Advancing Evidence Generation for Circulating Tumor DNA: Lessons Learned from A Multi-Assay Study of Baseline Circulating Tumor DNA Levels across Cancer Types and Stages.

Circulating tumor DNA (ctDNA) holds promise as a biomarker for predicting clinical responses to therapy in solid tumors, and multiple ctDNA assays are in development. However, the heterogeneity in ctDNA levels prior to treatment (baseline) across different cancer types and stages and across ctDNA assays has not been widely studied. Friends of Cancer Research formed a collaboration across multiple commercial ctDNA assay developers to assess baseline ctDNA levels across five cancer types in early- and late-stage disease. This retrospective study included eight commercial ctDNA assay developers providing summary-level de-identified data for patients with non-small cell lung cancer (NSCLC), bladder, breast, prostate, and head and neck squamous cell carcinoma following a common analysis protocol. Baseline ctDNA levels across late-stage cancer types were similarly detected, highlighting the potential use of ctDNA as a biomarker in these cancer types. Variability was observed in ctDNA levels across assays in early-stage NSCLC, indicative of the contribution of assay analytical performance and methodology on variability. We identified key data elements, including assay characteristics and clinicopathological metadata, that need to be standardized for future meta-analyses across multiple assays. This work facilitates evidence generation opportunities to support the use of ctDNA as a biomarker for clinical response.

Evaluation of tissue- and plasma-derived tumor mutational burden (TMB) and genomic alterations of interest in CheckMate 848, a study of nivolumab combined with ipilimumab and nivolumab alone in patients with advanced or metastatic solid tumors with high TMB.

BACKGROUND: An accumulation of somatic mutations in tumors leads to increased neoantigen levels and antitumor immune response. Tumor mutational burden (TMB) reflects the rate of somatic mutations in the tumor genome, as determined from tumor tissue (tTMB) or blood (bTMB). While high tTMB is a biomarker of immune checkpoint inhibitor (ICI) treatment efficacy, few studies have explored the clinical utility of bTMB, a less invasive alternative for TMB assessment. Establishing the correlation between tTMB and bTMB would provide insight into whether bTMB is a potential substitute for tTMB. We explored the tumor genomes of patients enrolled in CheckMate 848 with measurable TMB. The correlation between tTMB and bTMB, and the factors affecting it, were evaluated. METHODS: In the phase 2 CheckMate 848 (NCT03668119) study, immuno-oncology-naïve patients with advanced, metastatic, or unresectable solid tumors and tTMB-high or bTMB-high (≥10 mut/Mb) were prospectively randomized 2:1 to receive nivolumab plus ipilimumab or nivolumab monotherapy. Tissue and plasma DNA sequencing was performed using the Foundation Medicine FoundationOne CDx and bTMB Clinical Trial Assays, respectively. tTMB was quantified from coding variants, insertions, and deletions, and bTMB from somatic base substitutions. Correlations between tTMB and bTMB were determined across samples and with respect to maximum somatic allele frequency (MSAF). Assay agreement and variant composition were also evaluated. RESULTS: A total of 1,438 and 1,720 unique tissue and blood samples, respectively, were obtained from 1,954 patients and included >100 screened disease ontologies, with 1,017 unique pairs of tTMB and bTMB measurements available for assessment. Median tTMB and bTMB were 3.8 and 3.5 mut/Mb, respectively. A significant correlation between tTMB and bTMB (r=0.48, p<0.0001) was observed across all sample pairs, which increased to r=0.54 (p<0.0001) for samples with MSAF≥1%. Assay concordance was highest for samples with MSAF≥10% across multiple disease ontologies and observed for both responders and non-responders to ICI therapy. The variants contributing to tTMB and bTMB were similar. CONCLUSIONS: We observed that tTMB and bTMB had a statistically significant correlation, particularly for samples with high MSAF, and that this correlation applied across disease ontologies. Further investigation into the clinical utility of bTMB is warranted.

Pan-Cancer Analysis of Copy-Number Features Identifies Recurrent Signatures and a Homologous Recombination Deficiency Biomarker to Predict Poly (ADP-Ribose) Polymerase Inhibitor Response.

