WO2021228888A1 - Biomarqueurs pour maladie hyperprogressive et réponse thérapeutique après immunothérapie - Google Patents

Biomarqueurs pour maladie hyperprogressive et réponse thérapeutique après immunothérapie Download PDF

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WO2021228888A1
WO2021228888A1 PCT/EP2021/062527 EP2021062527W WO2021228888A1 WO 2021228888 A1 WO2021228888 A1 WO 2021228888A1 EP 2021062527 W EP2021062527 W EP 2021062527W WO 2021228888 A1 WO2021228888 A1 WO 2021228888A1
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biomarkers
treatment
expression level
immunotherapeutic agent
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Andrey KHMELEVSKIY
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Asylia Diagnostics
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • 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
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the invention relates to the field of diagnostics to guide cancer immunotherapy. More specifically, the invention provides a method for determining the likelihood of hyperprogression and/or progressive disease of cancer cells in response to immunotherapy, such as immune checkpoint inhibitor treatment. Furthermore, the invention also provides a method for determining the likelihood of a positive response to immunotherapy, such as immune checkpoint inhibitor treatment.
  • Cancer is a major public health problem. In 2015, 1.3 million people died from cancer in the European Union, which equated to more than one quarter (25.4%) of the total number of deaths. Many treatments have been devised for various cancers.
  • ICIs Immune checkpoint inhibitors
  • CTL-4 cytotoxic T- lymphocyte antigen-4
  • PD-1/PD-L1 programmed cell death-1
  • NSCLC non-small cell lung carcinoma
  • TMB tumor mutation burden
  • MSI microsatellite instability
  • W02014151006 provides biomarkers for patient selection and prognosis in cancer.
  • this patent application is limited to predicting the responsiveness of an individual with a disease to treatment with a PD-L1 axis binding antagonist.
  • W02019012147 proposes a radiomics based biomarker for detecting the presence and density of tumor infiltrating CD8 T-cells to prognose the survival and/or the treatment efficiency of cancer patients treated with immunotherapy such as anti-PD-1/PD-L1 monotherapy.
  • US20180107786 discloses a method for generating an immune score based on tumor infiltrating lymphocytes, T-cell receptor signaling and mutation burden.
  • hyperprogressive disease varied between clinicians and research groups.
  • a hyperprogressive disease was measured as a time-to-treatment failure (TTF) ⁇ 2 months, >50% increase in tumor burden compared to pre-immunotherapy imaging (Kato et al., 2017).
  • TTF time-to-treatment failure
  • TGKR tumor progression pace
  • TGT time to treatment failure
  • HPD is now defined based on RECIST as PD in the first 8 weeks after treatment initiation and minimum increase in the measurable lesions of 10 mm plus: a) increase of >40% in sum of target lesions compared to baseline (which represents doubling in unidimensional target lesions compared to classical RECIST PD criterion [20%]); and/or b) increase of >20% in sum of target lesions compared to baseline (the classical RECIST PD criterion) plus the appearance of new lesions in at least 2 different organs. Unlike pseudoprogression, patients displaying hyper progression present worse survival outcomes.
  • the inventors have addressed the challenges for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent by identifying biomarkers that are able to predict whether a subject will develop hyperprogressive disease after receiving treatment with an immunotherapeutic agent.
  • the inventors identified biomarkers that are able to predict whether a subject will respond or not respond to treatment with an immunotherapeutic agent.
  • a first aspect of the present invention relates to methods for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent or for deciding whether a subject is eligible for an treatment with an immunotherapeutic agent.
  • Said methods are based on the analysis of the expression level of one or more biomarkers, more specifically based on the analysis of the RNA expression level of one or more protein coding genes, and wherein on the basis of said expression level it is predicted whether the subject will develop a predisposition for generating hyperprogressive disease and/or whether the subject is eligible for treatment with an immunotherapeutic agent.
  • the invention thus relates to a method for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent or for deciding whether a subject is eligible for treatment with an immunotherapeutic agent, such as an immune checkpoint inhibitor, said method comprising analyzing the expression level of one or more of the biomarkers, preferably protein coding genes, selected from IL-17A, IFN1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 in a sample of the subject.
  • an immunotherapeutic agent such as an immune checkpoint inhibitor
  • the present invention relates to a method for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent, or for deciding whether a subject is eligible for treatment with an immunotherapeutic agent, said method comprising analyzing the expression level of the biomarkers IL-17A, IFN1 , IFNB1 , IL-6 in a sample of the subject.
  • the method further comprises the analysis of the expression level of one or more of the biomarkers selected from the group consisting of ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 in a sample of the subject.
  • the expression level of the biomarkers IL-17A, IFN1 , IFNB1 , IL- 6 is analyzed, in combination with the expression level of one or more of the biomarkers selected from the group consisting of ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 in order to determine whether the subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent or for deciding whether the subject is eligible for treatment with an immunotherapeutic agent.
  • the expression level of the biomarkers IL-17A, IFN1 , IFNB1 , IL-6 is analyzed, in combination with the expression level of at least 3, at least 4, at least 5, at least 6, at least 7, at least 8 or at least 9, of the biomarkers selected from the group consisting of ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 .
  • a method for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent, or for deciding whether a subject is eligible for treatment with an immunotherapeutic agent, said method comprising analyzing the expression level of the biomarkers IL-17A, IFN1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 in a sample of the subject.
  • the expression level of the biomarkers is compared to a corresponding reference value or threshold value that is characteristic of a subject with a known response to the treatment with the immunotherapeutic agent.
  • the expression level of the analyzed biomarkers is compared to a corresponding reference value or threshold value that is characteristic of a subject with a known response to treatment with the immunotherapeutic agent.
  • a risk score is obtained on the basis ofthe expression level ofthe analyzed biomarkers, said risk score representing the likelihood for developing hyperprogressive disease in the subject or for responding to the treatment with the immunotherapeutic agent by the subject.
  • kits for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent or for deciding whether a subject is eligible for treatment with an immunotherapeutic agent comprises means for measuring the expression level of one or more of the biomarkers, preferably protein coding genes, IL-17A, IFNA1 , IFNB and IL-6 in a sample of the subject; optionally further complemented with means for measuring the expression level of one or more of the biomarkers, preferably protein coding genes, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 ; and/or a reference value or threshold value for each of the biomarkers, preferably protein coding genes. Also the use of said kit in a method for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent or for deciding whether a subject is eligible for
  • Another aspect of the present application relates to a method for determining whether a subject is predisposed for responding to treatment with an immunotherapeutic agent.
  • Said method comprises the analysis ofthe expression level of one or more biomarkers, preferably protein coding genes selected from TLR9, IL23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A, in a sample of the subject.
  • the expression level of at least three, at least four, at least five, or at least six, of the biomarkers is analyzed in a sample of the subject and on the basis of said analysis a risk score is obtained, said risk score representing the likelihood for responding to the treatment with the immunotherapeutic agent in the subject.
  • the method comprises analyzing the expression level of the biomarkers, preferably protein coding genes, TLR9, IL-23A, CLEC4C, and CCR4, in a sample of the subject.
  • the method further comprises analyzing the expression level of the biomarkers, preferably protein coding genes, TLR9, IL-23A, CLEC4C, and CCR4, in combination with the analysis of the expression level of one or more of the biomarkers, preferably protein coding genes, selected from the group consisting of SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A, in a sample of the subject in order to determine whether a subject is predisposed for responding to treatment with an immunotherapeutic agent.
  • the expression level of the biomarkers is analyzed, in combination with the expression level of at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 10, or at least 11 , of the biomarkers, preferably protein coding genes, selected from the group consisting of SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A.
