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Matchan, A.

Publications and source records attributed to Matchan, A..

2 recordsLinked to original sources

Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR/Cas9 screening

Many gene fusions have been reported in tumours and for most their role remains unknown. As fusions can be used clinically for diagnostic and prognostic purposes, and are targets for treatment, it is crucial to assess their functional implications in cancer. To investigate the role of fusions in tumor cell fitness, we developed a systematic analysis utilising RNA-sequencing data from 1,011 human cancer cell lines to functionally link 8,354 gene fusion events with genomic data, sensitivity to >350 anti-cancer drugs and CRISPR-Cas9 loss-of-fitness information. Established clinically-relevant fusions were readily identified. Overall, functional fusions were rare, including those involving cancer driver genes, suggesting that many fusions are dispensable for tumor cell fitness. Novel therapeutically actionable fusions involving RAF1, BRD4 and ROS1 were verified in new histologies. In addition, recurrent YAP1-MAML2 fusions were identified as activators of Hippo-pathway signaling in multiple cancer types, supporting therapeutic targeting of Hippo signalling. Our approach discriminates functional fusions, identifying new drivers of carcinogenesis and fusions that could have important clinical implications. SignificanceWe identify fusions as new potential candidates for drug repurposing and drivers of carcinogenesis. These results support histology agnostic marker-driven precision cancer medicine. Most fusions are not functional with implications for interpreting cancer fusions reported from clinical sequencing studies.

cancer biology↗

Transcription factor activities enhance markers of drug response in cancer

Transcriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.

cancer biology↗