Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer

Programmed Death Ligand-1 and Tumor Mutation Burden Testing of Patients With Lung Cancer for Selection of Immune Checkpoint Inhibitor Therapies: Guideline From the College of American Pathologists, Association for Molecular Pathology, International Association for the Study of Lung Cancer, Pulmonary Pathology Society, and LUNGevity Foundation

CONTEXT.—

Rapid advancements in the understanding and manipulation of tumor-immune interactions have led to the approval of immune therapies for patients with non-small cell lung cancer. Certain immune checkpoint inhibitor therapies require the use of companion diagnostics, but methodologic variability has led to uncertainty around test selection and implementation in practice.

OBJECTIVE.—

To develop evidence-based guideline recommendations for the testing of immunotherapy/immunomodulatory biomarkers, including programmed death ligand-1 (PD-L1) and tumor mutation burden (TMB), in patients with lung cancer.

DESIGN.—

The College of American Pathologists convened a panel of experts in non-small cell lung cancer and biomarker testing to develop evidence-based recommendations in accordance with the standards for trustworthy clinical practice guidelines established by the National Academy of Medicine. A systematic literature review was conducted to address 8 key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach, recommendations were created from the available evidence, certainty of that evidence, and key judgments as defined in the GRADE Evidence to Decision framework.

RESULTS.—

Six recommendation statements were developed.

CONCLUSIONS.—

This guideline summarizes the current understanding and hurdles associated with the use of PD-L1 expression and TMB testing for immune checkpoint inhibitor therapy selection in patients with advanced non-small cell lung cancer and presents evidence-based recommendations for PD-L1 and TMB testing in the clinical setting.

KRAS and TP53 co-mutation predicts benefit of immune checkpoint blockade in lung adenocarcinoma

BACKGROUND

Predictive biomarkers in use for immunotherapy in advanced non-small cell lung cancer are of limited sensitivity and specificity. We analysed the potential of activating KRAS and pathogenic TP53 mutations to provide additional predictive information.

METHODS

The study cohort included 713 consecutive immunotherapy patients with advanced lung adenocarcinomas, negative for actionable genetic alterations. Additionally, two previously published immunotherapy and two surgical patient cohorts were analyzed. Therapy benefit was stratified by KRAS and TP53 mutations. Molecular characteristics underlying KRASmut/TP53mut tumours were revealed by the analysis of TCGA data.

RESULTS

An interaction between KRAS and TP53 mutations was observed in univariate and multivariate analyses of overall survival (Hazard ratio [HR] = 0.56, p = 0.0044 and HR = 0.53, p = 0.0021) resulting in a stronger benefit for KRASmut/TP53mut tumours (HR = 0.71, CI 0.55-0.92). This observation was confirmed in immunotherapy cohorts but not observed in surgical cohorts. Tumour mutational burden, proliferation, and PD-L1 mRNA were significantly higher in TP53-mutated tumours, regardless of KRAS status. Genome-wide expression analysis revealed 64 genes, including CX3CL1 (fractalkine), as specific transcriptomic characteristic of KRASmut/TP53mut tumours.

CONCLUSIONS

KRAS/TP53 co-mutation predicts ICI benefit in univariate and multivariate survival analyses and is associated with unique molecular tumour features. Mutation testing of the two genes can be easily implemented using small NGS panels.

Recommendations for Tumor Mutational Burden Assay Validation and Reporting: A Joint Consensus Recommendation of the Association for Molecular Pathology, College of American Pathologists, and Society for Immunotherapy of Cancer

Tumor mutational burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in several tumor types. Several laboratories offer TMB testing, but there is significant variation in how TMB is calculated, reported, and interpreted among laboratories. TMB standardization efforts are underway, but no published guidance for TMB validation and reporting is currently available. Recognizing the current challenges of clinical TMB testing, the Association for Molecular Pathology convened a multidisciplinary collaborative working group with representation from the American Society of Clinical Oncology, the College of American Pathologists, and the Society for the Immunotherapy of Cancer to review the laboratory practices surrounding TMB and develop recommendations for the analytical validation and reporting of TMB testing based on survey data, literature review, and expert consensus. These recommendations encompass pre-analytical, analytical, and postanalytical factors of TMB analysis, and they emphasize the relevance of comprehensive methodological descriptions to allow comparability between assays.

Understanding the Impact of Population and Cancer Type on Tumor Mutation Burden Scores: A Comprehensive Whole-Exome Study in Cancer Patients From India

PURPOSE

The purpose of this study was to understand the impact of population diversity and geographic variation on tumor mutation burden (TMB) scores across cancers and its implication on stratification of patients for immune checkpoint inhibitor (ICI) therapy.

MATERIALS AND METHODS

This retrospective study used whole-exome sequencing (WES) to profile 1,233 Indian patients with cancer across 30 different cancer types and to estimate their TMB scores. A WES-based pipeline was adopted, along with an indigenously developed strategy for arriving at true somatic mutations. A robust unsupervised machine learning approach was used to understand the distribution of TMB scores across different populations and within the population.

RESULTS

The results of the study showed a biphasic distribution of TMB scores in most cancers, with different threshold scores across cancer types. Patients with cancer in India had higher TMB scores compared with the Caucasian patients. We also observed that the TMB score value at 90th percentile (predicting high efficacy to ICI) was high in four different cancer types (sarcoma, ovary, head and neck, and breast) in the Indian cohort as compared with The Cancer Genome Atlas or public cohort. However, in lung and colorectal cancers, the TMB score distribution was similar between the two population cohorts.

CONCLUSION

The findings of this study indicate that it is crucial to benchmark both cancer-specific and population-specific TMB distributions to establish a TMB threshold for each cancer in various populations. Additional prospective studies on much larger population across different cancers are warranted to validate this observation to become the standard of care.

