILs as calculated by LiquidTME.
• (a) Melanoma patients are classified as immunotherapy Responders (R) vs. Nonresponders (NR) with ctilDNA percentage indicated in red.
• Receiver operating characteristic (ROC) analysis of response status based on ctilDNA yields an area under the curve (AUC) of 0.94 with a P value of 0.04, indicating ctilDNA level is serving as a strong classifier of response
• FIG. 5 Differentially methylated CpG sites in purified leukocyte subsets after methylation sequencing.
• FIG. 6 Ultra-High-Resolution Digital Cytometry via detecting co-associated CpGs within methylation sequencing read-pairs, and using these to assign each read to the matching reference cell type/state.
• Bulk leukocyte mixtures were sequenced by whole genome bisulfite sequencing (WGBS).
• Ultra-High-Resolution Digital Cytometry was performed utilizing different numbers of co-associated CpGs per read-pair, and correlated with flow cytometric ground-truth. Pearson r and associated P-value are shown to quantify the strength of the correlation.
• FIG. 7 Ultra-High-Resolution Digital Cytometry in Relative and Absolute modes. Bulk leukocyte mixtures were sequenced by whole genome bisulfite sequencing (WGBS). Ultra-High-Resolution Digital Cytometry was performed, with detection of co-associated CpGs per read-pair, followed by assigning each read- pair to its matching reference cell type/state. Results are shown in Relative Mode (left) where the reference-assigned reads are quantified with respect to each other, and Absolute Mode (right) where the reference-assigned fragments were normalized to the total number of unique reads with overlapping CpG positions. In both cases, ultra-high-resolution digital cytometry results were correlated with flow cytometric ground-truth. Pearson r and associated P-value are shown to quantify the strength of the correlation.
• FIG. 8 is an illustration showing tumors shed cells and genetic material into the bloodstream (circulation). ctDNA has been previously described, but here it was discovered that that ctilDNA is also present in the peripheral blood.
• FIG. 9 is a map showing clonally-related CD8 T cells across tissue compartments and T cell exhausting signatures. RNA-seq reveals TIL-specific cell states, distinct from normal. Left: Single cell RNA sequencing identifies a CD8 TIL gene expression profile, distinct from normal. Clones are distinguished by color. Right: Gene set enrichment analysis showing that exhaustion genes are upregulated in CD8 TILs compared to normal CD8 T cells.
• FIG. 10 is a flow chart and a series of graphs showing modeling of ctilDNA detection. Theoretical detection limit modeling of LiquidTME. A) Top: Typical cell- free DNA yield and sequencing depth from 10 mL of blood.
• FIG. 11 Strategy used to develop and validate the LiquidTME assay and application of LiquidTME clinically.
• FIG. 12 Liquid biopsy reveals TME signal in blood plasma.
• PBMCs peripheral blood mononuclear cells
• WGBS plasma cell-free DNA whole genome bisulfite
• FIG. 13 TIL signal measured by LiquidTME correlates with melanoma immunotherapy response.
• FIG. 14 is an illustration depicting the development of an assay for noninvasive TME profiling and measurement of the technical and in vivo performance.
• FIG. 15. Cryopreservation does not introduce epigenetic artifacts.
• Heat map shows methylation rat across the genome in 3 fresh samples, 3 frozen cell samples, and 3 frozen DNA samples from same donor.
• FIG. 16 Visualization of differential methylation of the PDCD1 gene in CD8 T cells.
• Top 3 3 CD8 T TIL samples purified from independent CRC patient tumors.
• Bottom 7 3 top PBL CD8 T samples are from these same CRC patients; 4 bottom samples are from BLUEPRINT healthy donors.
• FIG. 17 Strategy to develop the LiquidTME assay; technical optimization and testing; validation of our technique; and application of LiquidTME clinically.
• FIG. 18 Enumeration of leukocyte subsets by CIBERSORT deconvolution of whole blood methylation profiles.
• (a,b) Scatter plots showing deconvolution performance in relation to flow cytometry in two publicly available datasets: Chakravarthy et al. (a) and Accomando et al. (b).
• LiquidMIDOS will be an all-in-one liquid biopsy technology poised to revolutionize the diagnosis, monitoring, management, and ultimately survival of sepsis patients.
• FIG. 20 A deadly hyper-immune response typically dominates during the first few days of sepsis (A). This is followed by a hypo-immune phase that can be self limited (B) or deadly (C) due to T cell dysfunction/exhaustion which increases the risk of secondary infection, which could potentially be ameliorated with immunotherapy (D). Adapted from Boomer et al, 2014.
• FIG. 21 Plasma cfDNA sources in sepsis. Modified from Crowley et al, 2013.
• FIG. 22 Liver-derived plasma cell-free DNA levels (Y-axis) in hospitalized patients correlate significantly with serum ALT (X-axis), a gold-standard liver damage biomarker. From Moss etal, 2018.
• FIG. 23 Left: Cell-free DNA yield from 10 mL of blood, which following bisulfite conversion and library preparation, undergoes whole genome bisulfite sequencing. Middle: Estimated sources of plasma cell-free DNA and their relative percentages in a sepsis patient, based on Moss et al and Grumaz et al. Right: Binomial probability of detecting each queried cell-free DNA compartment as a function of the number of specific reporters.
• FIG. 24 FACS-sorting scheme for exhausted T cells from tissue using canonical surface marker staining.
• FIG. 25 Plasma cell-free DNA vs. tumor ground-truth for 9 colorectal cancer patients with cfDNA-detected epithelial signal (Left) and tissue lymphocyte signal (Right). Data analyzed in rank space (shown; Spearman p) and non-rank space (Pearson r).
• FIG. 26 Whole genome sequencing detected spiked-in sheared microbial DNA from S. aureus, S. epidermidis, and Adenovirus B (diluted into human plasma between 32 and 1,000 molecules per microliter) with high sensitivity, and specificity as assessed by sequencing 4 independent healthy donor plasma cell-free DNA samples.
• FIG. 27 is a block diagram schematically illustrating a system in accordance with one aspect of the disclosure.
• FIG. 28 is a block diagram schematically illustrating a computing device in accordance with one aspect of the disclosure.
• FIG. 29 is a block diagram schematically illustrating a remote or user computing device in accordance with one aspect of the disclosure.
• FIG. 30 is a block diagram schematically illustrating a server system in accordance with one aspect of the disclosure.
• the present disclosure is based, at least in part, on the discovery that cell states can be measured in a tissue or bodily fluid. It is noted that the scope of the method is not limited to DNA methylation or plasma-derived cell-free DNA. It can be applied to any sequenced nucleic acid mixture (i.e. , DNA or RNA) from any cellular or cell-free DNA source (i.e., any bodily fluid or tissue source). Although examples disclosed here use bisulfite/methylation sequencing, this method can be used with any type of next-generation sequencing or microarray technology known in the art (see e.g., Rajesh et al.
• the presently disclosed method enables detection and profiling of a tumor microenvironment (including tumor infiltrating leukocytes and tumor cell states) using a blood based liquid biopsy approach. This is performed through methylation sequencing of plasma-derived cell-free DNA (see e.g., FIG. 8 and FIG. 21 showing genetic material shed from cells, such as cancer cells, microbial cells, infected cells, etc. that can be detected by this method). Individual single cell states are profiled from bulk using either genome-wide or targeted bisulfite sequencing (e.g., leukocyte and tumor cell states by counting or, optionally, deconvolving plasma methylation sequencing data).
• genome-wide or targeted bisulfite sequencing e.g., leukocyte and tumor cell states by counting or, optionally, deconvolving plasma methylation sequencing data.
• This method is not deconvolution, rather it is single molecule counting, which allows us to enumerate and classify molecules (DNA or RNA) into reference bins on a molecule-by-molecule level.
• the method involves counting, not deconvolution. We start with individual molecules, and by enumerating and classifying them one by one, learn how the full system is comprised molecule-by- molecule. This makes this method extremely high resolution.
• a machine learning model may be used to enumerate and classify DNA or RNA molecules into reference bins.
• the machine learning model may be trained using DNA or RNA molecules obtained from isolated cell types or cell states as described herein.
• deconvolution starts by looking at the entire bulk sequenced mixture as a whole, then optimally tries to weigh and add cell-type- specific signatures together in order to achieve the mixture-representing matrix.
• Cellular states can be defined as context-dependent versions of a given cell type (e.g., normal vs. tumor-associated CD8 T cells). This unique capability allows the presently disclosed noninvasive approach to measure the non-malignant cells within a tumor and distinguish them from their normal tissue counterparts. It is presently believed that this is the first time this has been accomplished. Previous studies have exclusively focused on distinguishing cell types, tissue types, and cancer vs. normal cells -- all of these classifications are less granular than cellular states.
• the disclosed method is dependent on prior knowledge of cell state-specific signatures (e.g., from known cells). These signatures allow this approach to enumerate specific cell types and cellular states directly from methylation signals in cell-free DNA. Such signatures can be derived by physically isolating cell states of interest by FACS or by inferring them via single-cell bisulfite sequencing.
• FACS cell state-specific signatures
• these methods have major shortcomings, including the variable loss of specific cell types by tissue dissociation, the sensitivity, and specificity of the antibody panel (needed for FACS), the low amounts of tissue typically obtained from tumor biopsies, etc. We have therefore developed a novel alternative to complement these techniques. Our approach is based on inferring cell state signatures directly from bulk tumor methylation profiles.
