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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.

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. 794. This article is highlighted in the In This Issue feature, p. 781

Background: Metastatic or surgically unresectable urothelial cancer (UC) is a disease with high unmet need. Nivolumab monotherapy demonstrated clinical benefit in patients with platinum-resistant UC (CheckMate 275; NCT02387996). Because not all patients respond to nivolumab therapy, identifying biomarkers for response is of critical importance. In the CheckMate 275 cohort, higher tumor mutational burden (TMB) was associated with longer overall survival (OS) (1). Previous studies have shown that certain mutational signatures, combinations of mutation types arising from specific mutagenesis processes, were associated with mutational burden and clinical outcome in UC (2,3). We hypothesized that specific mutational signatures may be associated with response to nivolumab in CheckMate 275. Methods: Whole exome sequencing data and clinical annotations for 139 archival, tumor-normal paired samples from patients enrolled in the CheckMate 275 cohort were collected and analyzed. Sequencing reads were processed and somatic variants called as described previously (4). Percentage of mutations across 30 mutational signatures were generated using deconstructSigs, which infers signature activity with given known signatures (5). Association of the most prevalent mutational signatures with TMB, previously known clinical biomarkers, and clinical efficacy (OS, progression free survival [PFS], and objective response [OR]) were examined. Results: We identified age-related (signature 1), endogenous mutagenesis-related APOBEC (signatures 2 and 13) and UV-induced (signature 7) as the most prevalent mutational signatures in UC. When examining their correlations with TMB, signature 1 showed negative correlation, signatures 2 and 13 showed high positive correlation, and signature 7 showed low correlation. Higher signature 2 scores were associated with better OS, but were poor predictors for PFS and OR. Overall, while signature 2 added predictive value over previously known clinical biomarkers such as PD-L1 expression, serum hemoglobin level, and presence of liver metastasis, it did not improve on the predictive performance of TMB. Conclusions: An APOBEC-related mutational signature (signature 2) was associated with OS in patients with advanced UC treated with nivolumab. This signature was correlated with TMB and did not improve upon TMB as a predictive biomarker. Our work demonstrates the importance of including TMB as a covariate when analyzing mutational signatures. References: 1. Galsky MD et al. Ann Oncol 2017;28(suppl 5). Abstract 848PD 2. Glaser AP et al. Oncotarget 2018;9:4537-48 3. Roberts SA et al. Nat Genet 2013;45:970-6 4. Carbone DP et al. NEJM 2017;376:2415-26 5. Forbes SA et al. Nucleic Acids Res 2017;45:D777-83 Citation Format: G. Celine Han, Peter M. Szabo, Abdel Saci, Alice M. Walsh, Natallia Kalinava, Ariella Sasson, Bruce Fischer, Matthew D. Galsky, Padmanee Sharma. Mutational signatures as biomarkers of response to nivolumab in metastatic bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4890.

Background: Integrated analysis of multi-omic data is important in understanding oncogenesis, pathology, and drug mechanism of action. Formalin-fixed, paraffin-embedded (FFPE) tissues comprise the bulk of archival specimens in hospitals and clinical trials. Commercial kits are commonly used to extract RNA, DNA, or protein for biomarker studies; however, high-quality extractions are challenging due to crosslinking. We explored the feasibility of performing proteomic, transcriptomic, and genetic analysis from limited FFPE samples collected in clinical trials through a pilot study in colorectal cancer (CRC) and normal tissue. Methods: Qiagen kits were employed, with some modifications, to extract nucleic acid and protein from FFPE sample lysates that would be amenable to next-generation sequencing and liquid chromatography/mass-spectrometry (LC/MS) proteomic profiling. Commercially sourced FFPE slides from CRC biopsies and normal colon, heart, and skin samples were processed (n = 10 each; ~600-mm2 by 5-μm/slice). RNA-seq library preparation was performed using the Illumina® TruSeq Access kit. Label-free LC/MS proteomic analysis was carried out using trypsin/Lys-C-digested protein fragments on an EASY-nLC™ 1200 coupled to a Q Exactive™ Plus (Thermo Fisher). MS data were processed with MaxQuant to estimate protein abundance (Cox and Mann, Nat Biotech 2008). Results: Of 40 samples tested, 38 yielded quantifiable nucleic acid and protein of sufficient quality for transcriptional and proteomic analyses. Mean RNA yield was ~300 ng (150 ng-1.2 µg). RNA degradation was significant, with a mean DV>200 of 45% (Agilent Bioanalyzer). Mean DNA yield was 530 ng, with a mean fragment size of 2 kb. Mean protein yield was 50 µg (1 µg-300 µg). RNA libraries had a mapping rate range of 85%-90% and a coding rate range of 70%-80%; ~16,000 genes were reliable detected (log2 TPM > 1). The number of unique proteins detected ranged from 3,000 to 4,000 for CRC, normal colon, and heart samples; skin samples averaged 1,000 proteins and had the lowest overall yield. Correlation of RNA and protein expression for the same genes was weak (Spearman's rho ~0.3-0.4) at the per-sample level but statistically significant. Using linear models, we identified differentially expressed transcripts and proteins between the CRC and normal colon samples. Pathway enrichment analysis of both modalities indicated changes in cell cycle, which is consistent with rapid growth of tumor cells. Changes in nucleoside metabolism, extracellular matrix remodeling, and innate immune response were more apparent at the protein level. Conclusion: We found that multi-omic analysis was feasible with FFPE samples, and proteomics can be used to validate RNA results. Additionally, proteomics reveal post-translational events, such as extracellular matrix remodeling, that provide unique insights into cancer pathology. Citation Format: Vishal Patel, Ji Gao, Mingyi Liu, Aiqing He, Xi-Tao Wang, Kathryn Vanderlaag, Ariella Sasson, Stefan Kirov, Sunil Kuppasani, Omar J. Jabado, Kandasamy Ravi, Ashok Dongre, Julie Carman, Heidi LeBlanc. Integrated analysis of colorectal carcinoma by co-extraction of RNA, DNA and protein from FFPE tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2707.