Predicting response to immune checkpoint blockade in NSCLC with tumour-only RNA-seq

BACKGROUND

Targeted RNA sequencing (RNA-seq) from FFPE specimens is used clinically in cancer for its ability to estimate gene expression and to detect fusions. Using a cohort of NSCLC patients, we sought to determine whether targeted RNA-seq could be used to measure tumour mutational burden (TMB) and the expression of immune-cell-restricted genes from FFPE specimens and whether these could predict response to immune checkpoint blockade.

METHODS

Using The Cancer Genome Atlas LUAD dataset, we developed a method for determining TMB from tumour-only RNA-seq and showed a correlation with DNA sequencing derived TMB calculated from tumour/normal sample pairs (Spearman correlation = 0.79, 95% CI [0.73, 0.83]. We applied this method to targeted sequencing data from our patient cohort and validated these results against TMB estimates obtained using an orthogonal assay (Spearman correlation = 0.49, 95% CI [0.24, 0.68]).

RESULTS

We observed that the RNA measure of TMB was significantly higher in responders to immune blockade treatment (P = 0.028) and that it was predictive of response (AUC = 0.640 with 95% CI [0.493, 0.786]). By contrast, the expression of immune-cell-restricted genes was uncorrelated with patient outcome.

CONCLUSION

TMB calculated from targeted RNA sequencing has a similar diagnostic ability to TMB generated from targeted DNA sequencing.

Evaluating Multiple Next-Generation Sequencing-Derived Tumor Features to Accurately Predict DNA Mismatch Repair Status

Identifying tumor DNA mismatch repair deficiency (dMMR) is important for precision medicine. Tumor features, individually and in combination, derived from whole-exome sequenced (WES) colorectal cancers (CRCs) and panel-sequenced CRCs, endometrial cancers (ECs), and sebaceous skin tumors (SSTs) were assessed for their accuracy in detecting dMMR. CRCs (n = 300) with WES, where mismatch repair status was determined by immunohistochemistry, were assessed for microsatellite instability (MSMuTect, MANTIS, MSIseq, and MSISensor), Catalogue of Somatic Mutations in Cancer tumor mutational signatures, and somatic mutation counts. A 10-fold cross-validation approach (100 repeats) evaluated the dMMR prediction accuracy for i) individual features, ii) Lasso statistical model, and iii) an additive feature combination approach. Panel-sequenced tumors (29 CRCs, 22 ECs, and 20 SSTs) were assessed for the top performing dMMR predicting features/models using these three approaches. For WES CRCs, 10 features provided >80% dMMR prediction accuracy, with MSMuTect, MSIseq, and MANTIS achieving ≥99% accuracy. The Lasso model achieved 98.3% accuracy. The additive feature approach, with three or more of six of MSMuTect, MANTIS, MSIseq, MSISensor, insertion-deletion count, or tumor mutational signature small insertion/deletion 2 + small insertion/deletion 7 achieved 99.7% accuracy. For the panel-sequenced tumors, the additive feature combination approach of three or more of six achieved accuracies of 100%, 95.5%, and 100% for CRCs, ECs, and SSTs, respectively. The microsatellite instability calling tools performed well in WES CRCs; however, an approach combining tumor features may improve dMMR prediction in both WES and panel-sequenced data across tissue types.

Preclinical investigation of patient-derived cervical cancer organoids for precision medicine

OBJECTIVE

Advanced cervical cancer is still difficult to treat and in the case of recurrent cancer, it is desirable to utilize personalized treatment rather than uniform treatment because the type of recurrence is different for each individual. Therefore, this study aimed to establish a patient-derived organoid (PDO) platform to determine the effects of chemotherapy, radiation therapy, and targeted therapy in cervical cancer.

METHODS

We established organoids from 4 patients with various types of cervical cancer. The histopathological and gene profiles of these organoid models were compared to determine their characteristics and the maintenance of the patient phenotype. Each type of organoid was also subjected to anticancer drug screening and radiation therapy to evaluate its sensitivity.

RESULTS

We established PDOs to recapitulate the main elements of the original patient tumors, including the DNA copy number and mutational profile. We selected 7 drugs that showed growth inhibition in cervical cancer organoids out of 171 using an Food and Drug Administration-approved drug library. Moreover, adenocarcinoma and large-cell neuroendocrine carcinoma showed resistance to radiation therapy. whereas squamous cell carcinoma and villoglandular carcinoma showed a significant response to radiotherapy.

CONCLUSION

Our results showed that patient-derived cervical cancer organoids can be used as a platform for drug and radiation sensitivity testing. These findings suggest that patient-derived cervical cancer organoids could be used as a personalized medicine platform and may provide the best treatment options for patients with various subtypes of cervical cancer.

Proceedings From the ASCO/College of American Pathologists Immune Checkpoint Inhibitor Predictive Biomarker Summit

PURPOSE

Immune checkpoint inhibition (ICI) therapy represents one of the great advances in the field of oncology, highlighted by the Nobel Prize in 2018. Multiple predictive biomarkers for ICI benefit have been proposed. These include assessment of programmed death ligand-1 expression by immunohistochemistry, and determination of mutational genotype (microsatellite instability or mismatch repair deficiency or tumor mutational burden) as a reflection of neoantigen expression. However, deployment of these assays has been challenging for oncologists and pathologists alike.

METHODS

To address these issues, ASCO and the College of American Pathologists convened a virtual Predictive Factor Summit from September 14 to 15, 2021. Representatives from the academic community, US Food and Drug Administration, Centers for Medicare and Medicaid Services, National Institutes of Health, health insurance organizations, pharmaceutical companies, in vitro diagnostics manufacturers, and patient advocate organizations presented state-of-the-art predictive factors for ICI, associated problems, and possible solutions.

RESULTS

The Summit provided an overview of the challenges and opportunities for improvement in assay execution, interpretation, and clinical applications of programmed death ligand-1, microsatellite instability-high or mismatch repair deficient, and tumor mutational burden-high for ICI therapies, as well as issues related to regulation, reimbursement, and next-generation ICI biomarker development.

CONCLUSION

The Summit concluded with a plan to generate a joint ASCO/College of American Pathologists strategy for consideration of future research in each of these areas to improve tumor biomarker tests for ICI therapy.

