71 Tumors with higher heterogeneity were associated with superior survival outcome amongst stage I lung cancer patients with low tumor mutational burden (TMB)

Background Tumor mutational burden (TMB) has been shown to predict response to immune checkpoint inhibitors.1 Furthermore, the FDA has approved the use of TMB as a biomarker for response to pembrolizumab in solid tumors.2 Simultaneously, the relationship between tumor heterogeneity and outcome has been studied across a range of cancer indications and has shown predictive value.3 For Lung Squamous Cell Carcinoma (LUSC) the utility of heterogeneity metrics has not been established. To study this relationship we used both TMB and tumor heterogeneity to stratify patients, compare outcomes, explore differences in immune cell enrichment, and predict driver genes. Methods We obtained Tumor Cancer Genome Atlas (TCGA) LUSC SNP, CNV, and RNASeq data from the GDC Data Portal4 and clinical data from the PanCancer Atlas dataset through cBioPortal.5 TMB was calculated by dividing the number of mutations by 38 to yield a mut/Mb value. To estimate tumor heterogeneity we ran PyClone, an algorithm that estimates the number of tumor clones.6 PyClone uses a random seed and output for the same sample may differ. We ran each sample in triplicate on three separate days yielding 9 runs per sample, yielding an average PyClone clone number. Clones with >2 mutations were counted. Using p-value minimization we chose 5 for the TMB cutoff and 4.6 for the PyClone cutoff. This yielded 4 groups: HTHP, HTLP, LTHP, and LTLP, where H - high, L- low, T-TMB, and P-Pyclone. Immune cell enrichment analysis was accomplished with ssGSEA via the GenePattern platform.7 Driver gene prediction was performed with OncoDriveClust8 via the R package maftools.9 Results A statistically significant difference was found in progression free survival (PFS) between stage I LTHP (LTHPI, N = 15) and stage I LTLP (LTLPI, N = 77) patients (51.27 months vs. 25.4 months, p-value = 0.0059). Intriguingly, highly heterogeneous tumors revealed superior survival outcomes compared to less heterogeneous tumors in this subgroup. LTLPI patients were enriched for immature B cells, regulatory T cells, and myeloid derived suppressor cells (figure 1). Three driver genes were predicted for the LTLPI cohort (NFE2L2, PIK3CA, and TP53), while none were predicted for the LTHPI cohort. Conclusions Contrary to previous literature, superior survival outcomes were observed in high tumor heterogeneity, low TMB Stage I LUSC patients. Early stage patients can be stratified using heterogeneity metrics like PyClone. Given the presence of specific driver genes and an immunosuppressive tumor microenvironment, this population warrants further investigation for therapeutic implications. Acknowledgements This research was supported in part through the computational resources and staff contributions provided by the Genomics Compute Cluster which is jointly supported by the Feinberg School of Medicine, the Center for Genetic Medicine, and Feinberg’s Department of Biochemistry and Molecular Genetics, the Office of the Provost, the Office for Research, and Northwestern Information Technology. The Genomics Compute Cluster is part of Quest, Northwestern University’s high performance computing facility, with the purpose to advance research in genomics. Trial Registration N/A References Samstein RM, Lee C-H, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nature Genetics 2019;51(2):202–6. Center for Drug Evaluation and Research. FDA approves pembrolizumab for adults and children With TMB-H solid tu [Internet]. U.S. Food and Drug Administration. FDA; [cited 2021 Jul 28]. Available from: https://www.fda.gov/drugs/drug-approvals-and-databases/fda-approves-pembrolizumab-adults-and-children-tmb-h-solid-tumors Morris LGT, Riaz N, Desrichard A, Senbabaoglu Y, Hakimi AA, Makarov V, et al. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 2016;7(9):10051–63. GDC. [cited 2021Jul28]. Available from: https://portal.gdc.cancer.gov/ cBioPortal for cancer genomics [Internet]. cBioPortal for Cancer Genomics. [cited 2021Jul28]. Available from: https://www.cbioportal.org/ Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, et al. PyClone: Statistical inference of CLONAL population structure in cancer. Nature Methods 2014;11(4):396–8. GenePattern [Internet]. GenePattern sign in. [cited 2021Jul28]. Available from: https://cloud.genepattern.org/gp/pages/index.jsf Tamborero D, Gonzalez-Perez A, Lopez-Bigas N. OncodriveCLUST: Exploiting the Positional clustering of somatic mutations to identify CANCER GENES. Bioinformatics. 2013;29(18):2238–44. Mayakonda A, Lin D-C, Assenov Y, Plass C, Koeffler HP. Maftools: Efficient and comprehensive analysis of somatic variants in cancer. Genome Research 2018;28(11):1747–56. Ethics Approval N/A Consent N/A