ArticleScientific reports2019
Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The underexplored links between cancer and the internal body climate: Implications for cancer prevention and treatment.Frontiers in oncology · 2022Pooled it
- Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data.BMC bioinformatics · 2025Article
- Evolutionary measures show that recurrence of DCIS is distinct from progression to breast cancer.Breast cancer research : BCR · 2025Article
- Defining precancer: a grand challenge for the cancer community.Nature reviews. Cancer · 2024Review
- Evolutionary Measures Show that Recurrence of DCIS is Distinct from Progression to Breast Cancer.medRxiv : the preprint server for health sciences · 2024Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Definition of a novel breast tumor-specific classifier based on secretome analysis.Breast cancer research : BCR · 2022Article
- Evaluating the state of the science for adeno-associated virus integration: An integrated perspective.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Review
- A new method to accurately identify single nucleotide variants using small FFPE breast samples.Briefings in bioinformatics · 2021Article
- Identifying modules of cooperating cancer drivers.Molecular systems biology · 2021Article
- Precise base editing for theeLife · 2021Article
- A novel workflow for cancer blood biomarker identification.Annals of translational medicine · 2020Article
- Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms.Journal of genetics and genomics = Yi chuan xue bao · 2020Review
- Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU.Scientific reports · 2020Article
- Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations.PLoS computational biology · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual instances of cancer: we search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. We developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity (95% Confidence Interval (CI) = 89-92%) and 93% specificity (95% CI = 91-94%) on average for seventeen cancer types. We then present an approach based on mutational profile that can be used to distinguish between driver and passenger mutations within these genes. These combinations, with experimental validation, can aid in better diagnosis, provide insights into the etiology of cancer, and provide a rational basis for designing targeted combination therapies.
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