Clinical utility of tumour mutational burden on efficacy of immune checkpoint inhibitors in malignant solid tumours: protocol for a systematic review and meta-analysis

INTRODUCTION

A major development in solid malignancy treatment is the application of immune checkpoint inhibitors (ICIs), which have produced durable responses and increased survival rates. However, the therapeutic effect of ICIs has great heterogeneity in patients with cancer. We propose a systematic review to evaluate the predictive value of tumour mutation burden (TMB) on efficacy of ICIs.

METHODS AND ANALYSIS

A systematic literature search will be conducted in the PubMed, OVID, Web of Science, Embase and Cochrane Central Register of Controlled Trials Library databases up to 31 May 2022. We will compare the efficacy of ICIs between TMB high group and TMB low group in terms of the HRs of overall survival (OS) and progression-free survival (PFS), and the OR of the objective response rate/overall response rate (ORR). The HRs of PFS and OS, and the OR of ORR, will be measured by an inverse variance weighted fixed effects model (I²≤50%) or a DerSimonian-Laird random effects model (I²>50%). In addition, subgroup analysis, sensitivity analysis, heterogeneity analysis and publication bias will be conducted. We plan to conduct a subgroup analysis on age, sex, area, number of patients (high/low TMB), cancer type, tumour size, stage, line of therapy, TMB sequencing method, type of immunotherapy and follow-up period.

ETHICS AND DISSEMINATION

Ethical approval and informed consent are not needed, as the study will be a literature review and will not involve direct contact with patients or alterations to patient care. This systematic review is anticipated to be finished in December 2023, and the results will be published in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42021262480.

Homologous recombination deficiency is inversely correlated with microsatellite instability and identifies immunologically cold tumors in most cancer types

Homologous recombination deficiency (HRD) leads to DNA double‐strand breaks and can be exploited by the use of poly (ADP‐ribose) polymerase (PARP) inhibitors to induce synthetic lethality. Extending the original therapeutic concept, the role of HRD is currently being investigated in clinical trials testing immune checkpoint blockers alone or in combination with PARP inhibitors, but the relationship between HRD and immune cell context in cancer is incompletely understood. We analyzed the association between immune cell composition, gene expression, and HRD in 9,041 tumors of 32 solid cancer types from The Cancer Genome Atlas (TCGA). The numbers of genomic scars were quantified by the HRD sum score (HRDsum) including loss of heterozygosity, large‐scale state transitions, and telomeric allelic imbalance. The T‐cell inflamed gene expression profile correlated weakly, but significantly positively, with HRDsum across cancer types (ρ = 0.17). Within individual cancer types, a significantly positive correlation was observed only in breast cancer, ovarian cancer, and four other cancer types, but not in the remaining 26 cancer types. HRDsum and tumor mutational burden (TMB) correlated significantly positively across cancer types (ρ = 0.42) and within 18 cancer types. HRDsum and a proliferation metagene correlated significantly positively across cancer types (ρ = 0.52) and within 20 cancer types. Mismatch repair deficiency and HRD as well as proofreading deficiency showed a high level of exclusivity. High HRD scores were associated with an immunologically activated tumor microenvironment only in a minority of cancer types. Our data favor the combination of genetic markers, complex genomic markers (including HRDsum and TMB), and other molecular markers (including proliferation scores) for a precise and comprehensive read‐out of the tumor biology and an individually tailored treatment.

Open the Technical Black Box of Tumor Mutational Burden (TMB): Factors Affecting Harmonization and Standardization of Panel-Based TMB

As tumor mutational burden (TMB) has been approved as a predictive biomarker for immune checkpoint inhibitors (ICIs), next-generation sequencing (NGS) TMB panels are being increasingly used clinically. However, only a few of them have been validated in clinical trials or authorized by administration. The harmonization and standardization of TMB panels are thus essential for clinical implementation. In this review, preanalytic, sequencing, bioinformatics and interpretative factors are summarized to provide a comprehensive picture of how the different factors affect the estimation of panel-based TMB. Among the factors, poor DNA quality, improper formalin fixation and residual germline variants after filtration may overestimate TMB, while low tumor purity may decrease the sensitivity of the TMB panel. In addition, a small panel size leads to more variability when comparing with true TMB values detected by whole-exome sequencing (WES). A panel covering a genomic region of more than 1Mb is more stable for harmonization and standardization. Because the TMB estimate reflects the sum of effects from multiple factors, deliberation based on laboratory and specimen quality, as well as clinical information, is essential for decision making.

Biomarker analysis from CheckMate 214: nivolumab plus ipilimumab versus sunitinib in renal cell carcinoma

BACKGROUND

The phase 3 CheckMate 214 trial demonstrated higher response rates and improved overall survival with nivolumab plus ipilimumab versus sunitinib in first-line therapy for advanced clear-cell renal cell carcinoma (RCC). An unmet need exists to identify patients with RCC who are most likely to benefit from treatment with nivolumab plus ipilimumab.

METHODS

In exploratory analyses, pretreatment levels of programmed death ligand 1 were assessed by immunohistochemistry. Genomic and transcriptomic biomarkers (including tumor mutational burden and gene expression signatures) were also investigated.

RESULTS

Biomarkers previously associated with benefit from immune checkpoint inhibitor-containing regimens in RCC were not predictive for survival in patients with RCC treated with nivolumab plus ipilimumab. Analysis of gene expression identified an association between an inflammatory response and progression-free survival with nivolumab plus ipilimumab.

CONCLUSIONS

The exploratory analyses reveal relationships between molecular biomarkers and provide supportive data on how the inflammation status of the tumor microenvironment may be important for identifying predictive biomarkers of response and survival with combination immunotherapy in patients with RCC. Further validation may help to provide biomarker-driven precision treatment for patients with RCC.

Molecular Pathology of Lung Cancer

This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.

