Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator

Investigating the origins of the mutational signatures in cancer

Most of the risk factors associated with chronic and complex diseases, such as cancer, stem from exogenous and endogenous exposures experienced throughout an individual's life, collectively known as the exposome. These exposures can modify DNA, which can subsequently lead to the somatic mutations found in all normal and tumor tissues. Understanding the precise origins of specific somatic mutations has been challenging due to multitude of DNA adducts (i.e. the DNA adductome) and their diverse positions within the genome. Thus far, this limitation has prevented researchers from precisely linking exposures to DNA adducts and DNA adducts to subsequent mutational outcomes. Indeed, many common mutations observed in human cancers appear to originate from error-prone endogenous processes. Consequently, it remains unclear whether these mutations result from exposure-induced DNA adducts, or arise indirectly from endogenous processes or are a combination of both. In this review, we summarize approaches that aim to bridge our understanding of the mechanism by which exposure leads to DNA damage and then to mutation and highlight some of the remaining challenges and shortcomings to fully supporting this paradigm. We emphasize the need to integrate cellular DNA adductomics, long read-based mapping, single-molecule duplex sequencing of native DNA molecules and advanced computational analysis. This proposed holistic approach aims to unveil the causal connections between key DNA modifications and the mutational landscape, whether they originate from external exposures, internal processes or a combination of both, thereby addressing key questions in cancer biology.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

The evolutionary history of metastatic pancreatic neuroendocrine tumours reveals a therapy driven route to high-grade transformation

Tumour evolution with acquisition of more aggressive disease characteristics is a hallmark of disseminated cancer. Metastatic pancreatic neuroendocrine tumours (PanNETs) in particular may progress from a low/intermediate to a high-grade disease. The aim of this work was to understand the molecular mechanisms underlying metastatic progression as well as PanNET transformation from a low/intermediate to a high-grade disease. We performed multi-omics analysis (genome/exome sequencing, total RNA-sequencing and methylation array) of 32 longitudinal samples from six patients with metastatic low/intermediate grade PanNET. The clonal composition of tumour lesions and underlying phylogeny of each patient were determined with bioinformatics analyses. Findings were validated in post-alkylating chemotherapy samples from 24 patients with PanNET using targeted next generation sequencing. We validate the current PanNET evolutionary model with MEN1 inactivation that occurs very early in tumourigenesis. This was followed by pronounced genetic diversity on both spatial and temporal levels, with parallel and convergent tumour evolution involving the ATRX/DAXX and mechanistic target of the rapamycin (mTOR) pathways. Following alkylating chemotherapy treatment, some PanNETs developed mismatch repair deficiency and acquired a hypermutational phenotype. This was validated among 16 patients with PanNET who had high-grade progression after alkylating chemotherapy, of whom eight had a tumour mutational burden >50 (50%). In comparison, among the eight patients who did not show high-grade progression, 0 had a tumour mutational burden >50 (0%; odds ratio 'infinite', 95% confidence interval 1.8 to 'infinite', p = 0.02). Our findings contribute to broaden the understanding of metastatic/high-grade PanNETs and suggests that therapy driven disease evolution is an important hallmark of this disease. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Molecular classification of ovarian high-grade serous/endometrioid carcinomas through multi-omics analysis: JGOG3025-TR2 study

BACKGROUND

Considerable interobserver variability exists in diagnosis of ovarian high-grade endometrioid carcinoma (HGEC) and high-grade serous carcinoma (HGSC) due to histopathological similarities. While homologous recombination deficiency (HRD) correlates with drug sensitivity in HGSC, the molecular features of HGEC are unclear.

METHODS

Fresh-frozen samples from 15 ovarian HGECs and 274 ovarian HGSCs in the JGOG-TR2 cohort were submitted to targeted DNA sequencing, RNA sequencing, DNA methylation array, and SNP array. We additionally analyzed 555 ovarian HGSCs from TCGA-OV and 287 endometrial high-grade carcinomas from TCGA-UCEC.

RESULTS

Unsupervised clustering using copy number signatures identified four distinct tumor groups (C1, C2, C3 and C4). C1 (n = 41) showed CCNE1 amplification and poor survival. C2 (n = 160) and C3 (n = 59) showed high BRCA1/2 alteration frequency with low and moderate ploidy, respectively. C4 (n = 22) was characterized by favorable outcome, higher HGEC proportion, no BRCA1/2 alteration or CCNE1 amplification, and low levels of HRD score, ploidy, intra-tumoral heterogeneity, cell proliferation rate, and WT1 gene expression. Notably, C4 exhibited a normal endometrium-like DNA methylation profile, thus, defined as "HGEC-type" tumors, which were also identified in TCGA-OV and TCGA-UCEC.

CONCLUSIONS

Ovarian "HGEC-type" tumors present a non-HRD status, favorable prognosis, and endometrial differentiation, possibly constituting a subset of clinically diagnosed HGSCs.

Constitutional mismatch repair deficiency mimicking Lynch syndrome is associated with hypomorphic mismatch repair gene variants

Lynch syndrome (LS) and constitutional mismatch repair deficiency (CMMRD) are distinct cancer syndromes caused, respectively, by mono- and bi-allelic germline mismatch repair (MMR) variants. LS predisposes to mainly gastrointestinal and genitourinary cancers in adulthood. CMMRD predisposes to brain, haematological, and LS-spectrum cancers from childhood. Two suspected LS patients with first cancer diagnosis aged 27 or 38 years were found to be homozygous for an MMR (likely) pathogenic variant, MSH6 c.3226C>T (p.(Arg1076Cys)), or variant of uncertain significance (VUS), MLH1 c.306G>A (p.(Glu102=)). MLH1 c.306G>A was shown to cause leaky exon 3 skipping. The apparent genotype-phenotype conflict was resolved by detection of constitutional microsatellite instability in both patients, a hallmark feature of CMMRD. A hypomorphic effect of these and other variants found in additional late onset CMMRD cases, identified by literature review, likely explains a LS-like phenotype. CMMRD testing in carriers of compound heterozygous or homozygous MMR VUS may find similar cases and novel hypomorphic variants. Individualised management of mono- and bi-allelic carriers of hypomorphic MMR variants is needed until we better characterise the associated phenotypes.

Evaluating topography of mutational signatures with SigProfilerTopography

The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome's architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.