The evolution of metastatic upper tract urothelial carcinoma through genomic-transcriptomic and single-cell protein markers analysis.

The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are not well understood, and there is a lack of knowledge regarding the genomic and transcriptomic differences between primary and metastatic UTUC. To address these gaps, we integrate whole-exome sequencing, RNA sequencing, and Imaging Mass Cytometry using lanthanide metal-conjugated antibodies of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. We perform a spatially-resolved single-cell analysis of cancer, immune, and stromal cells to understand the evolution of primary to metastatic UTUC. We discover that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature are stable across primary and matched metastatic UTUC. Molecular and immune subtypes are consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single cells. Molecular subtypes at the single-cell level are highly conserved between primary and metastatic UTUC tumors within the same patient.

The genomic landscape of metastatic clear cell renal cell carcinoma after systemic therapy.

Primary clear cell renal cell carcinoma (ccRCC) has been previously characterized, but the genomic landscape of metastatic ccRCC is largely unexplored. Here, we performed whole exome sequencing (WES) in 68 samples from 44 patients with ccRCC, including 52 samples from a metastatic site. SETD2, PBRM1, APC and VHL were the most frequently mutated genes in the metastatic ccRCC cohort. RBM10 and FBXW7 were also among the 10 most frequently mutated genes in metastatic tissues. Recurrent somatic copy number variations (CNV) were observed at the previously identified regions 3p25, 9p21 and 14q25, but also at 6p21 (CDKN1A) and 13q14 (RB1). No statistically significant differences were found between samples from therapy-naïve and pretreated patients. Clonal evolution analyses with multiple samples from 13 patients suggested that early appearance of CNVs at 3p25, 9p21 and 14q25 may be associated with rapid clinical progression. Overall, the genomic landscapes of primary and metastatic ccRCC seem to share frequent CNVs at 3p25, 9p21 and 14q25. Future work will clarify the implication of RBM10 and FBXW7 mutations and 6p21 and 13q14 CNVs in metastatic ccRCC.

5-Hydroxymethylation highlights the heterogeneity in keratinization and cell junctions in head and neck cancers.

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.

Preanalytic variables in quality and quantity of nucleic acids extracted from FNA specimens of thyroid gland nodules collected in CytoLyt: Cellularity and storage time.

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.

Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution.

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 (WES) data from primary tumors in The Cancer Genome Atlas (TCGA) (n=3,534) and advanced/metastatic tumors from Weill Cornell Medicine (WCM 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 between cancer types. A cancer-specific threshold was able to adjust for the different TMB distributions, while 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 to the static pan-cancer threshold. Additionally, no significant difference in survival outcomes was found with the cancer-specific threshold compared to the pan-cancer one. Further, the cancer-specific threshold maintains higher predicted neoantigen load for the TMB high samples compared to the TMB low samples, even when the threshold is lower than the pan-cancer threshold. CONCLUSION: TMB is relative to the context of cancer type, metastatic state, and disease stage. Compared to a pan-cancer threshold, a cancer-specific threshold classifies more patients as TMB high while maintaining clinical outcomes that were not significantly different. Furthermore, the cancer-specific threshold identifies patients with a high number of predicted neoantigens. Due to the potential impact in cancer patient care, TMB status should be determined in a cancer-specific manner.