Wedding of Molecular Alterations and Immune Checkpoint Blockade

Genomics as a Matchmaker

Elena Fountzilas, MD, PhD; Razelle Kurzrock, MD; Henry Hiep Vo, PhD; Apostolia-Maria Tsimberidou MD, PhD

J Natl Cancer Inst. 2021;113(12):1634-1647.

Abstract and Introduction

Abstract

The development of checkpoint blockade immunotherapy has transformed the medical oncology armamentarium. But despite its favorable impact on clinical outcomes, immunotherapy benefits only a subset of patients, and a substantial proportion of these individuals eventually manifest resistance. Serious immune-related adverse events and hyperprogression have also been reported. It is therefore essential to understand the molecular mechanisms and identify the drivers of therapeutic response and resistance. In this review, we provide an overview of the current and emerging clinically relevant genomic biomarkers implicated in checkpoint blockade outcome. US Food and Drug Administration–approved molecular biomarkers of immunotherapy response include mismatch repair deficiency and/or microsatelliteinstability and tumor mutational burden of at least 10 mutations/megabase. Investigational genomic-associated biomarkers for immunotherapy response include alterations of the following genes/associated pathways: chromatin remodeling (ARID1A, PBRM1, SMARCA4, SMARCB1, BAP1), major histocompatibility complex, specific (eg, ultraviolet, APOBEC) mutational signatures, T-cell receptor repertoire, PDL1, POLE/POLD1, and neo-antigens produced by the mutanome, those potentially associated with resistance include β2-microglobulin, EGFR, Keap1, JAK1/JAK2/interferon-gamma signaling, MDM2, PTEN, STK11, and Wnt/Beta-catenin pathway alterations. Prospective clinical trials are needed to assess the role of a composite of these biomarkers to optimize the implementation of precision immunotherapy in patient care.

Introduction

Immunotherapies such as checkpoint inhibitors have demonstrated durable responses in selected patients with diverse tumor types.

Abstract and Introduction
FDA-approved Biomarkers
Investigational Biomarkers Conferring Sensitivity to Immunotherapy
Investigational Biomarkers Conferring Resistance to Immunotherapy
Other Immune-related Biomarkers
Discussion
References

Table 1. Genomic biomarkers for response or resistance to checkpoint blockade ^a
Genomic biomarker	Cancer type	Role	Predictive value and comments	Selected references
FDA-approved biomarkers associated with response
Deficient mismatch repair/microsatellite instability	Across solid tumor types, adult and pediatric	MSH2, MSH5, MLH1, PMS2, and EPCAM Repairs mismatches in microsatellites	Predicts response FDA approved for pembrolizumab	Le, et al. 2020 (10) Marabelle, et al. 2019 (6) Overman, et al. 2018 (11) Lenz, et al. 2020 (12)
TMB (>10 mutations/mb) regardless of microsatellite status	Across solid tumor types, adult and pediatric	Increased mutations/neo-antigens	Predicts response FDA-approved: pembrolizumab and TMB >10 mutations/mb	Goodman, et al. 2017 (8) Hellman, et al. 2018 (13) Gandara, et al. 2018 (14)
Reported/Investigational response alterations
Chromatin remodeling (SWI/SNF complex)
ARID1A	Across solid tumors (eg, ovarian clear cell, endometrial, and gastric)	SWI/SNF chromatin remodeling	ARID1A deficiency leads to impaired MMR function	Shen, et al. 2018 (15) Okamura, et al. 2020 (16) Kim, et al. 2019 (17)
PBRM1	Clear cell renal cancer	SWI/SNF chromatin remodeling	Contradictory data; some papers suggest that PBRM1 alterations predict response to immunotherapy, others do not	Miao, et al. 2018 (18) Braun, et al. 2019 (19) Liu, et al. 2020 (20)
SMARCA4	Driver in small cell ovarian cancer with hypercalcemia (found in uterine and thoracic sarcomas [undifferentiated], NSCLC, bladder, colorectal)	SWI/SNF chromatin remodeling	SMARC4-deficient tumors have immunogenic microenvironment May predict immunotherapy response Requires validation	Jelinic, et al. 2018 (21) Naito, et al. 2019 (22) Iijima, et al. 2020 (23)
SMARCB1	Rhabdoid tumors	SWI/SNF chromatin remodeling	Preliminary: SMARCB1 loss in rhabdoid tumors may correlate with immunotherapy response	Bakouny, et al. 2020 (24)
Other alterations
BAP1 alterations	Mesothelioma	Promotes immune inflammatory environment in mesothelioma	Predicts response Requires validation	Alley, et al. 2017 (25) Scherperel, et al. 2019 (26) Shrestha, et al. 2019 (27)
Major histocompatibility complex class-I (MHC-I) genotype	Across solid tumors	Efficient presentation of driver neoantigens to CD8+ T cells	Predicts response in patients with high TMB Requires validation	Goodman, et al. 2020 (28)
Mutational signatures APOBEC-related ultraviolet-related	Across solid tumors	Associated with high immunogenicity	Predicts response independently of TMB Requires validation	Boichard, et al. 2020 (29) Pham, et al. 2019 (30)
Mutational signatures ultraviolet-related	Across solid tumors	Associated with high immunogenicity	Predicts response in patients with low TMB Requires validation	Pham, et al. 2019 (30)
PD-L1 amplification	Across solid tumors (and in Hodgkin lymphoma)	PD-L1 ligand is important in the immune checkpoint machinery	Predicts response Requires validation	Goodman, et al. 2018 (31)
POLE/POLD1	Across solid tumors	High tumor mutational rates, high TIL rates, and increased expression of cytotoxic T-cell markers	Predicts response	Wang, et al. 2019 (32) Yao, et al. 2019 (33)
Biomarkers associated with resistance/Hyperprogression
Beta-2 microglobulin mutations	Melanoma	Defects in antigen presentation, escape of immune recognition	Predicts resistance Requires validation	Zaretsky, et al. 2016 (34) Sade-Feldman, et al. 2017 (35) Rodig, et al. 2018 (36)
EGFR alterations	Across tumor types	Unclear	Predicts resistance and hyperprogression Requires validation	Kato, et al. 2017 (3) Ferrara, et al. 2018 (37)
KEAP1 mutations	NSCLC	Associated with "cold" tumor microenvironment	Not clear if KEAP1 alterations are predictive or prognostic	Chen, et al. 2020 (38) Arbour, et al. 2018 (39) Rizvi, et al. 2019 (40) Papillon-Cavanagh, et al. 2020 (41)
JAK1/2 loss	Across tumor types (melanoma, colorectal)	Defects in interferon-receptor signaling pathways	Predicts resistance Requires validation	Zaretsky, et al. 2016 (34) Shin, et al. 2017 (42) Horn, et al. 2018 (43)
MDM2 amplification	Melanoma	Unclear	Predicts hyperprogression Requires validation	Kato, et al. 2017 (3) Kato, et al. 2018 (44)
PTEN loss	Melanoma	Upregulation of immunosuppressive cytokines; may decrease CD8+ T cell infiltration	Predicts resistance Requires validation	Peng, et al. 2016 (45) Zhao, et al. 2019 (46)
STK11 mutations with KRAS alterations	Lung	Altered cytokines/chemokines, metabolic restriction of T cells, impaired antigenicity	Not clear if STK11 alterations are predictive or prognostic	Skoulidis, et al. 2018 (47) Papillon-Cavanagh, et al. 2020 (41)
Wnt/Beta-catenin pathway alterations	Melanoma, colon cancer	Decreases T-cell infiltration	Predicts resistance Requires validation	Spranger, et al. 2015 (48) Abril-Rodriguez, et al. 2020 (49)