  • a method for determining whether a subject is predisposed for responding to treatment with an immunotherapeutic agent, said method comprising analyzing the expression level of the biomarkers, preferably protein coding genes, TLR9, IL-23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A, in a sample of the subject.
  • the biomarkers preferably protein coding genes, TLR9, IL-23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A
  • the expression level of the analyzed biomarkers is compared to a corresponding reference value or threshold value that is characteristic of a subject with a known response to treatment with the immunotherapeutic agent.
  • a risk score is obtained on the basis of the expression level of the analyzed biomarkers, said risk score representing the likelihood for responding to the treatment with the immunotherapeutic agent in the subject.
  • kits for determining whether a subject is predisposed for responding to treatment with an immunotherapeutic agent comprises means for measuring the expression level of one or more of the biomarkers, preferably protein coding genes, TLR9, IL- 23A, CLEC4C, and CCR4 in a sample of the subject; optionally further complemented with means for measuring the expression level of one or more of the biomarkers, preferably protein coding genes, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A; and/ora reference value orthreshold value for each of the biomarkers, preferably protein coding genes. Also the use of said kit in a method for determining whether a subject is predisposed for responding to treatment with an immunotherapeutic agent is disclosed.
  • the risk score is further improved by immunohistochemistry data of the tumor.
  • the methods of the application are further characterized in that the risk score is obtained and calculated using a pre-trained machine learning model and using the expression level of the biomarkers as input values.
  • the risk score is produced by a machine learning model that uses the normalized expression levels of the biomarker signature as input data. More specifically, the machine learning model uses a pre-trained architecture to calculate the risk score from 0 to 1 .
  • the methods according to the present application are characterized in that the immunotherapeutic agent is an immune checkpoint inhibitor or a combination of several different immune checkpoint inhibitors.
  • the immunotherapeutic agent is selected from the group consisting of a PD-1 targeting agent, a PD- L1 targeting agent, and a CTLA-4 targeting agent, or a combination thereof.
  • a PD-1 targeting agent or a PD-L1 targeting agent is combined with a CTLA-4 targeting agent as immunotherapeutic treatment.
  • a sample of a subject is analyzed to quantify the expression level of one or more biomarkers; in particular one or more protein coding genes.
  • Said sample is preferably a tumor tissue sample, such as a biopsy sample, derived from the subject; preferably the subject diagnosed with cancer.
  • said sample is a tumor tissue sample derived from cancer patients diagnosed with bladder cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, prostate cancer, thyroid cancer, uterine cancer, ovarian cancer, colorectal cancer, breast cancer, head and neck cancer, skin cancer; even more preferably cancer patients diagnosed with skin cancer or melanoma, lung cancer such as lung adenocarcinoma, lung squamous cell carcinoma, non-small cell lung carcinoma (NSCLC).
  • NSCLC non-small cell lung carcinoma
  • Figure 1 Boxplots for RNA expression for various membrane proteins, interleukins, and cytokines expressed by myeloid cells split by different response types.
  • Figure 6 Feature importance (absolute values) for the model highlighting the most important features for the target classes.
  • A - R vs NR;
  • B - HP vs non HPD.
  • Figure 7 Heatmap of differentially expressed genes showing the separation of HPD vs. non-HPD cases and clustering of patients according to the biomarker signature.
  • Figure 8 Individual boxplots and ANOVA tests demonstrating separation of HPD vs. non-HPD cases for PD1/PD-L1 treated patient cohort for gene expression of the top 4 markers (IL-17A, IFNA1 , IFNB1 and IL-6).
  • Figure 9 Discriminatory power of predictive signature for HPD vs non-HPD classes. Gene expression of the top 4 markers (IL-17A, IFNA1 , IFNB1 and IL-6) contribute over 90% of explanatory power.
  • Figure 10 Significantly enriched pathways based on top differentially expressed biomarkers (downregulated).
  • a compartment refers to one or more than one compartment.
  • the value to which the modifier “about” refers is itself also specifically disclosed.
  • % by weight refers to the relative weight of the respective component based on the overall weight of the formulation.
  • the terms “one or more” or “at least one”, such as one or more or at least one member(s) of a group of members, is clear perse, by means of further exemplification, the term encompasses inter alia a reference to any one of said members, or to any two or more of said members, such as, e.g., any >3, >4, >5, >6 or >7 etc. of said members, and up to all said members.
  • Gene is generally used herein to encompass a polynucleotide that encodes a gene product, e.g., a nucleic acid sequence defining an open reading frame.
  • Diagnosis as used herein generally includes determination of a subject's susceptibility to a disease or disorder, determination as to whether a subject is presently affected by a disease or disorder, as well as to the prognosis of a subject affected by a disease or disorder.
  • sample or “biological sample” encompasses a variety of sample types obtained from an organism and can be used in a diagnostic or monitoring assay.
  • sample encompasses blood and other liquid samples of biological origin, solid tissue samples, such as a biopsy specimen or tissue cultures or cells derived therefrom and the progeny thereof.
  • sample encompasses samples that have been manipulated in any way after their procurement, such as by treatment with reagents, solubilization, or enrichment for certain components.
  • the term encompasses a clinical sample, and also includes cells in cell culture, cell supernatants, cell lysates, serum, plasma, biological fluids, and tissue samples.
  • cancer neoplasm
  • tumor tumor and tumor cells
  • cancer include immune cells in the tumor microenvironment.
  • differentially expressed gene is intended to encompass a polynucleotide that represents or corresponds to a gene that is differentially expressed in a tumour cell when compared with another cell of the same cell type.
  • Such differentially expressed gene may include an open reading frame encoding a gene product (e.g., a polypeptide), as well as introns of such genes and adjacent 5' and 3' non-coding nucleotide sequences involved in the regulation of expression, up to about 20 kb beyond the coding region, but possibly further in either direction.
  • a difference in expression level associated with a decrease in expression level of at least about 25%, usually at least about 50% to 75%, more usually at least about 90% or more is indicative of a differentially expressed gene of interest, i.e., a gene that is downregulated in the test sample relative to a control sample.
  • a difference in expression level associated with an increase in expression of at least about 25%, usually at least about 50% to 75%, more usually at least about 90% and may be at least about 1 ,5-fold, usually at least about 2-fold to about 10-fold, and may be about 100-fold to about 1 ,000-fold increase relative to a control sample is indicative of a differentially expressed gene of interest, i.e., an overexpressed or upregulated gene.
  • “Differentially expressed polynucleotide” as used herein means a nucleic acid molecule (RNA or DNA) comprising a sequence that represents a differentially expressed gene, e.g., the differentially expressed polynucleotide comprises a sequence (e.g., an open reading frame encoding a gene product) that uniquely identifies a differentially expressed gene so that detection of the differentially expressed polynucleotide in a sample is correlated with the presence of a differentially expressed gene or gene product of a differentially expressed gene in a sample.
  • RNA or DNA nucleic acid molecule
  • the differentially expressed polynucleotide comprises a sequence (e.g., an open reading frame encoding a gene product) that uniquely identifies a differentially expressed gene so that detection of the differentially expressed polynucleotide in a sample is correlated with the presence of a differentially expressed gene or gene product of a differentially expressed gene in a sample.
  • “Differentially expressed polynucleotides” is also meant to encompass fragments of the disclosed polynucleotides, e.g., fragments retaining biological activity, as well as nucleic acids that are homologous, substantially similar, or substantially identical (e.g., having about 90% sequence identity) to the disclosed polynucleotides.
  • microenvironment may refer to the tumor microenvironment as a whole or to an individual subset of cells within the microenvironment.
  • Immune cells found within the tumor microenvironment are macrophages, monocytes, mast cells, helper T cells, cytotoxic T cells, regulatory T cells, natural killer cells, myeloid-derived suppressor cells, regulatory B cells, neutrophils, dendritic cells, and fibroblasts.