PURPOSE: Copy-number (CN) features reveal the molecular state of cancers and may have predictive and prognostic value in the treatment of cancer. We sought to apply published CN analysis methods to a large pan-cancer data set and characterize ubiquitous CN signatures across tumor types, including potential utility for treatment selection. METHODS: We analyzed the landscape of CN features in 260,333 pan-cancer samples. We examined the association of 10 signatures with genomic alterations and clinical characteristics and trained a machine learning classifier using CN and insertion and deletion features to detect homologous recombination deficiency signature (HRDsig) positivity. Clinical outcomes were assessed using a real-world clinicogenomic database (CGDB) of comprehensive genomic profiling linked to deidentified, electronic health record-derived clinical data. RESULTS: CN signatures were prevalent across cancer types and associated with diverse processes including focal tandem duplications, seismic amplifications, genome-wide loss of heterozygosity (gLOH), and HRD. Our novel HRDsig outperformed gLOH in predicting BRCAness and effectively distinguished biallelic BRCA and homologous recombination-repair wild-type (HRRwt) samples pan-tumor, demonstrating high sensitivity to detect biallelic BRCA in ovarian (93%) and other HRD-associated cancers (80%-87%). Pan-tumor prevalence of HRDsig was 6.4%. HRRwt cases represented a significant fraction of the HRDsig-positive cohort, likely reflecting a population with nongenomic mechanisms of HRD. In ovarian and prostate CGDBs, HRDsig identified more patients than gLOH and had predictive value for poly (ADP-ribose) polymerase inhibitor (PARPi) benefit. CONCLUSION: Tumor CN profiles are informative, revealing diverse processes active in cancer. We describe the landscape of 10 CN signatures in a large pan-cancer cohort, including two associated with HRD. We trained a machine learning-based HRDsig that robustly identified BRCAness and associated with biallelic BRCA pan-tumor, and was predictive of PARPi benefit in real-world ovarian and prostate data sets.

A Novel HRD Signature Is Predictive of FOLFIRINOX Benefit in Metastatic Pancreatic Cancer.

BACKGROUND: Pancreatic cancer (PC) represents an aggressive disease with median overall survival (OS) of less than 1 year in the front-line setting. FOLFIRINOX and gemcitabine and paclitaxel (GP) are standard of care options for these patients; however, optimal selection of therapy is challenging. METHODS: Comprehensive genomic profiling was performed on 8358 PC patients. Outcomes were available for 1149 metastatic PC patients treated with 1L FOLFIRINOX or GP. A scar-based measure of HRD was called using a machine learning-based algorithm incorporating copy number and indel features. RESULTS: A scar-based HRD signature (HRDsig) was identified in 9% of patients. HRDsig significantly co-occurred with biallelic alterations in BRCA1/2, PALB2, BARD1, and RAD51C/D, but encompassed a larger population than that defined by BRCA1/BRCA2/PALB2 (9% vs. 6%). HRDsig was predictive of 1L FOLFIRNOX chemotherapy benefit with doubled OS relative to gemcitabine and paclitaxel (GP) (rwOS aHR 0.37 [0.22-0.62]), including 25% of the population with long-term (2 year+) survival in a real-world cohort of patients. Less benefit from FOLFIRINOX was observed in the HRDsig(-) population. Predictive value was seen in both the BRCA1/2/PALB2 mutant and wildtype populations, suggesting additional value to mutational profiling. CONCLUSION: A scar-based HRD biomarker was identified in a significant fraction of PC patients and is predictive of FOLFIRINOX benefit. Incorporating a biomarker like HRDsig could identify the right patients for platinum chemotherapy and potentially reduce FOLFIRINOX use by over 40%, minimizing toxicities with similar survival outcomes. Confirmatory studies should be performed.

A longitudinal circulating tumor DNA-based model associated with survival in metastatic non-small-cell lung cancer.

One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0-5.3), P < 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7-6.4), P < 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83-7.60), P = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials.

Comprehensive pan-cancer genomic landscape of KRAS altered cancers and real-world outcomes in solid tumors.

