Biology subjects

Misetic, H.

Publications and source records attributed to Misetic, H..

4 recordsLinked to original sources

bioRxiv2024

Context-dependent effects of CDKN2A and other 9p21 gene losses during the evolution of oesophageal cancer

CDKN2A is a tumour suppressor located in chromosome 9p21 and frequently lost in Barretts oesophagus (BO) and oesophageal adenocarcinoma (OAC). How CDKN2A and other 9p21 gene co-deletions affect OAC evolution remains understudied. We explored the effects of 9p21 loss in OACs and cancer progressor and non-progressor BOs with matched genomic, transcriptomic, and clinical data. Despite its cancer driver role, CDKN2A loss in BO prevents OAC initiation by counter-selecting subsequent TP53 alterations. 9p21 gene co-deletions predict poor patient survival in OAC but not BO through context-dependent effects on cell cycle, oxidative phosphorylation, and interferon response. Immune quantifications using bulk transcriptome, RNAscope and high-dimensional tissue imaging showed that IFNE loss reduces immune infiltration in BO but not OAC. Mechanistically, CDKN2A loss suppresses the maintenance of squamous epithelium, contributing to a more aggressive phenotype. Our study demonstrates context-dependent roles of cancer genes during disease evolution, with consequences for cancer detection and patient management.

cancer biology↗

bioRxiv2023

Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment

The crosstalk between cancer and the tumour immune microenvironment (TIME) has attracted significant interest because of its impact on cancer evolution and response to treatment. Despite this, cancer-specific tumour-TIME interactions and their mechanisms of action are still poorly understood. Here we identified the interactions between cancer-specific genetic drivers and anti- or pro-tumour TIME features in individual samples of 32 cancer types. The resulting 477 TIME drivers are multifunctional genes whose alterations are selected early in cancer evolution and recur across and within cancer types. Moreover, the anti-tumour TIME driver burden is predictive of overall response to immunotherapy. Focusing on head and neck squamous cancer (HNSC), we rebuilt the functional networks linking specific TIME driver alterations to the TIME state. We showed that TIME driver alterations predict the immune profiles of HNSC molecular subtypes, and that deregulation of keratinization, apoptosis and interferon signalling underpin specific driver-TIME interactions. Overall, our study provides a comprehensive resource of TIME drivers giving mechanistic insights into their immune-regulatory role.

cancer biology↗

bioRxiv2021

Comparative assessment of genes driving cancer and somatic evolution.

Genetic alterations of somatic cells can drive nonmalignant clone formation and promote cancer initiation. However, the link between these processes remains unclear hampering our understanding of tissue homeostasis and cancer development. Here we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and noncancer somatic evolution in 122 cancer types and 12 noncancer tissues. Mapping the alterations of these genes in 7953 pancancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and noncancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and noncancer somatic drivers, their literature support and properties are accessible at http://www.network-cancer-genes.org/.

cancer biology↗

bioRxiv2020

Pan-cancer detection of driver genes at the single-patient resolution

BackgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. ResultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. ConclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

cancer biology↗