Genomic characteristics of invasive mucinous adenocarcinoma of the lung with multiple pulmonary sites of involvement

Invasive mucinous adenocarcinoma (IMA) of the lung frequently presents with diffuse pneumonic-type features or multifocal lesions, which are regarded as a pattern of intrapulmonary metastases. However, the genomics of multifocal IMAs have not been well studied. We performed whole exome sequencing on samples taken from 2 to 5 regions in seven patients with synchronous multifocal IMAs of the lung (24 regions total). Early initiating driver events, such as KRAS, NKX2-1, TP53, or ARID1A mutations, are clonal mutations and were present in all multifocal IMAs in each patient. The tumor mutational burden of multifocal IMAs was low (mean: 1.13/mega base), but further analyses suggested intra-tumor heterogeneity. The mutational signature analysis found that IMAs were predominantly associated with endogenous mutational process (signature 1), APOBEC activity (signatures 2 and 13), and defective DNA mismatch repair (signature 6), but not related to smoking signature. IMAs synchronously located in the bilateral lower lobes of two patients with background usual interstitial pneumonia had different mutation types, suggesting that they were double primaries. In conclusion, genomic evidence found in this study indicated the clonal intrapulmonary spread of diffuse pneumonic-type or multifocal IMAs, although they can occur in multicentric origins in the background of usual interstitial pneumonia. IMAs exhibited a heterogeneous genomic landscape despite the low somatic mutation burden. Further studies are warranted to determine the clinical significance of the genomic characteristics of IMAs in expanded cohorts.

Real-world survival analysis by tumor mutational burden in non-small cell lung cancer: a multisite U.S. study

BACKGROUND

Tumor mutational burden (TMB) is a potential biomarker to predict tumor response to immuno-oncology agents in patients with metastatic non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A multi-site cohort study evaluated patients diagnosed with stage IV NSCLC between 2012 and 2019 who had received comprehensive genomic profiling (CGP) and any NSCLC-related treatment at 9 U.S. cancer centers. Baseline characteristics and clinical outcomes were compared between patients with TMB <10 and TMB ≥10.

RESULTS

Among the 667 patients with CGP results, most patients received CGP from Foundation Medicine (64%) or Caris (20%). Patients with TMB ≥10 (vs. TMB <10) were associated with a positive smoking history. TMB was associated with ALK (p = 0.01), EGFR (p < 0.01), and TP53 (p < 0.05) alterations. TMB >10 showed a significant association towards longer overall survival (OS) (HR: 0.43, 95% CI: 0.21-0.88, p = 0.02) and progression-free survival (PFS) (HR: 0.43, 95% CI: 0.21-0.85, p = 0.02) in patients treated with first-line immunotherapy and tested by Foundation Medicine or Caris at treatment initiation.

CONCLUSIONS

TMB levels greater than or equal to 10 mut/Mb, when tested by Foundation Medicine or Caris at treatment initiation, were significantly associated with improved OS and PFS among patients treated with first-line immunotherapy-containing regimens. Additional prospective research is warranted to validate this biomarker along with PD-L1 expression.

Prediction performance of twelve tumor mutation burden panels in melanoma and non-small cell lung cancer

As a potential biomarker to predict the response to immunotherapy, tumor mutation burden (TMB) which can be estimated by the cancer gene panel (CGP) has received considerable attention. However, it is not clear which CGP is better in predicting the efficacy of immunotherapy. To evaluate the twelve CGPs, we compared them on 13 datasets of melanoma and non-small cell lung cancer (NSCLC) from the perspective of gene composition, reliability of measuring TMB and prediction performance of patient treatment benefits. The larger CGPs generally performed better, but their proportions of driver genes and function densities were smaller. The CGPs performed differently on melanoma and NSCLC patients treated with two blockades. Moreover, their ability to classify and predict patients with or without long-term clinical benefits was similar but not good enough, so it is necessary to explore a higher-performance biomarker.

TMB and Inflammatory Gene Expression Associated with Clinical Outcomes following Immunotherapy in Advanced Melanoma

Outcomes for patients with melanoma have improved over the past decade as a result of the development and FDA approval of immunotherapies targeting cytotoxic T lymphocyte antigen-4 (CTLA-4), programmed death-1 (PD-1), and programmed death ligand 1 (PD-L1). However, these therapies do not benefit all patients, and an area of intensive research investigation is identifying biomarkers that can predict which patients are most likely to benefit from them. Here, we report exploratory analyses of the associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival, and overall survival in patients with advanced melanoma treated as part of the CheckMate 066 and 067 phase III clinical trials evaluating immuno-oncology therapies. In patients enrolled in CheckMate 067 receiving the anti-PD-1 inhibitor nivolumab (NIVO) alone or in combination with the anti-CTLA-4 inhibitor ipilimumab (IPI) or IPI alone, longer survival appeared to associate with high (>median) versus low (≤median) TMB and with high versus low inflammatory signature scores. For NIVO-treated patients, the results regarding TMB association were confirmed in CheckMate 066. In addition, improved survival was observed with high TMB and absence of BRAF mutation. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immune checkpoint blockade in advanced melanoma.

Immunotherapy in Treating EGFR-Mutant Lung Cancer: Current Challenges and New Strategies