Recent clinical development of KRAS inhibitors has heightened interest in the genomic landscape of KRAS-altered cancers. We performed a pan-cancer analysis of KRAS-altered samples from 426,706 adult patients with solid or hematologic malignancies using comprehensive genomic profiling; additional analyses included 62,369 liquid biopsy and 7241 pediatric samples. 23% of adult pan-cancer samples had KRAS alterations; 88% were mutations, most commonly G12D/G12V/G12C/G13D/G12R, and prevalence was similar in liquid biopsies. Co-alteration landscapes were largely similar across KRAS mutations but distinct from KRAS wild-type, though differences were observed in some tumor types for tumor mutational burden, PD-L1 expression, microsatellite instability, and other mutational signatures. Prognosis of KRAS-mutant versus other genomic cohorts of lung, pancreatic, and colorectal cancer were assessed using a real-world clinicogenomic database. As specific KRAS inhibitors and combination therapeutic strategies are being developed, genomic profiling to understand co-alterations and other biomarkers that may modulate response to targeted or immunotherapies will be imperative.

Atezolizumab versus chemotherapy in advanced or metastatic NSCLC with high blood-based tumor mutational burden: primary analysis of BFAST cohort C randomized phase 3 trial.

Tumor mutational burden (TMB) is being explored as a predictive biomarker for cancer immunotherapy outcomes in non-small cell lung cancer. BFAST (NCT03178552)-an open-label, global, multicohort trial-evaluated the safety and efficacy of first-line targeted therapies or immunotherapy in patients with unresectable Stage IIIB or IV advanced or metastatic non-small cell lung cancer who were selected for biomarker status using blood-based targeted next-generation sequencing. In the Phase 3 cohort C evaluating blood-based (b)TMB as a biomarker of atezolizumab efficacy, patients with bTMB of ≥10 (N = 471) were randomized 1:1 to receive atezolizumab or platinum-based chemotherapy per local standard of care. Cohort C did not meet its primary endpoint of investigator-assessed progression-free survival in the population with bTMB of ≥16 (hazard ratio, 0.77; 95% confidence interval: 0.59, 1.00; P = 0.053). Adverse events leading to treatment withdrawal occurred in 10% of patients in the atezolizumab arm and 20% in the chemotherapy arm. Adverse events of special interest occurred in 42% of patients in the atezolizumab arm and 26% in the chemotherapy arm. A prespecified exploratory analysis compared the bTMB clinical trial assay with the FoundationOne Liquid Companion Diagnostic assay and showed high concordance between assays. Additional exploration of bTMB to identify optimal cutoffs, confounding factors, assay improvements or cooperative biomarkers is warranted.

Deletions on 9p21 are associated with worse outcomes after anti-PD-1/PD-L1 monotherapy but not chemoimmunotherapy.

NCCN guidelines for first-line treatment of advanced non-squamous non-small-cell lung cancer (NSCLC) patients without targetable driver alterations includes either immunotherapy alone or in combination with chemotherapy. In this study, we investigated genomic predictors of survival after immunotherapy to guide this treatment decision. Cox proportional hazards regression was used to identify genomic correlates of survival in a cohort of EGFR/ALK-, non-squamous NSCLC patients treated with first-line pembrolizumab monotherapy (mono-IO) or pembrolizumab in combination with carboplatin/cisplatin and pemetrexed (chemo-IO) within a real-world clinico-genomic database. The effect of deletions on 9p21 was further evaluated in five additional tumor types. Among mono-IO treated non-squamous NSCLC patients, tumors with 9p21.3 gene deletions (CDKN2A, CDKN2B, MTAP) were associated with worse survival compared to the corresponding deletion-negative tumors (CDKN2A deletion HR = 1.8, P = 0.001). However, this association was not observed among chemo-IO treated patients (CDKN2A deletion HR = 1.1, P = 0.4). This finding remained after adjusting for clinical and genomic features including TMB and PD-L1. Deletions at 9p21.3 were not associated with differences in TMB, PD-L1, or tumor inflammation. Due to the high incidence of 9p21.3 deletions across tumor types, we performed a pan-cancer analysis and found CDKN2A deletion-positive tumors had worse survival following first-line immunotherapy treatment in multiple tumor types (HR = 1.4, P < 0.001). These results indicate deletions at 9p21.3 are a putative negative predictor of clinical benefit from first-line immune checkpoint inhibitors and may have utility in choosing between mono-IO vs chemo-IO regimens in NSCLC.

