Tissue tumor mutational burden (tTMB) is calculated to aid in cancer treatment selection. High tTMB predicts a favorable response to immunotherapy in patients with non-small cell lung cancer. Blood TMB (bTMB) from circulating tumor DNA is reported to have similar predictive power and has been proposed as an alternative to tTMB. Across many studies not only are tTMB and bTMB not concordant but also as reported previously by our group predict conflicting outcomes. This implies that bTMB is not a substitute for tTMB, but rather a composite index that may encompass tumor heterogeneity. Here, we provide a thorough overview of the predictive power of TMB, discuss the use of tumor heterogeneity alongside TMB to predict treatment response and review several methods of tumor heterogeneity assessment. Furthermore, we propose a hypothetical method of estimating tumor heterogeneity and touch on its clinical implications.
BackgroundTumor 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.MethodsWe 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.9ResultsA 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.Abstract 71 Figure 1Immune Cell Gene Set EnrichmentConclusionsContrary 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.AcknowledgementsThis 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 RegistrationN/AReferencesSamstein 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-tumorsMorris LGT, Riaz N, Desrichard A, Şenbabaoğlu 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.jsfTamborero 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 ApprovalN/AConsentN/A
BackgroundTumor heterogeneity assessment may help predict response to immunotherapy. In melanoma mouse models, tumor heterogeneity impaired immune response.1 In addition, among lung cancer patients receiving immunotherapy, the high clonal neoantigen group had favorable survival and outcomes.2 Ideal methods of quantifying tumor heterogeneity are multiple biopsies or autopsy. However, these are not feasible in routine clinical practice. Circulating tumor DNA (ctDNA) is emerging as an alternative. Here, we reviewed the current state of tumor heterogeneity quantification from ctDNA. Furthermore, we propose a new tumor heterogeneity index(THI) based on our own scoring system, utilizing both ctDNA and tissue DNA.MethodsSystematic literature search on Pubmed was conducted up to August 18, 2020. A scoring system and THI were theoretically derived.ResultsTwo studies suggested their own methods of assessing tumor heterogeneity. One suggested clustering mutations with Pyclone,3 and the other suggested using the ratio of allele frequency (AF) to the maximum somatic allele frequency (MSAF).4 According to the former, the mutations in the highest cellular prevalence cluster can be defined as clonal mutations. According to the latter, the mutations with AF/MSAF<10% can be defined as subclonal mutations. To date, there have been no studies on utilizing both ctDNA and tissue DNA simultaneously to quantify tumor heterogeneity. We hypothesize that a mutation found in only one of either ctDNA or tissue DNA has a higher chance of being subclonal.We suggest a scoring system based on the previously mentioned methods to estimate the probability for a mutant allele to be subclonal. Adding up the points that correspond to the conditions results in a subclonality score (table 1). In a given ctDNA, the number of alleles with a subclonality score greater than or equal to 2 divided by the total number of alleles is defined as blood THI (bTHI) (figure 1). We can repeat the same calculation in a given tissue DNA for tissue THI (tTHI) (figure 2). Finally, we define composite THI (cTHI) as the mean of bTHI and tTHI.Abstract 18 Table 1Subclonality scoreAbstract 18 Figure 1Hypothetical distribution of all alleles found in ctDNA bTHI = the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in ctDNA = 10/20 =50%Abstract 18 Figure 2Hypothetical distribution of all alleles found in tissue DNA tTHI= the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in tissue DNA = 16/40 = 40% cTHI= (bTHI + tTHI)/2 = 45%ConclusionsTumor heterogeneity is becoming an important biomarker for predicting response to immunotherapy. Because autopsy and multiple biopsies are not feasible, utilizing both ctDNA and tissue DNA is the most comprehensive and practical approach. Therefore, we propose cTHI, for the first time, as a quantification measure of tumor heterogeneity.ReferencesWolf Y, Bartok O. UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma. Cell 2019;179:219–235.McGranahan N, Swanton C. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016;351:1463–1469.Ma F, Guan Y. Assessing tumor heterogeneity using ctDNA to predict and monitor therapeutic response in metastatic breast cancer. Int J Cancer 2020;146:1359–1368.Liu Z, Xie Z. Presence of allele frequency heterogeneity defined by ctDNA profiling predicts unfavorable overall survival of NSCLC. Transl Lung Cancer Res 2019;8:1045–1050.
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