  • the tumor microenvironment is generally defined as a complex mixture of cells, soluble factors, signaling molecules, extracellular matrices, and mechanical cues that promote neoplastic transformation, support tumor growth and invasion, and protect the tumor from host immunity.
  • the terms “marker(s)”, “biomarker(s)” refer to a gene or genes or a protein, polypeptide, or peptide expressed by the gene or genes whose expression level, alone or in combination with other genes, is correlated with the risk of developing hyperprogression or be non- responsive to an immunotherapeutic treatment.
  • the correlation can relate to either an increased or decreased expression of the gene (e.g. increased or decreased levels of mRNA orthe peptide encoded by the gene).
  • in vitro refers to an artificial environment and to processes or reactions that occur within an artificial environment.
  • in vitro environments can consist of, but are not limited to, test tubes and cell culture.
  • in vivo refers to the natural environment (e.g., an animal or a cell) and to processes or reaction that occur within a natural environment.
  • in silico refer to artificial environment, wherein a procedure is performed or modelled using a computer system, thereby partly or entirely avoiding the need of physically manipulating the data (e.g. genes, polynucleotides, proteins, ).
  • “Hyperprogressive”, “hyperprogressor”, “hyperprogression” is defined as a syndrome, which is measured as: a time-to-treatment failure (TTF) ⁇ 2 months; or/and > 50% increase in tumor burden compared to pre-immunotherapy imaging; or/and a > 2-fold increase in tumor progression pace; or/and tumor growth kinetics (TGK) ratio > 2 - 29%, where TGKR is the ratio of the slope of tumor growth before treatment and the slope of tumor growth on treatment; or/and as overall survival less than 3 months after commencing treatment.
  • TTF time-to-treatment failure
  • TGK tumor growth kinetics
  • biomarkers for determining whether a subject is predisposed for generating hyperprogressive disease when receiving an immunotherapeutic treatment or for deciding whether a subject is eligible for an immunotherapeutic treatment. Also biomarkers for determining whether a subject is predisposed of responding to an immunotherapeutic treatment were identified.
  • Immunotherapy using immune checkpoint inhibitors has changed the treatment landscape for many tumor types, particularly in the metastatic setting. Though, while meaningful, durable responses are achieved in some patients, a majority of patients do not respond, even worrisome, some patients develop hyperprogressive disease, a phenomenon clinically defines as an unexpected acceleration of the tumor kinetics measured on imaging with dynamic parameters. Hyperprogressive disease (HPD) following treatment with immunotherapy such as immune checkpoint inhibitors (ICIs) often leads to patient death within 24-65 days after the ICI treatment (Ferrara et al., 2020).
  • HPD hyperprogressive disease
  • ICIs immune checkpoint inhibitors
  • biomarkers that predict whether a subject would develop HPD after receiving an immunotherapeutic treatment, or that facilitate to decide whether a subject is eligible for an immunotherapeutic treatment, or that predict whether a subject is predisposed for responding to an immunotherapeutic treatment.
  • the current application provides a method for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent, or for deciding whether a subject is eligible for treatment with an immunotherapeutic agent, said method comprising the analysis of the expression level of one or more biomarkers, preferably one or more protein coding genes, in a sample of the subject.
  • the expression level of one or more of the following biomarkers will be assessed: IL-17A, IFN1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and/or TGFB1 , in a sample of the subject.
  • the expression level of the biomarkers preferably protein encoding genes, IL-17A, IFN1 , IFNB1 , IL-6 will be assessed, whether or not in combination with one or more of the following biomarkers, preferably protein coding genes, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and/or TGFB1.
  • biomarkers preferably protein coding genes, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and/or TGFB1.
  • the expression level of the biomarkers IL-17A, IFN1 , IFNB1 , IL-6 will be assessed, whether or not in combination with at least 3, at least 4, at least 5, at least 6, at least 7, at least 8 or at least 9 of the following biomarkers, preferably protein coding genes, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and/or TGFB1.
  • the expression level of the biomarkers preferably protein coding genes, IL-17A, IFN1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 will be assessed.
  • the expression level of the biomarkers is compared to a corresponding reference value or threshold value that is characteristic of a subject with a known response to treatment with an immunotherapeutic agent, in particular an immune checkpoint inhibitor.
  • a risk score is obtained and said risk score representing the likelihood for developing a hyperprogressive disease in the subject, or for responding or not responding to the immunotherapeutic treatment by the subject.
  • the risk score is compared with a threshold score that is calculated based on the expression analysis of the biomarkers in a sample obtained from a subject with a known response to treatment with an immunotherapeutic agent.
  • biomarkers preferably protein coding genes, including IL-17A, IFN1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 , thus comprises the predictive biomarkers of one aspect of the current application.
  • these predictive biomarkers are used for determining whether a subject is predisposed for generating hyperprogression and/or progressive disease when receiving treatment with an immunotherapeutic agent. More preferably, this list of predictive biomarkers is used for classifying a subject into a responder, non-responder or hyperprogressor toward treatment with an immunotherapeutic agent. These predictive biomarkers are further used for deciding whether a subject is eligible for treatment with an immunotherapeutic agent.
  • the current invention provides a method for determining whether a subject will respond to treatment with an immunotherapeutic agent, said method comprising the analysis of the expression level of one or more biomarkers, preferably protein coding genes, selected from TLR9, IL23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A, in a sample of the subject.
  • biomarkers preferably protein coding genes, selected from TLR9, IL23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A
  • the expression level of TLR9, IL23A, CLEC4C, CCR4 will be assessed, whether or not in combination with one or more of the following biomarkers, preferably protein coding genes: SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and/or FCGR3A.
  • biomarkers preferably protein coding genes: SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and/or FCGR3A.
  • the expression level of TLR9, IL23A, CLEC4C, CCR4 will be assessed, whether or not in combination with at least 3, at least 4, at least 5, at least 6, at least 7, at least 8 or at least 9 of the following biomarkers, preferably protein coding genes, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and/or FCGR3A.
  • biomarkers preferably protein coding genes, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and/or FCGR3A.
  • the expression level of the biomarkers preferably protein encoding genes, TLR9, IL23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A will be assessed.
  • the expression level of the biomarkers will be compared to a corresponding reference value or threshold value that is characteristic of a subject with a known response to the immunotherapeutic treatment.
  • a risk score is obtained and said risk score representing the likelihood for responding to treatment with the immunotherapeutic agent in the subject.
  • said risk score may be compared with a threshold score that is calculated based on the expression analysis of the biomarkers in a sample obtained from a subject with a known response to treatment with the immunotherapeutic agent.
  • the list of 16 biomarkers including TLR9, IL23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, FCGR3A thus comprises the predictive biomarkers of one aspect of the current application.
  • these predictive biomarkers are used for determining whether a subject will respond to treatment with an immunotherapeutic agent. More preferably, this list of predictive biomarkers is used for classifying a subject into a responder, non-responder or hyperprogressor toward treatment with an immunotherapeutic agent.
  • the expression level analysis of the different biomarkers to generate a risk score representing the likelihood for developing hyperprogressive disease in a subject or for representing whether a subject is eligible for treatment with an immunotherapeutic agent, or for responding to treatment with an immunotherapeutic agent in the subject is based on the multifactorial character of cancer disease.
  • the tumor microenvironment modifies the malignancy of tumors. This environment is populated by many macrophages and dendritic cells, which promote tumor progression to malignancy and increase metastatic potential.
  • tumor-associated macrophages TAMs
  • tumor-associated dendritic cells TACs
  • TADCs tumor-associated dendritic cells
  • the biomarker signature associated with the risk score representing the likelihood for developing a hyperprogressive disease is mostly associated with genes and/or proteins related to the cell surface, cell adhesion and mediation of intercellular interactions.