Lung cancer is the leading cause of cancer-related deaths worldwide. Immune checkpoint inhibitors, including monoclonal antibodies against programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1), have dramatically improved the survival and quality of life of a subset of non-small cell lung cancer (NSCLC) patients. Multiple predictive biomarkers have been proposed to select the patients who may benefit from the immune checkpoint inhibitors. EGFR-mutant NSCLC is the most prevalent molecular subtype in Asian lung cancer patients. However, patients with EGFR-mutant NSCLC show poor response to anti-PD-1/PD-L1 treatment. While small-molecule EGFR tyrosine kinase inhibitors (TKIs) are the preferred initial treatment for EGFR-mutant NSCLC, acquired drug resistance is severely limiting the long-term efficacy. However, there is currently no further effective treatment option for TKIs-refractory EGFR-mutant NSCLC patients. The reasons mediating the poor response of EGFR-mutated NSCLC patients to immunotherapy are not clear. Initial investigations revealed that EGFR-mutated NSCLC has lower PD-L1 expression and a low tumor mutational burden, thus leading to weak immunogenicity. Moreover, the use of PD-1/PD-L1 blockade prior to or concurrent with osimertinib has been reported to increase the risk of pulmonary toxicity. Furthermore, emerging evidence shows that PD-1/PD-L1 blockade in NSCLC patients can lead to hyperprogressive disease associated with dismal prognosis. However, it is difficult to predict the treatment toxicity. New biomarkers are urgently needed to predict response and toxicity associated with the use of PD-1/PD-L1 immunotherapy in EGFR-mutated NSCLC. Recently, promising data have emerged to suggest the potentiation of PD-1/PD-L1 blockade therapy by anti-angiogenic agents and a few other novel therapeutic agents. This article reviews the current investigations about the poor response of EGFR-mutated NSCLC to anti-PD-1/PD-L1 therapy, and discusses the new strategies that may be adopted in the future.

Predictive Value of Combining Biomarkers for Clinical Outcomes in Advanced Non-Small Cell Lung Cancer Patients Receiving Immune Checkpoint Inhibitors

INTRODUCTION

A high tumor mutational burden (TMB) (≥10 mut/Mb) has been associated with improved clinical benefit in non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICI) and is a tumor agnostic indication for pembrolizumab across tumor types. We explored whether combining TMB with programmed cell death ligand 1 (PD-L1) and pretreatment neutrophil-lymphocyte ratio (NLR) was associated with improved outcomes in ICI-treated NSCLC.

METHODS

We retrospectively analyzed patients treated with ICI with Foundation One genomic testing, including TMB. Optimal cutoff for prediction of response by TMB was determined by receiver operating characteristic analysis, and area under the curve (AUC) was calculated for all 3 biomarkers and combinations. Cox model was used to assess prognostic factors of overall survival (OS) and time to progression (TTP). Survival cutoffs calculated with Kaplan-Meier survival curves were TMB ≥10 mut/Mb, PD-L1 ≥50%, NLR <5, and combined biomarkers.

RESULTS

Data from 88 patients treated were analyzed. The optimal TMB cutoff was 9.24 mut/Mb (AUC, 0.62), improving to 0.74 combining all 3 biomarkers. Adjusted Cox model showed that TMB ≥10 mut/Mb was an independent factor of OS (hazard ratio [HR], 0.31; 95% confidence interval; 0.14-0.69; P = .004) and TTP (HR, 0.46; 95% CI, 0.27-0.77; P = .003). The combination of high TMB with positive PD-L1 and low NLR was significantly associated with OS (P = .038) but not TTP.

CONCLUSIONS

TMB has modest predictive and prognostic power for clinical outcomes after ICI treatment. The combination of TMB, PD-L1, and NLR status improves this power.

Correlations Between Tumor Mutation Burden and Immunocyte Infiltration and Their Prognostic Value in Colon Cancer

Background

Colon cancer has a huge incidence and mortality worldwide every year. Immunotherapy could be a new therapeutic option for patients with advanced colon cancer. Tumor mutation burden (TMB) and immune infiltration are considered critical in immunotherapy but their characteristics in colon cancer are still controversial.

Methods

The somatic mutation, transcriptome, and clinical data of patients with colon cancer were obtained from the TCGA database. Patients were divided into low or high TMB groups using the median TMB value. Somatic mutation landscape, differentially expressed genes, and immune-related hub genes, Gene Ontology and KEGG, gene set enrichment, and immune infiltration analyses were investigated between the two TMB groups. Univariate and multivariate Cox analyses were utilized to construct a prognostic gene signature. The differences in immune infiltration, and the expression of HLA-related genes and checkpoint genes were investigated between the two immunity groups based on single sample gene set enrichment analysis. Finally, a nomogram of the prognostic prediction model integrating TMB, immune infiltration, and clinical parameters was established. Calibration plots and receiver operating characteristic curves (ROC) were drawn, and the C-index was calculated to assess the predictive ability.

Results

Missense mutations and single nucleotide polymorphisms were the major variant characteristics in colon cancer. The TMB level showed significant differences in N stage, M stage, pathological stage, and immune infiltration. CD8⁺ T cells, activated memory CD4⁺ T cells, activated NK cells, and M1 macrophages infiltrated more in the high-TMB group. The antigen processing and presentation signaling pathway was enriched in the high-TMB group. Two immune related genes (CHGB and SCT) were identified to be correlated with colon cancer survival (HR = 1.39, P = 0.01; HR = 1.26, P = 0.02, respectively). Notably, the expression of SCT was identified as a risk factor in the immune risk model, in which high risk patients showed poorer survival (P = 0.04). High immunity status exhibited significant correlations with immune response pathways, HLA-related genes, and immune checkpoint genes. Finally, including nine factors, our nomogram prediction model showed better calibration (C-index = 0.764) and had an AUC of 0.737.

Conclusion

In this study, we investigated the patterns and prognostic roles of TMB and immune infiltration in colon cancer, which provided new insights into the tumor microenvironment and immunotherapies and the development of a novel nomogram prognostic prediction model for patients with colon cancer.

Predictive biomarkers for response to immune checkpoint inhibitors in lung cancer: PD-L1 and beyond

Immune checkpoint inhibitor (ICI) therapies, including the programmed cell death protein 1 (PD-1) axis blockade, are considered a major oncological breakthrough of the early twenty-first century and have led to remarkable response rates and survival in a subset of patients with non-small cell lung cancer (NSCLC). However, the available therapies work only for one in five unselected, advanced NSCLC patients; thus, patient selection needs to be performed with the use of efficient biomarkers. Although imperfect, programmed death-ligand 1 (PD-L1) expression by immunohistochemistry (IHC) on tumor cells and/or immune cells has been established as a predictive biomarker for response to the PD-1 axis blockade. There remain several pre-analytical, analytical, and post-analytical issues, however, before implementing a PD-L1 IHC assay(s) in the pathology laboratory. In addition, given the lack of robust sensitivity and specificity of PD-L1 IHC for predicting response to ICIs, other biomarkers including tumor mutation burden (TMB) are under investigation. In this review, issues associated with PD-L1 IHC and TMB estimations will be discussed, and other promising biomarkers for predicting response to ICIs will be briefly introduced.

