Cancer treatment and diagnosis

Abstract

To provide a method of treating, diagnosing, and prognosing cancer.SOLUTION: The present invention provides: a method for treating cancer based on a blood tumor mutational burden (bTMB) score or a maximum somatic allele frequency (MSAF) from samples from individuals (e.g., whole blood samples, plasma samples, serum samples, or combinations thereof); a method for determining whether an individual with cancer is likely to respond to treatment containing an immune checkpoint inhibitor (e.g., a PD-L1 axis binding antagonist); a method for predicting responsiveness of individuals with cancer to treatments containing immune checkpoint inhibitors (e.g., PD-L1 axis binding antagonists); a method for selecting the treatment for an individual with cancer; a method of providing a prognosis for individuals with cancer; and a method for monitoring the response of an individual with cancer.SELECTED DRAWING: None

Description

[Technical Field]

[0001] Provided herein are diagnostic, therapeutic, and prognostic methods for the treatment of cancer using immune checkpoint inhibitors (e.g., PD-L1 axis-binding antagonists). Specifically, the present invention provides methods for patient selection and diagnosis, therapeutic methods, and diagnostic kits. [Background technology]

[0002] Cancer remains one of the most deadly threats to human health. In the United States, cancer affects approximately 1.3 million new cases each year, making it the second leading cause of death after heart disease, accounting for approximately one in four deaths. It has even been predicted that cancer may surpass cardiovascular disease as the leading cause of death within five years. Solid tumors are responsible for the majority of these deaths. While significant advances have been made in medical treatments for certain cancers, the five-year overall survival rate for all cancers has improved by only about 10% over the past 20 years. In particular, malignant solid tumors metastasize and grow rapidly and uncontrollably, making their timely detection and treatment extremely challenging. Despite significant advances in cancer treatment, improved diagnostic methods remain a challenge.

[0003] Recent studies suggest that analysis of tumor mutation burden (TMB), a measure of tumor neoantigenicity obtained from tissue biopsies, has shown clinical utility in predicting outcomes in patients treated with PD-L1 axis-binding antagonists across a range of tumor types. However, some patients, for example, due to their own health status, are unfit or unwilling to undergo a biopsy to obtain tumor samples for somatic mutation analysis.

[0004] Therefore, there is an unmet need for an orthogonal, non-invasive diagnostic approach that allows for the analysis of TMB in patient samples without the need for tumor tissue biopsy. Summary of the Invention

[0005] The present invention provides therapeutic, diagnostic, and prognostic methods and compositions for treating individuals with cancer.

[0006] In one aspect, the invention features a method of identifying an individual having cancer who may benefit from a treatment comprising an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof), the method including determining a blood tumor mutation burden (bTMB) score from a sample from the individual, wherein a bTMB score from the sample that is at or exceeds a reference bTMB score identifies the individual as one who may benefit from a treatment comprising an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a PD-L1 axis-binding antagonist.

[0007] In another aspect, the invention features a method of selecting a therapy for an individual having cancer, the method including determining a bTMB score from a sample from the individual, wherein a bTMB score from the sample that is at or exceeds a reference bTMB score identifies the individual as one who may benefit from treatment including an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody), an antagonist directed against a coinhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof). In some embodiments, the immune checkpoint inhibitor is a PD-L1 axis-binding antagonist.

[0008] In some embodiments of any of the aforementioned aspects, the bTMB score determined from the sample is at or exceeds a reference bTMB score, and the method further comprises administering to the individual an effective amount of a PD-L1 axis-binding antagonist. In some embodiments, the bTMB score determined from the sample is less than the reference bTMB score.

[0009] In another aspect, the invention features a method of treating an individual having cancer, the method including: (a) determining a bTMB score from a sample from the individual, where the bTMB score from the sample is at or exceeds a reference bTMB score; and (b) administering to the individual an effective amount of a PD-L1 axis binding antagonist.

[0010] In another aspect, the invention features a method of treating an individual having cancer, comprising administering to the individual an effective amount of an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof), wherein prior to administration, a bTMB score that is at or exceeds a baseline bTMB score has been determined from a sample from the individual. In some embodiments, the immune checkpoint inhibitor is a PD-L1 axis-binding antagonist.

[0011] In some embodiments of any of the foregoing aspects, the baseline bTMB score is a bTMB score in a reference population of individuals with cancer, the population of individuals consisting of a first subset of individuals being treated with a PD-L1 axis-binding antagonist therapy and a second subset of individuals being treated with a non-PD-L1 axis-binding antagonist therapy, where the non-PD-L1 axis-binding antagonist therapy does not include an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody) or an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)). In some embodiments, the baseline bTMB score is The bTMB score in a reference population of individuals having cancer, the population consisting of a first subset of individuals being treated with a PD-L1 axis binding antagonist therapy and a second subset of individuals being treated with a non-PD-L1 axis binding antagonist therapy, where the non-PD-L1 axis binding antagonist therapy does not include a PD-L1 axis binding antagonist. In some embodiments, the reference bTMB score significantly separates the first subset of individuals from the second subset of individuals based on a significant difference in responsiveness to treatment with a PD-L1 axis binding antagonist therapy compared to responsiveness to treatment with a non-PD-L1 axis binding antagonist therapy. In some embodiments, responsiveness to treatment is an increase in progression-free survival (PFS). In some embodiments, responsiveness to treatment is an increase in overall survival (OS).

[0012] In some embodiments of any of the foregoing aspects, the reference bTMB score is a pre-assigned bTMB score. In some embodiments, the reference bTMB score is 4 to 30 (e.g., about 3.6 mut / Mb to about 26.7 mut / Mb). In some embodiments, the reference bTMB score is 8 to 30 (e.g., about 7.1 mut / Mb to about 26.7 mut / Mb). In some embodiments, the reference bTMB score is 10 to 20 (e.g., about 9 mut / Mb to about 17.8 mut / Mb). In some embodiments, the reference bTMB score is 10 (e.g., about 9 mut / Mb). In some embodiments, the reference bTMB score is 16 (e.g., about 14 mut / Mb). In some embodiments, the reference bTMB score is 20 (e.g., about 17.8 mut / Mb).

[0013] In some embodiments of any of the foregoing aspects, the bTMB score from the sample is 4 or greater (e.g., about 3.6 mut / Mb or greater). In some embodiments, the bTMB score from the sample is between 4 and 100 (e.g., about 3.6 mut / Mb to about 88.9 mut / Mb).

[0014] In some embodiments of any of the foregoing aspects, the bTMB score from the sample is 8 or greater (e.g., about 7.1 mut / Mb or greater). In some embodiments, the bTMB score from the sample is between 8 and 100 (e.g., between about 7.1 mut / Mb and about 88.9 mut / Mb).

[0015] In some embodiments of any of the aforementioned aspects, the bTMB score from the sample is less than 4 (e.g., less than about 3.6 mut / Mb). In some embodiments, the bTMB score from the sample is less than 8 (e.g., less than about 7.1 mut / Mb).

[0016] In some embodiments of any of the aforementioned aspects, the bTMB score (e.g., a reference bTMB score) is calculated based on a defined number of sequenced bases (e.g., FOUNDATION ONE The CDX (trademark) panel may evaluate, for example, about 100 kb to about 10 Mb, about 200 kb to about 10 Mb, about 300 kb to about 10 Mb, about 400 kb to about 10 Mb, about 500 kb to about 10 Mb, about 600 kb to about 10 Mb, about 700 kb to about 10 Mb, about 800 kb to about 10 Mb, about 900 kb to about 10 Mb, about 1 Mb to about 10 Mb, about 100 kb to about 5 Mb, about 200 kb to about 5 Mb, about 300 kb to about 5 Mb, about 400 kb to about 5 Mb, about 500 kb to about 5 Mb, about 600 kb to about 5 Mb, about 700 kb to about 5 Mb, about 800 kb to about 5 Mb, about 900 kb to about 10 Mb, The number of somatic mutations is expressed as the number of somatic mutations counted over a range of about 1 Mb to about 5 Mb, or about 1 Mb to about 5 Mb, about 100 kb to about 2 Mb, about 200 kb to about 2 Mb, about 300 kb to about 2 Mb, about 400 kb to about 2 Mb, about 500 kb to about 2 Mb, about 600 kb to about 2 Mb, about 700 kb to about 2 Mb, about 800 kb to about 2 Mb (e.g., about 800 kb (e.g., about 795 kb)), about 900 kb to about 2 Mb, or about 1 Mb to about 2 Mb, e.g., about 1.1 Mb (e.g., about 1.125 Mb)), as assessed by, for example, a FOUNDATIONONE® panel. In some embodiments, the defined number of sequenced bases is about 100 kb, about 200 kb, about 300 kb, about 400 kb, about 500 kb, about 600 kb, about 700 kb, about 800 kb, about 900 kb, about 1 Mb, about 2 Mb, about 3 Mb, about 4 Mb, about 5 Mb, about 6 Mb, about 7 Mb, about 8 Mb, about 9 Mb, or about 10 Mb. In some embodiments, the number of somatic mutations is the number of counted single nucleotide variants (SNVs), or the sum of the number of counted SNVs and the number of indel mutations. In some embodiments, the number of somatic mutations is the number of counted SNVs. In some embodiments, the number of somatic mutations is the number of synonymous and non-synonymous SNVs and / or indels. In some embodiments, the bTMB score (e.g., a reference bTMB score) is an equivalent bTMB value, e.g., determined by whole-exome sequencing.

[0017] In another aspect, the invention features a method of identifying an individual having cancer who may benefit from a treatment comprising an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof), the method including determining an equivalent bTMB value from a sample from the individual, wherein an equivalent bTMB value from the sample that is at or exceeds the reference equivalent bTMB value identifies the individual as one who may benefit from a treatment comprising an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a PD-L1 axis-binding antagonist.

[0018] In another aspect, the invention features a method of selecting a therapy for an individual having cancer, the method including determining an equivalent bTMB value from a sample from the individual, wherein an equivalent bTMB value from the sample that is at or exceeds the reference equivalent bTMB value identifies the individual as one who would benefit from a treatment including an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody), an antagonist directed against a coinhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof). In some embodiments, the immune checkpoint inhibitor is a PD-L1 axis-binding antagonist.

[0019] In some embodiments of any of the aforementioned aspects, the equivalent bTMB value determined from the sample is at or exceeds a reference equivalent bTMB value, and the method further comprises administering to the individual an effective amount of a PD-L1 axis-binding antagonist. In some embodiments, the equivalent bTMB value determined from the sample is less than the reference equivalent bTMB value.

[0020] In another aspect, the invention features a method of treating an individual having cancer, the method including: (a) determining an equivalent bTMB value from a sample from the individual, where the equivalent bTMB value from the sample is at or exceeds a reference equivalent bTMB value; and (b) administering to the individual an effective amount of an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody), an antagonist directed against a coinhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof). In some embodiments, the immune checkpoint inhibitor is a PD-L1 axis-binding antagonist. In some embodiments, the method further includes monitoring the individual's response to treatment with the immune checkpoint inhibitor (e.g., the PD-L1 axis-binding antagonist). In some embodiments, the monitoring comprises (a) determining the bTMB score in an additional sample obtained from the individual at a time point after administration of the immune checkpoint inhibitor (e.g., a PD-L1 axis binding antagonist); and (b) comparing the bTMB score in the additional sample to a reference bTMB score, thereby monitoring the individual's response to treatment with the immune checkpoint inhibitor (e.g., a PD-L1 axis binding antagonist).

[0021] In another aspect, the invention features a method of treating an individual having cancer, the method including administering to the individual an effective amount of an immune checkpoint inhibitor (e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof), where prior to administration, an equivalent bTMB value that is at or exceeds a reference equivalent bTMB value has been determined from a sample from the individual. In some embodiments, the immune checkpoint inhibitor is a PD-L1 axis-binding antagonist. In some embodiments, the method further includes monitoring the individual's response to treatment with the immune checkpoint inhibitor (e.g., the PD-L1 axis-binding antagonist). In some embodiments, the monitoring comprises (a) determining the bTMB score in an additional sample obtained from the individual at a time point after administration of the immune checkpoint inhibitor (e.g., a PD-L1 axis binding antagonist); and (b) comparing the bTMB score in the additional sample to a reference bTMB score, thereby monitoring the individual's response to treatment with the immune checkpoint inhibitor (e.g., a PD-L1 axis binding antagonist).

[0022] In another aspect, the invention features a method of providing a prognosis for an individual with cancer, comprising determining an MSAF from a sample from the individual, wherein an MSAF from the sample that is at or exceeds a reference MSAF identifies the individual as likely to have a poor prognosis.

[0023] In another aspect, the invention features a method of monitoring the response of an individual having cancer to treatment with an anti-cancer therapy that includes a PD-L1 axis-binding antagonist, the method including: (a) determining a bTMB score in a sample obtained from the individual at a time point after administration of the anti-cancer therapy to the individual; and (b) comparing the bTMB score in the sample to a reference bTMB score, thereby monitoring the individual's response to treatment with the anti-cancer therapy.

[0024] In another aspect, the invention features a method of predicting disease progression in an individual with cancer, comprising determining a bTMB score in a sample obtained from the individual, wherein a bTMB score in the sample that is at or above a reference bTMB score identifies the individual as likely to exhibit disease progression. In some embodiments, disease progression is an increase in tumor burden. In some embodiments, the increase in tumor burden is characterized by an increase in sum of longest diameters (SLD). In some embodiments, disease progression is characterized by an increase in squamous morphology, e.g., as assessed by tumor histology. In other embodiments, disease progression is characterized by an increase in non-squamous morphology, e.g., as assessed by tumor histology.

[0025] In yet a further aspect, the invention features a method of predicting disease progression in an individual with cancer, comprising determining an MSAF in a sample obtained from the individual, wherein an MSAF in the sample that is at or exceeds a reference MSAF identifies the individual as likely to exhibit disease progression. In some embodiments, the disease progression is an increase in tumor burden. In some embodiments, the increase in tumor burden is characterized by an increase in sum of longest diameters (SLD). In some embodiments, the disease progression is characterized by an increase in squamous morphology, e.g., as assessed by tumor histology. In other embodiments, the disease progression is characterized by an increase in non-squamous morphology, e.g., as assessed by tumor histology. In some embodiments of any of the foregoing aspects, the reference equivalent bTMB value is a pre-assigned equivalent bTMB value. In some embodiments, the reference equivalent bTMB value corresponds to a reference bTMB score of 4 to 30 (e.g., about 3.6 mut / Mb to about 26.7 mut / Mb). In some embodiments, a reference-equivalent bTMB value corresponds to a reference bTMB score of 8 to 30 (e.g., about 7.1 mut / Mb to about 26.7 mut / Mb). In some embodiments, a reference-equivalent bTMB value corresponds to a reference bTMB score of 10 to 20 (e.g., about 9 mut / Mb to about 17.8 mut / Mb). In some embodiments, a reference-equivalent bTMB value corresponds to a reference bTMB score of 10 (e.g., about 9 mut / Mb). In some embodiments, a reference-equivalent bTMB value corresponds to a reference bTMB score of 16 (e.g., about 14 mut / Mb). In some embodiments, a reference-equivalent bTMB value corresponds to a reference bTMB score of 20 (e.g., about 17.8 mut / Mb).

[0026] In some embodiments of any of the foregoing aspects, the equivalent bTMB value from the sample corresponds to a bTMB score of 4 or greater (e.g., about 3.6 mut / Mb or greater). In some embodiments, the equivalent bTMB value from the sample is between 4 and 100 (e.g., between about 3.6 mut / Mb and about 88.9 mut / Mb).

[0027] In some embodiments of any of the foregoing aspects, the equivalent bTMB value from the sample is 8 or greater (e.g., about 7.1 mut / Mb or greater). In some embodiments, the equivalent bTMB value from the sample is between 8 and 100 (e.g., about 7.1 mut / Mb to about 88.9 mut / Mb).

[0028] In some embodiments of any of the foregoing aspects, the equivalent bTMB value from the sample is less than 4 (e.g., less than about 3.6 mut / Mb). In some embodiments, the equivalent bTMB value from the sample is less than 8 (e.g., less than about 7.1 mut / Mb).

[0029] In some embodiments of any of the foregoing aspects, the benefit from treatment comprising a PD-L1 axis binding antagonist is increased OS. In other embodiments of any of the foregoing aspects, the benefit from treatment comprising a PD-L1 axis binding antagonist is increased PFS. In some embodiments, the benefit from treatment comprising a PD-L1 axis binding antagonist is increased OS and PFS.

[0030] In some embodiments of any of the aforementioned aspects, the method further includes determining a maximum somatic allele frequency (MSAF) from a sample from the individual, wherein the MSAF from the sample is 1% or greater. In some embodiments, prior to administration, the sample from the individual is determined to have an MSAF of 1% or greater.

[0031] In other embodiments of any of the foregoing aspects, the method further comprises determining an MSAF from a sample from the individual, wherein the MSAF from the sample is less than 1%. In some embodiments, prior to administration, the sample from the individual is determined to have an MSAF of less than 1%.

[0032] In some embodiments of any of the aforementioned aspects, the method further includes determining MSAF from a sample from the individual, wherein the MSAF from the sample is determined to be 1% or greater, and the method further includes administering to the individual an effective amount of an anti-cancer therapy other than or in addition to a PD-L1 axis-binding antagonist.

[0033] In some embodiments of any of the aforementioned aspects, the method further comprises determining MSAF from a sample from the individual, wherein the MSAF from the sample is determined to be less than 1%, and the method further comprises administering to the individual an effective amount of a PD-L1 axis-binding antagonist.

[0034] In some embodiments of any of the aforementioned aspects, the determination of the MSAF precedes the determination of the bTMB score. In some embodiments, the MSAF is determined before the bTMB score.

[0035] In some embodiments of any of the foregoing aspects, the bTMB score from the sample has a prevalence of about 5% or greater in a reference population. In some embodiments, the bTMB score from the sample has a prevalence of about 5% to about 75% in a reference population. In some embodiments, the bTMB score from the sample has a prevalence of about 20% to about 30% in a reference population.

[0036] In some embodiments of any of the aforementioned aspects, the method further comprises determining a tissue tumor mutation burden (tTMB) score from a tumor sample from the individual. In other embodiments of any of the aforementioned aspects, prior to administration, a tTMB score has been determined from a sample from the individual. In some embodiments, a tTMB score from the tumor sample that is at or exceeds a reference tTMB score identifies the individual as one who may benefit from a treatment comprising a PD-L1 axis-binding antagonist. In some embodiments, the tTMB score determined from the tumor sample is at or exceeds a reference tTMB score. In some embodiments, the tTMB score determined from the tumor sample is less than the reference tTMB score. In some embodiments, the reference tTMB score is a tTMB score in a reference population of individuals with cancer, the population consisting of a first subset of individuals being treated with a PD-L1 axis-binding antagonist therapy and a second subset of individuals being treated with a non-PD-L1 axis-binding antagonist therapy, where the non-PD-L1 axis-binding antagonist therapy does not include a PD-L1 axis-binding antagonist. In some embodiments, the reference tTMB score significantly separates the first subset of individuals from the second subset of individuals based on a significant difference in responsiveness to treatment with a PD-L1 axis-binding antagonist therapy compared to responsiveness to treatment with a non-PD-L1 axis-binding antagonist therapy. In some embodiments, responsiveness to treatment is an increased PFS, an increased OS, and / or an increased overall response rate (ORR). In some embodiments, the tumor sample has been determined to have an increased level of somatic mutations compared to the reference level of somatic mutations. In some embodiments, the tumor sample is determined to have an increased level of somatic mutations in at least one gene listed in Table 1 compared to a baseline level of somatic mutations in at least one gene listed in Table 1. In some embodiments, the somatic mutations are protein-modifying somatic mutations or synonymous mutations. In some embodiments, the somatic mutations are protein-modifying somatic mutations. In some embodiments, the somatic mutations are substitutions, deletions, and / or insertions. In some embodiments, the substitutions, deletions, and / or insertions are in coding regions.In some embodiments, the deletions and / or insertions are indels. In some embodiments, the reference tTMB score is a pre-assigned tTMB score. In some embodiments, the reference tTMB score is about 5 to about 50 mutations per megabase (mut / Mb). In some embodiments, the reference tTMB score is about 8 to about 30 mut / Mb. In some embodiments, the reference tTMB score is about 10 to about 20 mut / Mb. In some embodiments, the reference tTMB score is about 10 mut / Mb. In some embodiments, the reference tTMB score is about 16 mut / Mb. In some embodiments, the reference tTMB score is about 20 mut / Mb. In some embodiments, the tTMB score from the tumor sample is about 5 mut / Mb or greater. In some embodiments, the tTMB score from the tumor sample is about 5 to about 100 mut / Mb. In some embodiments, the tTMB score from the tumor sample is about 10 mut / Mb or greater. In some embodiments, the tTMB score from the tumor sample is about 10 to about 100 mut / Mb. In some embodiments, the tTMB score from the tumor sample is about 16 mut / Mb or greater. In some embodiments, the reference tTMB score is about 16 mut / Mb. In some embodiments, the tTMB score from the tumor sample is about 20 mut / Mb or greater. In some embodiments, the reference tTMB score is about 20 mut / Mb. In some embodiments, the tTMB score or reference tTMB score is expressed as the number of somatic mutations counted per defined number of sequenced bases. In some embodiments, the defined number of sequenced bases is about 100 kb to about 10 Mb. In some embodiments, the defined number of sequenced bases is about 0.8 Mb. In some embodiments, the defined number of sequenced bases is about 1.1 Mb. In some embodiments, the tTMB score or reference tTMB score is an equivalent tTMB value. In some embodiments, the equivalent tTMB value is determined by whole exome sequencing (WES).

[0037] In some embodiments of any of the foregoing aspects, the tumor sample obtained from the patient is determined to have a detectable PD-L1 expression level in less than 1% of tumor cells in the tumor sample. In other embodiments of any of the foregoing aspects, the tumor sample obtained from the patient is determined to have a detectable PD-L1 expression level in 1% or more of tumor cells in the tumor sample. In some embodiments, the tumor sample obtained from the patient is determined to have a detectable PD-L1 expression level in 1% to less than 5% of tumor cells in the tumor sample. In some embodiments, the tumor sample obtained from the patient is determined to have a detectable PD-L1 expression level in 5% or more of tumor cells in the tumor sample. In some embodiments, the tumor sample obtained from the patient is determined to have a detectable PD-L1 expression level in 5% to less than 50% of tumor cells in the tumor sample. In some embodiments, the tumor sample obtained from the patient is determined to have a detectable PD-L1 expression level in 50% or more of tumor cells in the tumor sample.

[0038] In some embodiments of any of the foregoing aspects, a tumor sample obtained from a patient is determined to have detectable PD-L1 expression in tumor-infiltrating immune cells that are present in less than 1% of the tumor sample. In other embodiments of any of the foregoing aspects, a tumor sample obtained from a patient is determined to have detectable PD-L1 expression in tumor-infiltrating immune cells that are present in more than 1% of the tumor sample. In some embodiments, a tumor sample obtained from a patient is determined to have detectable PD-L1 expression in tumor-infiltrating immune cells that are present in 1% to less than 5% of the tumor sample. In some embodiments, a tumor sample obtained from a patient is determined to have detectable PD-L1 expression in tumor-infiltrating immune cells that are present in more than 5% of the tumor sample. In some embodiments, a tumor sample obtained from a patient is determined to have detectable PD-L1 expression in tumor-infiltrating immune cells that are present in 5% to less than 10% of the tumor sample. In some embodiments, a tumor sample obtained from a patient is determined to have detectable PD-L1 expression in tumor-infiltrating immune cells that are present in more than 10% of the tumor sample.

[0039] In some embodiments of any of the aforementioned aspects, the sample is a whole blood sample, a plasma sample, a serum sample, or a combination thereof. In some embodiments, the sample is an archived sample, a fresh sample, or a frozen sample.