  • the biomarker signature associated with the risk score representing the likelihood for responding to immunotherapeutic treatment is mostly associated with genes and/or proteins related to interferon- beta production and genes participating in effector immune response.
  • the methods as taught herein may comprise comparing the biomarker expression level to a corresponding reference value or threshold value that is characteristic of a subject with a known response to treatment with an immunotherapeutic agent, in particular immune checkpoint inhibitor.
  • Said reference or threshold value may represent the expression level of the one or more biomarkers of a subject with a known prognosis after treatment with the immunotherapeutic agent.
  • Said known prognosis can be a response to the treatment, a partial response to the treatment, no response to the treatment, or even the development of hyperprogressive disease in response to the treatment;
  • said reference or threshold value may represent the expression level of the one or more biomarkers of a healthy subject.
  • Said comparison may generally include any means to determine the presence or absence of at least one difference and optionally of the size of such difference between values or profiles being compared.
  • a comparison may include a visual inspection, an arithmetical or statistical comparison of measurements. Such statistical comparisons include, but are not limited to, applying an algorithm. If the values or biomarker expression profiles comprise at least one standard, the comparison to determine a difference in said values or biomarker expression profiles may also include measurements of these standard, such that measurement of the biomarker are correlated to measurements of the internal standards.
  • Reference values or threshold values for the expression level of any of the biomarker may be established according to known procedures previously employed for other biomarkers.
  • a reference value of the expression level of a biomarker for determining whether a subject is predisposed of generating hyperprogressive disease when receiving an immunotherapeutic treatment as taught herein may be established by determining the quantity or expression of said biomarker in a sample(s) from one individual or from a population (e.g., group) of individuals characterized by a known predisposition for generating hyperprogressive disease.
  • a reference value of the expression level of a biomarker for determining whether a subject is predisposed of responding to an immunotherapeutic treatment as taught herein may be established by determining the quantity or expression of said biomarker in a sample(s) from one individual or from a population (e.g., group) of individuals characterized by a known response to treatment with the immunotherapeutic agent.
  • population may comprise without limitation >2, >10, >100, or even several hundred of individuals or more.
  • reference value(s) or threshold value(s) as intended herein may convey absolute quantities of the biomarker as intended herein.
  • the quantity of the biomarker in a sample from a test subject may be determined directly relative to the reference value (e.g., in terms of increase or decrease, or fold-increase or fold-decrease).
  • this may allow the comparison of the quantity or expression level of the biomarker in the sample from the subject with the reference value (in other words to measure the relative quantity of the biomarker in the sample from the subject vis-a-vis the reference value) without the need first to determine the respective absolute quantities of the biomarker.
  • the present methods, uses, or products may involve finding a deviation or no deviation between the expression level of the one or more biomarkers in a sample of the subject as taught herein and a given reference value or threshold value.
  • a ’’deviation” of a first value from a second value or a “difference” between a first value and a second value may generally encompass any direction (e.g., increase: first value > second value; or decrease: first value ⁇ second value) and any extent of alteration.
  • a deviation or a difference may encompass a decrease in a first value by, without limitation, at least about 10% (about 0.9-fold or less), or by at least about 20% (about 0.8-fold or less), or by at least about 30% (about 0.7-fold or less), or by at least about 40% (about 0.6-fold or less), or by at least about 50% (about 0.5-fold or less), or by at least about 60% (about 0.4-fold or less), or by at least about 70% (about 0.3-fold or less), or by at least about 80% (about 0.2-fold or less), or by at least about 90% (about 0.1-fold or less), relative to a second value with which a comparison is being made.
  • a deviation or a difference may encompass an increase of a first value by, without limitation, at least about 10% (about 1 .1-fold or more), or by at least about 20% (about 1 .2-fold or more), or by at least about 30% (about 1 .3-fold or more), or by at least about 40% (about 1 .4-fold or more), or by at least about 50% (about 1 .5-fold or more), or by at least about 60% (about 1 .6- fold or more), or by at least about 70% (about 1 .7-fold or more), or by at least about 80% (about 1.8-fold or more), or by at least about 90% (about 1.9-fold or more), or by at least about 100% (about 2-fold or more), or by at least about 150% (about 2.5-fold or more), or by at least about 200% (about 3-fold or more), or by at least about 500% (about 6-fold or more), or by at least about 700% (about 8-fold or more), or like, relative to a second value with which a second value
  • a deviation or a difference may refer to a statistically significant observed alteration.
  • a deviation or a difference may refer to an observed alteration which falls outside of error margins of reference values in a given population (as expressed, for example, by standard deviation or standard error, or by a predetermined multiple thereof, e.g., +1xSD or +2xSD or +3xSD, or +1xSE or +2xSE or +3xSE).
  • Deviation or a difference may also refer to a value falling outside of a reference range defined by values in a given population (for example, outside of a range which comprises >40%, > 50%, >60%, >70%, >75% or >80% or >85% or >90% or >95% or even >100% of values in said population).
  • a deviation or a difference may be concluded if an observed alteration is beyond a given threshold or cut-off.
  • threshold or cut-off may be selected as generally known in the art to provide for a chosen accuracy, sensitivity and/or specificity of the prediction methods, e.g., accuracy, sensitivity and/or specificity of at least 50%, or at least 60%, or at least 70%, or at least 80%, or at least 85%, or at least 90%, or at least 95%.
  • receiver-operating characteristic (ROC) curve analysis can be used to select an optimal threshold or cut-off value of the quantity of a given biomarker for clinical use of the present diagnostic tests, based on acceptable global accuracy, sensitivity and/or specificity, or related performance measures which are well-known per se, such as positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR-), Youden index, or similar.
  • an optimal threshold or cut-off value may be selected for each individual biomarker as a local extremum of the receiver operating characteristic (ROC) curve, i.e. a point of local maximum distance to the diagonal line, as described in Robin X., PanelomiX: a threshold-based algorithm to create panels of biomarkers, 2013, Translational Proteomics, 1 (1):57-64.
  • a relevant threshold or cut-off value can be obtained by correlating the sensitivity and specificity and the sensitivity/specificity for any threshold or cut-off value.
  • a risk score will be generated.
  • the risk scores that are generated represent the likelihood for developing hyperprogressive disease or represent the likelihood for responding to immunotherapeutic treatment in the subject.
  • said risk score ranges from 0 to 1 and represents the likelihood for developing hyperprogressive disease in a subject, with 1 being 100% likelihood of developing hyperprogressive disease and 0 having no risk of developing hyperprogressive disease.
  • the risk score ranges from 0 to 1 and represents the likelihood for responding to immunotherapeutic treatment in the subject, with 1 being 100% likelihood for responding to treatment with an immunotherapeutic agent in the subject, and 0 being 0% likelihood for responding to treatment with an immunotherapeutic agent.
  • the likelihood values are produced by a machine learning model that uses normalized expression levels of one of the biomarker signatures as input data.
  • the machine learning model is a pre-trained machine learning model that uses a pretrained architecture to calculate the risk score from 0 to 1 .
  • This pre-trained architecture is trained and updated based on the pool of retrospective and prospective samples with already known annotation towards the development of hyperprogressive disease or towards the response to immunotherapeutic treatment, in particular this pre-trained architecture is trained and updated based on the pool of reference values or threshold values obtained from samples of subjects with already known annotation towards the development of hyperprogressive disease or towards the response to immunotherapeutic treatment.
  • the machine learning model is selected from (1) linear mixed-effects model with random effect, (2) differential expression analysis, (3) random forest machine learning model, (4) gradient boosting machine model and (5) novel deep regression model based pre-trained architectures such as VGG-16 and ResNet-50 architectures.