Association of CD274 (PD-L1) Copy Number Changes with Immune Checkpoint Inhibitor Clinical Benefit in Non-Squamous Non-Small Cell Lung Cancer.

BACKGROUND: We sought to characterize response to immune checkpoint inhibitor (ICI) in non-squamous non-small cell lung cancer (NSCLC) across various CD274 copy number gain and loss thresholds and identify an optimal cutoff. MATERIALS AND METHODS: A de-identified nationwide (US) real-world clinico-genomic database was leveraged to study 621 non-squamous NSCLC patients treated with ICI. All patients received second-line ICI monotherapy and underwent comprehensive genomic profiling as part of routine clinical care. Overall survival (OS) from start of ICI, for CD274 copy number gain and loss cohorts across varying copy number thresholds, were assessed. RESULTS: Among the 621 patients, patients with a CD274 CN greater than or equal to specimen ploidy +2 (N = 29) had a significantly higher median (m) OS when compared with the rest of the cohort (N = 592; 16.1 [8.9-37.3] vs 8.6 [7.1-10.9] months, hazard ratio (HR) = 0.6 [0.4-1.0], P-value = .05). Patients with a CD274 copy number less than specimen ploidy (N = 299) trended toward a lower mOS when compared to the rest of the cohort (N = 322; 7.5 [5.9-11.3] vs 9.6 [7.9-12.8] months, HR = 0.9 [0.7-1.1], P-value = .3). CONCLUSION: This work shows that CD274 copy number gains at varying thresholds predict different response to ICI blockade in non-squamous NSCLC. Considering these data, prospective clinical trials should further validate these findings, specifically in the context of PD-L1 IHC test results.

Blood-based tumor mutational burden as a biomarker for atezolizumab in non-small cell lung cancer: the phase 2 B-F1RST trial.

Tumor mutational burden (TMB) in circulating tumor DNA (ctDNA) has shown promise in predicting benefit from PD-L1/PD-1 inhibitors in retrospective studies. Aiming to assess blood TMB (bTMB) prospectively, we conducted B-F1RST ( NCT02848651 ), an open-label, phase 2 trial that evaluated bTMB as a predictive biomarker for first-line atezolizumab monotherapy in locally advanced or metastatic stage IIIB-IVB non-small cell lung cancer (n = 152). The co-primary endpoints were investigator-assessed objective response rate (ORR) per RECIST version 1.1 and investigator-assessed progression-free survival (PFS) between high and low bTMB subgroups at the pre-defined bTMB ≥ 16 (14.5 mutations per megabase) cutoff. Secondary endpoints included investigator-assessed PFS, overall survival (OS) and duration of response at various bTMB cutoffs, as well as safety. Investigator-assessed PFS in the bTMB ≥ 16 versus bTMB < 16 groups was not statistically significant. However, bTMB ≥ 16 was associated with higher ORR, and ORR improved as bTMB cutoffs increased. No new safety signals were seen. In exploratory analyses, patients with maximum somatic allele frequency (MSAF) < 1% had higher ORR than patients with MSAF ≥ 1%. However, further analysis showed that this effect was driven by better baseline prognostics rather than by MSAF itself. At 36.5-month follow-up, an exploratory analysis of OS found that bTMB ≥ 16 was associated with longer OS than bTMB < 16. Further study and assay optimization will be required to develop bTMB as a predictive, standalone biomarker of immunotherapy or for use in conjunction with other biomarkers.

Somatic HLA Class I Loss Is a Widespread Mechanism of Immune Evasion Which Refines the Use of Tumor Mutational Burden as a Biomarker of Checkpoint Inhibitor Response.

