PURPOSE To understand the clinical context of tumor mutational burden (TMB) when comparing a pan-cancer threshold and a cancer-specific threshold. MATERIALS AND METHODS Using whole exome sequencing data from primary tumors in The Cancer Genome Atlas (n = 3,534) and advanced and/or metastatic tumors from Weill Cornell Medicine Advanced (n = 696), TMB status was determined using a pan-cancer and cancer-specific threshold. Survival curves, number of samples classified as TMB high, and predicted neoantigens were used to evaluate the differences between thresholds. RESULTS The distribution of TMB varied dramatically among cancer types. A cancer-specific threshold was able to adjust for the different TMB distributions, whereas the pan-cancer threshold was often too stringent. The dynamic nature of the cancer-specific threshold resulted in more tumors being classified as TMB high compared with the static pan-cancer threshold. In addition, no significant difference in survival outcomes was found with the cancer-specific threshold compared with the pan-cancer threshold. Furthermore, the cancer-specific threshold maintained higher predicted neoantigen load for the TMB-high samples compared with the TMB-low samples, even when the threshold was lower than the pan-cancer threshold. CONCLUSION TMB is determined within the context of cancer type, metastatic state, and disease stage. Compared with a pan-cancer threshold, a cancer-specific threshold classifies more patients as TMB high while maintaining clinical outcomes that are not significantly different. Furthermore, the cancer-specific threshold identifies patients with a high number of predicted neoantigens. Because of the potential impact in the care of patients with cancer, TMB status should be determined in a cancer-specific manner.

Background Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer worldwide, with human papillomavirus (HPV)-related HNSCC rising to concerning levels. Extensive clinical, genetic and epigenetic differences exist between HPV-associated HNSCC and HPV-negative HNSCC, which is often linked to tobacco use. However, 5-hydroxymethylation (5hmC), an oxidative derivative of DNA methylation and its heterogeneity among HNSCC subtypes, has not been studied. Results We characterized genome-wide 5hmC profiles in HNSCC by HPV status and subtype in 18 HPV(+) and 18 HPV(−) well-characterized tumors. Results showed significant genome-wide hyper-5hmC in HPV(−) tumors, with both promoter and enhancer 5hmC able to distinguish meaningful tumor subgroups. We identified specific genes whose differential expression by HPV status is driven by differential hydroxymethylation. CDKN2A (p16), used as a key biomarker for HPV status, exhibited the most extensive hyper-5hmC in HPV(+) tumors, while HPV(−) tumors showed hyper-5hmC in CDH13, TIMP2, MMP2 and other cancer-related genes. Among the previously reported two HPV(+) subtypes, IMU (stronger immune response) and KRT (more keratinization), the IMU subtype revealed hyper-5hmC and up-regulation of genes in cell migration, and hypo-5hmC with down-regulation in keratinization and cell junctions. We experimentally validated our key prediction of higher secreted and intracellular protein levels of the invasion gene MMP2 in HPV(−) oral cavity cell lines. Conclusion Our results implicate 5hmC in driving differences in keratinization, cell junctions and other cancer-related processes among tumor subtypes. We conclude that 5hmC levels are critical for defining tumor characteristics and potentially used to define clinically meaningful cancer patient subgroups.

Background Morphologic and genetic analysis of thyroid nodules may be performed from a single vial. Preanalytic variables that affect nucleic acid extracted from a single vial are evaluated. Methods Thyroid fine‐needle aspiration (FNA) specimens collected in CytoLyt were evaluated. A ThinPrep slide was prepared. Extracted nucleic acids were analyzed using Oncomine Comprehensive Panel, version 2, after Ion AmpliSeq library preparation. A pathologist and a cytotechnologist enumerated specimen cellularity. Results Fifty‐six samples were collected from 55 nodules in 53 patients. Bethesda category correlated with cellularity (P = .01), and storage time (median, 43 days; range, 7‐77 days) was longer for specimens in categories II and III than for those in categories IV and VI (P = .01). The mean specimen DNA concentration was 4.5 ng/µL (range, 0‐23.8 ng/µL), and 25 (45%) had concentrations >3.3 ng/µL. The mean specimen RNA concentration was 4.8 ng/µL (range, 0‐42.4 ng/µL), and 31 (55%) had concentrations >1.4 ng/µL. Nucleic acid quantity increased with epithelial cellularity. Storage time weakly correlated with the quantity of extracted DNA, independent of cellularity, but not extracted RNA. Greater proportions of cell‐free DNA and lesser proportions of long, intact RNA fragments were extracted from a subset of samples with longer storage time. Among 15 single nucleotide variants, the median mutant allelic fraction was 15.1%. One false‐negative result was identified. Five specimens subsequently determined to harbor a genetic alteration failed quality metrics. Conclusions Cellularity and storage time affect the quantity and quality of nucleic acid extracted from thyroid FNA specimens collected in CytoLyt. Further investigation will serve to quantify the magnitude of such effects and to elucidate other contributing factors.