  • a combination of machine learning models can be applied wherein each model is run independently on each dataset and then collectively on aggregated data to ensure that the signature replicates well on the independent trials.
  • gene and label bootstrapping and independent cohort comparisons can be applied to exclude the probability of random fit of these models and to ensure that the one or more biomarkers are unlikely to appear in the data by chance alone.
  • the analysis in particular the biomarker expression analysis and calculation of the risk score, is thus trained and updated using machine learning, for example using the gradient boosting machine (GBM) classification methodology to learn the specific pattern of biomarker expression features responsible for the subject’s response to one or more immunotherapy or for the subject to develop hyperprogressive disease.
  • GBM gradient boosting machine
  • expression levels of one or more biomarkers selected from IL-17A, IFN1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 are used in the gradient boosting machine (GBM) classification methodology to learn the specific pattern of said biomarker expression features responsible for a subject to develop hyperprogressive disease.
  • GBM gradient boosting machine
  • expression levels of one or more biomarkers selected from TLR9, IL-23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A are used in the gradient boosting machine (GBM) classification methodology to learn the specific pattern of said biomarker expression features responsible for the subject response to one or more therapy.
  • GBM gradient boosting machine
  • the methods of the current invention wherein a risk score is obtained, are further supplemented with the use of other cancer diagnostics known by a person skilled in the art.
  • biomarker is widespread in the art and may broadly denote a biological molecule and/or detectable portion thereof whose qualitative and/or quantitative evaluation in a subject is predictive or informative (e.g., predictive, diagnostic and/or prognostic) with respect to one or more aspects of the subject’s phenotype and/or genotype, such as, for example, with respect to the status of the subject as to a given disease or condition.
  • the biomarkers as taught herein are protein coding genes and the expression level of said protein coding genes is evaluated.
  • RNA-based expression levels of the one or more protein coding genes are evaluated.
  • the expression level of the protein coding genes disclosed herein may be assessed by any method known in the art suitable for determination of specific gene expression levels in a sample. Such methods are well-known and routinely implemented in the art.
  • Gene expression analysis can for example be performed by reverse transcriptase real-time quantitative PCR, gene expression arrays, TruSeq gene expression analysis, in-situ hybridization, dye sequencing, pyrosequencing, CRISPR-Cas-based or any other form of transcriptome sequencing (total RNA-Seq, mRNA-Seq, gene expression profiling).
  • TruSeq Targeted RNA expression Kit enables highly customizable mid- to high-plex gene expression profiling studies which allow defining panels of 12-1 ,000 assays to target individual exons, isoforms, splice junctions, coding SNPs (cSNPs), gene fusions, and non-coding RNA transcripts, plus multiplex up to 384 samples. Also, a customized version of TruSeq Custom Amplicon Kit Dx can be used for the qualitative and/or quantitative assessment of a plurality of genes as listed herein.
  • TruSeq Custom Amplicon Kit Dx is an FDA-approved regulated and CE-IVD-marked amplicon sequencing kit that enables clinical labs to develop their own next-generation sequencing (NGS) assays for use on the FDA- regulated and CE-IVD-marked MiSeqDx and NextSeq 550dx instruments.
  • NGS next-generation sequencing
  • a biomarker as taught herein may be peptide-, polypeptide- and/or protein-based.
  • the reference to any marker, including any gene, peptide, polypeptide, protein corresponds to the marker, gene, peptide, polypeptide, protein, commonly known under the respective designations in the art.
  • the terms encompass such markers, genes, peptides, polypeptides, proteins of any organism where found, and particularly of animals, preferably warmblooded animals, more preferably vertebrates, yet more preferably mammals, including humans and non-human mammals, still more preferably of humans.
  • native sequences may differ between different species due to genetic divergence between such species. Moreover, native sequences may differ between or within different individuals of the same species due to normal genetic diversity (variation) within a given species. Also, native sequences may differ between or even within different individuals of the same species due to genetic alterations, or post- transcriptional or posttranslational modifications. Any such variants or isoforms of markers, genes, peptides, polypeptides, proteins are intended herein.
  • markers all sequences of markers, genes, peptides, polypeptides, or proteins found in or derived from nature are considered “native”.
  • the terms encompass the markers, genes, peptides, polypeptides, or proteins when forming a part of a living organism, organ, tissue or cell, when forming a part of a biological sample, as well as when at least partly isolated from such sources.
  • the terms also encompass markers, genes, peptides, polypeptides, or proteins when produced by recombinant or synthetic means.
  • the biomarkers as taught herein may be a human biomarker, such as in particular any of the biomarkers selected from IL17A, IFNA1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 , or selected from TLR9, IL23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A.
  • any marker, gene, peptide, polypeptide or protein, or fragment thereof may generally also encompass modified forms of said marker, gene, peptide, polypeptide, or protein, or fragment thereof, such as genetic alterations, such as mutations, or bearing post-expression modifications including, for example, phosphorylation, glycosylation, lipidation, methylation, cysteinylation, sulphonation, glutathionylation, acetylation, oxidation, and the like.
  • any marker, gene, peptide, polypeptide or protein also encompasses fragments thereof.
  • the reference herein to measuring (or measuring the quantity of), determining the presence, or determining the expression level of any one marker, gene, peptide, polypeptide or protein may encompass measuring, determining the presence or determining the expression level of the marker, gene, peptide, polypeptide, or protein, such as, e.g. measuring any mature and/or processed soluble/secreted form(s) thereof (e.g., plasma circulating form(s)) and/or measuring one or more fragments thereof.
  • any marker, gene, peptide, polypeptide or protein, and/or one or more fragments thereof may be measured collectively, such that the measured quantity corresponds to the sum amounts of the collectively measured species.
  • any marker, gene, peptide, polypeptide or protein and/or one or more fragments thereof may be measured each individually.
  • fragment with reference to a peptide, polypeptide, or protein generally denotes a N- and/or C-terminally truncated form of the peptide, polypeptide, or protein.
  • a fragment may comprise at least about 30%, e.g., at least about 50% or at least about 70%, preferably at least about 80%, e.g., at least about 85%, more preferably at least about 90%, and yet more preferably at least about 95% or even about 99% of the amino acid sequence length of said peptide, polypeptide, or 10 protein.
  • a fragment may include a sequence of > 5 consecutive amino acids, or > 10 consecutive amino acids, or > 20 consecutive amino acids, or > 30 consecutive amino acids, e.g., > 40 consecutive amino acids, such as for example > 50 consecutive amino acids, e.g., > 60, > 70, > 80, > 90, > 100, > 200, > 300 or > 400 consecutive amino acids of the corresponding full-length peptide, polypeptide, or protein.
  • any protein, polypeptide or peptide may also encompass variants thereof.
  • variant of a protein, polypeptide or peptide refers to proteins, polypeptides or peptides the sequence (i.e. amino acid sequence) of which is substantially identical (i.e. largely but not wholly identical) to the sequence of said recited protein or polypeptide, e.g. at least about 80% identical or at least about 85% identical, e.g. preferably at least about 90% identical, e.g., at least 91% identical, 92% identical, more preferably at least about 93% identical, e.g.
  • a variant may display such degrees of identity to a recited protein, polypeptide or peptide when the whole sequence of the recited protein, polypeptide or peptide is querried in the sequence alignment (i.e, overall sequence identity).
  • a variant of a protein, polypeptide or peptide may be a homologue (e.g, orthologue or paralogue) of said protein, polypeptide or peptide.
  • a homologue e.g, orthologue or paralogue
  • the term “homology” generally denotes structural similarities between macromolecules, particularly between two proteins or polypeptides, from same or different taxons, wherein said similarity is due to shared ancestry.