• the present disclosure provides for the noninvasive measurement of measuring cell states in bodily or biological fluids. More specifically, the enumeration of specific cell types and cellular states directly from methylation signals present in cell-free DNA.
• a cell state can be defined as the phenotype of a cell.
• the phenotype of a cell can be a 'homeo-static phenotype' implying plasticity resulting from a dynamically changing yet characteristic pattern of gene/protein expression.
• the methods described herein can be applied to many commercial/biomedical problems, including immunotherapy response assessment, immunotherapy toxicity assessment, response of any tumor to any drug, tracking the tumor microenvironment noninvasively in research, clinical, or commercial applications, and enabling a true liquid biopsy of the tumor that includes both cancer and tumor microenvironment profiling.
• This technology can be used in a broad variety of applications using any type of epigenetics data (i.e., whole genome bisulfite sequencing, reduced representation bisulfite sequencing, methylation microarrays, etc.) on any bodily fluid (e.g., urine, saliva, plasma, stool, etc.).
• epigenetics data i.e., whole genome bisulfite sequencing, reduced representation bisulfite sequencing, methylation microarrays, etc.
• bodily fluid e.g., urine, saliva, plasma, stool, etc.
• This method enables detection and profiling of the tumor microenvironment (including tumor infiltrating leukocytes and tumor cell states) using a liquid biopsy approach.
• the nucleic acid can be full length DNA, a DNA fragment, cell-free DNA, RNA, or cell-free nucleic acid fragment assigned to a cell type originating from a tumor cell, an infected cell, a damaged cell, a normal cell, a bacterial cell, an organ or tissue cell, a tissue cell that secretes cfDNA, microbes such as bacteria, viruses (DNA or RNA), fungi, or eukaryotic parasites, for example.
• the DNA fragment can be about 300 base pairs or less.
• the scope of the method is not limited to DNA methylation or plasma-derived cell-free DNA. It can be applied to any sequenced or microarray-profiled nucleic acid mixture from any cellular or cell-free DNA source (i.e. , any bodily fluid or tissue source).
• the CpG methylation sites can be co-associated (e.g., proximal or nearby to each other) between any number of base pairs along the length of a DNA molecule.
• the amount of base pairs between co-associated CpGs can be between about 1 base pair (bp) and about 1000 bps (proximal or nearby to each other), between 1 bp and about 500 bps, or between about 1 bp and about 300 bps.
• the nearby or proximal CpGs can be separated by about about 1 bp; about 2 bps; about 3 bps; about 4 bps; about 5 bps; about 6 bps; about 7 bps; about 8 bps; about 9 bps; about 10 bps; about 11 bps; about 12 bps; about 13 bps; about 14 bps; about 15 bps; about 16 bps; about 17 bps; about 18 bps; about 19 bps; about 20 bps; about 21 bps; about 22 bps; about 23 bps; about 24 bps; about 25 bps; about 26 bps; about 27 bps; about 28 bps; about 29 bps; about 30 bps; about 31 bps; about 32 bps; about 33 bps; about 34 bps; about 35 bps; about 36 bps; about 37 bps; about 38 bps; about 39 bps; about 40 bps; about 41 bps; about 42 bps; about 43 bps; about 44 bps; about 45 bps; about 46 bps; about 47 bps;
• a control sample or a reference sample as described herein can be a sample from a healthy subject.
• a reference value can be used in place of a control or reference sample, which was previously obtained from a healthy subject or a group of healthy subjects.
• a control sample or a reference sample can also be a sample with a known cellular or tumor composition.
• FIG. 27 depicts a simplified block diagram of a system 800 for implementing the methods described herein. As illustrated in FIG.
• the system 800 may be configured to implement at least a portion of the tasks associated with the disclosed method.
• the system 800 may include a computing device 802.
• the computing device 802 is part of a server system 804, which also includes a database server 806.
• the computing device 802 is in communication with a database 808 through the database server 806 via a network.
• the network 850 may be any network that allows local area or wide area communication between the devices.
• the network 850 may allow communicative coupling to the Internet through at least one of many interfaces including, but not limited to, at least one of a network, such as the Internet, a local area network (LAN), a wide area network (WAN), an integrated services digital network (ISDN), a dial-up-connection, a digital subscriber line (DSL), a cellular phone connection, and a cable modem.
• the user computing device 830 may be any device capable of accessing the Internet including, but not limited to, a desktop computer, a laptop computer, a personal digital assistant (PDA), a cellular phone, a smartphone, a tablet, a phablet, wearable electronics, smart watch, or other web- based connectable equipment or mobile devices.
• PDA personal digital assistant
• the computing device 802 is configured to perform a plurality of tasks associated with the method of detecting abundances of cell states and/or cell types as described herein.
• FIG. 28 depicts a component configuration 400 of a computing device 402, which includes a database 410 along with other related computing components.
• the computing device 402 is similar to computing device 802 (shown in FIG. 27).
• a user 404 may access components of the computing device 402.
• the database 420 is similar to the database 808 (shown in FIG. 27).
• the database 410 includes library data 418, algorithm data 412, ML model data 416, and sample data 420.
• the library data 418 includes entries of a library defining characteristics of different cell types or cell states for which the abundance is detected as described herein.
• Non-limiting examples of library data 418 include entries of a CpG library, entries of a methylation haplotype block (MHB) library, and a signature matrix.
• a CpG library is defined as a plurality of entries in which each entry includes a differentially methylated CpG site indicative of one of the cell types or cell states.
• the differentially methylated CpG sites are additionally co-associated CpG sites.
• a co-associated CpG site refers to a differentially methylated CpG site characterizing one of the cell types or cell states that is positioned at a distance of no more than about 200 bp from an additional differentially methylated CpG site characterizing the same cell type or cell state.
• an MHB library is defined as a plurality of entries in which each entry includes at least two co-associated CpG sites indicative of one of the cell types or cell states.
• a signature matrix comprises a plurality of differentially methylated CpG sites characterizing all of the at least one cell type or cell state. The signature matrix is used as part of a digital deconvolution method as described herein. Non-limiting examples of suitable digital deconvolution methods include CIBERSORTx.
• algorithm data 412 includes any parameters used to implement the methods as described herein.
• suitable algorithm data 412 include any values of parameters defining the calculation of abundance counts, relative abundances, absolute abundances, and any other relevant parameter.
• ML model data 416 include any values of parameters defining the machine learning models used to optimize CpG libraries, to perform digital deconvolution, and any other transformation, classification, or other task in accordance with the methods described herein.
• sample data 420 include any plurality of reads associated with the biological sample analysis in accordance with the methods described herein, including DNA sequences, RNA sequences, DNA methylation sequences, and any other suitable nucleic acid sequence.
• the computing device 402 also includes a number of components that perform specific tasks.
• the computing device 402 includes a data storage device 430, an abundance component 440, an analysis component 450, an ML component 470, and a communication component 460.
• the data storage device 430 is configured to store data received or generated by the computing device 402, such as any of the data stored in database 410 or any outputs of processes implemented by any component of the computing device 402.
• the abundance component 450 is configured to transform the plurality of reads associated with a sample into at least one abundance, at least one relative abundance, at least any absolute abundance, or any combination thereof for each of the at least one cell types or cell states to be detected in accordance with the methods described herein.
• the analysis component 450 is configured to perform any additional analysis of any of the abundances produced in association with the methods described.
• additional analyses performed using the analysis component 450 include diagnosis of a disease or disorder such as cancer or sepsis, classification of a patient into a category such as a responder or non-responder to a treatment, determination of a treatment efficacy, and any other suitable analysis.
• the ML component 470 is configured to implement any of the machine learning model-based transformations and analyses as described herein.
• transformations or analyses implemented using the ML component 470 include digital deconvolution of the cell types or cell states based on a plurality of reads in a mixed sample. Optimization of a CpG library or an MHB library, or any other suitable transformation or analysis is in accordance with the methods described herein.
• the communication component 460 is configured to enable communications of the computing device 402 over a network, such as network 850 (shown in FIG. 27), or a plurality of network connections using predefined network protocols such as TCP/IP (Transmission Control Protocol/Internet Protocol).
• a network such as network 850 (shown in FIG. 27)
• predefined network protocols such as TCP/IP (Transmission Control Protocol/Internet Protocol).
• FIG. 29 depicts a configuration of a remote or user computing device 502, such as the user computing device 830 (shown in FIG. 27).
• the computing device 502 may include a processor 505 for executing instructions.
• executable instructions may be stored in a memory area 510.
• Processor 505 may include one or more processing units (e.g., in a multi-core configuration).
• Memory area 510 may be any device allowing information such as executable instructions and/or other data to be stored and retrieved.
• Memory area 510 may include one or more computer-readable media.
• Computing device 502 may also include at least one media output component 515 for presenting information to a user 501.
• Media output component 515 may be any component capable of conveying information to user 501.
• media output component 515 may include an output adapter, such as a video adapter and/or an audio adapter.
• An output adapter may be operatively coupled to processor 505 and operatively coupleable to an output device such as a display device (e.g., a liquid crystal display (LCD), organic light emitting diode (OLED) display, cathode ray tube (CRT), or “electronic ink” display) or an audio output device (e.g., a speaker or headphones).