^aBAP1 = BRCA1-associated protein 1; EGFR = epidermal growth factor receptor; FDA = US Food and Drug Administration; JAK = Janus kinase; KEAP1 = Kelch-like ECH associated protein 1; mb = megabase; MDM2 = murine double minute 2; MHC-I = major histocompatibility complex class-I; MMR = mismatch repair; NSCLC = non-small cell lung cancer; PBRM1 = polybromo-1; PD-1 = programmed cell death-1; PD-L1 = programmed cell death-ligand 1; POLE = DNA polymerase epsilon; PTEN = phosphatase and TENsin homolog deleted on chromosome 10; SMARCB = SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily B; STK11 = serine/threonine kinase 11; TIL = tumor infiltrative lymphocyte; TMB = tumor mutational burden.

Table 2. Biomarkers beyond genomics for response and resistance to immune checkpoint inhibitors ^a
Biomarker	Cancer type	Role	Predictive value and comments	Selected references
FDA-approved biomarkers
PD-L1 expression	Bladder cancer, NSCLC, TNBC, cervical cancer, and gastric/GEJ cancer	Inhibits peripheral T-cell activation and suppresses immune surveillance	Predicts response to immune checkpoint inhibitors	Patel, et al. 2015 (5)
Reported/Investigational biomarkers
Gene expression signatures	Melanoma, NSCLC	Immune-related signatures	Predicts response Requires validation	Ayers, et al. 2017 (153) Fehrenbacher, et al. 2016 (154)
Gut microbiome	Melanoma	Modulate immune-mediated colitis	Predicts response or resistance Predicts toxicity	Chaput, et al. 2017 (155) Gopalakrishnan, et al. 2018 (156)
Neoantigen load	Across tumor types (melanoma, NSCLC)	Increased mutations/neoantigens	Predicts response to adoptive T-cell therapy and checkpoint inhibitors	Lauss, et al. 2017 (157) Rizvi, et al. 2015 (158)
PD-1 expression	Across tumor types (melanoma, gastric cancer, NSCLC)	PD-1 expression on CD8+ T cells negatively influences effector functions	Predicts response	Kumagai, et al. 2020 (159)
TCR diversity	Melanoma, NSCLC	Associated with immune expansion of T-cell clone that recognizes a specific tumor neoantigen	Predicts response	Tumeh, et al. 2014 (140) Hopkins, et al. 2018 (142) Forde, et al. 2018 (143)
Proinflammatory cytokines	Melanoma	Increase proinflammatory activities	Predicts toxicity	Lim, et al. 2019 (160) Fujimura, et al. 2018 (161)

^aFDA = US Food and Drug Administration; GEJ = gastro-esophageal junction; NSCLC = non-small cell lung cancer; PD-1 = programmed cell death-1; PD-L1 = programmed cell death-ligand 1; TCR = T-cell receptor; TNBC = triple-negative breast cancer.