Search bioRxiv⌕ Search

Biology subjects

Mourikis, T.

Publications and source records attributed to Mourikis, T..

2 recordsLinked to original sources

Whole Genome Doubling mitigates Muller’s Ratchet in Cancer Evolution

Whole genome doubling (WGD) is a prevalent macro-evolutionary event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and clinical prognostic relevance, the evolutionary selection pressures for WGD have not been investigated. Here, we explored whether WGD may act to mitigate the irreversible, inexorable ratchet-like, accumulation of deleterious mutations in essential genes. Utilizing 1050 tumor regions from 816 non-small cell lung cancers (NSCLC), we temporally dissect mutations to determine their temporal acquisition in relation to WGD. We find evidence for strong negative selection against homozygous loss of essential cancer genes prior to WGD. However, mutations in essential genes occurring after duplication were not subject to significant negative selection, consistent with WGD providing a buffering effect, decreasing the likelihood of homozygous loss. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify signals of positive selection in lung, breast, colorectal cancer and other cancer types, enabling the elucidation of novel tumour suppressor genes and a deeper characterization of known cancer genes.

genomics↗

Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma

The identification of somatic alterations with a cancer promoting role is challenging in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we developed a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously (mutations, copy number alterations and structural rearrangements). Analysing 261 EACs from the OCCAMS Consortium, we discovered a large number of novel cancer genes that, together with well-known drivers, help promote cancer. Validation using 107 additional EACs confirmed the robustness of the approach. Unlike known drivers whose alterations recur across patients, the large majority of the newly discovered cancer genes are rare or patient-specific. Despite this, they converge towards perturbing cancer-related processes, including intracellular signalling, cell cycle regulation, proteasome activity and Toll-like receptor signalling. Recurrence of process perturbation, rather than individual genes, divides EACs into six clusters that differ in their molecular and clinical features and suggest patient stratifications for personalised treatments. By experimentally mimicking or reverting alterations of predicted cancer genes, we validated their contribution to cancer progression and revealed EAC acquired dependencies, thus demonstrating their potential as therapeutic targets.

cancer biology↗