• a display device e.g., a liquid crystal display (LCD), organic light emitting diode (OLED) display, cathode ray tube (CRT), or “electronic ink” display
• an audio output device e.g., a speaker or headphones.
• computing device 502 may include an input device 520 for receiving input from user 501.
• Input device 520 may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a camera, a gyroscope, an accelerometer, a position detector, and/or an audio input device.
• a single component such as a touch screen may function as both an output device of media output component 515 and input device 520.
• Computing device 502 may also include a communication interface 525, which may be communicatively coupleable to a remote device.
• Communication interface 525 may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a mobile phone network (e.g., Global System for Mobile communications (GSM), 3G, 4G, or Bluetooth) or other mobile data network (e.g., Worldwide Interoperability for Microwave Access (WIMAX)).
• GSM Global System for Mobile communications
• 3G, 4G, or Bluetooth or other mobile data network
• WIMAX Worldwide Interoperability for Microwave Access
• Stored in memory area 510 are, for example, computer-readable instructions for providing a user interface to user 501 via media output component 515 and, optionally, receiving and processing input from input device 520.
• a user interface may include, among other possibilities, a web browser, and client application. Web browsers enable users 501 to display and interact with media and other information typically embedded on a web page or a website from a web server.
• a client application allows users 501 to interact with a server application associated with, for example, a vendor or business.
• FIG. 30 illustrates an example configuration of a server system 602.
• Server system 602 may include, but is not limited to, database server 806 and computing device 802 (both shown in FIG. 27). In some aspects, server system 602 is similar to server system 804 (shown in FIG. 27).
• Server system 602 may include a processor 605 for executing instructions. Instructions may be stored in a memory area 625, for example.
• Processor 605 may include one or more processing units (e.g., in a multi-core configuration).
• Processor 605 may be operatively coupled to a communication interface 615 such that server system 602 may be capable of communicating with a remote device such as user computing device 830 (shown in FIG. 27) or another server system 602.
• communication interface 615 may receive requests from user computing device 830 via a network 850 (shown in FIG. 27).
• Storage device 625 may be any computer-operated hardware suitable for storing and/or retrieving data.
• storage device 625 may be integrated in server system 602.
• server system 602 may include one or more hard disk drives as storage device 625.
• storage device 625 may be external to server system 602 and may be accessed by a plurality of server systems 602.
• storage device 625 may include multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration.
• Storage device 625 may include a storage area network (SAN) and/or a network attached storage (NAS) system.
• SAN storage area network
• NAS network attached storage
• processor 605 may be operatively coupled to storage device 625 via a storage interface 620.
• Storage interface 620 may be any component capable of providing processor 605 with access to storage device 625.
• Storage interface 620 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing processor 605 with access to storage device 625.
• ATA Advanced Technology Attachment
• SATA Serial ATA
• SCSI Small Computer System Interface
• Memory areas 510 (shown in FIG. 29) and 610 may include, but are not limited to, random access memory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and non-volatile RAM (NVRAM).
• RAM random access memory
• DRAM dynamic RAM
• SRAM static RAM
• ROM read-only memory
• EPROM erasable programmable read-only memory
• EEPROM electrically erasable programmable read-only memory
• NVRAM non-volatile RAM
• the computer systems and computer-implemented methods discussed herein may include additional, less, or alternate actions and/or functionalities, including those discussed elsewhere herein.
• the computer systems may include or be implemented via computer-executable instructions stored on non-transitory computer-readable media.
• the methods may be implemented via one or more local, remote, o cloud-based processors, transceivers, servers, and/or sensors (such as processors, transceivers, servers, and/or sensors mounted on vehicle or mobile devices, or associated with smart infrastructure or remote servers), and/or via computer executable instructions stored on non-transitory computer-readable media or medium.
• a computing device is configured to implement machine learning, such that the computing device “learns” to analyze, organize, and/or process data without being explicitly programmed.
• Machine learning may be implemented through machine learning (ML) methods and algorithms.
• a machine learning (ML) module is configured to implement ML methods and algorithms.
• ML methods and algorithms are applied to data inputs and generate machine learning (ML) outputs.
• Data inputs may include but are not limited to: images or frames of a video, object characteristics, and object categorizations.
• Data inputs may further include: sensor data, image data, video data, telematics data, authentication data, authorization data, security data, mobile device data, geolocation information, transaction data, personal identification data, financial data, usage data, weather pattern data, “big data” sets, and/or user preference data.
• ML outputs may include but are not limited to: a tracked shape output, categorization of an object, categorization of a type of motion, a diagnosis based on motion of an object, motion analysis of an object, and trained model parameters ML outputs may further include: speech recognition, image or video recognition, functional connectivity data, medical diagnoses, statistical or financial models, autonomous vehicle decision-making models, robotics behavior modeling, fraud detection analysis, user recommendations and personalization, game Al, skill acquisition, targeted marketing, big data visualization, weather forecasting, and/or information extracted about a computer device, a user, a home, a vehicle, or a part of a transaction.
• data inputs may include certain ML outputs.
• At least one of a plurality of ML methods and algorithms may be applied, which may include but are not limited to: linear or logistic regression, instance-based algorithms, regularization algorithms, decision trees, Bayesian networks, cluster analysis, association rule learning, artificial neural networks, deep learning, dimensionality reduction, and support vector machines.
• the implemented ML methods and algorithms are directed toward at least one of a plurality of categorizations of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning.
• ML methods and algorithms are directed toward supervised learning, which involves identifying patterns in existing data to make predictions about subsequently received data.
• ML methods and algorithms directed toward supervised learning are “trained” through training data, which includes example inputs and associated example outputs.
• the ML methods and algorithms may generate a predictive function that maps outputs to inputs and utilize the predictive function to generate ML outputs based on data inputs.
• the example inputs and example outputs of the training data may include any of the data inputs or ML outputs described above.
• a ML module may receive training data comprising customer identification and geographic information and an associated customer category, generate a model that maps customer categories to customer identification and geographic information, and generate a ML output comprising a customer category for subsequently received data inputs including customer identification and geographic information.
• ML methods and algorithms are directed toward unsupervised learning, which involves finding meaningful relationships in unorganized data. Unlike supervised learning, unsupervised learning does not involve user-initiated training based on example inputs with associated outputs. Rather, in unsupervised learning, unlabeled data, which may be any combination of data inputs and/or ML outputs as described above, is organized according to an algorithm-determined relationship.
• a ML module receives unlabeled data comprising customer purchase information, customer mobile device information, and customer geolocation information, and the ML module employs an unsupervised learning method such as “clustering” to identify patterns and organize the unlabeled data into meaningful groups. The newly organized data may be used, for example, to extract further information about a customer’s spending habits.
• ML methods and algorithms are directed toward reinforcement learning, which involves optimizing outputs based on feedback from a reward signal.
• ML methods and algorithms directed toward reinforcement learning may receive a user-defined reward signal definition, receive a data input, utilize a decision-making model to generate a ML output based on the data input, receive a reward signal based on the reward signal definition and the ML output, and alter the decision-making model so as to receive a stronger reward signal for subsequently generated ML outputs.
• the reward signal definition may be based on any of the data inputs or ML outputs described above.
• a ML module implements reinforcement learning in a user recommendation application.
• the ML module may utilize a decision-making model to generate a ranked list of options based on user information received from the user and may further receive selection data based on a user selection of one of the ranked options.
• a reward signal may be generated based on comparing the selection data to the ranking of the selected option.
• the ML module may update the decision making model such that subsequently generated rankings more accurately predict a user selection.
• any such resulting program, having computer-readable code means may be embodied or provided within one or more computer-readable media, thereby making a computer program product, i.e. , an article of manufacture, according to the discussed aspects of the disclosure.
• the computer-readable media may be, for example, but is not limited to, a fixed (hard) drive, diskette, optical disk, magnetic tape, semiconductor memory such as read only memory (ROM), and/or any transmitting/receiving medium, such as the Internet or other communication network or link.
• the article of manufacture containing the computer code may be made and/or used by executing the code directly from one medium, by copying the code from one medium to another medium, or by transmitting the code over a network.
• a processor may include any programmable system including systems using micro-controllers, reduced instruction set circuits (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor capable of executing the functions described herein.
• RISC reduced instruction set circuits
• ASICs application specific integrated circuits
• logic circuits and any other circuit or processor capable of executing the functions described herein.
• the above examples are examples only, and are thus not intended to limit in any way the definition and/or meaning of the term “processor.”
• the terms “software” and “firmware” are interchangeable and include any computer program stored in memory for execution by a processor, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory.
• RAM random access memory
• ROM memory read-only memory
• EPROM memory erasable programmable read-only memory
• EEPROM memory electrically erasable programmable read-only memory
• NVRAM non-volatile RAM
• a computer program is provided, and the program is embodied on a computer readable medium.
• the system is executed on a single computer system, without requiring a connection to a server computer.
• the system is being run in a Windows® environment (Windows is a registered trademark of Microsoft Corporation, Redmond, Washington).
• Windows is a registered trademark of Microsoft Corporation, Redmond, Washington.
• the system is run on a mainframe environment and a UNIX® server environment (UNIX is a registered trademark of X/Open Company Limited located in Reading, Berkshire, United Kingdom).