[0040] In some embodiments of any of the foregoing aspects, the cancer is selected from the group consisting of lung cancer, renal cancer, bladder cancer, breast cancer, colorectal cancer, ovarian cancer, pancreatic cancer, gastric cancer, esophageal cancer, mesothelioma, melanoma, head and neck cancer, thyroid cancer, sarcoma, prostate cancer, glioblastoma, cervical cancer, thymic cancer, leukemia, lymphoma, myeloma, mycosis fungoides, Merkel cell carcinoma, or a hematological malignancy. In some embodiments, the cancer is lung cancer, bladder cancer, melanoma, renal cancer, colorectal cancer, or head and neck cancer. In some embodiments, the lung cancer is non-small cell lung cancer (NSCLC). In some embodiments, the bladder cancer is bladder urothelial (transitional cell) carcinoma. In some embodiments, the melanoma is cutaneous melanoma. In some embodiments, the renal cancer is renal urothelial carcinoma. In some embodiments, the colorectal cancer is colon adenocarcinoma. In some embodiments, the head and neck cancer is head and neck squamous cell carcinoma (HNSCC).

[0041] In some embodiments of any of the foregoing aspects, the PD-L1 axis binding antagonist is selected from the group consisting of a PD-L1 binding antagonist, a PD-1 binding antagonist, and a PD-L2 binding antagonist. In some embodiments, the PD-L1 axis binding antagonist is a PD-L1 binding antagonist. In some embodiments, the PD-L1 binding antagonist inhibits the binding of PD-L1 to one or more of its ligand binding partners. In some embodiments, the PD-L1 binding antagonist inhibits the binding of PD-L1 to PD-1. In some embodiments, the PD-L1 binding antagonist inhibits the binding of PD-L1 to B7-1. In some embodiments, the PD-L1 binding antagonist inhibits the binding of PD-L1 to both PD-1 and B7-1. In some embodiments, the PD-L1 binding antagonist is an anti-PD-L1 antibody. In some embodiments, the anti-PD-L1 antibody is selected from the group consisting of atezolizumab (MPDL3280A), YW243.55.S70, MDX-1105, MEDI4736 (durvalumab), and MSB0010718C (avelumab). In some embodiments, the anti-PD-L1 antibody comprises the following hypervariable regions: (a) the HVR-H1 sequence of GFTFSDSWIH (SEQ ID NO: 19), (b) the HVR-H2 sequence of AWISPYGGSTYYADSVKG (SEQ ID NO: 20), (c) the HVR-H3 sequence of RHWPGGFDY (SEQ ID NO: 21), (d) the HVR-L1 sequence of RASQDVSTAVA (SEQ ID NO: 22), (e) the HVR-L2 sequence of SASFLYS (SEQ ID NO: 23), and (f) the HVR-L3 sequence of QQYLYHPAT (SEQ ID NO: 24). In some embodiments, the anti-PD-L1 antibody comprises (a) a heavy chain variable (VH) domain comprising an amino acid sequence that has at least 90% sequence identity to the amino acid sequence of SEQ ID NO: 3; (b) a light chain variable (VL) domain comprising an amino acid sequence that has at least 90% sequence identity to the amino acid sequence of SEQ ID NO: 4; or (c) a VH domain as in (a) and a VL domain as in (b).In some embodiments, the anti-PD-L1 antibody comprises (a) a heavy chain variable (VH) domain comprising an amino acid sequence that has at least 95% sequence identity to the amino acid sequence of SEQ ID NO: 3, (b) a light chain variable (VL) domain comprising an amino acid sequence that has at least 95% sequence identity to the amino acid sequence of SEQ ID NO: 4, or (c) a VH domain as in (a) and a VL domain as in (b). In some embodiments, the anti-PD-L1 antibody comprises (a) a heavy chain variable (VH) domain comprising an amino acid sequence that has at least 96% sequence identity to the amino acid sequence of SEQ ID NO: 3, (b) a light chain variable (VL) domain comprising an amino acid sequence that has at least 96% sequence identity to the amino acid sequence of SEQ ID NO: 4, or (c) a VH domain as in (a) and a VL domain as in (b). In some embodiments, the anti-PD-L1 antibody comprises (a) a heavy chain variable (VH) domain comprising an amino acid sequence that has at least 97% sequence identity to the amino acid sequence of SEQ ID NO: 3, (b) a light chain variable (VL) domain comprising an amino acid sequence that has at least 97% sequence identity to the amino acid sequence of SEQ ID NO: 4, or (c) a VH domain as in (a) and a VL domain as in (b). In some embodiments, the anti-PD-L1 antibody comprises (a) a heavy chain variable (VH) domain comprising an amino acid sequence that has at least 98% sequence identity to the amino acid sequence of SEQ ID NO: 3, (b) a light chain variable (VL) domain comprising an amino acid sequence that has at least 98% sequence identity to the amino acid sequence of SEQ ID NO: 4, or (c) a VH domain as in (a) and a VL domain as in (b). In some embodiments, the anti-PD-L1 antibody comprises (a) a heavy chain variable (VH) domain comprising an amino acid sequence having at least 99% sequence identity to the amino acid sequence of SEQ ID NO: 3, (b) a light chain variable (VL) domain comprising an amino acid sequence having at least 99% sequence identity to the amino acid sequence of SEQ ID NO: 4, or (c) a VH domain as in (a) and a VL domain as in (b). In some embodiments, the anti-PD-L1 antibody comprises (a) a VH domain comprising the amino acid sequence of SEQ ID NO: 3, (b) a VL domain comprising the amino acid sequence of SEQ ID NO: 4, or (c) a VH domain as in (a) and a VL domain as in (b).In some embodiments, the anti-PD-L1 antibody comprises (a) a VH domain comprising the amino acid sequence of SEQ ID NO:3, and (b) a VL domain comprising the amino acid sequence of SEQ ID NO:4. In some embodiments, the antibody is atezolizumab (MPDL3280A). In some embodiments, the PD-L1 axis binding antagonist is a PD-1 binding antagonist. In some embodiments, the PD-1 binding antagonist inhibits the binding of PD-1 to one or more of its ligand binding partners. In some embodiments, the PD-1 binding antagonist inhibits the binding of PD-1 to PD-L1. In some embodiments, the PD-1 binding antagonist inhibits the binding of PD-1 to PD-L2. In some embodiments, the PD-1 binding antagonist inhibits the binding of PD-1 to both PD-L1 and PD-L2. In some embodiments, the PD-1 binding antagonist is an anti-PD-1 antibody. In some embodiments, the anti-PD-1 antibody is selected from the group consisting of MDX-1106 (nivolumab), MK-3475 (pembrolizumab), CT-011 (pidilizumab), MEDI-0680 (AMP-514), PDR001, REGN2810, and BGB-108. In some embodiments, the PD-1 binding antagonist is an Fc fusion protein. In some embodiments, the Fc fusion protein is AMP-224.

[0042] In some embodiments of any of the aforementioned aspects, the non-PD-L1 axis binding antagonist is an anti-tumor agent, a chemotherapeutic agent, a growth inhibitory agent, an anti-angiogenic agent, radiation therapy, or a cytotoxic agent, hi some embodiments, the anti-cancer therapy other than, or in addition to, the PD-L1 axis binding antagonist is an anti-tumor agent, a chemotherapeutic agent, a growth inhibitory agent, an anti-angiogenic agent, radiation therapy, or a cytotoxic agent.

[0043] In some embodiments of any of the aforementioned aspects, the individual has not previously received treatment for cancer, hi some embodiments, the individual has not previously received a PD-L1 axis-binding antagonist.

[0044] In some embodiments of any of the aforementioned aspects, the treatment comprising the PD-L1 axis-binding antagonist is a monotherapy.

[0045] In some embodiments of any of the aforementioned aspects, the method further comprises administering to the individual an effective amount of an additional therapeutic agent, hi some embodiments, the additional therapeutic agent is an anti-tumor agent, a chemotherapeutic agent, a growth inhibitory agent, an anti-angiogenic agent, radiation therapy, or a cytotoxic agent.

[0046] In some embodiments of any of the aforementioned aspects, the individual is a human.

[0047] In another aspect, the invention features a kit for identifying an individual having cancer who may benefit from a therapy comprising a PD-L1 axis binding antagonist, the kit including: (a) a reagent for determining a bTMB score from a sample from the individual; and, optionally, (b) instructions for use of the reagent to identify an individual having cancer who may benefit from a therapy comprising a PD-L1 axis binding antagonist, wherein a bTMB score from the sample that is at or exceeds a reference bTMB score identifies the individual as one who may benefit from a therapy comprising a PD-L1 axis binding antagonist.

[0048] In another aspect, the invention features an assay for identifying an individual with cancer who is a candidate for treatment comprising a PD-L1 axis binding antagonist, the assay including determining a bTMB score from a sample from the individual, wherein a bTMB score from the sample that is at or exceeds a reference bTMB score identifies the individual as one who may benefit from treatment comprising a PD-L1 axis binding antagonist.

[0049] In another aspect, the invention features a PD-L1 axis binding antagonist for use in treating an individual having cancer, where a bTMB score that is at or above a reference bTMB score has been determined from a sample from the individual.

[0050] In another aspect, the invention provides the use of a PD-L1 axis binding antagonist in the manufacture of a medicament for treating an individual having cancer, wherein a bTMB score that is at or above a reference bTMB score has been determined from a sample from the individual.

[0051] In some embodiments of any of the foregoing aspects, the bTMB score (e.g., a reference bTMB score) is expressed as the number of somatic mutations counted over a defined number of sequenced bases (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel). In some embodiments, the bTMB score (e.g., a reference bTMB score) is an equivalent bTMB value, e.g., as determined by whole-exome sequencing. [Brief explanation of the drawings]

[0052] [Figure 1A] 1 is a graph showing the hazard ratio (HR) for progression-free survival (PFS) for patients in the POPLAR trial (clinical trial identification number NCT01903993) who were diagnostically positive (Dx+) based on a bTMB score at or above the indicated baseline blood tumor mutation burden (bTMB) score. ITT: intent to treat; BEP: biomarker-evaluable population. Stratified HRs are shown for ITT, while unstratified HRs are shown for subgroups with BEP and indicated baseline bTMB score. [Figure 1B] 1 is a graph showing the HR for overall survival (OS) for patients in the POPLAR trial of Dx+ based on a bTMB score at or above the indicated reference bTMB score. This figure shows unstratified hazard ratios. [Figure 1C] 1 is a table with forest plots showing OS for ITT, BEP, subgroups with less than bTMB16, and subgroups with more than bTMB16. [Figure 1D]1 is a graph showing Kaplan-Meier curves of PFS in the subgroups of bTMB<16 and bTMB≥16 in the atezolizumab treatment arm and docetaxel treatment arm. [Figure 1E] This is a series of graphs showing Kaplan-Meier curves for OS in the POPLAR trial for the atezolizumab treatment arm and the bTMB ≥ 16 subgroup (Figure 1E) (interaction P = 0.34) in the docetaxel treatment arm. The interaction P value is from a non-stratified proportional Cox model including treatment period, bTMB subgroup, and treatment by subgroup interaction. [Figure 1F] This is a series of graphs showing Kaplan-Meier curves for OS in the POPLAR trial for the atezolizumab treatment arm and the bTMB <16 subgroup (Figure 1F) in the docetaxel treatment arm (interaction P=0.34). The interaction P value is from a non-stratified proportional Cox model including treatment period, bTMB subgroup, and treatment by subgroup interaction. [Figure 2A] 1 is a graph showing the HR for PFS for patients in the OAK trial (clinical trial identification number NCT02008227) who are diagnostically positive (Dx+) based on a bTMB score at or above the indicated baseline blood tumor mutation burden (bTMB) score. [Figure 2B] 1 is a graph showing HRs for OS for patients in OAK trials who are diagnostically positive (Dx+) based on a blood tumor mutation burden (bTMB) score at or above the indicated baseline bTMB score. [Figure 2C]Graph showing multivariate adaptive regression spline (MARS) analysis of the relationship between PFS HR and bTMB score values ​​(≥4: n=441, ≥5: n=403, ≥6: n=371, ≥7: n=340, ≥8: n=302, ≥9: n=272, ≥10: n=251, ≥11: n=235, ≥12: n=211, ≥13: n=221, ≥14: n=222, ≥15: n=221, ≥16: n=222, ≥17: n=221, ≥18: n=222, ≥19: n=223, ≥19: n=224, ≥19: n=225, ≥19: n=226, ≥19: n=227, ≥19: n=228, n=200, 14 and above: n=188, 15 and above: n=166, 16 and above: n=158, 17 and above: n=147, 18 and above: n=136, 19 and above: n=121, 2 0 or more: n = 105, 21 or more: n = 97, 22 or more: n = 84, 23 or more: n = 76, 24 or more: n = 69, 25 or more: n = 62, 26 or more: n = 54 patients). [Figure 3A] This graph shows Kaplan-Meier curves of PFS for patients (nBEP = 211 patients) in the atezolizumab (MPDL3280A) treatment arm (black) and docetaxel control arm (gray) of the POPLAR trial, stratifying each arm according to bTMB score. Patients with a baseline bTMB score of 18 or greater are shown with a solid line (Dx+), and patients with a baseline bTMB score of less than 18 are shown with a dashed line (Dx-). A table listing the number of patients without a PFS event in each BEP subgroup at a given time point is also shown. The time points in each column correspond to the time points indicated along the x-axis of the graph above. A baseline bTMB score of 18 or greater had a prevalence of approximately 20% in this population (nITT = 287, patient samples (all) = 273, patient samples (samples contaminated by laboratory errors excluded) = 265, nDx+ = 52, HR = 0.57, interaction p-value for PFS = 0.11). Patient samples positive for mutations in EGFR or ALK were not excluded from the analysis. Patient samples with a maximum somatic allele frequency (MSAF) of less than 1% were excluded from the analysis. Sequence coverage was ≥ 800. [Figure 3B]1 is a table with a forest plot showing HRs for PFS in patients in the POPLAR trial treated with atezolizumab compared to docetaxel (control). HRs are listed across patient subgroups defined by a baseline bTMB score ("cutoff") of 18 or greater (Dx+) and a bTMB score below the cutoff of 18 (Dx-). [Figure 4A] This graph shows Kaplan-Meier curves of PFS for patients (nBEP = 583 patients) in the atezolizumab treatment arm (black) and docetaxel control arm (gray) of the OAK trial, with each arm stratified according to bTMB score. Patients with a bTMB score equal to or greater than 18 are shown with a solid line (Dx+), and patients with a bTMB score less than 18 are shown with a dashed line (Dx-). A table listing the number of patients without a PFS event in each BEP subgroup at a given time point is also shown. The time points in each column correspond to the time points indicated along the x-axis of the graph above. A bTMB score of 18 or higher had a prevalence of approximately 20% in the population without EGFR or ALK mutations (nITT = 850, patient samples (all) = 803, patient samples (samples contaminated by laboratory errors excluded) = 777, patient samples without EGFR or ALK mutations = 697, nDx+ = 132, HR = 0.66, interaction p-value for PFS = 0.068). Patient samples with MSAF < 1% were excluded from the analysis. Sequence coverage was ≥ 800. [Figure 4B] 1 is a table with a forest plot showing HRs for PFS for patients in the OAK trial treated with atezolizumab compared to docetaxel (control). HRs are listed across patient subgroups defined by baseline bTMB scores ("cutoff values") of 18 or greater (Dx+) and bTMB scores less than 18 (Dx-). [Figure 5A]This graph shows Kaplan-Meier curves of PFS for patients (nBEP = 583 patients) in the atezolizumab treatment arm (black) and docetaxel control arm (gray) of the OAK trial, with each arm stratified according to bTMB score. Patients with a bTMB score equal to or greater than 16 are shown with a solid line (Dx+), and patients with a bTMB score less than 16 are shown with a dashed line (Dx-). A table listing the number of patients without a PFS event in each BEP subgroup at a given time point is also shown. The time points in each column correspond to the time points indicated along the x-axis of the graph above. A bTMB score of 16 or higher had a prevalence of approximately 23% in the population without EGFR or ALK mutations (nITT = 850, patient samples (all) = 803, patient samples (samples contaminated by laboratory errors excluded) = 777, patient samples without EGFR or ALK mutations = 697, nDx+ = 158, HR = 0.65, interaction p-value for PFS = 0.036). Patient samples with MSAF < 1% were excluded from the analysis. Sequence coverage was ≥ 800. [Figure 5B] 1 is a table with a forest plot showing HRs for PFS in patients in the OAK trial treated with atezolizumab compared to docetaxel (control). HRs are listed across patient subgroups defined by baseline bTMB scores ("cutoff values") of 16 or greater (Dx+) and bTMB scores less than 16 (Dx-). [Figure 5C] Kaplan-Meier curves for OS in the bTMB <16 subgroup and bTMB ≥16 subgroup in the atezolizumab and docetaxel treatment arms. Interaction p-values ​​from an unstratified proportional Cox model including treatment period, bTMB subgroup, and treatment by subgroup interaction are shown. [Figure 5D] 1 is a table with forest plot showing unstratified HRs for OS for patients in the OAK trial treated with atezolizumab compared with docetaxel (control) in the ITT, BEP, bTMB 16 or greater subgroups, and bTMB < 16 subgroups. [Figure 6A]This graph shows Kaplan-Meier curves of PFS for patients (nBEP = 583 patients) in the atezolizumab treatment arm (black) and docetaxel control arm (gray) of the OAK trial, with each arm stratified according to bTMB score. Patients with a bTMB score equal to or greater than 14 are shown with a solid line (Dx+), and patients with a bTMB score less than 14 are shown with a dashed line (Dx-). A table listing the number of patients without a PFS event in each BEP subgroup at a given time point is also shown. The time points in each column correspond to the time points indicated along the x-axis of the graph above. A bTMB score of 14 or higher had a prevalence of approximately 27% in the population without EGFR or ALK mutations (nITT = 850, patient samples (all) = 803, patient samples (samples contaminated by laboratory errors excluded) = 777, patient samples without EGFR or ALK mutations = 697, nDx+ = 188, HR = 0.68, interaction p-value for PFS = 0.047). Patient samples with MSAF less than 1% were excluded from the analysis. Sequence coverage was 800 or higher. [Figure 6B] 1 is a table with a forest plot showing HRs for PFS in patients in the OAK trial treated with atezolizumab compared to docetaxel (control). HRs are listed across patient subgroups defined by baseline bTMB scores ("cutoff values") of 14 or greater (Dx+) and bTMB scores less than 14 (Dx-). [Figure 7A]This graph shows Kaplan-Meier curves for PFS of combined BEP for patients in the atezolizumab-treated arm (black) and docetaxel-controlled arm (gray) of the POPLAR and OAK trials (nBEP = 775 patients, HR = 0.62), stratifying each arm according to bTMB score. Patients with a baseline bTMB score of 14 or greater are indicated by a solid line (Dx+), while patients with a baseline bTMB score of less than 14 are indicated by a dashed line (Dx-). A table listing the number of patients without a PFS event in each BEP subgroup at a given time point is also shown. The time points in each column correspond to the time points indicated along the x-axis of the graph above. Patient samples with MSAF less than 1% were excluded from the analysis. Sequence coverage was 800 or greater. [Figure 7B] 1 is a table with a forest plot showing HRs for PFS in patients in the POPLAR and OAK trials treated with atezolizumab compared to docetaxel (control). HRs are listed across patient subgroups defined by baseline bTMB scores ("cutoff values") of 14 or greater (Dx+) and bTMB scores less than 14 (Dx-). [Figure 8] 1 is a graph showing confirmed best objective response rates (ORR) for BEP and bTMB subgroups in OAK. ORR was plotted for BEP, bTMB <16, and bTMB ≥16 subgroups in the atezolizumab and docetaxel treatment arms. [Figure 9A] Table showing bTMB quantiles by mutually exclusive PD-L1 IHC subgroups in OAK. [Figure 9B] Venn diagram showing 229 patients with bTMB and IHC data, 30 had both bTMB16 or higher and TC3 or IC3 PD-L1 expression as measured by the Ventana PD-L1 (SP142) assay (bTMB16 or higher (n=156), TC3 or IC3 (n=103)). [Figure 9C]Venn diagrams showing the overlap between bTMB level subgroups and various PD-L1 expression subgroups in the OAK study. These Venn diagrams show the overlap between high bTMB (≥16) and TC1 / 2 / 3 or IC1 / 2 / 3 PD-L1 expression as measured by the Ventana PD-L1 SP142 assay (Figure 9C). [Figure 9D] Venn diagrams showing the overlap between bTMB level subgroups and various PD-L1 expression subgroups in the OAK study. These Venn diagrams demonstrate the overlap between high bTMB (≥16) and TC2 / 3 or IC2 / 3 PD-L1 expression as measured by the Ventana PD-L1 SP142 assay (Figure 9D). [Figure 9E] Figure 1 shows raw bTMB scores plotted for each sample, grouped according to PD-L1 IHC subgroups. The boxes and lines within them represent the 25th, 50th, and 75th quartiles for each PD-L1 subgroup. Individual observations are represented by open circles. Within each mutually exclusive IHC subgroup, the lower and upper hinges represent the first and third quartiles, with the bar between them representing the median. The upper whisker extends from the hinge to a maximum value not exceeding 1.5 x the interquartile range (IQR) from the hinge (where the IQR is the distance between the first and third quartiles). The lower whisker extends from the hinge to a minimum value not exceeding 1.5 x the IQR of the hinge. [Figure 10]