Beyond Tumor PD-L1: Emerging Genomic Biomarkers for Checkpoint Inhibitor Immunotherapy

Despite the success of immune checkpoint blockade as a strategy for activating an antitumor immune response and promoting cancer regression, only a subset of patients have durable clinical benefit. Efforts are ongoing to identify robust biomarkers that can effectively predict treatment response to immune checkpoint inhibitors (ICIs). Although PD-L1 expression is useful for stratifying patients, it is an imperfect tool. Comprehensive next-generation sequencing platforms that are readily used in clinical practice to identify a tumor's potentially actionable genetic alterations also reveal tumor genomic features, including tumor mutation burden (TMB), that may impact the response to ICIs. High TMB enhances tumor immunogenicity through increased numbers of tumor neoantigens that may promote an immune response. Defective DNA repair, leading to microsatellite instability, is an endogenous mechanism for increased tumor TMB that augments response to anti-PD-1 blockade. Alternatively, DNA damage from exogenous factors is responsible for high TMB seen in melanoma, lung cancer, and urothelial carcinoma, among tumor subtypes with higher response rates to ICIs. In this review, we summarize data supporting the use of TMB as a biomarker as well as its known limitations. We also highlight specific tumor suppressor genes and oncogenes that are under investigation as biomarkers for ICI response and resistance. Efforts are ongoing to delineate which genomic tumor characteristics can eventually be utilized in clinical practice to ascertain the benefit of ICIs for an individual patient.

Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing-based estimates of TMB have largely replaced whole-exome sequencing-derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the efficacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMB-high tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice. SIGNIFICANCE: Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors.

Modeling performance of sample collection sites using whole exome sequencing metrics

Although next-generation sequencing assays are routinely carried out using samples from cancer trials, the sequencing data are not always of the required quality. There is a need to evaluate the performance of tissue collection sites and provide feedback about the quality of next-generation sequencing data. This study used a modeling approach based on whole exome sequencing quality control (QC) metrics to evaluate the relative performance of sites participating in the Bristol Myers Squibb Immuno-Oncology clinical trials sample collection. We identified several events for the sample swap. Overall, most sites performed well and few showed poor performance. These findings can increase awareness of sample failure and improve the quality of samples.

Clinical advantage of targeted sequencing for unbiased tumor mutational burden estimation in samples with low tumor purity

Background

Tumor mutational burden (TMB) measurement is limited by low tumor purity of samples, which can influence prediction of the immunotherapy response, particularly when using whole-exome sequencing-based TMB (wTMB). This issue could be overcome by targeted panel sequencing-based TMB (pTMB) with higher depth of coverage, which remains unexplored.

Methods

We comprehensively reanalyzed four public datasets of immune checkpoint inhibitor (ICI)-treated cohorts (adopting pTMB or wTMB) to test each biomarker’s predictive ability for low purity samples (cut-off: 30%). For validation, paired genomic profiling with the same tumor specimens was performed to directly compare wTMB and pTMB in patients with breast cancer (paired-BRCA, n=165) and ICI-treated patients with advanced non-small-cell lung cancer (paired-NSCLC, n=156).

Results

Low tumor purity was common (range 30%–45%) in real-world samples from ICI-treated patients. In the survival analyzes of public cohorts, wTMB could not predict the clinical benefit of immunotherapy when tumor purity was low (log-rank p=0.874), whereas pTMB could effectively stratify the survival outcome (log-rank p=0.020). In the paired-BRCA and paired-NSCLC cohorts, pTMB was less affected by tumor purity, with significantly more somatic variants identified at low allele frequency (p<0.001). We found that wTMB was significantly underestimated in low purity samples with a large proportion of clonal variants undetected by whole-exome sequencing. Interestingly, pTMB more accurately predicted progression-free survival (PFS) after immunotherapy than wTMB owing to its superior performance in the low tumor purity subgroup (p=0.054 vs p=0.358). Multivariate analysis revealed pTMB (p=0.016), but not wTMB (p=0.32), as an independent predictor of PFS even in low-purity samples. The net reclassification index using pTMB was 21.7% in the low-purity subgroup (p=0.016).

Conclusions

Our data suggest that TMB characterization with targeted deep sequencing might have potential strength in predicting ICI responsiveness due to its enhanced sensitivity for hard-to-detect variants at low-allele fraction. Therefore, pTMB could act as an invaluable biomarker in the setting of both clinical trials and practice outside of trials based on its reliable performance in mitigating the purity-related bias.

Neoadjuvant Nivolumab for Patients With Resectable Merkel Cell Carcinoma in the CheckMate 358 Trial

PURPOSE

Merkel cell carcinoma (MCC) is a rare, aggressive skin cancer commonly driven by the Merkel cell polyomavirus (MCPyV). The programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) immunosuppressive pathway is often upregulated in MCC, and advanced metastatic MCC frequently responds to PD-1 blockade. We report what we believe to be the first trial of anti-PD-1 in the neoadjuvant setting for resectable MCC.

METHODS

In the phase I/II CheckMate 358 study of virus-associated cancer types, patients with resectable MCC received nivolumab 240 mg intravenously on days 1 and 15. Surgery was planned on day 29. Tumor regression was assessed radiographically and microscopically. Tumor MCPyV status, PD-L1 expression, and tumor mutational burden (TMB) were assessed in pretreatment tumor biopsies.