• the application is flexible and designed to run in various different environments without compromising any major functionality.
• the system includes multiple components distributed among a plurality of computing devices.
• One or more components may be in the form of computer-executable instructions embodied in a computer-readable medium.
• the systems and processes are not limited to the specific aspects described herein.
• components of each system and each process can be practiced independent and separate from other components and processes described herein.
• Each component and process can also be used in combination with other assembly packages and processes.
• the present aspects may enhance the functionality and functioning of computers and/or computer systems.
• methods and algorithms of the invention may be enclosed in a controller or processor.
• methods and algorithms of the present invention can be embodied as a computer implemented method or methods for performing such computer-implemented method or methods, and can also be embodied in the form of a tangible or non-transitory computer readable storage medium containing a computer program or other machine-readable instructions (herein “computer program”), wherein when the computer program is loaded into a computer or other processor (herein “computer”) and/or is executed by the computer, the computer becomes an apparatus for practicing the method or methods.
• computer program computer program
• Storage media for containing such computer program include, for example, floppy disks and diskettes, compact disk (CD)-ROMs (whether or not writeable), DVD digital disks, RAM and ROM memories, computer hard drives and back-up drives, external hard drives, “thumb” drives, and any other storage medium readable by a computer.
• the method or methods can also be embodied in the form of a computer program, for example, whether stored in a storage medium or transmitted over a transmission medium such as electrical conductors, fiber optics or other light conductors, or by electromagnetic radiation, wherein when the computer program is loaded into a computer and/or is executed by the computer, the computer becomes an apparatus for practicing the method or methods.
• the method or methods may be implemented on a general purpose microprocessor or on a digital processor specifically configured to practice the process or processes.
• the computer program code configures the circuitry of the microprocessor to create specific logic circuit arrangements.
• Storage medium readable by a computer includes medium being readable by a computer per se or by another machine that reads the computer instructions for providing those instructions to a computer for controlling its operation. Such machines may include, for example, machines for reading the storage media mentioned above.
• compositions and methods described herein utilizing molecular biology protocols can be according to a variety of standard techniques known to the art (see e.g., Sambrook and Russel (2006) Condensed Protocols from Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN-10: 0879697717; Ausubel et al. (2002) Short Protocols in Molecular Biology, 5th ed., Current Protocols, ISBN-10: 0471250929; Sambrook and Russel (2001) Molecular Cloning: A Laboratory Manual, 3d ed., Cold Spring Harbor Laboratory Press, ISBN-10: 0879695773; Elhai, J. and Wolk, C. P. 1988. Methods in Enzymology 167, 747-754;
• numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth, used to describe and claim certain embodiments of the present disclosure are to be understood as being modified in some instances by the term “about.”
• the term “about” is used to indicate that a value includes the standard deviation of the mean for the device or method being employed to determine the value.
• the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment.
• the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.
• the terms “a” and “an” and “the” and similar references used in the context of describing a particular embodiment (especially in the context of certain of the following claims) can be construed to cover both the singular and the plural, unless specifically noted otherwise.
• the term “or” as used herein, including the claims, is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive.
• any forms or tenses of one or more of these verbs are also open-ended.
• any method that “comprises,” “has” or “includes” one or more steps is not limited to possessing only those one or more steps and can also cover other unlisted steps.
• any composition or device that “comprises,” “has” or “includes” one or more features is not limited to possessing only those one or more features and can cover other unlisted features.
• TILs tumor immune cells
• TME tumor microenvironment
• leukocytes white blood cells
• Some subsets can include naive and memory CD8 T cells and CD4 T cells, NK cells, naive and memory B cells, monocytes/macrophages, and granulocytes.
• the following example and the present disclosure provides for a solution to the problem of assessing response to treatment early.
• Early imaging assessment is challenging and confounded by factors like pseudoprogression.
• Other leading prediction measures like tumor PDL1, TMB, and tumor gene expression profiling are not sensitive or specific enough.
• tumor PDL1, TMB, and tumor gene expression profiling are not sensitive or specific enough.
• LiquidTME liquid biopsy of the tumor microenvironment
• CpGs adjacent to each other have been shown to share similar methylation patterns due to locally coordinated activity of methylation enzymes and CpGs function at a block level within promoters to regulate gene transcription.
• Cancer is the second most common cause of death in the United States 1 and immunotherapy is a powerful way to treat advanced stages of disease 2 3 . However, only a fraction of patients respond initially 4 , and in many cases an initial response is not durable 5 .
• CT imaging is the standard-of-care method for assessing immunotherapy response 6 7 , however early imaging assessment is unreliable 89 . We currently have no reliable way to predict immunotherapy response early.
• ctilDNA are cell-free DNA arising from TILs. This platform, LiquidTME, profiles and measures ctilDNA. The outcome is early immunotherapy response prediction.
• TILs Tumor infiltrating leukocytes
• TME tumor microenvironment
• TILs liquid biopsy analysis of methylation signatures in plasma cell-free DNA will enable accurate quantitation of TILs and reliably predict immunotherapy response.
• Philip et al. showed that tumor infiltrating CD8 T cells have a distinct chromatin profile compared to normal CD8 T cells 31 .
• TILs, both myeloid and lymphoid have also been shown to have distinct gene expression profiles from normal leukocytes by single cell RNA sequencing 32-34 .
• Our novel data also show that TILs have a distinct methylation profile compared to normal leukocytes and tumor cells, allowing us to quantify them via cell-free DNA liquid biopsy.
• LiquidTME for any cancer or disease state and showcase it here for colorectal cancer and melanoma pre-treatment to detect cell states noninvasively and predict response to different types of treatment including immune checkpoint blockade.
• LiquidTME will enable sensitive TIL quantitation and predict therapeutic response better than leading technologies.
• LiquidTME will complement current efforts being undertaken toward early cancer detection using cell-free DNA 40 .
• Our work will allow researchers for the first time to specifically assess TILs without requiring invasive tumor biopsy.
• the principles established here should generalize to nearly any disease etiology and therapy type, opening the door to routine, noninvasive TIL assessment in research and clinical settings.
• TILs tumor infiltrating leukocytes
• PBLs peripheral blood leukocytes
• WGBS whole genome bisulfite sequencing
• TILs have a distinct methylation profile by methylation profiling of sorted cells (see e.g., FIG. 1) (whole genome bisulfite sequencing (WGBS) on sorted colorectal cancer samples) and differential methylated region (DMR) analysis reveals a TIL-specific methylation pattern.
• WGBS whole genome bisulfite sequencing
• DMR differential methylated region
• TIL signatures are detected in plasma cell-free DNA from CRC patients.
• TIL signal can be detected in cell-free DNA using a liquid biopsy technology that we call LiquidTME.
• cfDNA plasma cell-free DNA
• WGBS lllumina NovaSeq S4 flow cell targeting 65 genome-wide coverage.
• TIL vs. PBL vs. tumor cell signatures shown in FIG. 1 using CIBERSORTx.
• TIL signatures in plasma predict immunotherapy response in melanoma
• LiquidTME can also be applied to melanoma immunotherapy response (see e.g., FIG. 4) as shown by the application of LiquidTME to Melanoma Plasma Samples Collected Pre- or Early On- Immunotherapy. It was shown that ctilDNA or tumor signal detected in 8 of 13 samples (62%) and ctilDNA levels in cell-free DNA correlate strongly with durable response among these 8 detectable patients.
• FIG. 5 depicts whole genome bisulfite sequencing (WGBS) methylation data showing differentially methylated CpGs in sorted leukocyte subsets.
• WGBS whole genome bisulfite sequencing
• Methylation profiling i.e. , WGBS
• co-associated CpGs i.e., 2, 3, 4 per read-pair
• Our method correlates well with flow cytometric ground truth across a range of co-associated CpGs (FIG. 6).
• Assign sequencing reads from bulk mixture to each cell type/state (based on detection of cell type/state-specific co-associated CpGs at the individual read level).
• LiquidTME will enable early immunotherapy response prediction, make serial profiling of TILs practical, and improve clinical decision-making and patient survival.
• the described technology enables robust ultra-high-resolution digital cytometry to measure cell states from methylation sequencing data. Given its ultra sensitivity, it can be applied to cell-free DNA, enabling noninvasive detection of rare cell states, such as those in the tumor microenvironment.
• the approach, called LiquidTME serves as a robust early predictor of immunotherapy response in cancer patients through ultra-sensitive tumor infiltrating leukocyte detection.
• Tumeh, P C., et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568-571 (2014).
• TME tumor microenvironment
• PD-1 and CTLA4 immune cells
• ICIs immune checkpoint inhibitors
• TILs tumor infiltrating leukocytes
• TME can also contain cells that promote resistance to immune checkpoint blockade, or lack cells with cancer-killing properties 3 10 21 .
• TME analysis requires invasive biopsy 11 , which is impractical to perform serially and can be dangerous to our patients 2324 .
• LiquidTME based on digital cytometric analysis of bisulfite- treated cell-free DNA (cfDNA) next-generation sequencing (NGS) to overcome this.
• LiquidTME can distinguish TILs from tumor cells and normal leukocytes using methylation signatures (see e.g., FIG. 14).