[0023] Figure 1 shows the probability density of patients from the OAK study with non-squamous and squamous tumors. EGFR-mutated and ALK-mutated tumors were excluded from this analysis. The mean bTMB count for tumors with non-squamous histology was 11.2 mutations, and the mean bTMB count for tumors with squamous histology was 12.4 mutations. [Figure 11-1]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-2]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-3]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-4]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-5]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-6]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-7]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-8]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-9]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-10]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-11]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-12]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 11-13]Figures 11A and 11B are graphs showing concordance analysis of bTMB thresholds of 10 or more (equivalent to approximately 9 mut / Mb) (Figure 11A) and 16 or more (equivalent to approximately 14 mut / Mb) (Figure 11B) for the FOUNDATIONONE® (F1) TMB workflow. Concordance was established by splitting samples after DNA extraction and evaluating the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. Using a bTMB cutpoint of 16 or more (interchangeably referred to herein as the "cutoff" or "cut-off"), 41 of 46 samples were true positives and 23 of 23 samples were true negatives (Figure 11B). All four false-negative samples had insertions or deletions that were counted by the F1 TMB and subsequently excluded by the bTMB assay, which reduces bTMB counts. The graphs in Figures 11A and 11B correspond to the same scatter plot with different quadrants overlaid. Figure 11C is a table showing the PPA and NPA of various cutpoints for the comparison of TMB calculated using the F1 calculation pipeline versus the bTMB calculation pipeline from split ctDNA samples run with each assay shown in Figure 11B. Figure 11D is a graph showing a receiver operator curve (ROC) analysis of bTMB sensitivity and 1-specificity values ​​across a range of bTMB cutpoints from 4 to 20. Samples were split after DNA extraction and evaluated using the previously validated F1 workflow as well as the bTMB workflow. Each workflow utilizes a distinct pipeline to calculate subsequent TMB values. The plot shows sensitivity (PPA) versus 1-specificity (1-NPA). 1-specificity is equivalent to the false positive rate, and specificity is equivalent to the true positive rate. TMB assessed by the F1 assay does not comprehensively test all potential neoantigens the immune system may encounter. Rather, by sequencing the coding regions of a non-random cancer-specific set of genes and identifying single nucleotide variants (SNVs), TMB can reflect the mutation rate in the genome and serve as a proxy for neoantigen burden.Figure 11E shows a pairwise comparison of tissue TMB (tTMB) and bTMB in patients POPLAR (N=74) and OAK (N=224) with appropriate data from both platforms. The number of detected mutations is represented on each axis of the graph in the left panel. For tTMB, the mutation count includes SNVs and insertions and deletions (indels) with AF ≥5%. For bTMB, the mutation count includes only SNVs with AF ≥0.5%. Spearman's correlation = 0.59 (CI 95%: 0.49, 0.67). The dashed line represents the cutpoint of ≥16. PPA and NPA are shown in the table in the right panel. Figure 11F shows a pairwise comparison of patient tTMB and bTMB from POPLAR and OAK with appropriate data (SNP concordance, quality control passed) from both platforms (N=259). The number of detected mutations is represented on each axis. For tTMB mutations, the count includes SNVs and insertions and deletions (indels) with AF ≥ 5%. For bTMB mutations, the count includes only SNVs with AF ≥ 0.5%. Spearman's rank correlation = 0.64 (CI 95%: 0.56, 0.71). The dashed line represents the cutpoint of ≥ 16. PPA was 64% (CI 95%: 54, 74), and NPA was 88% (CI 95%: 83, 92). One sample had very high bTMB(152) and tTMB(133), so this sample was excluded from the graph for presentation purposes. Figure 11G shows a pairwise comparison of tTMB (SNVs only) and bTMB. The tTMB calculation algorithm counts both indels and SNVs, while the bTMB calculation algorithm counts only SNVs. Therefore, we compared the correlation between these two measures using only SNVs (N = 258; Spearman correlation = 0.65; CI 95%: 0.57, 0.71). One sample had very high bTMB (152) and tTMB (133), so this sample was excluded from the graph for presentation purposes. Figure 11H shows a comparison of TMB (number of mutations) calculated using the F1 assay versus the bTMB assay from split ctDNA samples (N = 69).The four discordant samples that exceeded the threshold for F1 TMB but not for bTMB are largely explained by indels that were included in the F1 TMB calculation but excluded from the bTMB calculation. Figure 11I is a table showing the PPA and PPV from a comparison of the FOUNDATIONACT® (FACT) assay and the bTMB assay. Samples were split and analyzed according to both assays, and the concordance of somatic variants from the FACT assay that were also detected by the bTMB assay was used to calculate the PPA. Somatic variants present in the FACT-restricted region of the bTMB assay that were also detected by the FACT assay were used to calculate the PPV. Data are plotted according to the overall concordance by analyzing individual variants as well as the percentage of all evaluated samples with perfect concordance between these two assays. The percentage of shared variants within the overlapping bait region between both assays is 93%. Restricting the allele frequency cutoff to at least 1% increases the concordance to 99%. Figure 11J is a series of graphs showing the variant allele frequency (VAF) of concordant variants detected in the overlap region of FACT and bTMB. For undetected variants, the VAF is 0.0. Known artifacts are excluded. The upper panel (N=202) represents the full distribution of concordant variants in bTMB and FACT, while the lower panel shows a zoomed-in view of low allele frequency variants. Figure 11K is a graph showing a comparison of TMB counts between patient tissue and blood samples with high (>30) total mutation counts from tissue. Samples are ordered by increasing tTMB. Figures 11L and 11M are a series of graphs showing blood TMB / tissue TMB concordance versus time between sample collection and MSAF. Pairwise correlations between bTMB-tTMB concordance (as percentage of shared variants) and interval (days on a log scale) (Figure 11L) and MSAF (Figure 11M) between tumor tissue and blood sample collection are shown.In Figure 11L, the dashed line indicates 100 days; Spearman's correlation (bootstrap CI 95%) was -0.25 (-0.37, -0.13) overall, 0.06 (-0.18, 0.3) for tissue samples collected less than 100 days before blood, and -0.3 (-0.42, -0.16) for tissue samples collected more than 100 days before blood. In Figure 11M, Spearman's correlation (bootstrap) was 0.30 (CI 95%: 0.19, 0.41). Figure 11N is a Venn diagram showing SNVs detected by the tTMB and bTMB assays. [Figure 12] Figure 1 shows median exon coverage as a factor of cfDNA input. In an analysis of 1,076 clinical samples, a median exon coverage of 100% was achieved with over 800x when ≥20 ng of cfDNA was used as input to library construction. [Figure 13] Figures 13A and 13B are graphs showing the accuracy analysis of bTMB and MSAF. Accuracy was assessed according to assay result reproducibility at two distinct bTMB thresholds: ≥10 (equivalent to approximately 9 mut / Mb) and ≥16 (equivalent to approximately 14 mut / Mb), as well as a quality control metric of 1% circulating tumor DNA (ctDNA) estimated by MSAF. For bTMB accuracy testing, 40 replicates with at least triplicate samples spanning a range of clinically significant bTMB values ​​were compared with the majority call. For MSAF accuracy testing, 37 replicates with at least triplicate samples spanning a range of clinically significant values ​​were compared with the majority call. Each data point represents a different replicate. [Figure 14] Figure 1 shows the reproducibility of the bTMB assay according to thresholds of 10 and 16 as a function of tumor content in the sample as estimated by MSAF. For both bTMB cutpoints of 10 or more ("bTMB10") and 16 or more ("bTMB16"), reproducibility of at least 80% was achieved with an MSAF of at least 1%. [Figure 15]Figure 1 shows simulated assay performance versus panel size. The simulated sensitivity and specificity of the TMB assay are shown according to the size of the panel used for its calculation. To generate these values, TMB values ​​were calculated from whole-exome sequencing (WES) data for 25 patient samples from The Cancer Genome Atlas. Each patient was selected to represent a range of TMB values, ranging from 100 mut / Mb to 1 mut / Mb. In addition, TMB values ​​were calculated from random sampling of the WES data by restricting the counting region to 5 Mb to 50 Kb. Within each restricted target region, a total of 250 million random samples were generated using a Poisson distribution to calculate equivalent tTMB values. The fraction of these target-restricted TMB values ​​that remained consistent with the TMB values ​​obtained from WES using a cutpoint of 14 mut / Mb (a total of 16 mutations in the bTMB assay) was calculated for each respective simulated panel size. Results were compared to the real-world distribution of TMB values ​​obtained from patients with non-small cell lung cancer using the Foundation Medicine database (n=19,320). Sensitivity was calculated as the fraction of true positives divided by the sum of all true positives and false negatives, and specificity was calculated as the number of true negatives divided by the sum of all true negatives and false positives. The plotted values ​​represent the results obtained from this analysis, and the common area represents the panel size required to maintain at least 80% sensitivity and specificity. [Figure 16]Figure 1 shows the distribution of bTMB and MSAF coefficients of variation (CV) at various coverage levels. To assess the minimum coverage required to maintain precision, in silico downsampling of 80 replicates from eight different samples with a bTMB score of 10 or greater and an MSAF of 1% or greater was performed to achieve median sequence coverage ranging from 800x to 2000x. The CV% of bTMB and MSAF values ​​was calculated from downsampled specimens at various sequence coverage levels. Minimum coverage was defined as the lower limit from the downsampling exercise that still achieved precision, defined by a CV of 30% or less for bTMB and MSAF values. The minimum sequence coverage required to maintain precision for bTMB and MSAF values ​​was confirmed to be 800x. [Figure 17] Graph showing a pairwise comparison of the mass of cfDNA extracted from plasma samples versus the bTMB score in the OAK test. The bTMB score is plotted against the total cfDNA extracted from plasma. There was a small, but statistically significant, positive Spearman correlation between the total extracted cfDNA and the bTMB score (Spearman r=0.15, [CI95%: 0.07, 0.23]). [Figure 18A] 18A-18C are a series of graphs showing the correlation between bTMB and MSAF (FIG. 18A). [Figure 18B] 18B is a series of graphs showing the correlation between bTMB and sum of longest diameters (SLD) (FIG. 18B). [Figure 19] FIG. 1 is a schematic showing the patient population of the interim analysis from the BF1RST study. [Figure 20] 1 is a table showing baseline demographics and clinical characteristics from the interim analysis population of the B-F1RST study. [Figure 21] 1 is a graph showing ORR by bTMB subgroup in the interim analysis population of the BF1RST study. [Figure 22]Figure 1 shows the reduction in sum of longest diameters (SLD) from baseline by bTMB subgroup in the interim analysis population. a 15 patients had MSAF less than 1%. b 4 patients did not have valid samples. Only patients with post-baseline target lesion measurements are shown in this graph (n=70). [Figure 23A] 23A is a series of graphs showing the change in tumor burden over time by bTMB subgroup for the bTMB subgroup less than 16 (FIG. 23A) in the interim analysis population in the B-F1RST study. [Figure 23B] 23B is a series of graphs showing the change in tumor burden over time by bTMB subgroup for the 16 or more bTMB subgroups in the interim analysis population in the B-F1RST study (FIG. 23B). [Figure 24] 1 is a graph showing Kaplan-Meier curves of PFS in the bTMB subgroup of less than 16 and the bTMB subgroup of 16 or more in the interim analysis population in the B-F1RST study. [Figure 25] Figure 1 shows a forest plot of PFS according to the bTMB cutoff score in the interim analysis population of the B-F1RST study. a Unstratified hazard ratio (CI90%). [Figure 26A] 1 is a graph showing Kaplan-Meier curves of OS for the bTMB <16 subgroup and the bTMB ≥16 subgroup in the interim analysis population of the B-F1RST study. [Figure 26B] b Forest plot of OS according to bTMB cutoff score in the interim analysis population in the B-F1RST study. b Unstratified hazard ratio (CI90%). [Figure 27] 1 is a graph showing AEs observed in 10% or more of the safety-evaluable interim analysis population in Study B-F1RST. DETAILED DESCRIPTION OF THE INVENTION

[0053] I. Introduction The present invention provides therapeutic, diagnostic, and prognostic methods and compositions for cancer. The invention involves, at least in part, determining the total number of somatic mutations in a sample obtained from an individual to derive a blood tumor mutation burden (bTMB) score that can be used as a biomarker (e.g., a predictive biomarker) in treating the individual with cancer, diagnosing the individual with cancer, and determining whether the individual with cancer is receiving an immune checkpoint inhibitor, such as a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab (MPDL3280A)), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist. and determining which individuals with cancer are likely to respond to treatment with an anti-cancer therapy comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab), an antagonist directed against a coinhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof; optimizing the therapeutic efficacy of an anti-cancer therapy comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab), an antagonist directed against a coinhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof; and selecting a therapy for an individual with cancer. The present invention also provides methods for providing a prognosis for an individual with cancer, as well as methods for monitoring an individual's response to treatment comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab (MPDL3280A)), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof.

[0054] II. Definition It is to be understood that aspects and embodiments of the invention described herein include aspects and embodiments "comprising," "consisting of," and "consisting essentially of." As used herein, the singular forms "a," "an," and "the" include plural referents unless otherwise indicated.

[0055] The term "about" as used herein refers to a normal error range for each value, readily known to those skilled in the art. Reference herein to "about" a value or parameter includes (and describes) embodiments directed to the value or parameter itself. For example, a description referring to "about X" includes a description of "X."

[0056] As used herein, the terms "blood tumor mutation burden score," "blood tumor mutation burden score," and "bTMB score," which may be used interchangeably, refer to a numerical value reflecting the number of somatic mutations detected in a blood sample (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) obtained from an individual (e.g., an individual at risk for or with cancer). The bTMB score can be measured, for example, based on the whole genome or exome, or based on a subset of the genome or exome (e.g., a predetermined gene set). In certain embodiments, the bTMB score can be measured based on intergenic sequences. In some embodiments, a bTMB score measured based on a subset of the genome or exome can be extrapolated to determine the bTMB score for the whole genome or exome. In certain embodiments, the predetermined gene set does not include the whole genome or exome. In other embodiments, the subgenomic interval set does not include the whole genome or exome. In some embodiments, the predetermined gene set includes multiple genes that, in mutant form, are associated with affecting cell division, growth, or survival, or are associated with cancer. In some embodiments, the predetermined gene set comprises at least about 50 or more, about 100 or more, about 150 or more, about 200 or more, about 250 or more, about 300 or more, about 350 or more, about 400 or more, about 450 or more, or about 500 or more genes. In some embodiments, the predetermined gene set covers about 1 Mb (e.g., about 1.1 Mb, e.g., about 1.125 Mb).

[0057] In some embodiments, the bTMB score is determined by measuring the number of somatic mutations in cell-free DNA (cfDNA) in the sample. In some embodiments, the bTMB score is determined by measuring the number of somatic mutations in circulating tumor DNA (ctDNA) in the sample. In some embodiments, the number of somatic mutations is the number of counted single nucleotide variants (SNVs), or the sum of the number of counted SNVs and the number of indel mutations. In some embodiments, the bTMB score refers to the number of accumulated somatic mutations in a tumor. Thus, the bTMB score can be used as a surrogate for the number of neoantigens on oncogenic (e.g., tumor) cells. The bTMB score can also be used as a surrogate for the mutation rate within a tumor, which is a surrogate for the number of neoantigens on oncogenic (e.g., tumor) cells. In some embodiments, a bTMB score that is at or exceeds the reference bTMB score identifies an individual as one who may benefit from treatment including an immune checkpoint inhibitor, such as a PD-L1 axis-binding antagonist (e.g., atezolizumab). In some embodiments, a bTMB score below the baseline bTMB score identifies an individual as one who may benefit from treatment that includes an anti-cancer therapy other than, or in addition to, a PD-L1 axis-binding antagonist. In some embodiments, the bTMB score can be used to monitor the response of individuals with cancer to treatment that includes an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., atezolizumab).

[0058] As used herein, the term "reference bTMB score" refers to a bTMB score to which another bTMB score is compared, e.g., for making a diagnosis, prediction, prognosis, and / or treatment decision. For example, the reference bTMB score can be a bTMB score in a reference sample, a reference population, and / or a predetermined value. In some examples, the reference bTMB score is a cutoff value that significantly separates a first subset of individuals in a reference population who are being treated with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, from a second subset of individuals in the same reference population who are being treated with an immune checkpoint inhibitor, e.g., a non-PD-L1 axis-binding antagonist therapy that does not include a PD-L1 axis-binding antagonist, based on a significant difference between the individual's responsiveness to treatment with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, and the individual's responsiveness to treatment with a non-PD-L1 axis-binding antagonist therapy that is at, above, and / or below the cutoff value. In some instances, the individual's responsiveness to treatment with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, is significantly improved compared to the individual's responsiveness to treatment with a non-PD-L1 axis-binding antagonist therapy at or above the cutoff value. In some instances, the individual's responsiveness to treatment with a non-PD-L1 axis-binding antagonist therapy is significantly improved compared to the individual's responsiveness to treatment with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, below the cutoff value.

[0059] Those skilled in the art will appreciate that the numerical value of the reference bTMB score may be correlated with the type of cancer (e.g., lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer (e.g., renal urothelial carcinoma), bladder cancer (e.g., bladder urothelial (transitional cell) carcinoma), breast cancer, colorectal cancer (e.g., colon adenocarcinoma), ovarian cancer, pancreatic cancer, gastric cancer, esophageal cancer, mesothelioma, melanoma (e.g., cutaneous melanoma), head and neck cancer (e.g., head and neck squamous cell carcinoma (HNSCC)), thyroid cancer, sarcoma (e.g., soft tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, osteosarcoma, chondrosarcoma, angiosarcoma, endothelial sarcoma, lymphangiosarcoma, lymphoma, thyroid cancer ... intraluminal sarcoma, leiomyosarcoma, or rhabdomyosarcoma), prostate cancer, glioblastoma, cervical cancer, thymic carcinoma, leukemia (e.g., acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic myelogenous leukemia (CML), chronic eosinophilic leukemia, or chronic lymphocytic leukemia (CLL)), lymphoma (e.g., Hodgkin's lymphoma or non-Hodgkin's lymphoma (NHL)), myeloma (e.g., multiple myeloma (MM)), mycosis fungoides, Merkel cell carcinoma, hematologic malignancies, blood tissue cancers, B-cell cancers, bronchial cancer, gastric cancer, brain or central nervous system cancers, peripheral nervous system cancers, foetal Uterine or endometrial cancer, oral or pharyngeal cancer, liver cancer, testicular cancer, biliary tract cancer, small intestine or appendix cancer, salivary gland cancer, adrenal gland cancer, adenocarcinoma, inflammatory myofibroblastic tumor, gastrointestinal stromal tumor (GIST), colon cancer, myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), polycythemia vera, chordoma, synovial tumor, Ewing's tumor, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatic carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder cancer, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharynx It will be understood that the bTMB score may vary depending on the tumor type (e.g., cranioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid carcinoma, small cell carcinoma, essential thrombocythemia, idiopathic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familial hypereosinophilia, neuroendocrine carcinoma, or carcinoid tumor), the methodology used to measure the bTMB score, and / or the statistical method used to generate the bTMB score.

[0060] The term "equivalent bTMB value" refers to a numerical value equivalent to a bTMB score expressed as the number of somatic mutations counted over a defined number of sequenced bases (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel). It should be understood that the bTMB score is generally linearly related to the size of the sequenced genomic region. Such an equivalent bTMB value indicates a comparable tumor mutation burden compared to the bTMB score and can be used interchangeably in the methods described herein to, for example, predict a cancer patient's response to an immune checkpoint inhibitor (e.g., an anti-PD-L1 antibody, e.g., atezolizumab). As an example, in some embodiments, the equivalent bTMB value is a normalized bTMB value, which can be calculated by dividing the somatic variant (e.g., somatic mutation) count by the number of sequenced bases. For example, the equivalent bTMB value can be expressed, for example, as number of mutations / megabase. For example, a bTMB score of about 25 (determined as the number of somatic mutations counted over about 1.1 Mb) corresponds to an equivalent bTMB value of about 23 mutations / Mb. It should be understood that the bTMB scores described herein (e.g., expressed as the number of somatic mutations counted over a defined number of sequenced bases (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel) encompass equivalent bTMB values ​​obtained using different methodologies (e.g., whole-exome sequencing or whole-genome sequencing). As an example, for a whole-exome panel, the target region may be approximately 50 Mb, and a sample with about 500 detected somatic mutations would have an equivalent bTMB value of about 10 mutations / Mb.In some embodiments, the bTMB score, determined as the number of somatic mutations counted over a defined number of sequenced bases in a subset of the genome or exome (e.g., a predetermined gene set) (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel), deviates from the bTMB score determined by whole-exome sequencing by less than about 30% (e.g., less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, less than about 5%, less than about 4%, less than about 3%, less than about 2%, less than about 1%, or less than that). See, e.g., Chalmers et al. Genome Medicine 9:34, 2017.

[0061] As used herein, the terms "maximum somatic allele frequency" and "MSAF," which may be used interchangeably, refer to the maximum frequency, expressed as a fraction or percentage, of alleles (i.e., genetic variants with somatic mutations (e.g., base substitutions in coding regions and / or indel mutations in coding regions)) detected in samples (e.g., whole blood samples, plasma samples, serum samples, or combinations thereof) from an individual that are less than about 40% (e.g., less than 40%, less than 30%, less than 20%, less than 10%, less than 5%, or less than 1%). The allele frequency of a somatic mutation can be calculated by dividing the number of sequence reads that exhibit the somatic mutation to the total reads aligned to a particular region of the human genome. In some examples, the MSAF is derived from a maximum somatic allele frequency in a sample that is less than about 20%. In some embodiments, this value is the fraction of all cfDNA in a sample from a subject that carries that allele. In some embodiments, this value is the fraction of ctDNA in a sample from a subject that carries that allele. In some embodiments, this value is used to estimate the total tumor content in the sample. In some embodiments, the method includes determining the allele frequency of each somatic modification detected in the sample. For example, a sample with multiple somatic modifications may present those modifications as a distribution of somatic allele frequencies, perhaps according to their original clonal frequency in the cancer (e.g., tumor). In some embodiments, this value is expressed as a function of a predetermined gene set, e.g., coding regions of a predetermined gene set. In other embodiments, this value is expressed as a function of sequenced subgenomic intervals, e.g., sequenced coding subgenomic intervals. In some embodiments, MSAF can be used to provide a prognosis for an individual with cancer.

[0062] As used herein, the terms "tissue tumor mutation burden score" and "tTMB score," which may be used interchangeably, refer to the level (e.g., number) of alterations (e.g., one or more alterations, e.g., one or more somatic alterations) per preselected unit (e.g., megabase) in a predetermined gene set (e.g., coding regions of a predetermined gene set) detected in a tumor tissue sample (e.g., a formalin-fixed and paraffin-embedded (FFPE) tumor sample, an archival tumor sample, a fresh tumor sample, or a frozen tumor sample). The tTMB score can be measured, for example, based on the whole genome or exome, or based on a subset of the genome or exome. In certain embodiments, a tTMB score measured based on a subset of the genome or exome can be extrapolated to determine the whole genome or exome mutation load. In some embodiments, the tTMB score refers to the level of accumulated somatic mutations in an individual (e.g., an animal (e.g., a human)). The tTMB score may refer to the accumulated somatic mutations in a patient with cancer (e.g., lung cancer, e.g., NSCLC). In some embodiments, the tTMB score refers to the accumulated mutations in an individual's entire genome. In some embodiments, the tTMB score refers to the accumulated mutations in a particular tissue sample collected from an individual (e.g., a tumor tissue sample biopsy, e.g., a lung cancer tumor sample, e.g., an NSCLC tumor sample).

[0063] As used herein, the term "reference tTMB score" refers to a tTMB score to which another tTMB score is compared, e.g., to make a diagnostic, predictive, prognostic, and / or therapeutic decision. For example, a reference tTMB score can be a tTMB score in a reference sample, a reference population, and / or a predetermined value. In some examples, the reference tTMB score is a cutoff value that significantly separates a first subset of individuals (e.g., patients) in a reference population who are being treated with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, from a second subset of individuals (e.g., patients) in the same reference population who are being treated with an immune checkpoint inhibitor, e.g., a non-PD-L1 axis-binding antagonist therapy that does not include a PD-L1 axis-binding antagonist, based on a significant difference between the individual's responsiveness to treatment with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, and the individual's responsiveness to treatment with a non-PD-L1 axis-binding antagonist therapy that is at or above the cutoff value and / or below the cutoff value. In some examples, the individual's responsiveness to treatment with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, is significantly improved compared to the individual's responsiveness to treatment with a non-PD-L1 axis-binding antagonist therapy that is at or above the cutoff value. In some instances, the individual's responsiveness to treatment with a non-PD-L1 axis-binding antagonist therapy is significantly improved compared to the individual's responsiveness to treatment with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, below the cutoff value.

[0064] Those skilled in the art will appreciate that the numerical value of the reference tTMB score may be used to assess the risk of developing a cancer type (e.g., lung cancer (e.g., non-small cell lung cancer (NSCLC) or small cell lung cancer), kidney cancer (e.g., renal urothelial carcinoma or renal cell carcinoma (RCC)), bladder cancer (e.g., bladder urothelial (transitional cell) carcinoma (e.g., locally advanced or metastatic urothelial carcinoma, including primary (1L) or secondary or higher-level (2L+) locally advanced or metastatic urothelial carcinoma)), breast cancer (e.g., human epidermal growth factor receptor-2 (HER2)+ breast cancer or hormone receptor positive (HR+) breast cancer), colorectal cancer (e.g., colon adenocarcinoma), ovarian cancer, Pancreatic cancer, gastric cancer, esophageal cancer, mesothelioma, melanoma (e.g., cutaneous melanoma), skin cancer (e.g., cutaneous squamous cell carcinoma), head and neck cancer (e.g., head and neck squamous cell carcinoma (HNSCC)), thyroid cancer, sarcoma (e.g., soft tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, osteosarcoma, chondrosarcoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, leiomyosarcoma, or rhabdomyosarcoma), prostate cancer, glioblastoma, cervical cancer, thymic carcinoma, leukemia (e.g., acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), chronic eosinophilic leukemia, or or chronic lymphocytic leukemia (CLL)), lymphoma (e.g., Hodgkin's lymphoma or non-Hodgkin's lymphoma (NHL)), myeloma (e.g., multiple myeloma (MM)), mycosis fungoides, Merkel cell carcinoma, hematologic malignancies, blood tissue cancer, B-cell cancer, bronchial cancer, gastric cancer, brain or central nervous system cancer, peripheral nervous system cancer, uterine or endometrial cancer, oral or pharyngeal cancer, liver cancer, testicular cancer, biliary tract cancer, small intestine or appendix cancer, salivary gland cancer, adrenal gland cancer, adenocarcinoma, inflammatory myofibroblastic tumor, gastrointestinal stromal tumor (GIST), colon cancer, myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), polycythemia vera , chordoma, synovial tumor, Ewing's tumor, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatocarcinoma, cholangiocarcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid carcinoma, small cell carcinoma, essential thrombocythemia, idiopathic myeloid metaplasia, hypereosinophilic syndrome,It will be understood that the tTMB score may vary depending on the type of tumor (e.g., systemic mastocytosis, familial hypereosinophilia, neuroendocrine carcinoma, or carcinoid tumor), the methodology used to measure the tTMB score, and / or the statistical method used to generate the tTMB score.