RESULTS

Thirty-nine patients with American Joint Committee on Cancer stage IIA-IV resectable MCC received ≥ 1 nivolumab dose. Three patients (7.7%) did not undergo surgery because of tumor progression (n = 1) or adverse events (n = 2). Any-grade treatment-related adverse events occurred in 18 patients (46.2%), and grade 3-4 events in 3 patients (7.7%), with no unexpected toxicities. Among 36 patients who underwent surgery, 17 (47.2%) achieved a pathologic complete response (pCR). Among 33 radiographically evaluable patients who underwent surgery, 18 (54.5%) had tumor reductions ≥ 30%. Responses were observed regardless of tumor MCPyV, PD-L1, or TMB status. At a median follow-up of 20.3 months, median recurrence-free survival (RFS) and overall survival were not reached. RFS significantly correlated with pCR and radiographic response at the time of surgery. No patient with a pCR had tumor relapse during observation.

CONCLUSION

Nivolumab administered approximately 4 weeks before surgery in MCC was generally tolerable and induced pCRs and radiographic tumor regressions in approximately one half of treated patients. These early markers of response significantly predicted improved RFS. Additional investigation of these promising findings is warranted.

Mucinous Histology, BRCA1/2 Mutations, and Elevated Tumor Mutational Burden in Colorectal Cancer

Mucinous colorectal carcinomas (MC) constitute 10% of colorectal malignancies. Recently, an increased risk of colorectal cancer has been demonstrated in germline BRCA1/2 mutation carriers. Furthermore, BRCA1/2 germline mutation carriers have exhibited a higher-than-expected frequency of MC tumors. Here, we investigate the relationship between BRCA mutations and mucinous histology in colorectal carcinoma patients, using both an existing cohort of sequenced colorectal tumors and a prospective case-control study comparing MC and conventional adenocarcinoma (AC) patients tested for BRCA mutations. We discovered that MC tumors exhibit a statistically significantly higher incidence of BRCA mutations in addition to a higher average mutation count when compared to AC tumors in the existing cohort. The strongest predictor of the mutation count was mucinous histology, independently of other variables including microsatellite instability. Contrary to our hypothesis, the first association did not recur in the prospective case-control study, likely due to our pathological definition of MC tumors and small sample size. Finally, we observed a higher tumor mutational burden (TMB) in MC tumors compared with AC tumors. We suggest that the association between MC histology, BRCA mutations, and increased TMB may open the door to the utilization of simple tests (such as histopathologic characterization) to detect patients who may benefit from immunotherapy in colorectal cancer.

Tumour mutational burden as a biomarker for immunotherapy: Current data and emerging concepts

Treatment with immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) or its ligand (PD-L1) can generate durable responses in various cancer types, but only in a subset of patients. The use of predictive biomarkers for response to PD-1/PD-L1 inhibitors is critical for patient selection. Expression of PD-L1 has demonstrated utility in patient selection. Tumour mutational burden (TMB) is an emerging biomarker for response to PD-1/PD-L1 inhibitors. The evaluation of this biomarker is based on the hypothesis that a high number of mutations in somatic exonic regions will lead to an increase in neoantigen production, which could then be recognised by CD8⁺ T cells, resulting in improved immune responses. In this review, we will discuss rationale and implementation of TMB usage in patients, development of different methods to assess it, current limitations and technical issues to use this biomarker as a diagnostic test and propose future perspectives beyond TMB.

Harmonization and Standardization of Panel-Based Tumor Mutational Burden Measurement: Real-World Results and Recommendations of the Quality in Pathology Study

INTRODUCTION

Tumor mutational burden (TMB) is a quantitative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole-exome sequencing (wesTMB) and gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research have jointly addressed the need for harmonization among TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment.

METHODS

A total of 20 samples from three tumor types (lung adenocarcinoma, head and neck squamous cell carcinoma, and colon adenocarcinoma) with available WES data were analyzed for psTMB using six panels across 15 testing centers. Interlaboratory and interplatform variation, including agreement on variant calling and TMB classification, were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated.

RESULTS

Sixteen samples had low interlaboratory and interpanel psTMB variation, with 87.7% of pairwise comparisons revealing a Spearman's ρ greater than 0.6. A wesTMB cut point of 199 missense mutations projected to psTMB cut points between 7.8 and 12.6 mutations per megabase pair; the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cut points of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation artifacts, DNA input, sequencing depth, genome coverage, and variant allele frequency cut points.

CONCLUSIONS

This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important parameters for reliable tissue TMB assessment that require careful control. As complex or composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and Friends of Cancer Research also delineate a general framework and blueprint for the evaluation of such assays.

Quantifying potential confounders of panel-based tumor mutational burden (TMB) measurement

OBJECTIVES

Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing.

MATERIAL AND METHODS

We separated different potential confounders of psTMB measurement including "panel size", "germline mutation filtering", "biological variance" and "technical variance" and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders.

RESULTS

Using a typical large panel (size 1-1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential.

CONCLUSION

A statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice.

Assessment of Tumor Mutational Burden in Pediatric Tumors by Real-Life Whole-Exome Sequencing and In Silico Simulation of Targeted Gene Panels: How the Choice of Method Could Affect the Clinical Decision?

Background: Tumor mutational burden (TMB) is an emerging genomic biomarker in cancer that has been associated with improved response to immune checkpoint inhibitors (ICIs) in adult cancers. It was described that variability in TMB assessment is introduced by different laboratory techniques and various settings of bioinformatic pipelines. In pediatric oncology, no study has been published describing this variability so far. Methods: In our study, we performed whole exome sequencing (WES, both germline and somatic) and calculated TMB in 106 patients with high-risk/recurrent pediatric solid tumors of 28 distinct cancer types. Subsequently, we used WES data for TMB calculation using an in silico approach simulating two The Food and Drug Administration (FDA)-approved/authorized comprehensive genomic panels for cancer. Results: We describe a strong correlation between WES-based and panel-based TMBs; however, we show that this high correlation is significantly affected by inclusion of only a few hypermutated cases. In the series of nine cases, we determined TMB in two sequentially collected tumor tissue specimens and observed an increase in TMB along with tumor progression. Furthermore, we evaluated the extent to which potential ICI indication could be affected by variability in techniques and bioinformatic pipelines used for TMB assessment. We confirmed that this technological variability could significantly affect ICI indication in pediatric cancer patients; however, this significance decreases with the increasing cut-off values. Conclusions: For the first time in pediatric oncology, we assessed the reliability of TMB estimation across multiple pediatric cancer types using real-life WES and in silico analysis of two major targeted gene panels and confirmed a significant technological variability to be introduced by different laboratory techniques and various settings of bioinformatic pipelines.

