ArticleNature communications2019
Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 47 citations in OpenAlex.
- Article
- Identification of cancer mini-drivers by deciphering selective landscape in the cancer genome.Briefings in bioinformatics · 2026Article
- Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks.Briefings in bioinformatics · 2024Article
- SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification.International journal of molecular sciences · 2024Article
- The pluripotency factor NANOG contributes to mesenchymal plasticity and is predictive for outcome in esophageal adenocarcinoma.Communications medicine · 2024Article
- Intragenic Rearrangement Burden Associates with Immune Cell Infiltration and Response to Immune Checkpoint Blockade in Cancer.Cancer immunology research · 2024Article
- A decade of the Oesophageal Cancer Clinical and Molecular Stratification Consortium.Nature medicine · 2024Article
- Mechanistic insights into the interactions between cancer drivers and the tumour immune microenvironment.Genome medicine · 2023Article
- Oncogenic ERRB2 signals through the AP-1 transcription factor to control mesenchymal-like properties of oesophageal adenocarcinoma.NAR cancer · 2023Article
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- Somatic whole genome dynamics of precancer in Barrett's esophagus reveals features associated with disease progression.Nature communications · 2022Article
- Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource.Genome biology · 2022Review
- Intrinsic Cellular Susceptibility to Barrett's Esophagus in Adults Born with Esophageal Atresia.Cancers · 2022Article
- Article
- [Influence of molecular pathology on oncological surgery of tumors of the upper gastrointestinal tract].Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen · 2021Review
- Article
- Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts.Gastroenterology · 2021Article
- Review
- Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma.Cancers · 2021Article
- The modern approach to esophageal palliative and emergency surgery.Annals of translational medicine · 2021Review
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Authors and funding
14 authors at 20 institutions in 2 countries.
Funding
Abstract
The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.