  • a functional fragment and/or variant may retain at least about 20%, e.g., at least 30%, or at least about 40%, or at least about 50%, e.g., at least 60%, more preferably at least about 70%, e.g., at least 80%, yet more preferably at least about 85%, still more preferably at least about 90%, and most preferably at least about 95% or even about 100% or higher of the intended biological activity or functionality compared to the corresponding protein, polypeptide or peptide.
  • the risk score obtained in any of the methods of the application can further be improved by immunohistochemistry or protein expression data of the tumor of the subject.
  • the Cytokine 30-Plex Human Panel and immunohistochemistry stainings can be used for additional qualitative and/or quantitative assessment of the sample of the subject.
  • the current invention also provides a computer-implemented method for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent, wherein a sample obtained from said subject is analyzed, the method comprising: (a) providing the quantified expression level of one or more biomarkers, preferably protein encoding genes, from the group consisting of IL-17A, IFN1 , IFNB1 , IL-6; even more preferably consisting of IL-17A, IFN1 , IFNB1 , IL-6, said biomarkers being quantified in a sample of the subject; (b) optionally, providing the quantified expression level of one or more biomarkers selected from ICOSLG, CCL20, ID01, FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 , said biomarkers being quantified in a sample of the subject; (c) normalizing the quantified expression levels whereby normalization occurs via comparison with data obtained from
  • a computer-implemented method for determining whether a subject will respond to treatment with an immunotherapeutic agent, wherein a sample obtained from said subject is analyzed, the method comprising: (a) providing the quantified expression level of one or more biomarkers, preferably protein encoding genes from the group consisting of TLR9, IL-23A, CLEC4C, CCR4; even more preferably consisting of TLR9, IL-23A, CLEC4C, CCR4, said biomarkers being quantified in a sample of the subject; (b) optionally providing the quantified expression level of one or more biomarkers selected from SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A, said biomarkers being quantified in a sample of the subject; (c) normalizing the quantified expression levels whereby normalization occurs via comparison with data obtained from corresponding assessments and expression levels from a
  • kits for determining whether a subject is predisposed for generating hyperprogressive diseases when receiving treatment with an immunotherapeutic agent or for deciding whether a subject is eligible for treatment with an immunotherapeutic agent comprises means for measuring the expression level of the biomarkers IL-17A, IFNA1 , IFNB and IL-6 in a sample of the subject.
  • said kit further comprises means for measuring the expression level of one or more of the biomarkers selected from ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 .
  • Said kit may further also comprise a reference value or threshold value for the biomarkers IL-17A, IFNA1 , IFNB and IL-6.
  • the kit may further also comprise a reference value or threshold value for one or more of the biomarkers selected from the group consisting of ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 .
  • kits for determining whether a subject is predisposed for responding to treatment with an immunotherapeutic agent comprises means for measuring the expression level of the biomarkers TLR9, IL-23A, CLEC4C, CCR4.
  • said kit comprises means for measuring the expression level of one or more of SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A.
  • Said kit may further comprise a reference value or threshold value for the biomarkers TLR9, IL-23A, CLEC4C, CCR4.
  • the kit may also comprise a reference value or threshold value for one or more of the biomarkers selected from the group consisting of SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A.
  • the kit of the present application may comprise ready-to-use substrate solutions, wash solutions, dilution buffers and instructions.
  • the kit may also comprise positive and/or negative control samples.
  • the kit comprises instructions for use.
  • the instructions for use included in the kit are unambiguous, concise and comprehensible to those skilled in the art.
  • the instructions typically provide information on kit contents, how to collect the sample, methodology, experimental read-outs and interpretation thereof and cautions and warnings.
  • kit of parts and “kit” as used throughout this specification refer to a product containing components necessary for carrying out the specified methods (e.g., method for determining whether a subject is predisposed for generating hyperprogressive disease, method for deciding whether a subject is eligible for an immunotherapeutic treatment or method for determining whether a subject is predisposed for responding to an immunotherapeutic treatment), packed so as to allow their transport and storage.
  • Materials suitable for packing the components comprised in a kit include crystal, plastic (e.g., polyethylene, polypropylene, polycarbonate), bottles, flasks, vials, ampules, paper, envelopes, or other types of containers, carriers or supports.
  • kits comprises a plurality of components
  • at least a subset of the components e.g., two or more of the plurality of components
  • all of the components may be physically separated, e.g. comprises in or on separate containers, carriers or supports.
  • the components comprised in a kit may be sufficient or may not be sufficient for carrying out the specified methods, such that external reagents or substances may not be necessary or may be necessary for performing the methods, respectively.
  • kits are employed in conjunction with standard laboratory equipment, such as liquid handling equipment, environment (e.g., temperature) controlling equipment, analytical instruments, etc.
  • kits may also include some or all of solvents, buffers (such as for example without limitation histidine-buffers, citrate-buffers, succinate-buffers, acetate-buffers, phosphate- buffers, formate buffers, benzoate buffers, TRIS ((Tris(hydroxymethyl)-aminomethan) buffers or maleate buffers, or mixtures thereof), enzymes (such as for example but without limitation thermostable DNA polymerase), detectable labels, detection reagents, and control formulations (positive and/or negative), useful in the specified methods.
  • buffers such as for example without limitation histidine-buffers, citrate-buffers, succinate-buffers, acetate-buffers, phosphate- buffers, formate buffers, benzoate buffers, TRIS ((Tris(hydroxymethyl)-aminomethan) buffers or maleate buffers, or mixtures thereof
  • enzymes such as for example but without limitation thermostable DNA polymerase
  • detectable labels such as for
  • kits may also include instructions for use thereof, such as a printed insert or on a computer readable medium.
  • instructions for use thereof such as a printed insert or on a computer readable medium.
  • the terms may be used interchangeably with the term “article of manufacture”, which broadly encompasses any man-made tangible structural product, when used in the present context.
  • the kit further comprises a computer readable storage medium having recorded thereon one or more programs for carrying out the methods as taught herein.
  • kits for determining whether a subject is predisposed for generating hyperprogressive disease, for deciding whether a subject is eligible for an immunotherapeutic treatment or for determining whether a subject is predisposed for responding to an immunotherapeutic treatment.
  • the inventors of the present application identified several biomarkers for determining whether a subject is predisposed for generating hyperprogressive disease when receiving treatment with an immunotherapeutic agent, or for deciding whether a subject is eligible for treatment with an immunotherapeutic agent, or for predicting a subject’s response to immunotherapeutic agent. More specifically, the biomarkers were identified to be specifically suitable for predicting responses after treatment with immune checkpoint inhibitors.
  • the immunotherapeutic agent is therefore an immune checkpoint inhibitor or a combination of several immune checkpoint inhibitors. Even further, the immune checkpoint inhibitors can be selected from PD-1 targeting agents, PD-L1 targeting agents or CTLA-4 targeting agents, or a combination thereof.
  • the immunotherapeutic treatment is a combination of a PD-1 targeting agent or a PD-L1 targeting agent in combination with a CTLA-4 targeting agent.
  • the PD-1 targeting agents are monoclonal antibodies against PD-1 , such as for example pembrolizumab, nivolumab or cemiplimab.
  • the PD-L1 targeting agents are monoclonal antibodies against PD-L1 , such as for example Atezolizumab, Avelumab, Durvalumab.
  • the CTLA-4 targeting agents are monoclonal antibodies against CTLA-1 , such as for example ipilimumab.
  • Immunotherapeutic drugs like immune checkpoint inhibitors, target certain immune cells that need to be activated or inactivated to start an immune response.