• Physiologic cfDNA in the blood is thought to arise from cell death 29 32
• Malignant tumors also shed DNA into the circulation (ctDNA), where it can be isolated, quantitated, and sequenced 29 35 .
• Mechanisms of release of ctDNA into the bloodstream are related to tumor cell death 29 33 .
• the challenge with ctDNA detection is that levels in the blood plasma are low, typically comprising a minority of normal cell-free DNA molecules 32 .
• Modern NGS-based techniques have thus been developed which enable ctDNA detection as low as ⁇ 0.01 % of total cell-free DNA, low enough to detect post-treatment molecular residual disease (MRD) 36 ⁇ 37 .
• MRD molecular residual disease
• ICIs are currently transforming cancer care and have improved the outcomes of a subset of patients with advanced cancer 4 ’ 5 ’ 38 . Still, immunotherapy response in individual patients is unpredictable, with overall rates ranging from 1 % to 60%, and most cancer types having a response rate of 5-20% 39 .
• response assessment cannot be performed reliably for ⁇ 3 months after starting treatment because standard-of-care CT imaging cannot reliably distinguish between true progression and pseudoprogression at earlier timepoints 40 42 .
• this first scan may still be subject to pseudoprogression 40 42
• current radiographic guidelines recommend that in cases of suspected progression, a second scan should be ordered at least one month later ( ⁇ 4 months after starting immunotherapy) to provide confirmation 41 43 .
• Melanoma is the fifth most common cancer in the United States and a poster child for immunotherapy response, with objective response rates as high as ⁇ 60% with combination ICIs 43 . Despite this, clinical outcomes remain poor with a 4-year survival rate of only ⁇ 50% 46 . Cell-free DNA and ctDNA concentrations are typically elevated in advanced-stage patients, with multiple papers demonstrating the ability to assess this compartment by plasma liquid biopsy 9 ’ 47 52 . Given poor clinical outcomes, high cfDNA content, and a clear role for immunotherapy, it is worth focusing on this cancer type for these studies.
• Bisulfite sequencing involves treatment of DNA with bisulfite to identify methylated bases, followed by NGS to identify patterns of DNA methylation. These methylation patterns can be used to identify tissue-of-origin 53 54 .
• Recent publications demonstrate the utility of methylation profiling to detect tumor cell-derived cfDNA S5 ⁇ 57 . Still, the composition of the TME has not been profiled epigenetically from cell- free DNA. We plan to bridge this gap here using a novel approach.
• Philip et al. used ATAC-seq to demonstrate distinct epigenetic programs in tumor-specific CD8 T cells indicative of cellular dysfunction 58 .
• ATAC-seq was used to demonstrate distinct epigenetic programs in tumor-specific CD8 T cells indicative of cellular dysfunction 58 .
• scRNA-seq data from T cells isolated from hepatocellular cancer patients (Zheng et al. 59 ) and identified stereotypic differences between CD8 T cells of the same clonotype found in >1 tissue compartment: tumor, adjacent normal, and/or peripheral blood (FIG. 9). Markers associated with T cell exhaustion and dysfunction 7 (i.e.
• TILs as well as those associated with tumor reactivity 60 (i.e., CD103 and CD39) were consistently upregulated in tumor CD8 T cells but were low or absent in the same clonotypes from adjacent normal and PBL compartments. This data suggests we can distinguish TILs from PBLs using epigenetics.
• Factors underlying the detection limit of cell-free DNA applications include:
• TME signatures can be detected in cfDNA
• TIL signal was not detectable in the PBL compartment. Also, as we would expect, the PBL signal was lower in tumor than in the periphery. Only TIL and tumor signals in cell-free DNA correlated positively with flow cytometry and imaging in matched tumors. We can significantly extend this work to optimize our assay, determine whether multiple TIL subsets can be quantitated in plasma, and demonstrate clinical utility.
• TIL-, PBL- and melanoma-specific methylation signatures for the purpose of deconvolution, and (2) design an optimized sequencing panel with the genomic bandwidth to profile melanoma tumor cells, TILs, and PBLs with high analytical sensitivity.
• TIL profile from pre-treatment cfDNA (i.e., elevated ctilDNA content like FIG. 2B) that corresponds to durable clinical benefit to ICI treatment, (2) validate it using a held-out cohort, and (3) demonstrate more accurate response and outcomes prediction than the other technologies tested.
• pre-treatment cfDNA i.e., elevated ctilDNA content like FIG. 2B
• sensitivity remains suboptimal, then we can implement methods to improve the analytical limit of detection such as bioinformatic background error correction 1 , addition of DMRs/reporters to the capture panel, greater sequencing depth, and optimization of deconvolution through machine learning.
• bioinformatic background error correction 1 addition of DMRs/reporters to the capture panel, greater sequencing depth, and optimization of deconvolution through machine learning.
• This technology is a highly innovative combination of cfDNA bisulfite sequencing and digital cytometry to profile the TME in solid tumor cancer patients by liquid biopsy for the first time. This approach will help address a major unmet need: predicting ICI response early.
• Immune checkpoint inhibitors have transformed modern cancer treatment as the only therapeutic in years to provide durable remission and significant survival benefit across many cancer types. Despite their success, most patients do not respond to these drugs, there is a serious risk of immune-related toxicity, and we are unable to reliably predict response or toxicity early.
• the key to unlocking the full potential of immune checkpoint inhibitors is through understanding the tumor microenvironment (TME).
• TME tumor microenvironment
• the only way to analyze the TME is through invasive biopsy which is impractical to perform serially and can cause harm to the patient. Solution
• LiquidTME liquid biopsy method for tumor microenvironment profiling based on next-generation methylation sequencing of cell-free DNA.
• This method which we call LiquidTME, will be developed in the context of colorectal and lung cancers (two of the most common cancers worldwide) but will be directly extensible to nearly any malignancy. If successful, our approach will enable tumor microenvironment analysis through a simple blood test, which should have a direct clinical impact by enabling earlier and more precise assessment of the thousands of cancer patients being treated with immunotherapy.
• TME tumor microenvironment
• PD-1 and CTLA4 immune cells
• Immune checkpoint inhibitors block these receptors and transform a subset of tumor infiltrating leukocytes (TILs) in the TME into cancer-killing cells, a phenomenon that has revolutionized the field of oncology 2,3 .
• ctDNA cell-free circulating tumor DNA
• Mechanisms of release of ctDNA into the bloodstream are related to tumor cell death 2630 .
• the challenge with ctDNA detection is that levels in the blood plasma are low, typically comprising ⁇ 1% of normal cell-free DNA molecules 26 .
• Modern NGS-based techniques have thus been developed which enable ctDNA detection as low as ⁇ 0.01 % of total cell-free DNA, low enough to detect post-treatment molecular residual disease (MRD) 31,32 .
• the epigenome is comprised of chemical compounds bound to the DNA molecule that direct which parts of the genome are turned on or off 33 .
• Each cell type has a unique epigenomic signature 33 which we can profile by analyzing the methylation pattern on DNA using a method called bisulfite sequencing 34,35 .
• bisulfite sequencing 34,35 we will use these epigenomic signatures to distinguish cell types through machine learning based cellular deconvolution, similar conceptually to CIBERSORT 3637 , but applied to the minuscule levels of ctilDNA present in blood plasma.
• FIG. 10 mathematical modeling exercise using this approach.
• Our model suggests a high chance of success as the detection limit required to track ctilDNA is within the range of what we reliably achieve with ctDNA 26,31,32 .
• TILs tumor infiltrating leukocytes
• PBL peripheral blood leukocyte
• CD8 T cells with the same T cell receptor still show striking epigenomic differences between tumor and normal, indicating their ultimate site of tumor vs. normal tissue/blood residence is a major determinant of their expression signature despite their clonal genomic identity.
• T cell exhaustion and dysfunction 5 i.e., ICOS, PD1 and CTLA4
• tumor reactivity 45 i.e., CD103 and CD39
• This technology is based on the premise that ultra-sensitive detection and profiling of TME-derived ctilDNA will enable early and precise cancer treatment response and toxicity assessment.
• Our approach will utilize machine learning to combine data from methylation sequencing studies (e.g., ENCODE 46 , BLUEPRINT 47 , NIH Roadmap Epigenomics Project 33 ) with our own data that we generate through methylation sequencing of patient samples, with innovative technical methods to sensitively and specifically detect individual TME cellular subsets (i.e., CD8 T cells, CD4 T cells, NK cells, B cells, monocytes/macrophages, cancer-associated fibroblasts) from cell-free DNA.
• TME cellular subsets i.e., CD8 T cells, CD4 T cells, NK cells, B cells, monocytes/macrophages, cancer-associated fibroblasts
• Immune checkpoint inhibitors are transforming cancer care and have improved the outcomes of a multitude of patients with advanced-stage cancer 2,3 .
• immunotherapy has improved survival dramatically in patients with both locally advanced and advanced disease 4952 , enabling many to live longer than ever thought possible.
• immunotherapy response in individual patients is unpredictable, with overall rates ranging between 1 % to 50%, and most cancer types having a response rate of 5-20% 53 .
• response assessment cannot be performed reliably for ⁇ 3 months after starting treatment because standard-of-care CT imaging cannot distinguish between true progression and pseudoprogression at earlier timepoints 5456 .