Neoantigen presentation arises as a result of tumor-specific mutations and is a critical component of immune surveillance that can be abrogated by somatic LOH of the human leukocyte antigen class I (HLA-I) locus. To understand the role of HLA-I LOH in oncogenesis and treatment, we utilized a pan-cancer genomic dataset of 83,644 patient samples, a small subset of which had treatment outcomes with immune checkpoint inhibitors (ICI). HLA-I LOH was common (17%) and unexpectedly had a nonlinear relationship with tumor mutational burden (TMB). HLA-I LOH was frequent at intermediate TMB, yet prevalence decreased above 30 mutations/megabase, suggesting highly mutated tumors require alternate immune evasion mechanisms. In ICI-treated patients with nonsquamous non-small cell lung cancer, HLA-I LOH was a significant negative predictor of overall survival. Survival prediction improved when combined with TMB, suggesting TMB with HLA-I LOH may better identify patients likely to benefit from ICIs. SIGNIFICANCE: This work shows the pan-cancer landscape of HLA-I LOH, revealing an unexpected "Goldilocks" relationship between HLA-I LOH and TMB, and demonstrates HLA-I LOH as a significant negative predictor of outcomes after ICI treatment. These data informed a combined predictor of outcomes after ICI and have implications for tumor vaccine development.This article is highlighted in the In This Issue feature, p. 211.

Prevalence of High Tumor Mutational Burden and Association With Survival in Patients With Less Common Solid Tumors.

Importance: Tumor mutational burden (TMB) is a potential biomarker associated with response to immune checkpoint inhibitor therapies. The prognostic value associated with TMB in the absence of immunotherapy is uncertain. Objective: To assess the prevalence of high TMB (TMB-H) and its association with overall survival (OS) among patients not treated with immunotherapy with the same 10 tumor types from the KEYNOTE-158 study. Design, Setting, and Participants: This retrospective cohort study evaluated the prognostic value of TMB-H, assessed by Foundation Medicine (FMI) and defined as at least 10 mutations/megabase (mut/Mb) in the absence of immunotherapy. Data were sourced from the deidentified Flatiron Health-FMI clinicogenomic database collected up to July 31, 2018. Eligible patients were aged 18 years or older with any of the following solid cancer types: anal, biliary, endometrial, cervical, vulvar, small cell lung, thyroid, salivary gland, mesothelioma, or neuroendocrine tumor. Patients with microsatellite instability-high tumors were excluded from primary analysis. For OS analysis, patients were excluded if immunotherapy started on the FMI report date or earlier or if patients died before January 1, 2012, and patients were censored if immunotherapy was started later than the FMI report date. Data were analyzed from November 2018 to February 2019. Main Outcomes and Measures: Overall survival was analyzed using the Kaplan-Meier method and Cox proportional hazards model, adjusting for age, sex, cancer types, practice type, and albumin level. Results: Of 2589 eligible patients, 1671 (64.5%) were women, and the mean (SD) age was 63.7 (11.7) years. Median (interquartile range) TMB was 2.6 (1.7-6.1) mut/Mb, and 332 patients (12.8%) had TMB-H (≥10 mut/Mb). Prevalence of TMB-H was highest among patients with small cell lung cancer (40.0%; 95% CI, 34.7%-45.6%) and neuroendocrine tumor (29.3%; 95% CI, 22.8%-36.6%) and lowest was among patients with mesothelioma (1.2%; 95% CI, 0.3%-4.4%) and thyroid cancer (2.7%; 95% CI, 1.2%-5.7%). Adjusted hazard ratio for OS of patients not treated with immunotherapy with TMB-H vs those without TMB-H was 0.94 (95% CI, 0.77-1.13). Comparable results were observed when including patients with high microsatellite instability tumors and calculating OS from first observed antineoplastic treatment date. Conclusions and Relevance: These findings suggest that prevalence of TMB-H varies widely depending on tumor type and TMB-H does not appear to be a factor associated with OS among patients across these cancer types treated in the absence of immunotherapy.

Identification and Utilization of Biomarkers to Predict Response to Immune Checkpoint Inhibitors.