[0065] The term "equivalent tTMB value" refers to a numerical value equivalent to a tTMB score, which can be calculated by dividing the somatic variant (e.g., somatic mutation) count by the number of sequenced bases (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel). It should be understood that the tTMB score is generally linearly related to the size of the sequenced genomic region. Such an equivalent tTMB value indicates a comparable tumor mutation burden compared to the tTMB score and can be used interchangeably in the methods described herein to, for example, predict a cancer patient's response to an immune checkpoint inhibitor (e.g., an anti-PD-L1 antibody, e.g., atezolizumab). As an example, in some embodiments, the equivalent tTMB value is a normalized tTMB value, which can be calculated by dividing the somatic variant (e.g., somatic mutation) count by the number of sequenced bases. For example, an equivalent tTMB value can be expressed as the number of somatic mutations counted over a defined number of sequenced bases (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel). For example, a tTMB score of about 25 (determined as the number of somatic mutations counted over about 1.1 Mb) corresponds to an equivalent tTMB value of about 23 mutations / Mb. It should be understood that the tTMB scores described herein (e.g., TMB scores expressed as the number of somatic mutations counted over a defined number of sequenced bases (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel)) encompass equivalent tTMB values ​​obtained using different methodologies (e.g., whole-exome sequencing or whole-genome sequencing). As an example, for a whole exome panel, the target region may be approximately 50 Mb, and a sample with approximately 500 somatic mutations detected would have an equivalent tTMB value with a tTMB score of approximately 10 mutations / Mb.In some embodiments, the tTMB score, determined as the number of somatic mutations counted over a defined number of sequenced bases in a subset of the genome or exome (e.g., a predetermined gene set) (e.g., about 1.1 Mb (e.g., about 1.125 Mb) as assessed by a FOUNDATIONONE® panel), deviates from the tTMB score determined by whole-exome sequencing by less than about 30% (e.g., less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, less than about 5%, less than about 4%, less than about 3%, less than about 2%, less than about 1%, or less than that). See, e.g., Chalmers et al. Genome Medicine 9:34, 2017.

[0066] The term "somatic mutation" or "somatic modification" refers to a genetic modification that occurs in somatic tissue (e.g., cells outside the germline). Examples of genetic modifications include, but are not limited to, point mutations (e.g., exchange of a single nucleotide for another nucleotide (e.g., silent mutations, missense mutations, and nonsense mutations)), insertions and deletions (e.g., addition and / or removal of one or more nucleotides (e.g., indels)), amplifications, gene duplications, copy number alterations (CNAs), translocations, and splice variants. In some embodiments, an indel can be a frameshift or in-frame mutation of one or more nucleotides (e.g., about 1-40 nucleotides).The presence of certain mutations may be associated with a disease state (e.g., cancer, e.g., lung cancer (e.g., non-small cell lung cancer (NSCLC)), renal cancer (e.g., renal urothelial carcinoma), bladder cancer (e.g., bladder urothelial (transitional cell) carcinoma), breast cancer, colorectal cancer (e.g., colon adenocarcinoma), ovarian cancer, pancreatic cancer, gastric cancer, esophageal cancer, mesothelioma, melanoma (e.g., cutaneous melanoma), head and neck cancer (e.g., head and neck squamous cell carcinoma (HNSCC)), thyroid cancer, sarcoma (e.g., soft tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, osteosarcoma, chondrosarcoma, angiosarcoma, endothelial cancer, sarcoma, lymphangiosarcoma, lymphangioendothelial sarcoma, leiomyosarcoma, or rhabdomyosarcoma), prostate cancer, glioblastoma, cervical cancer, thymic carcinoma, leukemia (e.g., acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic myelogenous leukemia (CML), chronic eosinophilic leukemia, or chronic lymphocytic leukemia (CLL)), lymphoma (e.g., Hodgkin's lymphoma or non-Hodgkin's lymphoma (NHL)), myeloma (e.g., multiple myeloma (MM)), mycosis fungoides, Merkel cell carcinoma, hematologic malignancies, blood tissue cancers, B-cell carcinoma, bronchial carcinoma, gastric cancer, brain or central nervous system cancer, peripheral nervous system cancer, uterine or endometrial cancer, oral or pharyngeal cancer, liver cancer, testicular cancer, biliary tract cancer, small intestine or appendix cancer, salivary gland cancer, adrenal gland cancer, adenocarcinoma, inflammatory myofibroblastic tumor, gastrointestinal stromal tumor (GIST), colon cancer, myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), polycythemia vera, chordoma, synovial tumor, Ewing's tumor, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatic carcinoma, bile duct carcinoma , choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder cancer, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid carcinoma, small cell carcinoma, essential thrombocythemia, idiopathic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familial hypereosinophilia, neuroendocrine carcinoma, or carcinoid tumor).

[0067] In certain embodiments, the somatic modification is a silent mutation (e.g., a synonymous modification). In other embodiments, the somatic modification is a non-synonymous single nucleotide variant (SNV). In other embodiments, the somatic modification is a passenger mutation (e.g., a modification that has no detectable effect on the fitness of the clone). In certain embodiments, the somatic modification is a variant of unknown significance (VUS), e.g., a modification whose pathogenicity cannot be confirmed or excluded. In certain embodiments, the somatic modification has not been identified as being associated with a cancer phenotype.

[0068] In certain embodiments, the somatic modification is not associated with or is not known to be associated with affecting cell division, growth, or survival, while in other embodiments, the somatic modification is associated with affecting cell division, growth, or survival.

[0069] In certain embodiments, the number of somatic modifications excludes one or more functional modifications within the subgenomic interval.

[0070] As used herein, the terms "sub-genomic interval" and "subgenomic interval," which may be used interchangeably, refer to a portion of a genomic sequence. In some embodiments, a subgenomic interval can be a single nucleotide position, e.g., a nucleotide position variant that shows an association (positive or negative) with a tumor phenotype. In some embodiments, a subgenomic interval includes more than one nucleotide position. Such embodiments include sequences at least 2, 5, 10, 50, 100, 150, or 250 nucleotide positions in length. A subgenomic interval can include an entire gene or a preselected portion thereof, e.g., a coding region (or portion thereof), a preselected intron (or portion thereof), or an exon (or portion thereof). A subgenomic interval can include all or a portion of a naturally occurring fragment of nucleic acid, e.g., genomic DNA. For example, a subgenomic interval can correspond to a fragment of genomic DNA that is subjected to a sequencing reaction. In certain embodiments, a subgenomic interval is a contiguous sequence from a genomic source. In other embodiments, a subgenomic interval includes sequences that are not contiguous in the genome; for example, it may include a junction formed at an exon-exon junction in a cDNA.

[0071] In one embodiment, the subgenomic interval may include a single nucleotide position; an intragenic or intergenic region; an exon or intron, or a fragment thereof, typically an exonic sequence or a fragment thereof; a coding or non-coding region, such as a promoter, enhancer, 5' untranslated region (5'UTR), or 3' untranslated region (3'UTR), or a fragment thereof; a cDNA or a fragment thereof; an SNV; an SNP; a somatic mutation, a germline mutation, or both; a modification, such as a point mutation or a single mutation; a deletion mutation (e.g., an in-frame deletion, a gene deletion, a intragenic deletion, whole gene deletion); insertion mutation (e.g., intragenic insertion); inversion mutation (e.g., intrachromosomal inversion); linked mutation; linked insertion mutation; inverted duplication mutation; tandem duplication (e.g., intrachromosomal tandem duplication); translocation (e.g., chromosomal translocation, non-inverted locus); rearrangement (e.g., genomic rearrangement (e.g., rearrangement of one or more introns or fragments thereof; rearranged introns may include the 5'-UTR and / or 3'-UTR)); alteration of gene copy number; alteration of gene expression; alteration of RNA levels; or combinations thereof.

[0072] "Gene copy number" refers to the number of DNA sequences in a cell that encode a particular gene product. Generally, for a given gene, a mammal has two copies of each gene. Copy number can be increased, for example, by gene amplification or duplication, or decreased by deletion.

[0073] In some embodiments, the functional modification is a modification that affects cell division, growth, or survival (e.g., promotes cell division, growth, or survival) compared to a reference sequence (e.g., a wild-type sequence or an unmutated sequence). In certain embodiments, the functional modification is identified as being included in a database of functional modifications, such as the COSMIC database (see Forbes et al. Nucl. Acids Res. 43(D1):D805-D811, 2015, incorporated herein by reference in its entirety). In other embodiments, the functional modification is a modification with a known functional state (e.g., occurring as a known somatic modification in the COSMIC database). In certain embodiments, the functional modification is a modification with a possible functional state (e.g., a truncation in a tumor suppressor gene). In certain embodiments, the functional modification is a driver mutation (e.g., a modification that confers a selective advantage to a clone in its microenvironment, e.g., by increasing cell survival or reproduction). In other embodiments, the functional modification is a modification that can cause clonal expansion. In certain embodiments, the functional alteration is one that can cause one, two, three, four, five, or all six of: (a) self-sufficiency in growth signals; (b) reduced, e.g., insensitivity to, anti-growth signals; (c) reduced apoptosis; (d) increased replicative capacity; (e) sustained angiogenesis; or (f) tissue invasion or metastasis.

[0074] In certain embodiments, the functional modification is not a passenger mutation (e.g., a modification that has no detectable effect on the fitness of a clone of cells). In certain embodiments, the functional modification is not a variant of unknown significance (VUS) (e.g., a modification whose pathogenicity cannot be confirmed or excluded).

[0075] In certain embodiments, functional modifications in a plurality (e.g., about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more) of preselected oncogenes within a predetermined gene set are excluded. In certain embodiments, all functional modifications in preselected genes (e.g., oncogenes) within a predetermined gene set are excluded. In certain embodiments, multiple functional modifications in multiple preselected genes (e.g., oncogenes) within a predetermined gene set are excluded. In certain embodiments, all functional modifications in all genes (e.g., oncogenes) within a predetermined gene set are excluded.

[0076] In certain embodiments, the number of somatic modifications excludes germline mutations within the subgenomic interval.

[0077] In certain embodiments, the germline modification is a SNP, base substitution, insertion, deletion, indel, or silent mutation (e.g., a synonymous mutation).

[0078] In certain embodiments, germline modifications are excluded by using a method that does not use comparison with a matched normal sequence. In other embodiments, germline modifications are excluded by a method that includes the use of an algorithm, such as the somatic-germline-zygosity (SGZ) algorithm (see Sun et al. Cancer Research 2014;74(19S):1893-1893). In certain embodiments, germline modifications are identified as being included in a database of germline modifications, such as the dbSNP database (see Sherry et al. Nucleic Acids Res. 29(1):308-311, 2001, which is incorporated herein by reference in its entirety). In other embodiments, germline modifications are identified as being included in two or more counts in the ExAC database (see Exome Aggregation Consortium et al. bioRxiv preprint, October 30, 2015, which is incorporated herein by reference in its entirety). In some embodiments, the germline modifications are identified as being included in the 1000 Genome Project database (McVean et al. Nature 491, 56-65, 2012, incorporated herein by reference in its entirety). In some embodiments, the germline modifications are identified as being included in the ESP database (Exome Variant Server, NHLBI GO Exome Sequencing Project (ESP), Seattle, WA).

[0079] The term "PD-L1 axis binding antagonist" refers to a molecule that inhibits the interaction of a PD-L1 axis binding partner with one or more of its binding partners, so as to abrogate T cell dysfunction resulting from signaling along the PD-1 signaling axis, thereby restoring or enhancing T cell function. As used herein, PD-L1 axis binding antagonists include PD-L1 binding antagonists and PD-1 binding antagonists, as well as molecules that interfere with the interaction between PD-L1 and PD-1 (e.g., PD-L2-Fc fusions).

[0080] The term "dysfunction" in the context of immune dysfunction refers to a state of decreased immune responsiveness to antigenic stimulation. This term encompasses the common elements of both "exhaustion" and / or "anergy," in which antigen recognition may occur but the subsequent immune response is ineffective in controlling infection or tumor growth.

[0081] As used herein, the term "dysfunctional" also includes refractoriness or unresponsiveness to antigen recognition, specifically, an impaired ability to translate antigen recognition into downstream T cell effector functions such as proliferation, cytokine production (e.g., IL-2), and / or target cell killing.

[0082] The term "anergy" refers to the incomplete or insufficient signaling delivered through the T cell receptor (e.g., intracellular Ca in the absence of Ras activation). 2+ T cell anergy refers to a state of unresponsiveness to antigenic stimulation resulting from an increase in T cell proliferation (increase in T cell proliferation). T cell anergy can also occur upon stimulation with antigen in the absence of costimulation, rendering the cells refractory to subsequent activation by antigen, even in the context of costimulation. The unresponsive state can often be abrogated by the presence of interleukin-2. Anergic T cells do not undergo clonal expansion and / or acquire effector function.

[0083] The term "exhaustion" refers to T cell exhaustion, a state of T cell dysfunction resulting from persistent TCR signaling, which occurs during many chronic infections and cancer development. It is distinct from anergy in that it results from persistent signaling rather than defective or insufficient signaling. It is defined by effector dysfunction, persistent expression of inhibitory receptors, and a transcriptional state distinct from that of functional effector or memory T cells. Exhaustion prevents optimal control of infection and tumors. Exhaustion can result from both extrinsic negative regulatory pathways (e.g., immunomodulatory cytokines) and cell-intrinsic negative regulatory (costimulatory) pathways (PD-1, B7-H3, B7-H4, etc.).

[0084] "Immunogenicity" refers to the ability of a particular substance to induce an immune response. Tumors are immunogenic, and enhancing tumor immunogenicity promotes the clearance of tumor cells by the immune response. An example of enhancing tumor immunogenicity is treatment with a PD-L1 axis-binding antagonist.

[0085] As used herein, the term "immune checkpoint inhibitor" refers to a therapeutic agent that targets at least one immune checkpoint protein to alter the regulation of an immune response, e.g., downregulate or inhibit an immune response. Immune checkpoint proteins are known in the art and include, but are not limited to, cytotoxic T-lymphocyte antigen 4 (CTLA-4), programmed cell death 1 (PD-1), programmed cell death-ligand 1 (PD-L1), programmed cell death-ligand 2 (PD-L2), V-domain Ig suppressor of T-cell activation (VISTA), B7-H2, B7-H3, B7-H4, B7-H6, 2B4, ICOS, HVEM, CD160, gp49B, PIR-B, KIR family receptors, TIM-1, TIM-3, TIM-4, LAG-3, BTLA, SIRP alpha (CD47), CD48, 2B4 (CD244), B7.1, B7.2, ILT-2, ILT-4, TIGIT, LAG-3, BTLA, IDO, OX40, and A2aR. In some examples, immune checkpoint proteins are expressed on the surface of activated T cells. Therapeutic agents that can function as immune checkpoint inhibitors useful in the methods of the present invention include, but are not limited to, therapeutic agents that target one or more of CTLA-4, PD-1, PD-L1, PD-L2, VISTA, B7-H2, B7-H3, B7-H4, B7-H6, 2B4, ICOS, HVEM, CD160, gp49B, PIR-B, KIR family receptors, TIM-1, TIM-3, TIM-4, LAG-3, BTLA, SIRP alpha (CD47), CD48, 2B4 (CD244), B7.1, B7.2, ILT-2, ILT-4, TIGIT, LAG-3, BTLA, IDO, OX40, and A2aR. In some examples, immune checkpoint inhibitors enhance or suppress the function of one or more targeted immune checkpoint proteins. In some examples, the immune checkpoint inhibitor is a PD-L1 axis binding antagonist described herein.

[0086] As used herein, a "PD-L1 binding antagonist" is a molecule that reduces, blocks, inhibits, abrogates, or prevents signaling resulting from the interaction of PD-L1 with one or more of its binding partners, e.g., PD-1 and / or B7-1. In some embodiments, a PD-L1 binding antagonist is a molecule that inhibits the binding of PD-L1 to its binding partners. In particular aspects, PD-L1 binding antagonists inhibit the binding of PD-L1 to PD-1 and / or B7-1. In some embodiments, PD-L1 binding antagonists include anti-PD-L1 antibodies and antigen-binding fragments thereof, immunoadhesins, fusion proteins, oligopeptides, small molecule antagonists, polynucleotide antagonists, and other molecules that reduce, block, inhibit, abrogate, or prevent signaling resulting from the interaction of PD-L1 with one or more of its binding partners, e.g., PD-1 and / or B7-1. In one embodiment, the PD-L1 binding antagonist reduces negative signals mediated by or through PD-L1 or cell surface proteins expressed on T lymphocytes and other cells via PD-1, thereby reducing dysfunction of dysfunctional T cells (e.g., enhancing effector responses to antigen recognition). In some embodiments, the PD-L1 binding antagonist is an anti-PD-L1 antibody. In a particular aspect, the anti-PD-L1 antibody is YW243.55.S70, as described herein. In another particular aspect, the anti-PD-L1 antibody is MDX-1105, as described herein. In yet another particular aspect, the anti-PD-L1 antibody is atezolizumab (CAS Registry Number: 1422185-06-5), also known as MPDL3280A, as described herein. In yet another particular aspect, the anti-PD-L1 antibody is MEDI4736 (dorvalumab), as described herein. In yet another specific embodiment, the anti-PD-L1 antibody is MSB0010718C (avelumab) as described herein.

[0087] As used herein, a "PD-1 binding antagonist" is a molecule that reduces, blocks, inhibits, abrogates, or prevents signaling resulting from the interaction of PD-1 with one or more of its binding partners, e.g., PD-L1 and / or PD-L2. In some embodiments, a PD-1 binding antagonist is a molecule that inhibits the binding of PD-1 to its binding partners. In particular aspects, a PD-1 binding antagonist inhibits the binding of PD-1 to PD-L1 and / or PD-L2. For example, PD-1 binding antagonists include anti-PD-1 antibodies and antigen-binding fragments thereof, immunoadhesins, fusion proteins, oligopeptides, small molecule antagonists, polynucleotide antagonists, and other molecules that reduce, block, inhibit, abrogate, or prevent signaling resulting from the interaction of PD-1 with PD-L1 and / or PD-L2. In one embodiment, the PD-1 binding antagonist reduces negative signals mediated by or through cell surface proteins expressed on T lymphocytes and other cells via PD-1 or PD-L1, thereby reducing dysfunction of dysfunctional T cells. In some embodiments, the PD-1 binding antagonist is an anti-PD-1 antibody. In a specific aspect, the PD-1 binding antagonist is MDX-1106 (nivolumab), as described herein. In another specific aspect, the PD-1 binding antagonist is MK-3475 (pembrolizumab), as described herein. In another specific aspect, the PD-1 binding antagonist is CT-011 (pidilizumab), as described herein. In another specific aspect, the PD-1 binding antagonist is MEDI-0680 (AMP-514). In another specific aspect, the PD-1 binding antagonist is PDR001. In another specific embodiment, the PD-1 binding antagonist is REGN2810. In another specific embodiment, the PD-1 binding antagonist is BGB-108. In another specific embodiment, the PD-1 binding antagonist is AMP-224, as described herein.

[0088] The terms "programmed death-ligand 1" and "PD-L1," as used herein, refer to native sequence PD-L1 polypeptides, polypeptide variants (i.e., PD-L1 polypeptide variants), and fragments of native sequence polypeptides and polypeptide variants, as further defined herein. The PD-L1 polypeptides described herein may be isolated from a variety of sources, for example, from human tissue types or from another source, or prepared by recombinant or synthetic methods.

[0089] A "native sequence PD-L1 polypeptide" includes a polypeptide that has the same amino acid sequence as a corresponding PD-L1 polypeptide derived from nature.

[0090] By "PD-L1 polypeptide variant" or variations thereof is meant a PD-L1 polypeptide, generally an active PD-L1 polypeptide, as defined herein, that has at least about 80% amino acid sequence identity to any of the native sequence PD-L1 polypeptide sequences disclosed herein. Such PD-L1 polypeptide variants include, for example, PD-L1 polypeptides in which one or more amino acid residues have been added or deleted at the N- or C-terminus of the native amino acid sequence. Typically, a PD-L1 polypeptide variant will have at least about 80% amino acid sequence identity, or alternatively at least about 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity to a native sequence PD-L1 polypeptide sequence disclosed herein. Typically, PD-L1 polypeptide variants are at least about 10 amino acids in length, alternatively at least about 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 281, 282, 283, 284, 285, 286, 287, 288, or 289 amino acids in length or more. Optionally, a PD-L1 polypeptide variant will have no more than one conservative amino acid substitution compared to a native PD-L1 polypeptide sequence, alternatively no more than 2, 3, 4, 5, 6, 7, 8, 9, or 10 conservative amino acid substitutions compared to a native PD-L1 polypeptide sequence.

[0091] As used interchangeably herein, "polynucleotide" or "nucleic acid" refers to a polymer of nucleotides of any length, including DNA and RNA. Nucleotides can be deoxyribonucleotides, ribonucleotides, modified nucleotides or bases, and / or their analogs, or any substrate that can be incorporated into a polymer by DNA or RNA polymerase or by a synthetic reaction. Thus, for example, polynucleotides as defined herein include, but are not limited to, single- and double-stranded DNA, DNA containing single- and double-stranded regions, single- and double-stranded RNA, and RNA containing single- and double-stranded regions, and hybrid molecules containing DNA and RNA that may be single-stranded or, more typically, double-stranded, or contain single- and double-stranded regions. Additionally, as used herein, the term "polynucleotide" refers to triple-stranded regions that contain RNA or DNA, or both RNA and DNA. The strands within such regions may be from the same molecule or from different molecules. These regions can include all of one or more of these molecules, but more typically, only some of these molecules. One of the molecules in the triple helix region is often an oligonucleotide. The term "polynucleotide" specifically includes cDNA.

[0092] A polynucleotide may comprise modified nucleotides, such as methylated nucleotides and their analogs. If present, modifications to the nucleotide structure may be imparted before or after assembly of the polymer. The sequence of nucleotides may be interrupted by non-nucleotide components. A polynucleotide may be further modified after synthesis, such as by conjugation with a label. Other types of modifications include, for example, "cap" substitutions with one or more analogs of naturally occurring nucleotides, internucleotide modifications such as those with uncharged linkages (e.g., methyl phosphonates, phosphotriesters, phosphoamidates, carbamates, etc.) and charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.), those containing pendant moieties such as proteins (e.g., nucleases, toxins, antibodies, signal peptides, poly-L-lysine, etc.), those containing intercalating agents (e.g., acridine, psoralen, etc.), those containing chelating agents (e.g., metals, radioactive metals, boron, metal oxides, etc.), those containing alkylating agents, those with modified linkages (e.g., alpha anomeric nucleic acids, etc.), and unmodified forms of polynucleotide(s). Additionally, any of the hydroxyl groups normally present on the sugar can be replaced, for example, by a phosphonate group, a phosphate group, protected by a standard protecting group, activated to prepare for further linkage to an additional nucleotide, or conjugated to a solid or semi-solid support. The 5'- and 3'-terminal OH can be phosphorylated or substituted with an amine or organic capping group moiety having 1 to 20 carbon atoms. Other hydroxyls can also be derivatized to standard protecting groups. Polynucleotides can also contain analogous forms of ribose or deoxyribose sugars commonly known in the art, including, for example, 2'-O-methyl-, 2'-O-allyl-, 2'-fluoro-, or 2'-azido-ribose, carbocyclic sugar analogs, α-anomeric sugars, epimeric sugars such as arabinose, xylose, or lyxose, pyranose sugars, furanose sugars, sedoheptulose, acyclic analogs, and abasic nucleoside analogs such as methyl riboside. One or more phosphodiester linkages can be replaced by alternative linking groups.These alternative linking groups include, but are not limited to, embodiments in which phosphate is replaced by P(O)S ("thioate"), P(S)S ("dithioate"), (O)NR2 ("amidate"), P(O)R, P(O)OR', CO, or CH2 ("formacetal"), where each R or R' is independently H or substituted or unsubstituted alkyl (1-20C) (optionally containing an ether (-O-) linkage), aryl, alkenyl, cycloalkyl, cycloalkenyl, or araldyl. Not all linkages within a polynucleotide need be identical. A polynucleotide can contain one or more different types of modifications described herein and / or multiple modifications of the same type. The foregoing description applies to all polynucleotides referred to herein, including RNA and DNA.