• toxicity 20 On the flip-side of immunotherapy response is toxicity 20 . Rates of severe toxicity requiring hospitalization are ⁇ 60% in patients treated with combination immune checkpoint inhibitors (anti-CTLA4 and anti-PD1 ), and ⁇ 25% in those treated with a single agent 60,61 . Unfortunately multiple instances of death resulting from immune checkpoint blockade have also been documented 21,22 . In a large meta-analysis of 613 patients who experienced fatal immune checkpoint blockade-related toxicity, the median time to death after starting treatment was only 14.5 days in those receiving combination immune checkpoint inhibitors, and 40 days in those receiving either anti-PD1 or anti-CTLA4 alone 21 , highlighting that biomarkers must be developed to predict these as early as possible.
• LiquidTME a novel method for detecting tumor microenvironment- derived DNA in cell-free DNA
• LiquidTME entails purifying pre determined genomic regions that are highly enriched for DMRs which identify and distinguish tumor microenvironmental cellular subsets from their normal counterparts.
• LiquidTME will be ultra-sensitive and directly applicable to cancer patients, with the most immediate clinical role being the early prediction of immunotherapy response and toxicity.
• this technology can result in the delivery of an optimized method for profiling the tumor microenvironment noninvasively that has passed initial clinical validation applied to immunotherapy patients.
• LiquidTME is developed in the context of CRC and NSCLC.
• CRC colorectal cancer
• NSCLC non-small cell lung cancer
• LiquidTME for noninvasive TME profiling, we will follow the roadmap outlined in FIG. 17.
• MDSCs myeloid derived suppressor cells
• monocytes/macrophages granulocytes.
• CAFs cancer- associated fibroblasts
• FIG. 12 shows we were able to detect the TME signal within cell-free DNA without the major technical innovations discussed here.
• LiquidTME can be validated clinically by applying the assay to plasma samples from patients with advanced-stage CRC and NSCLC (FIG. 12). We also have cryopreserved banked tumor samples from these patients from the same timepoints. We will assess the accuracy and precision of our method in these clinical samples, as compared to flow cytometry on the dissociated tumor. In addition to flow cytometry, we will also perform CIBERSORT 3637 on the tumor samples as tissue dissociation can lead to variable loss of fragile cell types and distort flow cytometry results by favoring cell types that fit through the filter and instrument pores 37 . To clinically validate performance, we will compare concordance between our method applied to cell-free DNA and gold- standard tumor analysis.
• Exosomes are microvesicles that are present in plasma and can contain nucleic acids 72 .
• TME-derived cell-free DNA is enriched inside or outside of exosomes we will perform fractionation of plasma using previously described methods 73 and will sequence exosome-enriched and -depleted fractions.
• ctDNA and the data shown in FIG. 18 suggest significant enrichment of ctilDNA in the cell-free vs. cellular compartment of blood, we will confirm this and compare our results to gold-standard tumor assessment.
• LiquidTME could revolutionize immunotherapy response and toxicity assessment in two ways. First, it could serve as a primary assessment modality that provides precise data to the clinician at a timepoint when imaging and clinical assessment has been shown to be inadequate. Secondly, it could be used to serially track patients and supplement equivocal assessments from our standard clinical modalities, helping to distinguish borderline response from progression and predict the severity of potential symptomatic toxicity. Our work here can be generalized even more broadly, as noninvasive TME assessment can find utility in multiple research and clinical settings.
• This technology tracks a previously undescribed entity (ctilDNA), to do this robustly and comprehensively, and to apply our technology to a clinical challenge of utmost importance in the field of oncology.
• the presently described technology is exceptionally innovative since it is on the topic of a new and previously undescribed component of cell-free DNA, which arises from the tumor microenvironment, and we disclose a new technical method in order to profile and track it in the blood.
• Our method presents a potential solution to one of the most significant problems to arise in modern oncology, namely the prediction of which patients will respond to immunotherapy and which patients will be affected by severe toxicities from immunotherapy. If successful, LiquidTME will be a technological advance in immunotherapy response and toxicity assessment, having a palpable clinical impact. This would revolutionize oncologic practice by enabling us to more precisely select and monitor our patients and potentially impact the lives of thousands of individuals annually.
• our work here should generalize to nearly any cancer type and anti-cancer therapy, opening the door to routine and noninvasive tumor microenvironment assessment in both research and clinical settings.
• a variety of methods can be employed to increase sensitivity.
• the main drawbacks of these optimizations are that sequencing costs will increase. However, sequencing costs have been plummeting and are expected to continue to decrease 82 .
• the following example describes the development of an ultrasensitive framework for profiling tumor infiltrating leukocytes using cell-free DNA methylation profiles and evaluate the technical performance of noninvasive digital cytometry for profiling TILs in vitro and from patients with metastatic melanoma.
• TILs Tumor infiltrating leukocytes
• Liquid biopsies are an emerging class of techniques for noninvasive tumor profiling based on cell-free DNA, which is continually shed into the circulation from normal and malignant cells. Despite the potential of cell-free DNA to enable safe, noninvasive assessment of diverse physiological states over serial time points, there is currently no liquid biopsy method available for monitoring TIL composition.
• a genomics platform applied to cell-free DNA can enable noninvasive profiling of TIL subsets to precisely profile the tumor microenvironment. This can be achieved via bisulfite-treated next-generation sequencing of plasma-derived cell-free DNA, followed by deconvolution of cell composition from methylation signatures, which we will apply to metastatic melanoma as a proof-of-principle.
• We hypothesized that our method for “noninvasive digital cytometry” will enable accurate, biopsy-free monitoring of the tumor microenvironment without being limited to (1 ) small combinations of preselected marker genes (as flow cytometry is), (2) T/B cell receptor variable regions (as VDJ profiling is), or (3) viable single cells (as single cell RNA sequencing is).
• the kinetics of cell-free DNA release from TILs is unknown and whether methylation signatures can quantitatively capture specific non-malignant tumor cell types from cell-free DNA has not yet been established.
• A. Define cell type-specific methylation signatures that distinguish major TIL subsets from normal peripheral blood leukocytes and non-hematopoietic cells.
• whole genome bisulfite sequencing to sorted melanoma TIL subsets, malignant melanocytes, stromal cells, and normal peripheral blood leukocytes, to define TIL-specific methylation sites.
• TIL profiling in melanoma patients and evaluate concordance with paired tumors.
• PBMC peripheral blood mononuclear cell
• PBMC peripheral blood mononuclear cell
• TIL predictions by our method to orthogonal measures of TIL content in paired tumors (e.g., by flow cytometry), and will compare methylation signatures from cell-free DNA to cellular DNA (PBMCs) to determine which compartment better captures known TIL composition.
• TILs Tumor infiltrating leukocytes
• ICIs immune checkpoint inhibitors
• TIL composition Although a number of powerful techniques for characterizing TIL composition are available (e.g., flow cytometry, immunohistochemistry, CyTOF, single cell RNA sequencing), they generally require tumor biopsy or resection procedures that are invasive (14), associated with morbidity (15), and may not account for geographic tumor heterogeneity (16, 17). As a result, due to limited tumor availability, most analyses of human TIL composition are restricted to a single snapshot of tumor heterogeneity obtained from a single time point.
• flow cytometry e.g., flow cytometry, immunohistochemistry, CyTOF, single cell RNA sequencing
• the presently described technology can be a new technology for noninvasive TIL quantitation.
• the ability to noninvasively monitor TIL composition would provide an attractive solution to the above problem in both research and clinical settings.
• Previous studies of peripheral blood leukocytes (PBLs) in cancer patients have identified subpopulations that resemble those found in tumors and that have prognostic/predictive potential (18, 19); however, the cell type marker profiles employed in these studies are unlikely to be TIL-specific and the extent to which these cells truly capture tumor immune composition is unclear (20).
• TCR T cell receptor
• ICIs are currently transforming cancer care, and have improved the outcomes of a subset of patients with advanced cancer, giving them remarkable therapeutic responses and allowing a subset of these responders to achieve long-term survival (9, 31-33).
• ICI response rates for different cancers range from 1% to 50% (34), with response rates affected by multiple factors, including tumor PDL1 expression, tumor mutation burden, neoantigen load, and tumor histology (34-37).
• Standard-of-care for assessing ICI response is serial CT imaging that begins 2-3 months after initiating immunotherapy (38), and is assessed by RECIST 1.1 (39) or iRECIST (40) criteria.
• CT imaging is typically performed no earlier than 2-3 months after treatment initiation due to delayed radiographic responses and concern for pseudoprogression at earlier time points (13, 38, 41). This approach will allow investigators to explore methods of earlier immunotherapy response assessment in order to pivot sooner to more effective treatment modalities for progressors, who comprise the majority of patients.
• This technology will provide a platform for the following innovations:
• cell-free DNA harbors epigenetic signatures that are informative for tissue-of-origin, including methylated cytosines in CpG dinucleotides, which have distinct lineage-specific patterns and can be profiled using bisulfite sequencing (46).
• the Lo group showed that genome-wide bisulfite sequencing enabled tissue-of- origin identification of plasma-derived cell-free DNA in pregnant women, organ transplant patients, and hepatocellular carcinoma patients (47).
• Zhang and colleagues applied whole genome bisulfite sequencing with linkage disequilibrium principles to identify tightly coupled CpG sites, which they called methylation haplotype blocks (48).