Immune checkpoint inhibitors (ICPI) have revolutionized cancer therapy and provided clinical benefit to thousands of patients. Despite durable responses in many tumor types, the majority of patients either fail to respond at all or develop resistance to the ICPI. Furthermore, ICPI treatment can be accompanied by serious adverse effects. There is an urgent need for identification of patient populations that will benefit from ICPI as single agents and when used in combinations. As ICPI have achieved regulatory approvals, accompanying biomarkers including PD-L1 immunohistochemistry (IHC) and tumor mutational burden (TMB) have also received approvals for some indications. The ICPI pembrolizumab was the first example of a tissue-agnostic FDA approval based on tumor microsatellite instability (MSI)/deficient mismatch repair (dMMR) biomarker status, rather than on tumor histology assessment. Several other ICPI-associated biomarkers are in the exploratory stage, including quantification of tumor-infiltrating lymphocytes (TILs), gene expression profiling (GEP) of an inflamed microenvironment, and neoantigen prediction. TMB and PD-L1 expression can predict a subset of responses, but they fail to predict all responses to checkpoint blockade. While a single biomarker is currently limited in its ability to fully capture the complexity of the tumor-immune microenvironment, a combination of biomarkers is emerging as a method to improve predictive power. Here we review the steadily growing impact of comprehensive genomic profiling (CGP) for development and utilization of predictive biomarkers by simultaneously capturing TMB, MSI, and the status of genomic targets that confer sensitivity or resistance to immunotherapy, as well as detecting inflammation through RNA expression signatures.

Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study.

BACKGROUND: Tumour mutational burden (TMB) has been retrospectively correlated with response to immune checkpoint blockade. We prospectively explored the association of high tissue TMB (tTMB-high) with outcomes in ten tumour-type-specific cohorts from the phase 2 KEYNOTE-158 study, which assessed the anti-PD-1 monoclonal antibody pembrolizumab in patients with selected, previously treated, advanced solid tumours. METHODS: In the multi-cohort, open-label, non-randomised, phase 2 KEYNOTE-158 study, patients were enrolled from 81 academic facilities and community-based institutions across 21 countries in Africa, the Americas, Asia, and Europe. Eligible patients were aged 18 years or older, had a histologically or cytologically confirmed advanced (ie, unresectable or metastatic, or both) incurable solid tumour (eligible tumour types were anal, biliary, cervical, endometrial, mesothelioma, neuroendocrine, salivary, small-cell lung, thyroid, and vulvar), progression on or intolerance to one or more lines of standard therapy, had measurable disease per Response Evaluation Criteria in Solid Tumors (RECIST; version 1.1) assessed by independent central radiological review, Eastern Cooperative Oncology Group performance status of 0 or 1, life expectancy of at least 3 months, adequate organ function, and a tumour sample for biomarker analysis. Participants were given pembrolizumab 200 mg intravenously every 3 weeks for up to 35 cycles. Tissue TMB (tTMB) was assessed in formalin-fixed paraffin-embedded tumour samples using the FoundationOne CDx assay (Foundation Medicine, Cambridge, MA, USA). The prespecified definition of tTMB-high status was at least 10 mutations per megabase. The primary endpoint was the proportion of patients with an objective response (complete or partial response) as per Response Evaluation Criteria in Solid Tumours (version 1.1) by independent central review. This prespecified analysis assessed the association between antitumour activity and tTMB in treated patients with evaluable tTMB data. Efficacy was assessed in all participants who received at least one dose of pembrolizumab, had evaluable tTMB data, and were enrolled at least 26 weeks before data cutoff (June 27, 2019), and safety was assessed in all participants who received at least one dose of pembrolizumab and had tTMB-high status. KEYNOTE-158 is registered at ClinicalTrials.gov, NCT02628067, and is ongoing. FINDINGS: Between Jan 15, 2016, and June 25, 2019, 1073 patients were enrolled. 1066 participants were treated as of data cutoff (June 27, 2019), of whom 805 (76%) were evaluable for TMB, and 105 (13%) of 805 had tTMB-high status and were assessed for safety. 1050 (98%) of 1066 patients enrolled by at least 26 weeks before data cutoff, of whom 790 (75%) were evaluable for TMB and included in efficacy analyses. 102 (13%) of these 790 patients had tTMB-high status (≥10 mutations per megabase), and 688 (87%) patients had non-tTMB-high status (<10 mutations per megabase). Median study follow-up was 37·1 months (IQR 35·0-38·3). Objective responses were observed in 30 (29%; 95% CI 21-39) of 102 patients in the tTMB-high group and 43 (6%; 5-8) of 688 in the non-tTMB-high group. 11 (10%) of 105 patients had treatment-related serious adverse events. 16 (15%) participants had a grade 3-5 treatment-related adverse event, of which colitis was the only such adverse event that occurred in more than one patient (n=2). One patient had fatal pneumonia that was assessed by the investigator to be treatment related. INTERPRETATION: tTMB-high status identifies a subgroup of patients who could have a robust tumour response to pembrolizumab monotherapy. tTMB could be a novel and useful predictive biomarker for response to pembrolizumab monotherapy in patients with previously treated recurrent or metastatic advanced solid tumours. FUNDING: Merck Sharp & Dohme Corp, a subsidiary of Merck & Co, Inc.