Retracted Article: Long non-coding RNA MEG3 inhibits cell proliferation, migration, invasion and enhances apoptosis in non-small cell lung cancer cells by regulating the miR-31-5p/TIMP3 axis

Non-small cell lung cancer (NSCLC) is a malignant lung cancer and accounts for 80% of lung cancer-related deaths. Long non-coding RNA maternally expressed gene 3 (MEG3) has been identified as a tumor suppressor in multiple cancers. However, the regulatory mechanism of MEG3 in NSCLC development is still largely unknown. The expression levels of MEG3, microRNA-31-5p (miR-31-5p) and tissue inhibitor of metalloproteinase 3 (TIMP3) in NSCLC tumors and cells were measured by quantitative real time polymerase chain reaction (qRT-PCR). Cell viability, apoptosis, migration and invasion were detected by cell counting kit-8 (CCK-8), flow cytometry, western blotting and transwell assays, respectively. Xenograft mouse models were established by subcutaneously injecting NSCLC cells stably transfected with Lenti-pcDNA or Lenti-MEG3. The interaction between miR-31-5p and MEG3 or TIMP3 was validated by luciferase reporter and RNA immunoprecipitation (RIP) assays. MEG3 and TIMP3 levels were up-regulated, whereas miR-31-5p expression was down-regulated in NSCLC tumors and cells compared with normal tissues and cells. Overexpression of MEG3 repressed cell proliferation, migration and invasion, but induced apoptosis in NSCLC cells. More importantly, MEG3 effectively hindered tumor growth in vivo. Next, luciferase reporter and RIP assays confirmed the interaction between miR-31-5p and MEG3 or TIMP3. Pearson's correlation coefficient revealed that miR-31-5p was inversely correlated with MEG3 or TIMP3. Rescue experiments indicated that MEG3 regulated TIMP3 expression by sponging miR-31-5p in NSCLC cells. Thus, MEG3 inhibited cell proliferation, migration and invasion, but enhanced apoptosis in NSCLC cells through up-regulating TIMP3 expression by regulating miR-31-5p, indicating novel biomarkers for the therapy of NSCLC.

Non-small cell lung cancer (NSCLC) is a malignant lung cancer and accounts for 80% of lung cancer-related deaths.

Tumor Mutational Burden and Efficacy of Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis

Tumor mutational burden (TMB) is a genomic biomarker that predicts favorable responses to immune checkpoint inhibitors (ICIs). Here, we set out to assess the predictive value of TMB on long-term survival outcomes in patients undergoing ICIs. We systematically searched PubMed, Embase, CENTRAL and clinicaltrials.gov from inception to 6 August 2019. We included retrospective studies or clinical trials of ICIs that reported hazard ratios (HRs) for overall survival (OS) and/or progression-free survival (PFS) according to TMB. Data on 5712 patients from 26 studies were included. Among patients who received ICIs, high TMB groups showed better OS (HR 0.53, 95% CI 0.42 to 0.67) and PFS (HR 0.52, 95% CI 0.40 to 0.67) compared to low TMB groups. In patients with high TMB, those who received ICIs had a better OS (HR 0.69, 95% CI 0.50 to 0.95) and PFS (HR = 0.66, 95% CI = 0.47 to 0.92) compared to those who received chemotherapy alone, while in patients with low TMB, such ICI benefits of OS or PFS were not statistically significant. In conclusion, TMB may be an effective biomarker to predict survival in patients undergoing ICI treatment. The role of TMB in identifying patient groups who may benefit from ICIs should be determined in future randomized controlled trials.

Randomized Phase II Trial and Tumor Mutational Spectrum Analysis from Cabozantinib versus Chemotherapy in Metastatic Uveal Melanoma (Alliance A091201)

PURPOSE

The surface receptor MET is highly expressed on primary uveal melanoma; MET inhibitors demonstrated early clinical signals of efficacy in slowing uveal melanoma growth. The primary objective of our study was to compare the progression-free survival rate at 4 months (PFS4) of patients with uveal melanoma treated with cabozantinib or chemotherapy.

PATIENTS AND METHODS

Patients with metastatic uveal melanoma and RECIST measurable disease were randomized 2:1 to receive either cabozantinib (arm 1) versus temozolomide or dacarbazine (arm 2) with restaging imaging every two cycles. Cross-over from arm 2 to cabozantinib after progression was allowed (arm 2X). Available tumor specimens were analyzed by whole-exome sequencing (WES) and results were correlated with outcome.

RESULTS

Forty-six eligible patients were accrued with 31, 15, and 9 in arms 1, 2, and 2X, respectively. Median lines of prior therapy, including hepatic embolization, were two. Rates of PFS4 in arm 1 and arm 2 were 32.3% and 26.7% (P = 0.35), respectively, with median PFS time of 60 and 59 days (P = 0.964; HR = 0.99). Median overall survival (OS) was 6.4 months and 7.3 months (P = 0.580; HR = 1.21), respectively. Grade 3-4 Common Terminology Criteria for Adverse Events were present in 61.3%, 46.7%, and 37.5% in arms 1, 2, and 2X, respectively. WES demonstrated a mean tumor mutational burden of 1.53 mutations/Mb and did not separate OS ≤ or >1 year (P = 0.14). Known mutations were identified by WES and novel mutations were nominated.

CONCLUSIONS

MET/VEGFR blockade with cabozantinib demonstrated no improvement in PFS but an increase in toxicity relative to temozolomide/dacarbazine in metastatic uveal melanoma.