  • Monoclonal antibodies that target either PD-1 or PD-L1 can block these proteins, which are present on respectively T-cells and normal (or cancer) cells, and boost the immune response against cancer cells. These drugs are helpful in treating different types of cancer, including bladder cancer, non-small cell lung cancer, and Merkel cell skin cancer.
  • CTLA-4 is another protein on some T cells that, just like PD-1 , acts as a type of switch to keep the immune system in check.
  • Anti-CTLA4 treatment is often used to treat melanoma of the skin and some other cancers.
  • the PD-1 targeting agents, PD-L1 targeting agents or CTLA-4 targeting agents can be any targeting agent known in the art, such as an antibody or a fragment thereof that recognizes and blocks PD-1 , PD-L1 or CTLA-4.
  • the PD-1 targeting agents are monoclonal antibodies against PD-1 , such as for example pembrolizumab, nivolumab or cemiplimab.
  • the PD-L1 targeting agents are monoclonal antibodies against PD-L1 , such as for example Atezolizumab, Avelumab, Durvalumab.
  • the CTLA-4 targeting agents are monoclonal antibodies against CTLA-1 , such as for example ipilimumab.
  • immune checkpoint inhibitors are provided for use in the treatment of cancer in a subject wherein the subject has been found not to be predisposed for generating hyperprogressive disease using any of the methods for analyzing the expression level of the one or more biomarkers in a sample of the subject as disclosed herein.
  • immune checkpoint inhibitors are provided for use in the treatment of cancer, preferably melanoma and/or non-small cell lung cancer, in a subject wherein the subject has been found not to be predisposed for generating hyperprogressive disease using any of the methods for analyzing the expression level of the biomarkers IL-17A, IFNA1 , IFNB and IL-6, whether or not in combination with any of ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 in a sample of the subject.
  • the set of predictive biomarkers of current invention are preferably used to determine whether a subject is predisposed for generating hyperprogression when receiving one or more of said immunotherapeutic treatments, in particular one or more immune checkpoint inhibitors. More preferably the set of predictive biomarkers of current invention is used to determine whether a subject is predisposed for generating hyperprogression when receiving an immunotherapeutic treatment, such as treatment with an immune checkpoint inhibitor. Most preferably the set of predictive biomarkers of current invention is used to determine whether a subject is predisposed for generating hyperprogression when receiving a monotherapy.
  • a further aspect of the current invention concerns a method for treating a subject diagnosed with cancer with an immunotherapeutic agent.
  • the method comprises determining whether the subject is predisposed for generating hyperprogressive disease when receiving an immunotherapeutic agent, or for responding to treatment with an immunotherapeutic agent according to any of the methods as disclosed herein, and administering to the subject an immunotherapeutic agent when the subject is found to be responsive to immunotherapeutic treatment.
  • the immunotherapeutic agent is an immune checkpoint inhibitor, such as a PD-1 targeting agent, a PD-L1 targeting agent or a CTLA-4 targeting agent.
  • the immunotherapeutic agent is a combination of different immune checkpoint inhibitors, such as a PD-1 targeting agent or a PD-L1 targeting agent in combination with a CTLA- 4 targeting agent.
  • Another embodiment of the current invention provides a tumor response report, comprising the steps of isolating one or more samples from a tumor of a patient; generating from one or more samples, expression data, preferably gene expression data, even more preferably R,NA sequencing data, comprising information about a plurality of biomarkers, preferably protein coding genes, in particular about a plurality of protein coding genes genes selected from IL-17A, IFN1 , IFNB1 , IL-6, ICOSLG, CCL20, ID01 , FGF2, CXCL8, NRP1 , MX1 , VEGFA, CXCL12 and TGFB1 ; and/or a plurality of protein coding genes selected from TLR9, IL-23A, CLEC4C, CCR4, SIGLEC6, IL3RA, IL12A, TLR7, CXCR3, AXL, TLR8, SIGLEC1 , TCF4, CD22, ITGAX, and FCGR3A; and assessing the likelihood of the patient’
  • the sample is a tumor tissue sample derived subjects diagnosed with cancer.
  • the expression level of a cell signature is assessed in a biological sample that contains or is suspected to contain cancer cells.
  • the tissue sample may be, for example, a tissue resection, a tissue biopsy, or metastatic lesion obtained from a patient suffering from, suspected to suffer from, or diagnosed with cancer.
  • the sample is a sample of tissue, a resection or biopsy of a tumor, known or suspected metastatic cancer lesion or section, or a blood sample, e.g. a peripheral blood sample, known or suspected metastatic cancer lesion or section, or a blood sample, e.g. a peripheral blood sample, known or suspected to comprise circulating cancer cells.
  • the sample may comprise both cancer cells, i.e. tumor cells, and non-cancerous cells, and, in certain embodiments, comprises both cancerous and non-cancerous cells.
  • the sample obtained from the patient is collected prior to beginning any immunotherapy or other treatment regimen or therapy, e.g. chemotherapy, or radiation therapy for the treatment of cancer or the management or amelioration of a symptom thereof. Therefore, in some embodiments, the sample is collected before the administration of immunotherapeutic agents or other agents, or the start of immunotherapy or other treatment regimen.
  • immunotherapy or other treatment regimen or therapy e.g. chemotherapy, or radiation therapy for the treatment of cancer or the management or amelioration of a symptom thereof. Therefore, in some embodiments, the sample is collected before the administration of immunotherapeutic agents or other agents, or the start of immunotherapy or other treatment regimen.
  • the sample is a tumor tissue sample, such as a fresh-frozen tumor tissue sample, a fresh tumor tissue sample, or a formalin-fixed paraffin-embedded (FFPE) tumor tissue sample.
  • FFPE formalin-fixed paraffin-embedded
  • the subject is a subject diagnosed with cancer.
  • the cancer can be selected from bladder cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, prostate cancer, thyroid cancer, uterine cancer, ovarian cancer, colorectal cancer, breast cancer, head and neck cancer, skin cancer; even more preferably cancer patients diagnosed with skin cancer or melanoma, lung cancer such as lung adenocarcinoma, lung squamous cell carcinoma, non-small cell lung carcinoma (NSCLC).
  • the subject is a subject diagnosed with melanoma and/or lung cancer.
  • the subject is diagnosed with melanoma.
  • the subject is diagnosed with lung cancer, preferably non-small cell lung carcinoma (NSCLC).
  • the current invention is preferably depicted for patients diagnosed with non-small cell lung cancer and/or melanoma. These patients may have already undergone an anti-PD-1 , anti-PD-L1 and/or anti-CTLA-4 treatment, are undergoing such a treatment or are planned to be treated with such drugs.
  • the set of predictive biomarkers is optimized for such patients, and predicts with high specificity and sensitivity the response of the patient’s tumor towards such a treatment or the chance of developing hyperprogressive disease.
  • the different set of biomarkers of the current invention are used for the development of novel immunotherapeutic drugs.
  • One aspect of the current invention relates to a method for the development of predictive biomarkers involved in developing hyperprogression and/or progressive disease when receiving an immunotherapeutic treatment, wherein the method comprises the steps of (a) treating an animal model with an immunotherapeutic treatment; (b) analyzing the expression levels of the biomarkers, preferably protein coding genes, involved in developing hyperprogressive disease and/or responsiveness to the immunotherapeutic treatment; (c) selecting differentially expressed biomarkers, preferably protein coding genes, wherein said differentially expressed biomarkers are correlated with the predisposition of a subject being responsive, non-responsive, or hyperprogressive toward to the immunotherapeutic treatment.