• Methylation haplotype blocks were more accurate at discriminating between tissue-specific methylation patterns than conventional methylation metrics, and enabled cancer tissue of origin identification from cell-free DNA from patients with different malignancies (48). Despite these results, the composition of the tumor immune microenvironment has not been profiled by methylation signatures in cell-free DNA. This technology can yield a novel framework that addresses this gap using targeted bisulfite sequencing.
• CIBERSORT revealed important new associations between TILs and clinical outcomes (3).
• This method can be adapted for the deconvolution of cell-free DNA bisulfite sequencing data, allowing us to determine the proportions of distinct TIL subsets from cell type-specific methylation profiles identified in cell-free DNA.
• this approach can help address a major unmet need: monitoring TIL dynamics at high resolution over serial time points to advance biomarker discovery and precision cancer medicine.
• the experiments described here are to develop and experimentally evaluate the new platform for noninvasive profiling of TILs from melanoma patients.
• This research can involve an innovative combination of experimental and computational approaches, including tools developed by the investigative team, to build a novel genomics platform for profiling and decoding TIL-derived methylation signatures identified from plasma-derived cell-free DNA molecules.
• the research plan is schematically depicted in FIG. 14.
• Also described is the design and optimization of a next generation sequencing panel and corresponding computational framework to profile TIL-specific methylation sites from clinically practical amounts of plasma-derived cell-free DNA.
• High-throughput methylation profiling has revealed extraordinary insights into the epigenetic landscape of distinct tissue types and cellular lineages, including normal immune subsets (53).
• normal immune subsets 53.
• a comparative analysis of genome-wide methylation signatures in major melanoma TIL subsets versus their normal peripheral blood counterparts has not yet been described.
• PBMC peripheral blood mononuclear cell
• WGBS whole genome bisulfite sequencing
• WGBS has been shown to achieve better CpG coverage than reduced representation bisulfite sequencing, an alternate technique that uses restriction enzymes to enrich for CpG sites (54).
• WGBS will allow us to interrogate CpG sites at single nucleotide resolution across the entire genome and maximize the number of discriminatory markers that are detectable.
• DMRs differentially methylated regions
• 4050 coverage may be inadequate to robustly identify single and/or bi-allelic methylation events. If so, we will perform additional sequencing to target 65 coverage. Should specific TIL subsets be indistinguishable from normal leukocytes, we will consider eliminating them from further analysis or pooling them into broader lineages.
• reporter that would be needed to achieve various detection limits, considering: (1) a realistic cell-free DNA input amount ( ⁇ 32 ng cell-free DNA in 1 blood collection tube (28)), (2) the median circulating tumor DNA fraction in metastatic melanoma ( ⁇ 1% (45)) (3) estimates of TIL content in advanced melanoma tumors (3, 72, 73), (4) estimated cell-free DNA recovery rates after bisulfite conversion (20-60% (74)), and (5) published recovery rates of cell-free DNA using hybrid capture sequencing (40-60% (28)).
• TIL-, PBL- and melanoma tumor-specific methylation signatures for the purpose of deconvolution, and (2) design an optimized targeted hybrid-capture panel with the genomic bandwidth to profile TIL and PBL subsets.
• TIL profiling will have utility in vivo, it will be important to compare estimated TIL composition in the plasma of melanoma patients against orthogonal measures of TIL content in paired tumors (e.g., by flow cytometry). In addition, we will compare methylation signatures from cell-free DNA to cellular DNA (PBMCs) to determine which compartment better captures known TIL composition. These data can be useful for establishing baseline values for power calculations and dedicated biomarker studies.
• PBMCs cellular DNA
• Cell-free DNA will be extracted from ⁇ 5 ml of plasma using the QiaAmp Circulating Nucleic Acid Kit according to the manufacturer’s instructions, and stored at -80°C. Following isolation, DNA will be quantified by Qubit dsDNA High Sensitivity Kit (Life Technologies) and Bioanalyzer (Agilent), and inspected for expected fragment length distribution and yield.
• PD-1 blockade induces responses by inhibiting adaptive immune resistance.
• T cell receptor sequencing of early-stage breast cancer tumors identifies altered clonal structure of the T cell repertoire. Beausang JF, Wheeler AJ, Chan NH, Hanft VR, Dirbas FM, Jeffrey SS, Quake SR. Proceedings of the National Academy of Sciences. 2017; 114(48):E10409-E17.
• Liquid biopsies come of age: towards implementation of circulating tumour DNA. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N. Nat Rev Cancer. 2017 Apr;17(4):223-38. PMID:28233803.
• Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial.
• Neoantigens in cancer immunotherapy Schumacher TN, Schreiber RD. Science. 2015 Apr 3;348(6230):69-74. PMID:25838375.
• iRECIST guidelines for response criteria for use in trials testing immunotherapeutics. Seymour L, Bogaerts J, Perrone A, Ford R, Schwartz LH, Mandrekar S, Lin NU, Litiere S, Dancey J, Chen A, Hodi FS, Therasse P, Hoekstra OS, Shankar LK, Wolchok JD, Ballinger M, Caramella C, de Vries EG, group Rw. Lancet Oncol. 2017 Mar;18(3):e143-e52. PMID:28271869.
• Immune checkpoint blockade a common denominator approach to cancer therapy. Topalian SL, Drake CG, Pardoll DM. Cancer cell. 2015 Apr 13;27(4):450-61. Epub 2015/04/11. PMID:25858804.
• the GEM mapper fast, accurate and versatile alignment by filtration. Marco- Sola S, Sammeth M, Guigo R, Ribeca P. Nature Methods. 2012 10/28/online;9:1185.
• gemBS high throughput processing for DNA methylation data from bisulfite sequencing. Merkel A, Fernandez-Callejo M, Casals E, Marco-Sola S, Schuyler R, Gut IG, Heath SC. Bioinformatics. 2018:bty690-bty.
• NGSCheckMate software for validating sample identity in next-generation sequencing studies within and across data types.
• Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers.
• Tumor-associated B-cells induce tumor heterogeneity and therapy resistance.
• DNABarcodes an R package for the systematic construction of DNA sample tags. Buschmann T. Bioinformatics. 2017;33(6):920-2.
• EXAMPLE 5 DEVELOPING A LIQUID BIOPSY APPROACH FOR DIAGNOSING AND MONITORING SEPSIS
• Sepsis is the most common cause of death in United States hospitals and the number one cause of death worldwide, with 11.0 million sepsis-related deaths reported in 2017. Sepsis is difficult to diagnose and monitor in its early stages, because it is challenging to determine if a patient has an infection (microbial cultures take time to grow), where the infection site is (requires imaging and microbial cultures), and the sites and extent of end-organ damage (often determined clinically, i.e. altered mental status as a marker of brain damage). Unfortunately, when not detected early, patients miss critical early intervention and sepsis progresses rapidly to cause life-threatening multi-organ failure, septic shock, and immunosuppression leading to deadly secondary infections. There are no reliable biomarkers in clinical use for the early diagnosis and monitoring of sepsis.
• Liquid biopsy diagnosis of Microbial infection, Immune dysfunction, and Damage to Organs in Sepsis (LiquidMIDOS), which will enable the following via whole genome bisulfite sequencing of plasma cell-free DNA (FIG. 19): 1) Detection of the microbial etiology of sepsis; 2) Identification of the septic tissue site; 3) Determination of which organs are getting damaged and thus at risk for failure if not proactively managed; 4) Determination of whether the adaptive immune response against sepsis has become dysfunctional and exhausted, which could in the future be managed precisely with immunotherapy; 5) Detection of deadly secondary infection at its inception.
• Sepsis is the most common cause of hospital death in the United States and accounts for 1 in 5 of all deaths worldwide 4 . It is defined as life-threatening organ dysfunction caused by a dysregulated immune response to infection. There were 11 million sepsis-related deaths reported in 2017 4 . Sepsis-associated mortality rates are unacceptably high at 15-25%, and significantly higher for patients diagnosed with associated multi-organ failure 63 . Unfortunately the problem has grown more dire in the year 2020 with ICUs witnessing record numbers of sepsis cases and associated deaths 910 . The most important prognostic factor in sepsis is early intervention, which is impeded by diagnostic challenges. Early diagnosis and intervention are critical to maximize survival in this high-risk patient population.
• Diagnosing sepsis depends on a confirmed diagnosis of microbial infection. Infection is typically determined by bacterial cultures which take time to grow: usually 24-72 hours, with some organisms taking 5 days or longer to grow in culture. Bacterial cultures also do not account for other sources of sepsis such as viral infection which have accounted for an increased proportion of septic patients recently 10 . Biomarkers suggestive of systemic inflammation such as C-reactive protein, white blood cell count, and procalcitonin have also been tested but have limited sensitivity and specificity, especially at early timepoints and in immunosuppressed settings 11 14 . It is critical to confirm infection diagnosis early to prevent treatment delays and improve patient survival.
• the source of infection can also be difficult to determine early during sepsis, and can require an extensive workup involving chest X-rays, stool cultures, urine cultures, wound cultures, and blood cultures, leading to further diagnostic delays and confusion. Finding the site of infection is an important determinant of management and outcomes, with unknown and pulmonary sites of infection having the highest mortality rates 15 16 . With LiquidMIDOS, we will thus prioritize determining the infection site and source early.