[0093] As used herein, "oligonucleotide" generally refers to a short, single-stranded polynucleotide that is less than about 250 nucleotides in length, but not necessarily. An oligonucleotide may be synthetic. The terms "oligonucleotide" and "polynucleotide" are not mutually exclusive. The above description of polynucleotides is equally and fully applicable to oligonucleotides.

[0094] The term "primer" refers to a single-stranded polynucleotide that can hybridize to a nucleic acid and allow polymerization of a complementary nucleic acid, generally by providing a free 3'-OH group.

[0095] The term "small molecule" refers to any molecule having a molecular weight below about 2000 daltons, preferably below about 500 daltons.

[0096] The terms "host cell," "host cell line," and "host cell culture" are used interchangeably and refer to cells into which exogenous nucleic acid has been introduced, including the progeny of such cells. Host cells include "transformants" and "transformed cells," including the primary transformed cell and its progeny regardless of the number of passages. The progeny may not be completely identical in nucleic acid content to the parent cell, but may contain mutations. Mutant progeny that have the same function or biological activity as screened or selected for in the originally transformed cell are included herein.

[0097] As used herein, the term "vector" refers to a nucleic acid molecule capable of propagating another nucleic acid to which it is linked. The term includes vectors as self-replicating nucleic acid structures as well as vectors that integrate into the genome of a host cell into which they are introduced. Certain vectors are capable of directing the expression of nucleic acids to which they are operatively linked. Such vectors are referred to herein as "expression vectors."

[0098] An "isolated" nucleic acid refers to a nucleic acid molecule that has been separated from a component of its natural environment. Isolated nucleic acid includes a nucleic acid molecule contained in cells that ordinarily contain the nucleic acid molecule, but where the nucleic acid molecule is present extrachromosomally or at a chromosomal location that is different from its natural chromosomal location.

[0099] The term "antibody" as used herein is used in the broadest sense and encompasses a variety of antibody structures, including, but not limited to, monoclonal antibodies, polyclonal antibodies, multispecific antibodies (e.g., bispecific antibodies), and antibody fragments (so long as they exhibit the desired antigen-binding activity).

[0100] An "isolated" antibody is one that has been identified and separated and / or recovered from a component of its natural environment. Contaminant components of its natural environment are substances that would interfere with research, diagnostic, and / or therapeutic uses for the antibody, and may include enzymes, hormones, and other proteinaceous or nonproteinaceous solutes. In some embodiments, the antibody is purified (1) to greater than 95%, and in some embodiments greater than 99%, by weight of the antibody, e.g., as determined by the Lowry method; (2) to a degree sufficient to obtain at least 15 residues of N-terminal or internal amino acid sequence, e.g., using a spinning cup sequenator; or (3) to homogeneity by SDS-PAGE under reducing or non-reducing conditions, e.g., using Coomassie blue or silver staining. Isolated antibodies include antibodies in situ within recombinant cells, since at least one component of the antibody's natural environment will not be present. Ordinarily, however, isolated antibodies will be prepared by at least one purification step.

[0101] "Native antibodies" are typically heterotetrameric glycoproteins of about 150,000 daltons composed of two identical light chains (L) and two identical heavy chains (H). Each light chain is linked to a heavy chain by one covalent disulfide bond, although the number of disulfide bonds varies among the heavy chains of different immunoglobulin isotypes. Each heavy and light chain also has regularly spaced intrachain disulfide bridges. Each heavy chain has a variable domain (VH) at one end followed by several constant domains. Each light chain has a variable domain (VL) at one end and a constant domain at its other end, with the light chain constant domain aligned with the first heavy chain constant domain and the light chain variable domain aligned with the heavy chain variable domain. Particular amino acid residues are believed to form an interface between the light chain variable domain and the heavy chain variable domain.

[0102] The "light chains" of antibodies (immunoglobulins) from any mammalian species can be assigned to one of two clearly distinct types, called kappa ("κ") and lambda ("λ"), based on the amino acid sequences of their constant domains.

[0103] The term "constant domain" refers to the portion of an immunoglobulin molecule that has a more conserved amino acid sequence compared to the other portion of the immunoglobulin, the variable domain, which contains the antigen-binding site. The constant domain includes the CH1, CH2, and CH3 domains (collectively CH) of the heavy chain and the CHL (or CL) domain of the light chain.

[0104] The "variable region" or "variable domain" of an antibody refers to the amino-terminal domain of the heavy or light chain of the antibody. The variable domain of the heavy chain may be referred to as "VH." The variable domain of the light chain may be referred to as "VL." These domains are generally the most variable parts of an antibody and contain the antigen-binding site.

[0105] The term "variable" refers to the fact that certain portions of the variable domains differ extensively in sequence among antibodies and are used in the binding and specificity of each particular antibody for its particular antigen. However, variability is not evenly distributed throughout the variable domains of antibodies. It is concentrated in three segments called hypervariable regions (HVRs) in both the light-chain and heavy-chain variable domains. The more highly conserved portions of the variable domains are called framework regions (FRs). Native heavy and light chain variable domains each contain four FR regions, which adopt a primarily beta-sheet configuration and are connected by three HVRs, which form loops that connect, and in some cases form part of, the beta-sheet structure. The HVRs within each chain are held together in close proximity by the FR regions and, together with the HVRs of the other chain, contribute to the formation of the antigen-binding site of antibodies (see Kabat et al., Sequences of Proteins of Immunological Interest, Fifth Edition, National Institutes of Health, Bethesda, Md. (1991)). The constant domains are not involved directly in binding an antibody to an antigen, but exhibit various effector functions, such as participation of the antibody in antibody-dependent cellular toxicity.

[0106] As used herein, the terms "hypervariable region," "HVR," or "HV" refer to the region of an antibody variable domain that is hypervariable in sequence and / or forms structurally defined loops. Antibodies generally contain six HVRs: three in the VH (H1, H2, and H3) and three in the VL (L1, L2, and L3). In natural antibodies, H3 and L3 exhibit the highest diversity among these six HVRs, and H3 in particular is thought to play a unique role in conferring superior specificity to antibodies. See, e.g., Xu et al., Immunity 13:37-45 (2000); Johnson and Wu, in Methods in Molecular Biology 248:1-25 (Lo, ed., Human Press, Totowa, NJ, 2003). In fact, naturally occurring camelid antibodies consisting only of heavy chains are functional and stable in the absence of light chains. See, for example, Hamers-Casterman et al., Nature 363:446-448 (1993); Sheriff et al., Nature Struct. Biol. 3:733-736 (1996).

[0107] Several HVR delineations are in use and are encompassed herein. Kabat complementarity-determining regions (CDRs) are based on sequence variability and are the most commonly used (Kabat et al., Sequences of Proteins of Immunological Interest, 5th Ed. Public Health Service, National Institutes of Health, Bethesda, Md. (1991)). Chothia, instead, refers to the location of structural loops (Chothia and Lesk J. Mol. Biol. 196:901-917 (1987)). AbM HVRs represent a compromise between Kabat HVRs and Chothia structural loops and are used by Oxford Molecular's AbM antibody modeling software. "Contact" HVRs are based on analysis of available complex crystal structures. Residues from each of these HVRs are described below. TIFF2023103258000001.tif49170

[0108] HVRs may comprise the following "extended HVRs": 24-36 or 24-34 (L1), 46-56 or 50-56 (L2), and 89-97 or 89-96 (L3) in the VL, and 26-35 (H1), 50-65, or 49-65 (H2), and 93-102, 94-102, or 95-102 (H3) in the VH. The variable domain residues are numbered according to Kabat et al. (see above) for each of these definitions.

[0109] "Framework" or "FR" residues are those variable domain residues other than the HVR residues as herein defined.

[0110] The terms "variable domain residue numbering as in Kabat" or "amino acid position numbering as in Kabat," and variations thereof, refer to the numbering system used for the heavy or light chain variable domains of the compilation of antibodies in Kabat et al. (see above). Using this numbering system, the actual linear amino acid sequence may contain fewer or additional amino acids corresponding to a shortening of, or insertion into, the FRs or HVRs of the variable domain. For example, a heavy chain variable domain may contain a single amino acid insertion after residue 52 of H2 (residue 52a according to Kabat) and inserted residues after heavy chain FR residue 82 (e.g., residues 82a, 82b, and 82c, etc. according to Kabat). The Kabat numbering of residues can be determined for a given antibody by alignment of the antibody sequence with the "standard" Kabat numbering sequence at regions of homology.

[0111] The Kabat numbering system is commonly used when referring to residues within the variable domain (approximately residues 1-107 of the light chain and approximately residues 1-113 of the heavy chain) (e.g., Kabat et al., Sequences of Immunological Interest. 5th Ed. Public Health Service, National Institutes of Health, Bethesda, Md. (1991)). The "EU numbering system" or "EU index" is commonly used when referring to residues within the immunoglobulin heavy chain constant region (e.g., the EU index reported in Kabat et al., supra). "EU index as in Kabat" refers to the residue numbering of the human IgG1 EU antibody.

[0112] The terms "full length antibody," "intact antibody," and "whole antibody" are used interchangeably herein to refer to an antibody in its substantially intact form, rather than an antibody fragment as defined below. This term specifically refers to an antibody having a heavy chain that includes an Fc region.

[0113] An "antibody fragment" includes a portion of an intact antibody, preferably including its antigen-binding region. In some embodiments, the antibody fragments described herein are antigen-binding fragments. Examples of antibody fragments include Fab, Fab', F(ab')2, and Fv fragments; diabodies; linear antibodies; single-chain antibody molecules; and multispecific antibodies formed from antibody fragments.

[0114] Papain digestion of antibodies produces two identical antigen-binding fragments, called "Fab" fragments, each with a single antigen-binding site, and a residual "Fc" fragment, a name reflecting its ability to readily crystallize. Pepsin treatment yields an F(ab')2 fragment that has two antigen-binding sites and is still capable of cross-linking antigen. "Fv" is the minimum antibody fragment containing an intact antigen-binding site. In one embodiment, a two-chain Fv species consists of a dimer of one heavy- and one light-chain variable domain in tight, non-covalent association. In a single-chain Fv (scFv) species, one heavy- and one light-chain variable domain can be covalently linked by a flexible peptide linker such that the light and heavy chains can associate in a "dimeric" structure similar to that in a two-chain Fv species. It is in this configuration that the three HVRs of each variable domain interact to define an antigen-binding site on the surface of the VH-VL dimer. Collectively, the six HVRs confer antigen-binding specificity to the antibody. However, even a single variable domain (or half of an Fv comprising only three HVRs specific for an antigen) has the ability to recognize and bind antigen, although at a lower affinity than the entire binding site.

[0115] Fab fragments contain the heavy-chain variable domain and the light-chain variable domain, and also contain the light-chain constant domain and the first heavy-chain constant domain (CH1). Fab' fragments differ from Fab fragments by the addition of a few residues at the carboxy terminus of the heavy-chain CH1 domain including one or more cysteines from the antibody hinge region. Fab'-SH is the designation herein for Fab' in which the cysteine ​​residue(s) of the constant domains bear a free thiol group. F(ab')2 antibody fragments were originally produced as pairs of Fab' fragments with hinge cysteines between them. Other chemical linkages of antibody fragments are also known.

[0116] "Single-chain Fv" or "scFv" antibody fragments comprise the VH and VL domains of an antibody, wherein these domains are present in a single polypeptide chain. Generally, the scFv polypeptide further comprises a polypeptide linker between the VH and VL domains that enables the scFv to form the desired structure for antigen binding. For a review of scFvs, see, e.g., Pluckthun, in The Pharmacology of Monoclonal Antibodies, vol. 113, Rosenburg and Moore eds., (Springer-Verlag, New York, 1994), pp. 269-315.

[0117] The term "diabody" refers to an antibody fragment having two antigen-binding sites, which fragments comprise a heavy-chain variable domain (VH) connected to a light-chain variable domain (VL) in the same polypeptide chain (VH-VL). By using a linker that is too short to allow pairing between the two domains on the same chain, these domains are forced to pair with complementary domains on another chain, creating two antigen-binding sites. Diabodies may be bivalent or bispecific. Diabodies are described in more detail, for example, in EP 404,097, WO 1993 / 01161, Hudson et al., Nat. Med. 9:129-134 (2003), and Hollinger et al., Proc. Natl. Acad. Sci. USA 90:6444-6448 (1993). Triabodies and tetrabodies are also described in Hudson et al., Nat. Med. 9:129-134 (2003).

[0118] The "class" of an antibody refers to the type of constant domain or constant region possessed by its heavy chain. There are five major antibody classes: IgA, IgD, IgE, IgG, and IgM, and some of these can be further divided into subclasses (isotypes), e.g., IgG1, IgG2, IgG3, IgG4, IgA1, and IgA2. The heavy chain constant domains corresponding to the different classes of antibodies are called α, δ, ε, γ, and μ, respectively.

[0119] As used herein, the term "monoclonal antibody" refers to an antibody obtained from a substantially homogeneous antibody population, e.g., the individual antibodies within the population are identical except for possible mutations that may be present in minor amounts, e.g., naturally occurring mutations. Thus, the modifier "monoclonal" indicates the character of the antibody as not being a mixture of distinct antibodies. In certain embodiments, such monoclonal antibodies typically comprise an antibody comprising a polypeptide sequence that binds to a target, where the target-binding polypeptide sequence has been obtained by a process that includes selection of a single target-binding polypeptide sequence from a plurality of polypeptide sequences. For example, the selection process can be selection of a unique clone from a pool of multiple clones, e.g., hybridoma clones, phage clones, or recombinant DNA clones. It is understood that the selected target-binding sequence may be further modified, e.g., to improve affinity for the target, humanize the target-binding sequence, improve its production in cell culture, reduce its immunogenicity in vivo, or create a multispecific antibody, and that an antibody comprising a modified target-binding sequence is also a monoclonal antibody of the present invention. In contrast to polyclonal antibody preparations, which typically include different antibodies directed against different determinants (epitopes), each monoclonal antibody of a monoclonal antibody preparation is directed against a single determinant on an antigen. In addition to their specificity, monoclonal antibody preparations are advantageous in that they are typically uncontaminated by other immunoglobulins.

[0120] The modifier "monoclonal" indicates the character of the antibody as being obtained from a substantially homogeneous population of antibodies, and is not to be construed as requiring production of the antibody by any particular method. For example, the monoclonal antibodies used in accordance with the present invention can be produced by, for example, hybridoma techniques (e.g., Kohler and Milstein, Nature 256:495-97 (1975); Hongo et al., Hybridoma 14(3):253-260 (1995); Harlow et al., Antibodies: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 2nd ed. 1988); Hammerling et al., in: Monoclonal Antibodies and T-Cell Hybridomas 563-681 (Elsevier, NY, 1981)), recombinant DNA techniques (see, for example, U.S. Pat. No. 4,816,567), phage display techniques (e.g., Clackson et al., Nature, 352:624-628 (1991); Marks et al. al., J.Mol.Biol.222:581-597(1992), Sidhu et al., J.Mol.Biol.338(2):299-310(2004), Lee et al. al., J.Mol.Biol.340(5):1073-1093(2004), Fellouse,Proc.Natl.Acad.Sci.USA 101(34):12467-12472(2004), and Lee et al.,J.Immunol.Methods 284(1-2):119-132(2004)), and techniques for producing human or human-like antibodies in animals having some or all of the human immunoglobulin loci or genes encoding human immunoglobulin sequences (e.g., WO1998 / 24893, WO1996 / 34096, WO1996 / 33735, WO1991 / 10741, Jakobovits et al., Proc. Natl. Acad. Sci. USA 90:2551(1993), Jakobovits et al., Nature 362:255-258(1993), Bruggemann et al., Year in Immunol. 7:33 (1993), U.S. Patent Nos. 5,545,807, 5,545,806, 5,569,825, 5,625,126, 5,633,425, and 5,661,016, Marks et al., Bio / Technology 10:779-783 (1992), Lonberg et al., Nature 368:856-859 (1994), Morrison, Nature 368:812-813 (1994), Fishwild et al., Nature Biotechnol. 14:845-851 (1996), Neuberger, Nature Biotechnol. 14:826 (1996), and Lonberg et al. These antibodies can be produced by a variety of techniques, including by ion exchange (see, e.g., et al., Intern. Rev. Immunol. 13:65-93 (1995)).

[0121] The term "monoclonal antibodies" as used herein specifically includes "chimeric" antibodies in which a portion of the heavy and / or light chain is identical to or homologous to corresponding sequences in antibodies derived from a particular species or belonging to a particular antibody class or subclass, while the remainder of the chain(s) is identical to or homologous to corresponding sequences in antibodies derived from another species or belonging to another antibody class or subclass, as well as fragments of such antibodies, so long as they exhibit the desired biological activity (see, e.g., U.S. Pat. No. 4,816,567 and Morrison et al., Proc. Natl. Acad. Sci. USA 81:6851-6855 (1984)). Chimeric antibodies include PRIMATIZED® antibodies, in which the antigen-binding region of the antibody is derived from an antibody produced, for example, by immunizing macaque monkeys with the antigen of interest.

[0122] A "human antibody" is one having an amino acid sequence corresponding to an antibody produced by a human or human cell, or an antibody derived from a non-human source that utilizes the human antibody repertoire or other human antibody coding sequences. This definition of human antibody specifically excludes humanized antibodies, which contain non-human antigen-binding residues.

[0123] A "humanized" antibody refers to a chimeric antibody comprising amino acid residues from non-human HVRs and amino acid residues from human framework regions (FRs). In certain embodiments, a humanized antibody comprises substantially all of at least one, and typically two, variable domains, in which all or substantially all of the HVRs (e.g., CDRs) correspond to those of a non-human antibody and all or substantially all of the FRs correspond to those of a human antibody. A humanized antibody may optionally comprise at least a portion of an antibody constant region derived from a human antibody. A "humanized form" of an antibody, e.g., a non-human antibody, refers to an antibody that has undergone humanization.

[0124] The terms "anti-PD-L1 antibody" and "antibody that binds to PD-L1" refer to an antibody that can bind to PD-L1 with sufficient affinity such that the antibody, when targeted to PD-L1, is useful as a diagnostic and / or therapeutic agent. In one embodiment, the extent of binding of the anti-PD-L1 antibody to an unrelated, non-PD-L1 protein is less than about 10% of the binding of the antibody to PD-L1, as measured, for example, by radioimmunoassay (RIA). In certain embodiments, the anti-PD-L1 antibody binds to an epitope of PD-L1 that is conserved among PD-L1 from different species.

[0125] The terms "anti-PD-1 antibody" and "antibody that binds to PD-1" refer to an antibody that can bind to PD-1 with sufficient affinity so that the antibody, when targeted to PD-1, is useful as a diagnostic and / or therapeutic agent. In one embodiment, the extent of binding of the anti-PD-1 antibody to an unrelated, non-PD-1 protein is less than about 10% of the binding of the antibody to PD-1, as measured, for example, by radioimmunoassay (RIA). In certain embodiments, the anti-PD-1 antibody binds to an epitope of PD-1 that is conserved among PD-1 from different species.

[0126] A "blocking" or "antagonist" antibody is an antibody that inhibits or reduces the biological activity of the antigen to which it binds. Preferred blocking or antagonist antibodies substantially or completely inhibit the biological activity of the antigen.

[0127] "Affinity" refers to the strength of the sum of non-covalent interactions between a single binding site of a molecule (e.g., an antibody) and its binding partner (e.g., an antigen). Unless otherwise indicated, as used herein, "binding affinity" refers to the intrinsic binding affinity that reflects a 1:1 interaction between members of a binding pair (e.g., an antibody and an antigen). The affinity of a molecule X for its partner Y can generally be expressed as a dissociation constant (Kd). Affinity can be measured by common methods known in the art, including the methods described herein. Specific illustrative and exemplary embodiments for measuring binding affinity are described below.

[0128] As used herein, the terms "bind," "specifically bind to," or "specific for" refer to a measurable and reproducible interaction, such as binding between a target and an antibody, that determines the presence of the target in the presence of a heterogeneous population of molecules, including biological molecules. For example, an antibody that binds to or specifically binds to a target (which may be an epitope) is an antibody that binds to this target with higher affinity, with higher avidity, more readily, and / or for a longer duration than it binds to other targets. In one embodiment, the extent of binding of an antibody to an unrelated target is less than about 10% of the binding of the antibody to the target, as measured, for example, by radioimmunoassay (RIA). In certain embodiments, an antibody that specifically binds to a target has a dissociation constant (Kd) of 1 μM or less, 100 nM or less, 10 nM or less, 1 nM or less, or 0.1 nM or less. In certain embodiments, an antibody specifically binds to an epitope of a protein that is conserved among proteins from different species. In another embodiment, specific binding can include, but does not require, exclusive binding.

[0129] An "affinity matured" antibody refers to an antibody that contains one or more alterations in one or more hypervariable regions (HVRs) compared to a parent antibody that does not possess such alterations, which alterations improve the affinity of the antibody for its antigen.

[0130] An "antibody that binds to the same epitope" as a reference antibody refers to an antibody that blocks the binding of the reference antibody to its antigen by 50% or more in a competition assay; conversely, the reference antibody blocks the binding of the antibody to its antigen by 50% or more in a competition assay.

[0131] An "immunoconjugate" is an antibody conjugated to one or more heterologous molecule(s), including, but not limited to, a cytotoxic agent.

[0132] As used herein, the term "immunoadhesin" refers to an antibody-like molecule that combines the binding specificity of a heterologous protein (an "adhesin") with the effector functions of immunoglobulin constant domains. Structurally, immunoadhesins comprise a fusion of an amino acid sequence with the desired binding specificity other than the antigen recognition and binding site of an antibody (i.e., "heterologous") with an immunoglobulin constant domain sequence. The adhesin portion of an immunoadhesin molecule is typically a contiguous amino acid sequence that includes at least the binding site of a receptor or ligand. The immunoglobulin constant domain sequence in an immunoadhesin can be derived from any immunoglobulin, e.g., IgG1, IgG2 (including IgG2A and IgG2B), IgG3, or IgG4 subtypes, IgA (including IgA1 and IgA2), IgE, IgD, or IgM. Ig fusions preferably involve the substitution of a polypeptide or antibody domain described herein in place of at least one variable region within an Ig molecule. In a particularly preferred embodiment, the immunoglobulin fusion comprises the hinge, CH2, and CH3 regions, or the hinge, CH1, CH2, and CH3 regions, of an IgG1 molecule. See also U.S. Patent No. 5,428,130 for information on the production of immunoglobulin fusions. For example, immunoadhesins useful as therapeutic agents herein include polypeptides comprising the extracellular domain (ECD) or PD-1-binding portion of PD-L1 or PD-L2, or the extracellular or PD-L1- or PD-L2-binding portion of PD-1 (e.g., PD-L1 ECD-Fc, PD-L2 ECD-Fc, and PD-1 ECD-Fc, respectively), fused to a constant domain of an immunoglobulin sequence. Immunoadhesins combining the Ig Fc and ECD of a cell surface receptor are also referred to as soluble receptors.