  • Another aspect of the current invention provides a method for testing biomarkers involved in developing hyperprogressive disease or for predicting the responsiveness to immunotherapeutic treatment, wherein the method comprises the steps of (a) treating an animal model with an immunotherapeutic treatment; (b) analyzing the expression level of biomarkers, preferably protein coding genes involved in developing hyperprogressive disease and/or responsiveness to the immunotherapeutic treatment; (c) selecting differentially expressed biomarkers, preferably protein coding genes, wherein said differentially expressed biomarkers, which are correlated with the predisposition of the animal model being responsive, non-responsive, or hyperprogressive toward the immunotherapeutic treatment, are asserted.
  • animal models are selected from the group consisting of mouse, rat, rabbit, cat, dog, frog. These animal models can be utilized for the development and testing of the predictive biomarkers.
  • a preferred animal model is a mouse model.
  • the biomarkers of current invention are developed in an animal model.
  • the predictive biomarkers of current invention are developed in a mouse model.
  • Preferred mouse models are MISTRG, NSG-SGM3, NOG-EXL, BRG hlL-3 hGM-CSF.
  • the predictive biomarkers are tested in an animal model, preferably a mouse model.
  • Some embodiments of the application relate to the use of the previously described methods for predicting hyperprogression and/or non-responsiveness of a subject undergoing one or more immunotherapeutic treatments. Preferably, for predicting hyperprogression and/or nonresponsiveness of a subject undergoing anti-PD-1/PD-L1 monotherapy, or in combination with anti-CTLA-4 therapy.
  • Prognostically identifying which patients are going to experience hyperprogression or be nonresponders to such immunotherapeutic treatments is of medical and economical interest.
  • An optimal patient specific therapy can be determined which assures a better and safer therapy.
  • the present invention further provides a method for determining the response of a tumor to an immunotherapeutic treatment; the method comprising the steps of quantitative assessment of the expression level of a plurality of biomarkers, preferably protein coding genes involved in developing hyperprogressive disease and/or responsiveness to the immunotherapeutic treatment; and based on this quantitative assessment classifying the tumor as a responder or a nonresponder to the therapy, wherein the responder is further classified as being at risk for hyperprogression to said therapy.
  • the method further comprises the step of determining immunohistochemistry data for the tumor.
  • immunohistochemistry data is included in the characterization of the tumor and tumor microenvironment.
  • This extra dataset makes the identification of the tumor is a R, NR, or HP more reliable and valid, as repeated measurements generate a similar output.
  • the method further comprises the step of determining one or more possible immunotherapeutic treatment therapies.
  • the targeted ICI treatment other immunotherapeutic treatment therapies could be addressed in the treatment of cancer patients leading to a positive outcome of the cancer patients treatment.
  • the application further provides methods for administering an activating or suppressing immunotherapeutic treatment to patients with cancer, if the patient is determined to have a change in the level of expression of one or more gene signatures as disclosed herein.
  • the method of the present invention comprises the step of informing the patient that they have an increased likelihood of being responsive to therapy.
  • the method of the present invention comprises the step of recommending a particular therapeutic treatment to the patient.
  • the method of the present invention further comprises the step of administering a therapy to the patient if it is determined that the patient may benefit from the therapy, in particular the immunotherapy.
  • the immunotherapeutic agent comprises a checkpoint inhibitor, a chimeric antigen receptor T-cell therapy, an oncolytic vaccine, a cytokine agonist or a cytokine antagonist, or a combination thereof, or any other immunotherapy available in the art.
  • the activating immunotherapy may further comprise the use of checkpoint inhibitors.
  • Checkpoint inhibitors are readily available in the art and include, but are not limited to, a PD-1 inhibitor, PD-L1 inhibitor, PD- L2 inhibitor, a CTLA-4 inhibitor, or a combination thereof.
  • ICIs have only recently become used on a regular basis in a clinic. Therefore, the availability of reliable and thorough data on patients' health outcomes is still a challenge.
  • We identified key studies which have undertaken observational studies on patients exposed to ICIs (Rizvi et al. 2015; Chan, Wolchok, and Snyder 2015; Hugo et al. 2017; Hellmann et al. 2018; Goodman et al. 2017) and compiled a pooled dataset of RNA-sequencing data for further investigation.
  • RNA-sequencing transcriptional analysis
  • pretreatment biopsies tumor samples and adjacent normal tissue or peripheral blood mononuclear cells samples
  • FPKM normalized gene expression counts
  • LR statistical models - logistics regression
  • GBMC gradient boosting machine classifier
  • ROC AUC curves and various accuracy metrics were created for the LR and GBM models.
  • To train the models we split our dataset into test (20%) and train (80%) sets. We also run 10-fold CV on both models to ensure that the signal we get is stable regardless of the train/test target balance.
  • Each sample was assigned to one of three classes and two independent models were constructed - one for discrimination of HPD vs non-HPD and one for discrimination of R vs NR. Due to the absence of original TGR data, we classified as HPD any sample that had survival data of less than 2 months (time-to-treatment failure (TTF) ⁇ 2 months).
  • TTF time-to-treatment failure
  • the strongest gene contributing to the model classification performance for HPD vs non-HPD were IFNB1 , SIGLEC1 , VEGFA, ID01 , ICOSLG, TGFB1 , FGF2, IL6 ( Figure 6b).
  • the directionality was an down-regulation of CCL20, CXCL12, IFNB1 and up-regulation of VEGFA, FGF2, and ICOSLG in the HPD, and the reverse pattern in non-HPD.
  • FFPE tumor biopsies were used to extract RNA and obtain whole transcriptomics sequencing data. HPD suspects were qualified following the RECIST1 1-based definition based on PFS and OS data.
  • Transcriptomics data were processed using the following bioinformatics procedure.
  • Raw bulk RNA-seq reads were processed using nf-core/rnaseq pipeline to transform the raw RNA reads and to obtain the gene expression matrices.
  • the workflow included a series of steps of read quality control and read trimming (FastQC, Trim Galore!), aligning reads against the reference genome (STAR), calculating counts relative to genes (featureCounts), and quality control on the results (RSeQC, Qualimap, Preseq, edgeR, MultiQC). We have adjusted the pipeline to best suit our needs and available resources.
  • Bioinformatics and ML methods confirmed previously identified markers of HPD vs. non-HPD in PD1/PDL1 treated cohort from a total of 20000+ mapped expressed tags ( Figure 7 and Figure 8). Four core and supporting biomarkers were consistently visible across independent trials and on a combined dataset.
  • ML models with combined biomarkers achieved 81 % accuracy on a 50%-50% split of training/testing data (Figure 9 and Table 5).
  • the baseline model that only included PD-L1 expression and clinical variables was only able to discriminate non-HPD vs HPD cases at the accuracy of 69%.
  • the top predictive biomarkers were IL17A, IFNA1 , IFNB1 , and IL-6, and these markers were consistently identified in the top 10 discriminatory biomarkers in each trial separately.
  • the pathways enriched for the significantly differentially expressed markers ( Figure 10) highlighted enrichment for antigen processing and presentation, T cell receptor signaling, Th1 and Th2 cell differentiation and PD1/PDL1 checkpoint expression pathways.

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Abstract

L'invention concerne le domaine du diagnostic pour orienter une immunothérapie anticancéreuse. Plus particulièrement, l'invention concerne un procédé permettant de déterminer la probabilité d'une hyperprogression et/ou d'une maladie progressive des cellules cancéreuses en réponse à une immunothérapie, telle qu'un traitement par inhibiteur de point de contrôle immunitaire. En outre, l'invention concerne également un procédé permettant de déterminer la probabilité d'une réponse positive à une immunothérapie, telle qu'un traitement par inhibiteur de point de contrôle immunitaire.
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Cited By (1)

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CN114694745A (zh) * 2022-03-24 2022-07-01 至本医疗科技（上海）有限公司 预测免疫疗效的方法、装置、计算机设备和存储介质

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