High Tumor Mutational Burden Correlates with Longer Survival in Immunotherapy-Naïve Patients with Diverse Cancers.

Higher tumor mutational burden (TMB) has been correlated with response to checkpoint blockade immunotherapy. However, it is unclear whether TMB independently serves as a prognostic biomarker for outcomes in immunotherapy-naïve patients. Here, we evaluated the relationship between TMB and overall survival in 1,415 immunotherapy-naïve patients with diverse advanced malignancies. TMB was studied both as a tiered variable (low ≤5 mutations/Mb, intermediate >5 and <20, high ≥20 and <50, and very high ≥50) and as a continuous variable. Interestingly, we observed a parabolic correlation between TMB and overall survival, in which intermediate-range TMB correlated with decreased survival, whereas low and very high TMB correlated with improved outcomes (median survival: 238, 174, 195, and 350 weeks for low, intermediate, high, and very high TMB, respectively; multivariate P < 0.01). This corresponded to an HR of 1.29 (95% confidence interval, 1.07-1.54; P < 0.01) for intermediate-range TMB on multivariable survival analysis correcting for known confounders, including primary tumor of origin. These results demonstrate that TMB may have utility as a prognostic biomarker in immunotherapy-naïve patients, with a protective effect at higher TMBs, and that studies of survival in immunotherapy-treated patients may need to stratify or randomize by TMB in a nonlinear fashion to account for this confounding.

Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project.

BACKGROUND: Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms. METHODS: Eleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits. RESULTS: Study results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers. CONCLUSIONS: Increasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.

Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence.

Treatment with immune checkpoint inhibitors (ICPIs) extends survival in a proportion of patients across multiple cancers. Tumor mutational burden (TMB)-the number of somatic mutations per DNA megabase (Mb)-has emerged as a proxy for neoantigen burden that is an independent biomarker associated with ICPI outcomes. Based on findings from recent studies, TMB can be reliably estimated using validated algorithms from next-generation sequencing assays that interrogate a sufficiently large subset of the exome as an alternative to whole-exome sequencing. Biological processes contributing to elevated TMB can result from exposure to cigarette smoke and ultraviolet radiation, from deleterious mutations in mismatch repair leading to microsatellite instability, or from mutations in the DNA repair machinery. A variety of clinical studies have shown that patients with higher TMB experience longer survival and greater response rates following treatment with ICPIs compared with those who have lower TMB levels; this includes a prospective randomized clinical trial that found a TMB threshold of ≥10 mutations per Mb to be predictive of longer progression-free survival in patients with non-small cell lung cancer. Multiple trials are underway to validate the predictive values of TMB across cancer types and in patients treated with other immunotherapies. Here we review the rationale, algorithm development methodology, and existing clinical data supporting the use of TMB as a predictive biomarker for treatment with ICPIs. We discuss emerging roles for TMB and its potential future value for stratifying patients according to their likelihood of ICPI treatment response. IMPLICATIONS FOR PRACTICE: Tumor mutational burden (TMB) is a newly established independent predictor of immune checkpoint inhibitor (ICPI) treatment outcome across multiple tumor types. Certain next-generation sequencing-based techniques allow TMB to be reliably estimated from a subset of the exome without the use of whole-exome sequencing, thus facilitating the adoption of TMB assessment in community oncology settings. Analyses of multiple clinical trials across several cancer types have demonstrated that TMB stratifies patients who are receiving ICPIs by response rate and survival. TMB, alongside other genomic biomarkers, may provide complementary information in selecting patients for ICPI-based therapies.