[0133] "Fusion protein" and "fusion polypeptide" refer to a polypeptide having two moieties covalently linked together, each of which is a polypeptide with a distinct property. The property can be a biological property, such as activity in vitro or in vivo. The property can also be a simple chemical or physical property, such as binding to a target molecule, catalysis of a reaction, etc. The two moieties can be linked directly by a single peptide bond or through a peptide linker, but are in reading frame with each other.

[0134] "Percent (%) amino acid sequence identity" with respect to a polypeptide sequence identified herein is defined as the percentage of amino acid residues in a candidate sequence that are identical to the amino acid residues in the polypeptide being compared, after aligning the sequences and introducing gaps as necessary to achieve the maximum percent sequence identity, without considering any conservative substitutions as sequence identity. Alignment for purposes of determining percent amino acid sequence identity can be achieved in a variety of ways within the skill of the art, for example, using publicly available computer software such as BLAST, BLAST-2, ALIGN, or Megalign (DNASTAR) software. Those skilled in the art can determine appropriate parameters for measuring alignment, including any algorithms required to achieve maximum alignment across the entire length of the sequences being compared. However, for purposes herein, percent amino acid sequence identity values ​​are generated using the sequence comparison computer program ALIGN-2. The author of the ALIGN-2 sequence comparison computer program is Genentech, Inc., and the source code, along with user documentation, has been submitted to the U.S. Copyright Office (Washington, DC, 20559) and is registered under U.S. Copyright Registration No. TXU510087. The ALIGN-2 program is publicly available from Genentech, Inc., South San Francisco, California. The ALIGN-2 program should be compiled for use on a UNIX operating system, preferably Digital UNIX V4.0D. All sequence comparison parameters are set by the ALIGN-2 program and do not vary.

[0135] In situations where ALIGN-2 is used for amino acid sequence comparison, the % amino acid sequence identity of a given amino acid sequence A to, with, or against a given amino acid sequence B (alternatively, it may be expressed as a given amino acid sequence A having or containing a certain % amino acid sequence identity to, with, or against a given amino acid sequence B) is calculated as follows: 100 x fraction X / Y where X is the number of amino acid residues scored as perfect matches by the sequence alignment program ALIGN-2 in that program's alignment of A and B, and Y is the total number of amino acid residues in B. It is understood that if the length of amino acid sequence A is not equal to the length of amino acid sequence B, then the % amino acid sequence identity of A to B will not equal the % amino acid sequence identity of B to A. Unless specifically stated otherwise, all % amino acid sequence identity values ​​used herein are obtained as described in the immediately preceding paragraph using the ALIGN-2 computer program.

[0136] The term "detection" includes any means of detection, including direct and indirect detection.

[0137] The term "biomarker," as used herein, refers to, for example, a predictive, diagnostic, and / or prognostic indicator that can be detected in a sample, e.g., bTMB score, tTMB score, or PD-L1. A biomarker can serve as an indicator of a particular subtype of a disease or disorder (e.g., cancer) characterized by certain molecular, pathological, histological, and / or clinical features (e.g., responsiveness to therapy, including a PD-L1 axis-binding antagonist). In some embodiments, a biomarker is a collection of genes or a collection of mutations / alterations (e.g., somatic mutations) in a collection of genes. Biomarkers include, but are not limited to, polynucleotide (e.g., DNA and / or RNA), polynucleotide alterations (e.g., polynucleotide copy number alterations, e.g., DNA copy number alterations), polypeptides, polypeptide and polynucleotide modifications (e.g., post-translational modifications), carbohydrate, and / or glycolipid-based molecular markers.

[0138] The "amount" or "number" of somatic mutations associated with increased clinical benefit to an individual is a detectable level in a biological sample. These can be measured by methods known to those skilled in the art and disclosed herein. The amount of somatic mutations assessed can be used to determine response to treatment.

[0139] As used herein, "amplification" generally refers to the process of producing multiple copies of a desired sequence. "Multiple copies" means at least two copies. A "copy" does not necessarily imply perfect sequence complementarity or identity to the template sequence. For example, a copy may contain nucleotide analogs such as deoxyinosine, intentional sequence modifications (such as those introduced by primers containing sequences that are hybridizable to, but not complementary to, the template), and / or sequence errors that occur during amplification.

[0140] As used herein, the term "polymerase chain reaction" or "PCR" generally refers to a procedure in which minute, specific pieces of nucleic acid, RNA, and / or DNA are amplified, as described, for example, in U.S. Pat. No. 4,683,195. Generally, sequence information from the ends of the region of interest or beyond must be available so that oligonucleotide primers can be designed; the sequences of these primers are identical or similar to those of opposite strands of the template to be amplified. The 5' terminal nucleotides of these two primers can correspond to the ends of the material to be amplified. PCR can be used to amplify specific RNA sequences, specific DNA sequences from total genomic DNA, cDNA transcribed from total cellular RNA, bacteriophage, or plasmid sequences, etc. See generally Mullis et al., Cold Spring Harbor Symp. Quant. Biol. 51:263 (1987), and Erlich, ed., PCR Technology (Stockton Press, NY, 1989). As used herein, PCR is considered to be one example, but not the only example, of a nucleic acid polymerase reaction method for amplifying a nucleic acid test sample, which involves the use of known nucleic acids (DNA or RNA) as primers, and utilizes a nucleic acid polymerase to amplify or generate a specific piece of nucleic acid, or a specific piece of nucleic acid complementary to a specific nucleic acid.

[0141] The term "diagnosis" is used herein to refer to the identification or classification of a molecular or pathological state, disease, or condition (e.g., cancer). For example, "diagnosis" can refer to the identification of a particular type of cancer. "Diagnosis" can also refer to the classification of a particular subtype of cancer, for example, by histopathological criteria or by molecular features (e.g., a subtype characterized by the expression of one or a combination of biomarkers (e.g., particular genes or proteins encoded by those genes)).

[0142] The term "aiding in diagnosis" is used herein to refer to a method that aids in making a clinical decision regarding the presence or nature of a particular type of symptom or condition of a disease or disorder (e.g., cancer). For example, a method that aids in the diagnosis of a disease or condition (e.g., cancer) may include measuring certain somatic mutations in a biological sample from an individual.

[0143] As used herein, the term "sample" refers to a composition obtained or derived from a subject and / or individual of interest that contains cells and / or other molecular entities to be characterized and / or identified, e.g., based on physical, biochemical, chemical, and / or physiological properties. For example, the phrase "disease sample" and variations thereof refer to any sample obtained from a subject of interest that is expected to contain or is known to contain the cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymphatic fluid, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, plasma, serum, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tumor lysates, and tissue culture media, tissue extracts, e.g., homogenized tissue, tumor tissue, cell extracts, and combinations thereof. In some examples, the sample is a whole blood sample, a plasma sample, a serum sample, or a combination thereof.

[0144] As used herein, "tumor cell" refers to any tumor cell present in a tumor or a sample thereof. Tumor cells can be distinguished from other cells, e.g., stromal cells and tumor-infiltrating immune cells, that may be present in a tumor sample using methods known in the art and / or described herein.

[0145] As used herein, "reference sample," "reference cell," "reference tissue," "control sample," "control cell," or "control tissue" refers to a sample, cell, tissue, standard, or level used for comparison purposes.

[0146] "Correlate" or "correlating" means comparing, in any manner, the performance and / or results of a first analysis or protocol with the performance and / or results of a second analysis or protocol. For example, the results of a first analysis or protocol may be used in performing a second protocol and / or may be used to determine whether a second analysis or protocol should be performed. With respect to polypeptide analysis or protocol embodiments, the results of a polypeptide expression analysis or protocol may be used to determine whether a particular therapeutic regimen should be performed. With respect to polynucleotide analysis or protocol embodiments, the results of a polynucleotide expression analysis or protocol may be used to determine whether a particular therapeutic regimen should be performed.

[0147] "Individual response" or "response" may be assessed using any endpoint that indicates benefit to an individual, including, but not limited to, (1) some degree of inhibition of disease progression (e.g., cancer progression), including delay or complete cessation; (2) reduction in tumor size; (3) inhibition (i.e., reduction, delay, or complete cessation) of cancer cell invasion into adjacent peripheral organs and / or tissues; (4) inhibition (i.e., reduction, delay, or complete cessation) of metastasis; (5) some degree of alleviation of one or more symptoms associated with a disease or disorder (e.g., cancer); (6) increased or prolonged survival, including overall survival and progression-free survival; and / or (7) reduced mortality at a given time point after treatment.

[0148] A patient's "effective response" or patient "responsiveness" to treatment with an agent, and similar phrases, refers to a clinical or therapeutic benefit conferred on a patient at risk for or suffering from a disease or disorder, such as cancer. In one embodiment, such benefit includes any one or more of extending survival (including overall survival and / or progression-free survival), producing an objective response (including a complete or partial response), or ameliorating the signs or symptoms of cancer.

[0149] In some embodiments, a bTMB score determined using the methods disclosed herein that meets or exceeds a baseline bTMB score (e.g., a baseline bTMB score of about 4 to about 30, e.g., a baseline bTMB score of about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30) is used to identify patients predicted to be likely to respond to treatment with an agent (e.g., treatment comprising a PD-L1 axis binding antagonist, e.g., an anti-PD-L1 antibody). In some embodiments, a bTMB score determined using the methods disclosed herein that is less than a reference bTMB score (e.g., a reference bTMB score of about 4 to about 30, e.g., a reference bTMB score of about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30) is used to identify patients predicted to be more likely to respond to treatment with an anti-cancer therapy other than, or in addition to, a PD-L1 axis-binding antagonist. In some examples, the bTMB score determined from a sample from an individual is between about 8 and about 100 (e.g., 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100).

[0150] Generally, the bTMB score (e.g., a reference bTMB score) is linearly related to the size of the sequenced genomic region. The above exemplary numbers refer to bTMB scores obtained by sequencing approximately 1.1 Mb, for example, using the FOUNDATIONONE® panel. The bTMB score of a sample is expected to be approximately X times higher if X times more bases are sequenced. In some embodiments, a normalized bTMB value can be calculated by dividing the number of counted somatic mutations (e.g., mutations) by the number of sequenced bases, for example, the number of counted somatic mutations (e.g., mutations) per megabase. Thus, any of the aforementioned bTMB scores or reference bTMB scores can be an equivalent bTMB value, for example, an equivalent bTMB value determined by whole-exome sequencing. In some examples, the bTMB score (e.g., a reference bTMB score) can be about 400 to about 1500 (e.g., a bTMB score of about 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, or 1500), for example, in a whole-exome-based assay.

[0151] In some embodiments, the combination of the bTMB score determined using the methods disclosed herein and the MSAF is used to identify patients predicted to be likely to respond to treatment with an agent (e.g., treatment comprising a PD-L1 axis binding antagonist, e.g., an anti-PD-L1 antibody). In some embodiments, the combination of the bTMB score determined using the methods disclosed herein and the MSAF is used to identify patients predicted to be likely to respond to treatment with an anti-cancer therapy other than, or in addition to, a PD-L1 axis binding antagonist.

[0152] "Objective response" refers to a measurable response, including a complete response (CR) or a partial response (PR). In some embodiments, "objective response rate (ORR)" refers to the sum of the complete response (CR) rate and the partial response (PR) rate.

[0153] "Complete response" or "CR" means the disappearance of all signs of cancer in response to treatment (e.g., the disappearance of all target lesions). This does not necessarily mean that the cancer has been cured.

[0154] "Sustained response" refers to a sustained effect on tumor growth reduction after treatment is discontinued. For example, the tumor size can be the same size or smaller compared to the size at the beginning of the drug administration period. In some embodiments, the sustained response has a duration at least equal to the duration of treatment, at least 1.5, 2.0, 2.5, or 3.0 times longer than the duration of treatment, or longer.

[0155] As used herein, "reducing or inhibiting cancer recurrence" means reducing or inhibiting tumor or cancer recurrence or tumor or cancer progression. As disclosed herein, cancer recurrence and / or cancer progression includes, but is not limited to, cancer metastasis.

[0156] As used herein, "partial response" or "PR" refers to a reduction in the size of one or more tumors or lesions, or a reduction in the extent of cancer in the body, in response to treatment. For example, in some embodiments, PR refers to at least a 30% reduction in the sum of the longest diameters (SLD) of target lesions, relative to the baseline SLD.

[0157] As used herein, "stable disease" or "SD" refers to neither sufficient shrinkage of target lesions to merit PR nor sufficient growth to merit PD, with reference to the smallest SLD since the start of treatment.

[0158] As used herein, "progressive disease" or "PD" refers to at least a 20% increase in the SLD of a target lesion, referenced to the smallest SLD recorded since treatment initiation or the presence of one or more new lesions.

[0159] The term "survival" refers to a patient remaining alive and includes overall survival as well as progression-free survival.

[0160] As used herein, "progression-free survival" or "PFS" refers to the length of time during and after treatment during which the disease being treated (e.g., cancer) does not worsen. Progression-free survival can include periods during which patients experience a complete or partial response, as well as periods during which patients experience stable disease.

[0161] As used herein, "overall survival" or "OS" refers to the percentage of individuals in a group who may be alive after a particular period of time.

[0162] "Prolonged survival" refers to an increase in overall survival or progression-free survival in treated patients compared to untreated patients (i.e., compared to patients not treated with a drug), or compared to patients who do not have somatic mutations at a specified level, and / or compared to patients treated with an anti-tumor drug.

[0163] As used herein, the term "substantially the same" means that the degree of similarity between two values ​​is sufficiently high such that one of ordinary skill in the art would consider the difference between the two values ​​to be of little or no biological and / or statistical significance in relation to the biological property measured by those values ​​(e.g., Kd value or mutation level). The difference between the two values ​​is, for example, less than about 50%, less than about 40%, less than about 30%, less than about 20%, and / or less than about 10% as a function of the reference / comparator value.

[0164] As used herein, the phrase "substantially different" means that the degree of difference between two numerical values ​​is sufficiently high such that one of ordinary skill in the art would consider the difference between the two values ​​to be statistically significant in relation to the biological property measured by those values ​​(e.g., Kd value or mutation level). The difference between the two values ​​can be, for example, more than about 10%, more than about 20%, more than about 30%, more than about 40%, and / or more than about 50% as a function of the value of the reference / comparator molecule.

[0165] The word "label," as used herein, refers to a compound or composition that is directly or indirectly conjugated or fused to a reagent, such as a polynucleotide probe, or to an antibody, facilitating detection of the reagent to which it is conjugated or fused. The label may be detectable itself (e.g., a radioisotope label or a fluorescent label) or, in the case of an enzymatic label, may catalyze chemical modification of a substrate compound or composition that is detectable. The term is intended to encompass direct labeling of a probe or antibody by coupling (i.e., physically linking) a detectable substance to the probe or antibody, as well as indirect labeling of a probe or antibody by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of a primary antibody using a fluorescently labeled secondary antibody and end-labeling of a DNA probe with biotin such that it can be detected using fluorescently labeled streptavidin.

[0166] An "effective amount" refers to the amount of a therapeutic agent to treat or prevent a disease or disorder in a mammal. In the case of cancer, a therapeutically effective amount of a therapeutic agent may reduce the number of cancer cells, reduce primary tumor size, inhibit (i.e., slow to some extent, and preferably stop) cancer cell invasion into peripheral organs, inhibit (i.e., slow to some extent, and preferably stop) tumor metastasis, inhibit tumor growth to some extent, and / or alleviate to some extent one or more symptoms associated with the disorder. To the extent that a drug inhibits the growth of and / or kills existing cancer cells, the drug may be cytostatic and / or cytotoxic. For cancer therapy, in vivo efficacy may be measured, for example, by assessing survival time, time to disease progression (TTP), response rate (e.g., CR and PR), duration of response, and / or quality of life.

[0167] A "disorder" is any condition that would benefit from treatment, including, but not limited to, chronic and acute disorders or diseases, including conditions that predispose a mammal to the disorder in question.

[0168] The terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. This definition includes benign and malignant cancers. "Early-stage cancer" or "early-stage tumor" refers to a cancer that is not invasive or metastatic and is classified as stage 0, 1, or 2 cancer.Examples of cancer include lung cancer (e.g., non-small cell lung cancer (NSCLC)), kidney cancer (e.g., renal urothelial carcinoma), bladder cancer (e.g., bladder urothelial (transitional cell) carcinoma), breast cancer, colorectal cancer (e.g., colon adenocarcinoma), ovarian cancer, pancreatic cancer, gastric cancer, esophageal cancer, mesothelioma, melanoma (e.g., cutaneous melanoma), head and neck cancer (e.g., head and neck squamous cell carcinoma (HNSCC)), thyroid cancer, sarcoma (e.g., soft tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, osteosarcoma, chondrosarcoma, angiosarcoma, endothelial sarcoma, lymphangiosarcoma, lymphatic endothelial carcinoma, and lymphatic endothelial carcinoma). sarcoma, leiomyosarcoma, or rhabdomyosarcoma), prostate cancer, glioblastoma, cervical cancer, thymic carcinoma, leukemia (e.g., acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic myelogenous leukemia (CML), chronic eosinophilic leukemia, or chronic lymphocytic leukemia (CLL)), lymphoma (e.g., Hodgkin's lymphoma or non-Hodgkin's lymphoma (NHL)), myeloma (e.g., multiple myeloma (MM)), mycosis fungoides, Merkel cell carcinoma, hematologic malignancies, blood tissue cancers, B-cell cancers, bronchial cancer, stomach cancer, brain or central nervous system cancer, peripheral nervous system cancer, uterine or endometrial cancer, oral or pharyngeal cancer, liver cancer, testicular cancer, biliary tract cancer, small intestine or appendix cancer, salivary gland cancer, adrenal gland cancer, adenocarcinoma, inflammatory myofibroblastic tumor, gastrointestinal stromal tumor (GIST), colon cancer, myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), polycythemia vera, chordoma, synovial tumor, Ewing's tumor, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, cholangiocarcinoma, choriocarcinoma, seminoma, fetal Cancers include, but are not limited to, thyroid cancer, Wilms' tumor, bladder cancer, epithelial cancer, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, small cell carcinoma, essential thrombocythemia, idiopathic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familial hypereosinophilia, neuroendocrine carcinoma, or carcinoid tumor. More specific examples of such cancers include lung cancer, e.g., NSCLC, squamous cell carcinoma (e.g., epithelial squamous cell carcinoma), lung cancer, e.g., small cell lung cancer (SCLC), and lung adenocarcinoma and lung squamous cell carcinoma.In a specific example, the lung cancer is NSCLC, for example, locally advanced or metastatic NSCLC (e.g., stage IIIB NSCLC, stage IV NSCLC, or recurrent NSCLC). In some embodiments, the lung cancer (e.g., NSCLC) is unresectable / inoperable lung cancer (e.g., unresectable NSCLC). In some embodiments, the cancer is triple-negative metastatic breast cancer, including any histologically confirmed triple-negative (ER-, PR-, HER2-) breast adenocarcinoma with locally recurrent or metastatic disease (where locally recurrent disease is not amenable to resection for curative purposes).

[0169] As used herein, the term "tumor" refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. The terms "cancer," "cancerous," and "tumor" are not mutually exclusive as referred to herein.

[0170] The term "pharmaceutical formulation" refers to a preparation in which the biological activity of the active ingredient contained therein is in a form such that it is effective and which does not contain additional ingredients that are unacceptably toxic to the subject to which the formulation will be administered.

[0171] "Pharmaceutically acceptable carrier" refers to an ingredient in a pharmaceutical formulation other than an active ingredient that is non-toxic to a subject. Pharmaceutically acceptable carriers include, but are not limited to, buffers, excipients, stabilizers, or preservatives.

[0172] As used herein, "treatment" (and grammatical variations thereof, such as "treat" or "treating") refers to a clinical intervention to alter the natural course of the treated individual and may be performed prophylactically or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, prevention of disease onset or recurrence, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, prevention of metastasis, reduction in the rate of disease progression, improvement or palliation of the disease state, and remission or improved prognosis. In some embodiments, antibodies (e.g., anti-PD-L1 antibodies and / or anti-PD-1 antibodies) are used to delay disease onset or slow disease progression.

[0173] The term "anti-cancer therapy" refers to a therapy useful in the treatment of cancer. Examples of anti-cancer therapeutic agents include, but are not limited to, cytotoxic agents, chemotherapeutic agents, growth inhibitory agents, agents used in radiation therapy, anti-angiogenic agents, apoptotic agents, anti-tubulin agents, and other agents for treating cancer, such as, for example, anti-CD20 antibodies, platelet-derived growth factor inhibitors (e.g., GLEEVEC™ (imatinib mesylate)), COX-2 inhibitors (e.g., celecoxib), interferons, cytokines, antagonists (e.g., neutralizing antibodies) that bind to one or more of the following targets: PDGFR-β, BlyS, APRIL, BCMA receptor(s), TRAIL / Apo2, other biologically active and organic chemical agents, etc. Combinations thereof are also encompassed by the present invention.

[0174] The term "cytotoxic agent," as used herein, refers to a substance that inhibits or prevents the function of cells and / or causes destruction of cells. This term also includes radioactive isotopes (e.g., At 211 , I 131 , I 125 , Y 90 ,Re 186 ,Re 188 , Sm 153 , Bi 212 , P 32, and radioactive isotope Lu), chemotherapeutic agents such as methotrexate, adriamycin, vinca alkaloids (vincristine, vinblastine, etoposide), doxorubicin, melphalan, mitomycin C, chlorambucil, daunorubicin, or other intercalating agents, enzymes and fragments thereof, such as nucleases, antibiotics, and toxins, such as small molecule toxins or enzymatically active toxins of bacterial, fungal, plant, or animal origin (including fragments and / or variants thereof), as well as various antitumor or anticancer agents disclosed below. Other cytotoxic agents are described below. Tumoricidal agents cause the destruction of tumor cells.

