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.

Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications.

Although next-generation sequencing is widely used in cancer to profile tumors and detect variants, most somatic variant callers used in these pipelines identify variants at the lowest possible granularity, single-nucleotide variants (SNV). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variants (MNV). With this approach, the amino acid change from the individual SNV within a codon could be different from the amino acid change based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstream effects of the variants. Here, we analyzed 10,383 variant call files (VCF) from the Cancer Genome Atlas (TCGA) and found 12,141 incorrectly annotated MNVs. Analysis of seven commonly mutated genes from 178 studies in cBioPortal revealed that MNVs were consistently missed in 20 of these studies, whereas they were correctly annotated in 15 more recent studies. At the BRAF V600 locus, the most common example of MNV, several public datasets reported separate BRAF V600E and BRAF V600M variants instead of a single merged V600K variant. VCFs from the TCGA Mutect2 caller were used to develop a solution to merge SNV to MNV. Our custom script used the phasing information from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into MNV before variant annotation. This study shows that institutions performing NGS sequencing for cancer genomics should incorporate the step of merging MNV as a best practice in their pipelines. SIGNIFICANCE: Identification of incorrect mutation calls in TCGA, including clinically relevant BRAF V600 and KRAS G12, will influence research and potentially clinical decisions.

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.

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

INTRODUCTION: Tumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. OBJECTIVE: Here we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase III clinical trial. METHODS: In the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small-cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne® CDx assay. RESULTS: TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman's r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearman's r = 0.90). CONCLUSIONS: Parameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.

STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma.

KRAS is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that STK11/LKB1 (KL) or TP53 (KP) comutations define distinct subgroups of KRAS-mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups (P < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with KRAS-mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; P = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free (P < 0.001) and overall (P = 0.0015) survival compared with KRASMUT;STK11/LKB1WT LUAC. Among 924 LUACs, STK11/LKB1 alterations were the only marker significantly associated with PD-L1 negativity in TMBIntermediate/High LUAC. The impact of STK11/LKB1 alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In Kras-mutant murine LUAC models, Stk11/Lkb1 loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify STK11/LKB1 alterations as a major driver of primary resistance to PD-1 blockade in KRAS-mutant LUAC.Significance: This work identifies STK11/LKB1 alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in KRAS-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. Cancer Discov; 8(7); 822-35. ©2018 AACR.See related commentary by Etxeberria et al., p. 794This article is highlighted in the In This Issue feature, p. 781.

A 16-gene signature distinguishes anaplastic astrocytoma from glioblastoma.

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.

A DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-κB nexus.

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n = 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-κB pathway association with high-risk group. NF-κB inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-κB targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-κB activity by inhibiting AKT through a p53-PTEN-dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-κB-activated high-risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-κB pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target.

A ten-microRNA expression signature predicts survival in glioblastoma.

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n=222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR]=2.4; 95% CI=1.4-3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR=1.7; 95% CI=1.1-2.8; p=0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR=2.0; 95% CI=1.4-2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR=1.120; 95% CI=1.04-1.20; p=0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.