Characteristics of Pan-Cancer Patients With Ultrahigh Tumor Mutation Burden

Genomic and transcriptomic profiling reveal molecular characteristics of parathyroid carcinoma

Genomic and transcriptomic profiling has enhanced the diagnostic and treatment options for many cancers. However, the molecular characteristics of parathyroid cancer remain largely unexplored, thereby limiting the development of new therapeutic interventions. Herein, we conducted genomic and transcriptomic sequencing of 50 parathyroid tissues (12 carcinomas, 28 adenomas, and 10 normal tissues) to investigate the intrinsic and comparative molecular features of parathyroid carcinoma. We confirmed multiple two-hit mutation patterns in cell division cycle 73 (CDC73) that converged to biallelic inactivation, calling into question the presence of a second hit in other genes. In addition, allele-specific repression of CDC73 in copies with germline-truncating variants suggested selective pressure prior to tumorigenesis. Transcriptomic analysis identified upregulation of the expression of E2F targets, KRAS and TNF-alpha signaling, and epithelial-mesenchymal transition pathways in carcinomas compared to adenomas and normal tissues. A molecular classification model based on carcinoma-specific genes clearly separated carcinomas from adenomas and normal tissues, the clinical utility of which was demonstrated in two patients with uncertain malignant potential. A deeper analysis of gene expression and functional prediction suggested that Wilms tumor 1 (WT1) is a potential biomarker for CDC73-mutant parathyroid carcinoma, which was further validated through immunohistochemistry. Overall, our study revealed the genomic and transcriptomic profiles of parathyroid carcinoma and may help direct future precision diagnostic and therapeutic improvements.

Comprehensively Exploring the Mutational Landscape and Patterns of Genomic Evolution in Hypermutated Cancers

Simple Summary

To identify potential genetic markers for evaluating hypermutated cancers, we investigated driver mutations, mutational signatures, tumor-associated neoantigens, and molecular cancer evolution in the genetic variants of 533 cancer patients with six different cancer types. Driver mutations, including RET, CBL, and DDR2 gene mutations, were identified in the hypermutated cancers. Cancer driver mutations and mutational signatures are associated with sensitivity or resistance to immunotherapy, representing potential genetic markers in hypermutated cancers. Using computational predictions, we identified two tumor-associated neoantigens. Sequential mutations were used in a logistic model to predict hypermutated cancers according to genomic evolution. The sequential mutation order and coexisting genetic mutations were found to influence the hypermutation phenotype. Based on our observations, we developed a new concept for hypermutated cancers, whereby sequential mutations are significant for hypermutated cancers, which are mutationally heterogeneous. Through the comprehensive assessments of cancer gene panels, mutational pattern analysis was conducted as a basis for providing recommendations regarding therapeutic strategies for hypermutated cancer patients.

Abstract

Tumor heterogeneity results in more than 50% of hypermutated cancers failing to respond to standard immunotherapy. There are numerous challenges in terms of drug resistance, therapeutic strategies, and biomarkers in immunotherapy. In this study, we analyzed primary tumor samples from 533 cancer patients with six different cancer types using deep targeted sequencing and gene expression data from 78 colorectal cancer patients, whereby driver mutations, mutational signatures, tumor-associated neoantigens, and molecular cancer evolution were investigated. Driver mutations, including RET, CBL, and DDR2 gene mutations, were identified in the hypermutated cancers. Most hypermutated endometrial and pancreatic cancer patients carry genetic mutations in EGFR, FBXW7, and PIK3CA that are linked to immunotherapy resistance, while hypermutated head and neck cancer patients carry genetic mutations associated with better treatment responses, such as ATM and BRRCA2 mutations. APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) and DNA repair defects are mutational drivers that are signatures for hypermutated cancer. Cancer driver mutations and other mutational signatures are associated with sensitivity or resistance to immunotherapy, representing potential genetic markers in hypermutated cancers. Using computational prediction, we identified NF1 p.T700I and NOTCH1 p.V2153M as tumor-associated neoantigens, representing potential therapeutic targets for immunotherapy. Sequential mutations were used to predict hypermutated cancers based on genomic evolution. Using a logistic model, we achieved an area under the curve (AUC) = 0.93, accuracy = 0.93, and sensitivity = 0.81 in the testing set. The sequential patterns were distinct among the six cancer types, and the sequential mutation order of MSH2 and the coexisting BRAF genetic mutations influenced the hypermutated phenotype. The TP53~MLH1 and NOTCH1~TET2 sequential mutations impacted colorectal cancer survival (p-value = 0.027 and 0.0001, respectively) by reducing the expression of PTPRCAP (p-value = 1.06 × 10⁻⁶) and NOS2 (p-value = 7.57 × 10⁻⁷) in immunity. Sequential mutations are significant for hypermutated cancers, which are characterized by mutational heterogeneity. In addition to driver mutations and mutational signatures, sequential mutations in cancer evolution can impact hypermutated cancers. They characterize potential responses or predictive markers for hypermutated cancers. These data can also be used to develop hypermutation-associated drug targets and elucidate the evolutionary biology of cancer survival. In this study, we conducted a comprehensive analysis of mutational patterns, including sequential mutations, and identified useful markers and therapeutic targets in hypermutated cancer patients.