• organ damage Another important diagnostic factor in sepsis is organ damage. Dysfunction of a single organ can unfortunately progress to multiple organ dysfunction syndrome (MODS) in a septic patient who does not receive adequate upfront care in the acute setting. When this occurs, homeostasis can no longer be maintained, and the patient’s prognosis becomes dire. The greater the number of organ systems failing, the higher the mortality rate, with mortality reaching ⁇ 100% when >5 organ systems fail 7 . It is critical to identify organ damage early to prevent MODS and its associated high mortality rate.
• MODS organ dysfunction syndrome
• Sepsis is also an immunological conundrum, with the initial acute phase typically being hyper-immune with a dysregulated immune “cytokine storm” that requires intensive care and causes death from septic shock or multi-organ failure 5 ’ 18 ’ 19 . If the patient recovers from this, then this hyper-immune phase is followed days later by a hypo-immune phase characterized by exhausted and dysfunctional T cells, critical cells in the adaptive immune system, which puts patients at risk for deadly secondary infections (FIG. 20) 5 ’ 13 21 . The majority of these dysfunctional/exhausted T cells reside within tissue 20 , thus a method to detect them sensitively and proactively needs to be capable of querying their tissue sources.
• LiquidMIDOS will aid in the early diagnosis and monitoring of sepsis by: 1 ) Detecting the microbial etiology of sepsis; 2) Identifying the septic tissue site; 3) Determining which organs are being damaged; 4) Determining if the T cell response has become dysfunctional; 5) Detecting secondary infection (FIG. 19). LiquidMIDOS will achieve these goals through a single assay from a single blood draw that can be performed early and serially to improve patient survival.
• tissue cells secrete cfDNA tissue cells secrete cfDNA
• microbes including bacteria, DNA viruses, fungi, and eukaryotic parasites have also been shown to secrete cfDNA that can be measured through NGS 2S
• dysfunctional/exhausted T cells shed cell-free DNA that can be precisely measured by NGS through advanced analytical methods, and distinguished from the much more prevalent cfDNA arising from peripheral blood leukocytes (FIG. 21).
• FOG. 21 peripheral blood leukocytes
• the epigenome is comprised of chemical compounds bound to the DNA molecule that direct which parts of the genome are turned on vs. off 30 .
• Each cell and tissue type has its own unique epigenomic signature 30 which can be profiled by analyzing the methylation patterns on DNA using a method called bisulfite sequencing 31 ⁇ 32 .
• bisulfite sequencing 31 ⁇ 32 a method called bisulfite sequencing 31 ⁇ 32 .
• Recent published data shows the ability to sensitively detect cancer tissue- of-origin (from among the plethora of different human tissue types) using methylation-based plasma cell-free DNA analysis 1 ⁇ 23 ⁇ 33 . Additionally, we will achieve the broad dynamic range necessary to measure different levels of organ injury, as shown recently for liver damage (FIG. 22) 1 . Recent literature has also shown that genome-wide cell-free DNA sequencing approaches can achieve superb detection sensitivity, comparable to targeted ultra-deep sequencing, because while the sequencing depth is inferior using a genome-wide approach, sequencing the whole genome enables the ability to track a far greater number of specific reporters 34 . Furthermore, whole genome sequencing of plasma cell-free DNA can be used to sensitively detect multiple infectious microbial species, as shown previously 29 .
• LiquidMIDOS will be the first all- in-one method to integrate microbial, immune exhaustion and organ tissue analyses all from a single blood tube with a single assay.
• Factors underlying the detection limit of cell-free DNA analysis include the number of independent “reporters” that are interrogated 34 35 .
• Using a validated binomial model that was previously described for predicting circulating tumor DNA detection limits 35 we estimated the probability of cell-free DNA detection based on the number of unique compartment-specific reporters (i.e.
• organ tissue-specific differentially methylated regions, microbe-specific genomic sequences considering: (1 ) a realistic cell-free DNA input amount ( ⁇ 50 ng cell-free DNA in 1 blood collection tube 35 ), and (2) 10% 1 , 1% and 0.4% 2 of involved/damaged organ-specific cfDNA, exhausted/dysfunctional T cell-specific cfDNA and microbe-specific cfDNA, respectively.
• Our mathematical modeling suggests that >2 reporters per organ tissue type, >15 specific to exhausted T cells, and >40 microbe-specific genomic motifs will enable sensitive and specific detection with >90% probability.
• LiquidMIDOS will enable sensitive cell-free DNA detection, especially given the millions of potential compartment- specific reporters available genome-wide 34 .
• LiquidMIDOS blood-based all-in- one sepsis detection and monitoring assay
• LiquidMIDOS will be clinically useful, serving as a clinician’s “Swiss Army knife” for data-driven diagnosis, monitoring, and management of sepsis (Table 1).
• LiquidMIDOS optimal classification outpoint using Youden’s index (and report the associated sensitivity and specificity); we will do this with regard to each of the criteria displayed in Table 1 , as well as in a time-dependent manner (using our serial samples) to determine if the LiquidMIDOS classification scores change over time as would be expected in Table 1.
• LiquidMIDOS would still be possible to perform from a single blood draw, as the plasma and PBLs are isolated from the same tube of blood, although some of the workflow would need to be replicated (WGBS performed on plasma- and PBL- derived DNA separately). Still, to ensure our assay is as sensitive as possible, we will sequence, deconvolve and classify PBL-derived sheared DNA using the same workflow described above. We will thus query whether cell-free DNA is a superior analyte to PBLs for LiquidMIDOS, and will flexibly proceed with the most sensitive analyte in a setting-dependent manner.
• LiquidMIDOS LiquidMIDOS
• sepsis patients Similar to the training cohort, sepsis patients underwent daily plasma and PBL collection starting from day 1 of ICU admission.
• propensity score matching We will perform propensity score matching to ensure that cases and controls are overall matched in terms of clinical and epidemiological covariates other than sepsis-specific factors.
• LiquidMIDOS Cost of LiquidMIDOS to be $2,000 per assay based on estimates of library preparation, sequencing, and genomic analysis. As mentioned above, sepsis cases not diagnosed early had a significantly higher economic burden, costing $51 ,022 per patient, compared to $18,023 when sepsis was accurately diagnosed at the time of hospital admission 17 . Delayed diagnoses are associated with increased sepsis severity, longer hospital and ICU stays, and inferior survival. If we conservatively assume that among patients with late- diagnosed sepsis (costing $51,022 per patient), LiquidMIDOS serial monitoring x3 reduces the cost in 25% to the baseline level of $18,023 per patient (+ $6,000 of assay costs), then utilizing LiquidMIDOS would save on average $2,250 per patient.
• Sepsis is the most common cause of hospital death in the United States and accounts for 1 in 5 of all deaths worldwide 2 . It is an immunological conundrum, with the initial acute phase typically being hyper-immune with a dysregulated immune “cytokine storm” that requires intensive care and can lead to death from septic shock or multi-organ failure 3 5 . If the patient recovers from this, then this hyper immune phase is followed days later by a hypo-immune phase characterized by exhausted and dysfunctional T cells, critical cells in the adaptive immune system, which puts patients at risk for deadly secondary infections 3 7 (FIG. 20). The majority of these dysfunctional and exhausted T cells reside within organ tissue 6 .
• tissue cells shed cfDNA infectious microbes have also been shown to shed cfDNA that can be measured through NGS 2G .
• dysfunctional/exhausted T cells shed cell-free DNA that can be precisely measured by NGS through advanced analytical methods, and distinguished from the much more prevalent cfDNA arising from peripheral blood leukocytes (FIG. 21 ).
• FIG. 21 we describe the quantification of cell-free DNA arising from organ-specific tissues and exhausted T cells in order to noninvasively track organ damage and immune dysfunction/exhaustion, respectively, during sepsis.
• the epigenome is comprised of chemical compounds bound to the DNA molecule that direct which parts of the genome are turned on vs. off 21 .
• Each cell and tissue type has its own unique epigenomic signature 21 which can be profiled by analyzing the methylation patterns on DNA using a method called whole genome bisulfite sequencing (WGBS) 2223 .
• WGBS whole genome bisulfite sequencing
• methylation reporters could distinguish exhausted tissue lymphocytes from tissue-derived epithelial cells and normal peripheral blood leukocytes (PBLs).
• PBLs peripheral blood leukocytes
• WGBS flow cytometric approach to specifically sort these cells from tissue prior to sequencing
• DMR differential methylated region
• This technology can facilitate the development of noninvasive biomarkers to track sepsis patients.
• the results can further clarify how to develop and interpret these biomarkers, amplifying our understanding of when a septic patient is slipping into life-threatening multi-organ failure or developing increased risk for life- threatening secondary infection.
• cell-free DNA biomarkers begin to be utilized in the sepsis field, such as the Karius assay, which enables rapid and noninvasive determination of infectious etiologies using a plasma whole genome sequencing approach 20 .
• the sepsis field is absolutely ripe for improved precision diagnostic modalities, and the translational work described here can help facilitate that.
• this technology can be adapted to use cell-free DNA epigenomics to understand the multifactorial spatiotemporal underpinnings of sepsis, and by doing so to enable the development of precise biomarkers to improve patient outcomes.

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