[0175] A "chemotherapeutic agent" is a chemical compound useful in the treatment of cancer. Examples of chemotherapeutic agents include alkylating agents, such as thiotepa and CYTOXAN® cyclosphosphamide; alkyl sulfonates, such as busulfan, improsulfan, and piposulfan; aziridines, such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylameramines, such as altretamine, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, and trimethylolmelamine; acetonitrile, ... togenins (especially bullatacin and bullatacinone); delta-9-tetrahydrocannabinol (dronabinol, MARINOL®); beta-lapachone; lapachol; colchicine; betulinic acid; camptothecins (e.g., synthetic analogs topotecan (HYCAMTIN®), CPT-11 (irinotecan, CAMPTOSAR®), acetylcamptothecin, scopolectin, and 9-aminocamptothecin); bryostatin; kallistatin; CC -1065 (e.g., its synthetic analogs adozelesin, carzelesin, and bizelesin); podophyllotoxin; podophyllic acid; teniposide; cryptophycins (e.g., cryptophycin 1 and cryptophycin 8); dolastatins; duocarmycins (e.g., synthetic analogs KW-2189 and CB1-TM1); eleutherobin; pancratistatin; sarcodictyin; spongistatin; nitrogen mustards, e.g., chlorambucil, chlornaphazine, colofosfamidis amide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembicin, fenesterine, prednimustine, trofosfamide, uracil mustard; nitrosoureas, such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimnustine; antibiotics, such as enediyne antibiotics (e.g., calicheamicins, particularly calicheamicin γ1I and calicheamicin ω1I (see, e.g., Nicolaou et al., Angew. Chem Intl. Ed. Engl., 33:183-186 (1994)); dynemicins, such as dynemicin A; esperamicin;and neocarzinostatin chromophore and related chromoprotein enediyne antibiotic chromophores, aclacinomycin, actinomycin, anthramycin, azaserine, bleomycin, cactinomycin, carubicin, carminomycin, carzinophilin, chromomycin, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, ADRIAMYCIN® doxorubicin (e.g., morpholino-doxorubicin, cyanomorpholino-doxorubicin, 2-pyrrolino-doxorubicin, and deoxycyano-doxorubicin). cidoxorubicin), epirubicin, esorubicin, idarubicin, maceromycin, mitomycins, e.g., mitomycin C, mycophenolic acid, nogalamycin, olivomycin, peplomycin, potfilomycin, puromycin, queramycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin; antimetabolites, e.g., methotrexate and 5-fluorouracil (5-FU); folic acid analogues, e.g., denopterin, methotrexate, pteropterin, trimetrexate sate; purine analogues, for example, fludarabine, 6-mercaptopurine, thiamiprine, thioguanine; pyrimidine analogues, for example, ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, floxuridine; androgens, for example, calsterone, dromostanolone propionate, epithiostanol, mepitiostane, testolactone; antiadrenal agents, for example, aminoglutethimide, mitotane, trilostane; folic acid replenishers, for example, furoic acid; aceglatone; alginate Dofosfamide glycoside; Aminolevulinic acid; Eniluracil; Amsacrine; Bestravcil; Bisantrene; Edatraxate; Defofamine; Demecolcine; Diazicon; Elfomitine; Elliptinium acetate; Epothilone; Etoglucide; Gallium nitrate; Hydroxyurea; Lentinan; Lonidamine; Maytansinoids, such as maytansine and ansamitocin; Mitoguazone; Mitoxantrone; Mopidamunol; Nitracrine; Pentostatin; Fenamet; Pirarubicin; Rosoxantrone; 2-Ethylhydrazide; Procarbazine;PSK® polysaccharide complex (JHS Natural Products, Eugene, OR); razoxane; rhizoxin; schizofiran; spirogermanium; tenuazonic acid; triazicone; 2,2',2"-trichlorotriethylamine; trichothecenes (especially T-2 toxin, veraculin A, loridene A, and anguidene); urethane; vindesine (ELDISINE®, FILDESIN®); dacarbazine; mannomustine; mitobronitol; mitolactol; pipobroman; gacytosine; arabinoside ("Ara-C"); thiotepa; taxoids, such as TAXOL® paclitaxel (Bristol-Myers Squibb Oncology, Princeton, NJ), ABRAXANE™ Cremophor-free, an albumin-engineered nanoparticle formulation of paclitaxel (American Pharmaceutical Partners, Schaumberg, Illinois), and TAXOTERE® docetaxel (Rhone-Poulenc Taxanes, including those listed in Table 1 (Rorer, Antony, France); chlorambucil; gemcitabine (GEMZAR®); 6-thioguanine; mercaptopurine; methotrexate; platinum or platinum-based chemotherapy agents and platinum analogues, such as cisplatin, carboplatin, oxaliplatin (ELOXATIN™), satraplatin, picoplatin, nedaplatin, triplatin, and lipoplatin; vinblastine (VELBAN®); platinum; etoposide (VP-16); ifosfamide; mitoxantrone; vincristine (ONCOVIN®); oxaliplatin; leucovorin; vinorelbine (NAVELBINE®); novantrone; edatrexate; daunomycin; aminopterin; ibandronate; the topoisomerase inhibitor RFS 2000; difluoromethylornithine (DMFO); retinoids, e.g., retinoic acid; capecitabine (XELODA®); pharmaceutically acceptable salts, acids, or derivatives of any of the above;and combinations of two or more of the above, such as CHOP (an abbreviation for the combination therapy of cyclophosphamide, doxorubicin, vincristine, and prednisolone), and FOLFOX (an abbreviation for the treatment regimen of oxaliplatin (ELOXATIN™) in combination with 5-FU and leucovorin). Additional chemotherapeutic agents include, for example, cytotoxic agents available as antibody-drug conjugates, such as maytansinoids (DM1, for example) and the auristatins MMAE and MMAF.

[0176] "Chemotherapeutic agents" also include "antihormonal agents" or "endocrine therapy agents," which act to regulate, reduce, block, or inhibit the action of hormones that can promote cancer growth, often in the form of systemic or whole-body treatments, which themselves may be hormones. Examples include antiestrogens and selective estrogen receptor modulators (SERMs), such as tamoxifen (e.g., NOLVADEX® tamoxifen), EVISTA® raloxifene, droloxifene, 4-hydroxytamoxifen, trioxifene, ketoxifene, LY117018, onapristone, and FARESTON® toremifene; antiprogesterones; estrogen receptor down-regulators (ERDs); agents that function to suppress or shut down the ovaries, such as luteinizing hormone-releasing hormone (LHRH) agonists, such as LUPRON® and ELIGARD® leuprolide acetate, goserelin acetate, buserelin acetate, and tripterelin; other antiandrogens, such as flutamide, nilutamide, and bicalutamide; and aromatase inhibitors, which inhibit the enzyme aromatase, which regulates estrogen production in the adrenal glands, such as 4(5)-imidazole, aminoglutethimide, MEGASE® megestrol acetate, AROMASIN® exemestane, holmestein, fadrozole, RIVISOR® vorozole, FEMARA® letrozole, and ARIMIDEX® anastrozole.Additionally, this definition of chemotherapeutic agent includes bisphosphonates, such as clodronate (e.g., BONEFOS® or OSTAC®), DIDROCAL® etidronate, NE-58095, ZOMETA® zoledronic acid / zoledronate, FOSAMAX® alendronate, AREDIA® pamidronate, SKELID® tiludronate, or ACTONEL® risedronate; and troxacitabine (a 1,3-dioxolane nucleoside cytosine analog); antisense oligonucleotides, particularly those that inhibit genes in signal transduction pathways involved in abnormal cell proliferation. These include those that inhibit the expression of genes such as PKC-alpha, Raf, H-Ras, and epidermal growth factor receptor (EGFR); vaccines, such as the THERATOOPE® vaccine and gene therapy vaccines, such as the ALLOVECTIN® vaccine, the LEUVECTIN® vaccine, and the VAXID® vaccine; LURTOTECAN® topoisomerase 1 inhibitors; ABARELIX® rmRH; lapatinib ditosylate (an ErbB-2 and EGFR dual tyrosine kinase small molecule inhibitor, also known as GW572016); and pharmaceutically acceptable salts, acids, or derivatives of any of the above.

[0177] Chemotherapeutic agents also include antibodies, such as alemtuzumab (Campath), bevacizumab (AVASTIN®, Genentech); cetuximab (ERBITUX®, Imclone); panitumumab (VECTIBIX®, Amgen), rituximab (RITUXAN®, Genentech / Biogen Idec), pertuzumab (OMNITARG®, 2C4, Genentech), trastuzumab (HERCEPTIN®, Genentech), tositumomab (Bexxar, Corixia), and the antibody-drug conjugate, gemtuzumab ozogamicin (MYLOTARG®, Wyeth). Additional humanized monoclonal antibodies that have therapeutic potential as drugs in combination with the compounds of the invention include apolizumab, aselizumab, atlizumab, bapineuzumab, bivatuzumab mertansine, cantuzumab mertansine, cedelizumab, certolizumab pegol, cidfusituzumab, cidtuzumab, daclizumab, eculizumab, efalizumab, epratuzumab, erlizumab, felvizumab, fontolizumab, gemtuzumab ozogamicin, inotuzumab ozogamicin, ipilimumab, labetuzumab, lintuzumab, matuzumab, mepolizumab, motavizumab, motavizumab, natalizumab, nimotuzumab, and the like. Mab, norobizumab, numavizumab, ocrelizumab, omalizumab, palivizumab, pascolizumab, pecfusituzumab, pectuzumab, pexelizumab, ralivizumab, ranibizumab, reslivizumab, reslizumab, resivizumab, rovelizumab, ruplizumab, sibrotuzumab, siplizumab, sontuzumab These include tuximab, tacatuzumab tetraxetan, tadocizumab, talizumab, tefibazumab, tocilizumab, toralizumab, tucotuzumab celmoleukin, tuxituzumab, umavisumab, urtoxazumab, ustekinumab, visilizumab, and anti-interleukin-12 (ABT-874 / J695, Wyeth Research and Abbott Laboratories) (an exclusively human sequence recombinant full-length IgG1λ antibody genetically modified to recognize the interleukin-12 p40 protein).

[0178] Chemotherapeutic agents also include "EGFR inhibitors," alternatively referred to as "EGFR antagonists," which refer to compounds that bind to or otherwise directly interact with EGFR and block or reduce its signaling activity. Examples of such agents include antibodies and small molecules that bind to EGFR. Examples of antibodies that bind to EGFR include MAb 579 (ATCC CRL HB 8506), MAb 455 (ATCC CRL HB 8507), MAb 225 (ATCC CRL 8508), MAb 528 (ATCC CRL 8509) (see U.S. Pat. No. 4,943,533, Mendelsohn et al.) and variants thereof, such as chimerized 225 (C225 or cetuximab, ERBUTIX®) and reshaped human 225 (H225) (WO 96 / 40210, Imclone Systems, Inc.). Inc.); IMC-11F8, a fully human EGFR-targeting antibody (Imclone); antibodies that bind to type II mutant EGFR (U.S. Pat. No. 5,212,290); humanized and chimeric antibodies that bind to EGFR as described in U.S. Pat. No. 5,891,996; and human antibodies that bind to EGFR, e.g., ABX-EGF or panitumumab (see WO 98 / 50433, Abgenix / Amgen); EMD55900 (Stragliotto et al. Eur. J. Cancer 32A:636-640 (1996); EMD7200 (matuzumab) (a humanized EGFR antibody directed against EGFR that competes with both EGF and TGF-alpha for EGFR binding) (EMD / Merck); the human EGFR antibody, HuMax-EGFR (GenMab); the fully human antibodies known as E1.1, E2.4, E2.5, E6.2, E6.4, E2.11, E6.3, and E7.6.3 and described in US Pat. No. 6,235,883; MDX-447 (Medarex Inc); and mAb806 or humanized mAb806 (Johns et al., J. Biol. Chem. 279(29):30375-30384 (2004)).Anti-EGFR antibodies can be conjugated to cytotoxic agents, thus forming immunoconjugates (see, e.g., EP 659,439A2, Merck Patent GmbH). EGFR antagonists can be used in combination with other cytotoxic agents, such as those disclosed in U.S. Patent Nos. 5,616,582, 5,457,105, 5,475,001, 5,654,307, 5,679,683, 6,084,095, 6,265,410, 6,455,534, 6,521,620, 6,596,726, 6,713,484, 5,770,599, 6,140,332, 5, and 5,747,498, and the following PCT publications: WO98 / 14451, WO98 / 50038, WO99 / 09016, and WO99 / 24037.Specific small molecule EGFR antagonists include OSI-774 (CP-358774, erlotinib, TARCEVA® Genentech / OSI Pharmaceuticals); PD 183805 (CI 1033, 2-propenamide, N-[4-[(3-chloro-4-fluorophenyl)amino]-7-[3-(4-morpholinyl)propoxy]-6-quinazolinyl]-, dihydrochloride, Pfizer Inc.); ZD1839, gefitinib (IRESSA®) 4-(3'-chloro-4'-fluoroanilino)-7-methoxy-6-(3-morpholinopropoxy)quinazoline, AstraZeneca); ZM 105180 ((6-amino-4-(3-methylphenyl-amino)-quinazoline, Zeneca); BIBX-1382 (N8-(3-chloro-4-fluoro-phenyl)-N2-(1-methyl-piperidin-4-yl)-pyrimido[5,4-d]pyrimidine-2,8-diamine, Boehringer Ingelheim; PKI-166 ((R)-4-[4-[(1-phenylethyl)amino]-1H-pyrrolo[2,3-d]pyrimidin-6-yl]-phenol); (R)-6-(4-hydroxyphenyl)-4-[(1-phenylethyl)amino]-7H-pyrrolo[2,3-d]pyrimidine; CL-387785 (N-[4-[(3-bromophenyl)amino]-6-quinazolinyl]-2-butynamide); EKB-569 (N-[4-[(3-chloro-4-fluorophenyl)amino]-3-cyano-7-ethoxy-6-quinolinyl]-4-(dimethylamino)-2-butenamide) (Wyeth); AG1478 (Pfizer); AG1571 (SU 5271; Pfizer); and dual EGFR / HER2 tyrosine kinase inhibitors, such as lapatinib (TYKERB®, GSK572016, or N-[3-chloro-4-[(3-fluorophenyl)methoxy]phenyl]-6[5[[[2methylsulfonyl)ethyl]amino]methyl]-2-furanyl]-4-quinazolinamine).

[0179] Chemotherapeutic agents include "tyrosine kinase inhibitors," such as the EGFR-targeted drugs described in the previous paragraph; small molecule HER2 tyrosine kinase inhibitors, such as TAK165 available from Takeda; CP-724,714, an oral selective inhibitor of ErbB2 receptor tyrosine kinase (Pfizer and OSI); dual HER inhibitors, such as EKB-569 (available from Wyeth), which preferentially binds to EGFR but inhibits both HER2 and EGFR-overexpressing cells; lapatinib (GSK572016, available from Glaxo-SmithKline), an oral HER2 and EGFR tyrosine kinase inhibitor; PKI-166 (available from Novartis); pan-HER inhibitors, such as canertinib (CI-1033, Pharmacia); Raf-1 inhibitors, such as ISIS, which inhibits Raf-1 signaling. antisense drug ISIS-5132 available from Pharmaceuticals; non-HER-targeted TK inhibitors such as imatinib mesylate (GLEEVEC®, available from GlaxoSmithKline); multi-targeted tyrosine kinase inhibitors such as sunitinib (SUTENT®, available from Pfizer); VEGF receptor tyrosine kinase inhibitors such as vatalanib (PTK787 / ZK222584, available from Novartis / Schering AG); the MAPK extracellular regulated kinase I inhibitor CI-1040 (available from Pharmacia); quinazolines such as PD 153035, 4-(3-chloroanilino)quinazoline; pyridopyrimidines; pyrimidopyrimidines; pyrrolopyrimidines such as CGP 59326, CGP 60261, and CGP 62706; pyrazolopyrimidine, 4-(phenylamino)-7H-pyrrolo[2,3-d]pyrimidine; curcumin (diferuloylmethane, 4,5-bis(4-fluoroanilino)phthalimide); tyrphostins containing a nitrothiophene moiety; PD-0183805 (Warner-Lamber); antisense molecules (e.g., those that bind to HER-encoding nucleic acids); quinoxalines (U.S. Patent No. 5,804,396); tryphostins (U.S. Patent No. 5,804,396); ZD6474 (Astra Zeneca);PTK-787 (Novartis / Schering AG); pan-HER inhibitors, such as CI-1033 (Pfizer); Affinitac (ISIS 3521, Isis / Lilly); imatinib mesylate (GLEEVEC®); PKI 166 (Novartis); GW2016 (GlaxoSmithKline); CI-1033 (Pfizer); EKB-569 (Wyeth); Semaxinib (Pfizer); ZD6474 (AstraZeneca); PTK-787 (Novartis / Schering AG); INC-1C11 (Imclone), rapamycin (sirolimus, RAPAMUNE®); or the following patent publications: U.S. Pat. No. 5,804,396; WO 1999 / 09016 (American Cyanamid); WO 1998 / 43960 (American Cyanamid); WO1997 / 38983 (Warner Lambert); WO1999 / 06378 (Warner Lambert); WO1999 / 06396 (Warner Lambert); WO1996 / 30347 (Pfizer, Inc); WO1996 / 33978 (Zeneca); WO1996 / 3397 (Zeneca); and WO1996 / 33980 (Zeneca).

[0180] Chemotherapeutic agents include dexamethasone, interferon, colchicine, metoprine, cyclosporine, amphotericin, metronidazole, alemtuzumab, alitretinoin, allopurinol, amifostine, arsenic trioxide, asparaginase, live BCG, bevacuzimab, bexarotene, cladribine, clofarabine, darbepoetin alfa, denileukin, dexrazoxane, epoetin alfa, erotinib, filgrastim, histrelin acetate, ibritumomab, interferon alfa-2a, and interferon alfa- Also included are 2b, lenalidomide, levamisole, mesna, methoxsalen, nandrolone, nelarabine, nofetumomab, oprelvekin, palifermin, pamidronate, pegademase, pegaspargase, pegfilgrastim, pemetrexed disodium, plicamycin, porfimer sodium, quinacrine, rasburicase, sargramostim, temozolomide, VM-26, 6-TG, toremifene, tretinoin, ATRA, valrubicin, zoledronate, and zoledronic acid, and pharmaceutically acceptable salts thereof.

[0181] Chemotherapeutic agents include hydrocortisone, hydrocortisone acetate, cortisone acetate, tixocortol pivalate, triamcinolone acetonide, triamcinolone alcohol, mometasone, amcinonide, budesonide, desonide, fluocinonide, fluocinolone acetonide, betamethasone, betamethasone sodium phosphate, dexamethasone, dexamethasone sodium phosphate, fluocortolone, hydrocortisone-17-butyrate, hydrocortisone- 17-valerate, aclometasone propionate, betamethasone valerate, betamethasone dipropionate, prednicarbate, clobetasone-17-butyrate, clobetasol-17-propionate, fluocortolone caproate, fluocortolone pivalate, and fluprednidene acetate; immunoselective anti-inflammatory peptides (ImSAIDs), such as phenylalanine-glutamine-glycine (FEG) and its D-isomer (feG) (IMULAN) BioTherapeutics, LLC; antirheumatic drugs such as azathioprine, cyclosporine (cyclosporine A), D-penicillamine, gold salts, hydroxychloroquine, leflunomide, minocycline, sulfasalazine, tumor necrosis factor alpha (TNFα) blockers such as etanercept (ENBREL®), infliximab (REMICADE®), adalimumab (HUMIRA®), certolizumab pegol (CIMZIA®), golimumab (SI MPONI®), interleukin 1 (IL-1) blockers, e.g., anakinra (KINERET®), T-cell costimulation blockers, e.g., abatacept (ORENCIA®), interleukin 6 (IL-6) blockers, e.g., tocilizumab (ACTEMERA®); interleukin 13 (IL-13) blockers, e.g., lebrikizumab; interferon alpha (IFN) blockers, e.g., rontalizumab; beta 7 integrin blockers, e.g., rhuMAb Beta7; IgE pathway blockers, e.g., anti-M1 prime; secreted homotrimeric LTa3 and membrane-bound heterotrimeric LTa1 / β2 blockers, e.g., anti-lymphotoxin alpha (LTa);Various investigational drugs, such as thioplatin, PS-341, phenyl butyrate, ET-18-OCH3, and farnesyltransferase inhibitors (L-739749, L-744832); polyphenols, such as quercetin, resveratrol, piceatannol, epigallocatechin gallate, theaflavins, flavanols, procyanidins, betulinic acid, and their derivatives; autophagy inhibitors, such as chloroquine; delta-9-tetrahydrocannabinol (dronabinol, MARINOL®); beta-lapachone; lapachol; colchicine; betulinic acid; acetylcamptothecin, scopoletin, and 9-aminocamptothecin; podophyllotoxin; tegafur (UFTORAL®); bexarotene (TARGRETIN®); bisphosphonates, such as clodronate (e.g., BONEFOS® or OSTAC®). trademark), etidronate (DIDROCAL®), NE-58095, zoledronic acid / zoledronate (ZOMETA®), alendronate (FOSAMAX®), pamidronate (AREDIA®), tiludronate (SKELID®), or risedronate (ACTONEL®); and epidermal growth factor receptor (EGF-R); vaccines, e.g., THERATOPE® vaccine; perifosine, COX-2 inhibitors (e.g., celecoxib or etoricoxib), proteasome inhibitors (e.g., PS341); CCI-779; tipifarnib (R11577), or afenib, ABT510; Bcl-2 inhibitors, e.g., oblimersen sodium (GENASENSE®); pixantrone; farnesyltransferase inhibitors, e.g., lonafarnib (SCH) 6636, SARASAR™); and pharmaceutically acceptable salts, acids, or derivatives of any of the foregoing; and combinations of two or more of the foregoing.

[0182] The term "prodrug" as used herein refers to a precursor or derivative form of a pharmaceutically active substance that is less cytotoxic to tumor cells compared to the parent drug and can be enzymatically activated or converted into a more active parent form. See, for example, Wilman, "Prodrugs in Cancer Chemotherapy," Biochemical Society Transactions, 14, pp. 375-382, 615th Meeting Belfast (1986), and Stella et al., "Prodrugs: A Chemical Approach to Targeted Drug Delivery," Directed Drug Delivery, Borchardt et al., (ed.), pp. 247-267, Humana Press (1985). Prodrugs of the present invention include, but are not limited to, phosphate-containing prodrugs, thiophosphate-containing prodrugs, sulfate-containing prodrugs, peptide-containing prodrugs, D-amino acid modified prodrugs, glycosylated prodrugs, β-lactam-containing prodrugs, optionally substituted phenoxyacetamide-containing prodrugs, or optionally substituted phenylacetamide-containing prodrugs, 5-fluorocytosine, and other 5-fluorouridine prodrugs, which can be converted to more active cytotoxic free drugs. Examples of cytotoxic drugs that can be derivatized into prodrug forms for use in the present invention include, but are not limited to, the chemotherapeutic agents described above.

[0183] A "growth inhibitory agent," as used herein, refers to a compound or composition that inhibits the growth and / or proliferation of cells (e.g., cells whose growth is dependent on PD-L1 expression) in vitro or in vivo. Thus, a growth inhibitory agent may be one that significantly reduces the percentage of cells in S phase. Examples of growth inhibitory agents include agents that block cell cycle progression (at a place other than S phase), such as agents that induce G1 arrest and M-phase arrest. Classical M-phase blockers include the vincas (vincristine and vinblastine), taxanes, and topoisomerase II inhibitors, such as the anthracycline antibiotic doxorubicin ((8S-cis)-10-[(3-amino-2,3,6-trideoxy-α-L-lyxo-hexapyranosyl)oxy]-7,8,9,10-tetrahydro-6,8,11-trihydroxy-8-(hydroxyacetyl)-1-methoxy-5,12-naphthacenedione), epirubicin, daunorubicin, etoposide, and bleomycin. Agents that arrest G1, such as DNA alkylating agents such as tamoxifen, prednisone, dacarbazine, mechlorethamine, cisplatin, methotrexate, 5-fluorouracil, and ara-C, also cause S-phase arrest. Further information can be found in "The Molecular Basis of Cancer," Mendelsohn and Israel, eds., Chapter 1, entitled "Cell cycle regulation, oncogenes, and anticancer drugs" by Murakami et al. (WB Saunders: Philadelphia, 1995), especially chapter 13. Taxanes (paclitaxel and docetaxel) are anticancer drugs, both derived from the yew tree. Docetaxel (TAXOTERE®, Rhone-Poulenc Rorer), derived from the European yew, is a semisynthetic analog of paclitaxel (TAXOL®, Bristol-Myers Squibb). Paclitaxel and docetaxel promote the assembly of microtubules from tubulin dimers and stabilize microtubules by preventing depolymerization, thereby resulting in the inhibition of mitosis in cells.

[0184] "Radiation therapy" refers to the use of directed gamma or beta radiation to cause sufficient damage to cells so as to limit their ability to function normally or destroy them completely. It is understood that there are many methods known in the art for determining dosage and duration of treatment. A typical treatment is given as a single administration, with typical dosages ranging from 10 to 200 units (Gy) per day.

[0185] As used herein, the terms "individual," "patient," or "subject" are used interchangeably and refer to any single animal, more preferably a mammal (including, e.g., dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human animals such as non-human primates), for which treatment is desired. In a specific embodiment, a patient herein is a human.

[0186] As used herein, "administering" refers to a method of providing a subject (e.g., a patient) with a dosage of a compound (e.g., an antagonist) or pharmaceutical composition (e.g., a pharmaceutical composition comprising an antagonist). Administration can be by any suitable means, including parenteral, intrapulmonary, and intranasal, as well as intralesional administration if localized treatment is desired. Parenteral infusions include, for example, intramuscular, intravenous, intraarterial, intraperitoneal, or subcutaneous administration. Dosing can be by any suitable route, e.g., injection, such as intravenous or subcutaneous injection, depending in part on whether administration is brief or chronic. Various dosing schedules are contemplated herein, including, but not limited to, a single dose or multiple doses over various time points, bolus administration, and pulse infusion.

[0187] The term "concurrently" is used herein to refer to the administration of two or more therapeutic agents, where at least some of the administration overlaps in time. Thus, concurrent administration includes a dosing regimen in which the administration of one or more agent(s) continues after the administration of one or more other agent(s) is discontinued.

[0188] "Reduce or inhibit" means the ability to cause an overall decrease of 20%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, or more. Reduction or inhibition can refer, for example, to the symptoms of the disorder being treated, the presence or size of metastases, or the size of the primary tumor.

[0189] The term "package insert" is used to refer to instructions typically included in the commercial packaging of a therapeutic product that contain information regarding the indications, usage, dosage, administration, concomitant therapy, contraindications, and / or warnings for the use of such a therapeutic product.

[0190] A "sterile" preparation is one that is aseptic or free of all living microorganisms and their spores.

[0191] An "article of manufacture" is any article of manufacture (e.g., package or container) or kit that includes at least one reagent, e.g., an agent for treating a disease or disorder (e.g., cancer), or a probe that specifically detects a biomarker (e.g., PD-L1) described herein. In certain embodiments, the article of manufacture or kit is promoted, distributed, or sold as a unit for performing the methods described herein.

[0192] The phrase "based on," as used herein, means that information about one or more biomarkers is used to inform treatment decisions, information provided in package inserts, marketing / promotional guidance, etc.

[0193] III. Method treating individuals with cancer, and identifying individuals with cancer who may benefit from a therapy comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab (MPDL3280A)), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof; Diagnosing a patient with cancer and determining whether the individual with cancer is likely to respond to treatment with an anti-cancer therapy comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof; The present invention also provides methods for optimizing the therapeutic efficacy of anti-cancer therapies comprising a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof, for selecting a therapy for an individual with cancer, and for providing a prognosis for an individual with cancer. and monitoring an individual's response to treatment with an anti-cancer therapy comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof.

[0194] The methods and assays described herein are based on the finding that a circulating tumor mutation burden (bTMB) score determined from a sample from an individual (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) can be used to predict the therapeutic efficacy of an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist therapy, e.g., a PD-L1 axis-binding antagonist monotherapy or a combination therapy comprising a PD-L1 axis-binding antagonist (e.g., a PD-L1 axis-binding antagonist in combination with an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), and / or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)). Any of these methods may further comprise determining a maximum somatic allele frequency (MSAF). Any of these methods may further comprise determining a tTMB score. Any of the methods provided herein may further comprise administering to the individual a PD-L1 axis-binding antagonist (e.g., as described in Section IV, below). Accordingly, methods and assays for assessing bTMB in a sample from an individual are also provided herein. Any of the methods provided herein may comprise administering to the individual an anti-cancer therapy other than, or in addition to, an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., as described in Section IV, below), an antagonist directed against a coinhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof. Any of these methods may further comprise administering to the individual an effective amount of an additional therapeutic agent described herein.

[0195] A. Diagnostic Methods and Assays (i) Predictive diagnostic methods In a specific example, the methods and assays provided herein can be used to identify individuals with cancer who may benefit from treatment comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab (MPDL3280A)), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof, and the methods can be used to identify individuals with cancer who may benefit from treatment comprising an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab (MPDL3280A)), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof, a bTMB score from the sample that is at or exceeds the reference bTMB score identifies the individual as one who may benefit from treatment including an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof.

[0196] In a specific example, the methods and assays provided herein can be used to select a therapy for an individual with cancer, the method comprising determining a bTMB score from a sample from the individual, wherein a bTMB score from the sample that is at or exceeds a reference bTMB score identifies the individual as one who may benefit from treatment including an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist (e.g., an anti-PD-L1 antibody, e.g., atezolizumab), an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof.

[0197] In specific examples, the methods and assays provided herein can be used to diagnose patients with cancer, the methods including determining a bTMB score from a sample from an individual, wherein a bTMB score from the sample that is at or above a reference bTMB score identifies the individual as likely to have cancer. In some examples, a bTMB score below the reference bTMB score identifies the individual as unlikely to have cancer.

[0198] The methods provided herein may include determining a bTMB score from a sample from an individual (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof). The sample from the individual may be an archived sample, a fresh sample, or a frozen sample. The determining step may include determining the total number of somatic mutations (e.g., base substitutions in coding regions and / or indel mutations in coding regions) occurring in a given set of genes to derive a bTMB score from the sample from the individual. In some embodiments, the number of somatic mutations is the number of single nucleotide variants (SNVs) counted, or the sum of the number of SNVs and the number of indel mutations counted.

[0199] The number of somatic mutations can be determined qualitatively and / or quantitatively based on any suitable criteria known in the art, including, but not limited to, measurements of DNA, mRNA, cDNA, protein, protein fragment, and / or gene copy number levels in the individual. In some examples, a comprehensive genomic profile of the individual is determined. In some examples, a comprehensive genomic profile of a sample collected from the individual (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) is determined. In some examples, determining the genomic profile includes applying next-generation sequencing methods known in the art or described herein to identify genomic modifications (e.g., somatic mutations (e.g., base substitutions in coding regions and / or indel mutations in coding regions)). In some examples, the test involves a diverse set of about 300 genes (e.g., at least about 300 to about 400 genes, e.g., a diverse set of at least about 0.05 Mb to about 10 Mb (e.g., 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 Mb) covering at least about 300 genes (e.g., a diverse set of at least about 300 to about 400 genes, e.g., a diverse set of at least about 0.05 Mb to about 10 Mb). Simultaneously sequence the coding regions of at least about 500-fold (e.g., 500x, 550x, 600x, 650x, 700x, 750x, 800x, 850x, 900x, 950x, or 1,000x) of a total of 1000 genes (e.g., about 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, or 400 genes) to a typical median exon coverage depth of at least about 500x (e.g., 500x, 550x, 600x, 650x, 700x, 750x, 800x, 850x, 900x, 950x, or 1,000x). In other examples, the test may be performed using a gene sequence of about 400 genes, about 425 genes, about 450 genes, about 475 genes, about 500 genes, about 525 genes, about 550 genes, about 575 genes, about 600 genes, about 625 genes, about 650 genes, about 675 genes, about 700 genes, about 725 genes, about 750 genes, about 775 genes, about 800 genes, about 825 genes, about 850 genes, about 875 genes, about 900 genes, about 925 genes, about 950 genes, about 975 genes, about 1000 genes,Or the coding regions of more than 1000 genes are sequenced simultaneously. In some examples, the gene set includes one or more genes (e.g., cancer-related genes) listed in Table 1. In some examples, the gene set is a FOUNDATIONONE® panel gene set (see, e.g., Frampton et al. Nat. Biotechnol. 31:1023-31, 2013, which is incorporated herein by reference in its entirety). In some examples, the gene set is a FOUNDATIONONE® CDx panel gene set. In some embodiments, the test is performed on more than about 10 Mb of the individual, e.g., more than about 10 Mb, more than about 15 Mb, more than about 20 Mb, more than about 25 Mb, more than about 30 Mb, more than about 35 Mb, more than about 40 Mb, more than about 45 Mb, more than about 50 Mb, more than about 55 Mb, more than about 60 Mb, more than about 65 Mb, more than about 70 Mb, more than about 75 Mb, more than about 80 Mb The genome is sequenced to more than about 85 Mb, more than about 90 Mb, more than about 95 Mb, more than about 100 Mb, more than about 200 Mb, more than about 300 Mb, more than about 400 Mb, more than about 500 Mb, more than about 600 Mb, more than about 700 Mb, more than about 800 Mb, more than about 900 Mb, more than about 1 Gb, more than about 2 Gb, more than about 3 Gb, or more than about 3.3 Gb. In some examples, the bTMB score is determined by whole-exome sequencing. In some examples, the bTMB score is determined by whole-genome sequencing. It is now understood that the bTMB score can be calculated independently of gene identity. In some examples, each covered sequencing read represents a unique DNA fragment, allowing for sensitive and specific detection of genomic alterations that occur at low frequencies due to tumor heterogeneity, low tumor purity, and small sample volume. The determining step may include determining the number of somatic mutations in cell-free DNA (cfDNA) and / or circulating tumor DNA (ctDNA) isolated from a sample from the individual (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) to derive a bTMB score. In some embodiments, the amount of cfDNA isolated from the sample is at least about 5 ng (e.g., at least about 5 ng, at least about 10 ng, at least about 15 ng, at least about 20 ng, at least about 25 ng, at least about 30 ng, at least about 35 ng, at least about 40 ng,At least about 45 ng, at least about 50 ng, at least about 75 ng, at least about 100 ng, at least about 200 ng, at least about 300 ng, at least about 400 ng, or more. For example, in some embodiments, the amount of cfDNA isolated from a sample is at least about 20 ng. In some embodiments, the amount of cfDNA isolated from a sample is, for example, about 5 ng to about 100 ng (e.g., about 5 ng to about 100 ng, about 5 ng to about 90 ng, about 5 ng to about 80 ng, about 5 ng to about 70 ng, about 5 ng to about 60 ng, about 5 ng to about 50 ng, about 5 ng to about 40 ng, about 5 ng to about 30 ng, about 5 ng to about 20 ng, about 5 ng to about 15 ng, about 5 ng to about 10 ng, about 10 ng to about 100 ng, about 10 ng to about 90 ng, about 10 ng to about 80 ng, about 10 ng to about 10 g ~ about 70ng, about 10ng - about 60ng, about 10ng - about 50ng, about 10ng - about 40ng, about 10ng - about 30ng, about 10ng - about 20ng, about 15ng - about 100ng, about 15ng - about 90ng, about 15ng - about 80ng, about 15ng to about 70ng, about 15ng to about 60ng, about 15ng to about 50ng, about 20ng to about 100ng, about 20ng to about 90ng, about 20ng to about 80ng, about 20ng to about 70ng, about 20ng to about 60ng, about 20ng to about 50ng , about 20ng to about 40ng, about 20ng to about 30ng, about 25ng to about 100ng, about 25ng to about 90ng, about 25ng to about 80ng, about 25ng to about 70ng, about 25ng to about 60ng, about 25ng to about 50ng, about 25ng to about 4 0ng, approximately 25ng to approximately 30ng, approximately 30ng to approximately 100ng, approximately 30ng to approximately 90ng, approximately 30ng to approximately 80ng, approximately 30ng to approximately 70ng, approximately 30ng to approximately 60ng, approximately 30ng to approximately 50ng, approximately 30ng to approximately 40ng, approximately 30ng to About 35ng, ​​about 35ng to about 100ng, about 35ng to about 90ng, about 35ng to about 80ng, about 35ng to about 70ng, about 35ng to about 60ng, about 35ng to about 50ng, about 35ng to about 40ng, about 40ng to about 100ng, about 40ng to about 90ng, about 40ng to about 80ng, about 40ng to about 70ng, about 40ng to about 60ng, about 40ng to about 50ng, about 40ng to about 45ng, about 50ng to about 100ng, about 50ng to about 90ng, about 50ng to about 80ng,In some embodiments, the amount of cfDNA isolated from the sample is about 100 ng or more (e.g., about 100 ng or more, about 200 ng or more, about 300 ng or more, about 400 ng or more, about 500 ng or more, about 600 ng or more, about 700 ng or more, about 800 ng or more, about 900 ng or more, or about 100 ng or more).

[0200] Any suitable sample volume can be used in any of the above-mentioned methods.For example, in some examples, sample (for example, whole blood sample, plasma sample, serum sample, or combination thereof) can have a volume of about 1mL to about 50mL, for example, about 1mL, about 2mL, about 3mL, about 4mL, about 5mL, about 6mL, about 7mL, about 8mL, about 9mL, about 10mL, about 11mL, about 12mL, about 13mL, about 14mL, about 15mL, about 16mL, about 17mL, about 18mL, about 19mL, about 20mL, about 22mL, about 24mL, about 26mL, about 28mL, about 30mL, about 32mL, about 34mL, about 36mL, about 38mL, about 40mL, about 42mL, about 44mL, about 46mL, about 48mL, or about 50mL. In some examples, the sample (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) may be about 1 mL to about 50 mL, about 1 mL to about 40 mL, about 1 mL to about 30 mL, about 1 mL to about 20 mL, about 1 mL to about 10 mL, about 5 mL to about 50 mL, about 5 mL to about 40 mL, about 5 mL to about 30 mL, about 5 mL to about 20 mL, about 5 mL to about 10 mL, about 6 mL to about 50 mL L, about 6mL to about 40mL, about 6mL to about 30mL, about 6mL to about 20mL, about 6mL to about 10mL, about 7mL to about 50mL, about 7mL to about 40mL, about 7mL to about 30mL, about 7mL ~20mL, 7mL~10mL, 8mL~50mL, 8mL~40mL, 8mL~30mL, 8mL~20mL, 8mL~10mL, 9mL~50mL , about 9mL to about 40mL, about 9mL to about 30mL, about 9mL to about 20mL, about 9mL to about 10mL, about 5mL to about 15mL, about 5mL to about 14mL, about 5mL to about 13mL, about 5mL ~12mL, 5mL~11mL, 6mL~15mL, 6mL~14mL, 6mL~13mL, 6mL~12mL, 6mL~11mL, 7mL~15mL , about 7 mL to about 14 mL, about 7 mL to about 13 mL, about 7 mL to about 12 mL, about 7 mL to about 11 mL, about 8 mL to about 15 mL, about 8 mL to about 14 mL, about 8 mL to about 13 mL, about 8 mL to about 12 mL, about 8 mL to about 11 mL, about 9 mL to about 15 mL, about 9 mL to about 14 mL, about 9 mL to about 13 mL, about 9 mL to about 12 mL, or about 9 mL to about 11 mL.In some examples, the sample (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) has a volume of about 10 mL. For example, in some examples, a plasma sample has a volume of 10 mL.

[0201] In some embodiments of any of the aforementioned methods, the somatic mutations assessed in the assay are each about 0.1% or more, e.g., about 0.1% or more, about 0.2% or more, about 0.3% or more, about 0.4% or more, about 0.5% or more, about 0.6% or more, about 0.7% or more, about 0.8% or more, about 0.9% or more, about 1.0% or more, about 1.1% or more, about 1.2% or more, about 1.3% or more, about 1.4% or more, about 1.5% or more, about 1.6% or more, about 1.7% or more, about 1.8% or more, about 1.9% or more, about 2.0% or more, about 2.1% or more, about 2.2% or more, about 2.3% or more, about 2.4% or more, about 2.5% or more, about 2.6% or more, about 2.7% or more, about 2.8% or more, about 2.9% or more, about 3.0% or more, about 3.1% or more. Above, about 3.2% or more, about 3.3% or more, about 3.4% or more, about 3.5% or more, about 3.6% or more, about 3.7% or more, about 3.8% or more, about 3.9% or more, about 4.0% or more, about 4 .1% or more, about 4.2% or more, about 4.3% or more, about 4.4% or more, about 4.5% or more, about 4.6% or more, about 4.7% or more, about 4.8% or more, about 4.9% or more, about 5.0% or more or greater, about 6.0% or greater, about 7.0% or greater, about 8.0% or greater, about 9.0% or greater, about 10.0% or greater, about 11.0% or greater, about 12.0% or greater, about 13.0% or greater, about 14.0% or greater, about 15.0% or greater, about 16.0% or greater, about 17.0% or greater, about 18.0% or greater, about 19.0% or greater, about 20.0% or greater, or greater. For example, in some embodiments, the somatic mutations assessed in the assay each have an allele frequency of 0.5% or greater. TIFF2023103258000002.tif253170TIFF2023103258000003.tif254170TIFF2023103258000004.tif249170TIFF2023103258000005.tif193170

[0202] The determining step may include determining the highest relative frequency of alleles (i.e., genetic variants with somatic mutations (e.g., base substitutions in coding regions and / or indel mutations in coding regions)) from a sample from the individual (e.g., a whole blood sample, a plasma sample, a serum sample, or a combination thereof) to derive the MSAF. The somatic allele frequency of the next most commonly occurring mutation may also be determined from the sample from the individual. In some examples, the somatic allele frequency of each mutation detected from the sample from the individual is determined. In some examples, a sample with multiple somatic mutations will present those mutations as a distribution of somatic allele frequencies, likely according to their original clonal frequency in the cancer (e.g., tumor). In some examples, somatic allele frequencies greater than 40% (e.g., greater than 40%, 50% or greater, 60% or greater, 70% or greater, 80% or greater, 90% or greater, or 100%) are eliminated, and the variant with the next highest somatic allele frequency less than 40% (e.g., 40% or less) is determined to be the MSAF of the sample. In some instances, the MSAF is calculated from a maximum somatic allele frequency of less than 20% in a sample. Germline mutations may be found to have a somatic allele frequency distribution of about 50% to about 100%.

[0203] The determination of the MSAF can occur before, simultaneously with, or after the determination of the bTMB score from a sample from the individual.

[0204] In any of the foregoing examples, the individual may be diagnosed with, for example, lung cancer (e.g., non-small cell lung cancer (NSCLC)), renal cancer (e.g., renal urothelial carcinoma), bladder cancer (e.g., bladder urothelial (transitional cell) carcinoma), breast cancer, colorectal cancer (e.g., colon adenocarcinoma), ovarian cancer, pancreatic cancer, gastric cancer, esophageal cancer, mesothelioma, melanoma (e.g., cutaneous melanoma), head and neck cancer (e.g., head and neck squamous cell carcinoma (HNSCC)), thyroid cancer, sarcoma (e.g., soft tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, osteosarcoma, chondrosarcoma, angiosarcoma, endothelial sarcoma, lymphoma, leukemia ... lymphangiosarcoma, lymphangiosarcoma, leiomyosarcoma, or rhabdomyosarcoma), prostate cancer, glioblastoma, cervical cancer, thymic carcinoma, leukemia (e.g., acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic myelogenous leukemia (CML), chronic eosinophilic leukemia, or chronic lymphocytic leukemia (CLL)), lymphoma (e.g., Hodgkin's lymphoma or non-Hodgkin's lymphoma (NHL)), myeloma (e.g., multiple myeloma (MM)), mycosis fungoides, Merkel cell carcinoma, hematologic malignancies, blood tissue cancers, B-cell cancers, Bronchial carcinoma, gastric cancer, brain or central nervous system cancer, peripheral nervous system cancer, uterine or endometrial cancer, oral cavity or pharyngeal cancer, liver cancer, testicular cancer, biliary tract cancer, small intestine or appendix cancer, salivary gland cancer, adrenal gland cancer, adenocarcinoma, inflammatory myofibroblastic tumor, gastrointestinal stromal tumor (GIST), colon cancer, myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), polycythemia vera, chordoma, synovial tumor, Ewing's tumor, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatic carcinoma, cholangiocarcinoma, choriocarcinoma, cerebrospinal fluid carcinoma, sarcoidosis ... The patient may have a cancer selected from: myeloma, embryonal carcinoma, Wilms' tumor, bladder cancer, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, small cell carcinoma, essential thrombocythemia, idiopathic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familial hypereosinophilia, neuroendocrine carcinoma, or carcinoid tumor.

[0205] In some examples, the individual has progressed after treatment for cancer with a platinum-containing regimen (e.g., a regimen including a platinum-based chemotherapy agent, e.g., a regimen including cisplatin-based chemotherapy). In other examples, the individual may be ineligible for treatment with a platinum-containing regimen (e.g., a regimen including a platinum-based chemotherapy agent, e.g., a regimen including cisplatin-based chemotherapy) and / or has not received prior treatment for cancer. In some examples, the individual has not received prior treatment with an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist, an antagonist directed against a co-inhibitory molecule (e.g., a CTLA-4 antagonist (e.g., an anti-CTLA-4 antibody), a TIM-3 antagonist (e.g., an anti-TIM-3 antibody), or a LAG-3 antagonist (e.g., an anti-LAG-3 antibody)), or any combination thereof.

[0206] In any of the aforementioned methods, the sample (e.g., blood sample) obtained from the patient is selected from the group consisting of whole blood, plasma, serum, or a combination thereof. In some examples, the sample is an archived blood sample, a fresh blood sample, or a frozen blood sample.

[0207] In any of the foregoing examples, the reference bTMB score may be a marker for a cancer (e.g., lung cancer (e.g., non-small cell lung cancer (NSCLC)), renal cancer (e.g., renal urothelial carcinoma), bladder cancer (e.g., bladder urothelial (transitional cell) carcinoma), breast cancer, colorectal cancer (e.g., colon adenocarcinoma), ovarian cancer, pancreatic cancer, gastric cancer, esophageal cancer, mesothelioma, melanoma (e.g., cutaneous melanoma), head and neck cancer (e.g., head and neck squamous cell carcinoma (HNSCC)), thyroid cancer, sarcoma (e.g., soft tissue sarcoma, fibrosarcoma, myxosarcoma, liposarcoma, osteogenic sarcoma, osteosarcoma, chondrosarcoma, angiosarcoma, endothelial sarcoma, rheumatoid arthritis ... lymphangiosarcoma, lymphangiosarcoma, leiomyosarcoma, or rhabdomyosarcoma), prostate cancer, glioblastoma, cervical cancer, thymic carcinoma, leukemia (e.g., acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic myeloid leukemia (CML), chronic eosinophilic leukemia, or chronic lymphocytic leukemia (CLL)), lymphoma (e.g., Hodgkin's lymphoma or non-Hodgkin's lymphoma (NHL)), myeloma (e.g., multiple myeloma (MM)), mycosis fungoides, Merkel cell carcinoma, hematologic malignancies, blood tissue cancer, B-cell cancer, bronchial carcinoma, stomach cancer, brain or esophageal cancer, Central nervous system cancer, peripheral nervous system cancer, uterine or endometrial cancer, oral or pharyngeal cancer, liver cancer, testicular cancer, biliary tract cancer, small intestine or appendix cancer, salivary gland cancer, adrenal gland cancer, adenocarcinoma, inflammatory myofibroblastic tumor, gastrointestinal stromal tumor (GIST), colon cancer, myelodysplastic syndrome (MDS), myeloproliferative disorder (MPD), polycythemia vera, chordoma, synovial tumor, Ewing's tumor, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatic carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, bladder cancer, epithelial the bTMB score in a reference population of individuals with cancer, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, neuroblastoma, retinoblastoma, follicular lymphoma, diffuse large B-cell lymphoma, mantle cell lymphoma, hepatocellular carcinoma, thyroid cancer, small cell carcinoma, essential thrombocythemia, idiopathic myeloid metaplasia, hypereosinophilic syndrome, systemic mastocytosis, familial hypereosinophilia, neuroendocrine carcinoma, or carcinoid tumors), wherein the population of individuals is or have been treated with an immune checkpoint inhibitor, e.g.,The method comprises: a first subset of individuals being treated with a PD-L1 axis-binding antagonist therapy; and a second subset of individuals being treated with a non-PD-L1 axis-binding antagonist therapy, wherein the non-PD-L1 axis-binding antagonist therapy does not include an immune checkpoint inhibitor, e.g., a PD-L1 axis-binding antagonist. In some examples, the baseline bTMB score significantly separates the first subset of individuals from the second subset of individuals based on a significant difference in responsiveness to treatment with the PD-L1 axis-binding antagonist therapy compared to responsiveness to treatment with the non-PD-L1 axis-binding antagonist therapy. In some examples, responsiveness to treatment is an increase in progression-free survival (PFS) and / or an increase in overall survival (OS). In some examples, the baseline bTMB score can be a pre-assigned bTMB score. The reference bTMB score can be 4 to 30 (e.g., 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and 30, e.g., 8 to 30, e.g., 10 to 16, or e.g., 10 to 20). In some examples, the reference bTMB score can be 10 to 20 (e.g., 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20). In other examples, the reference bTMB score can be 16 to 20 (e.g., 16, 17, 18, 19, or 20). For example, in some examples, the reference population of individuals has lung cancer (e.g., non-small cell lung cancer (NSCLC)), renal cancer (e.g., renal urothelial carcinoma), and a reference bTMB score of 9 or greater. In some exampl...

Claims

[Claim 1] An invention described in the specification.

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