Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data
Abstract Genome data from cancer patients represents relationships between the presence of a gene mutation and cancer occurrence in a patient. Different types of cancer in human are thought to be caused by combinations of two to nine gene mutations. Identifying these combinations through traditional...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06043-1 |
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| author | Vladyslav Oles Sajal Dash Ramu Anandakrishnan |
| author_facet | Vladyslav Oles Sajal Dash Ramu Anandakrishnan |
| author_sort | Vladyslav Oles |
| collection | DOAJ |
| description | Abstract Genome data from cancer patients represents relationships between the presence of a gene mutation and cancer occurrence in a patient. Different types of cancer in human are thought to be caused by combinations of two to nine gene mutations. Identifying these combinations through traditional exhaustive search requires the amount of computation that scales exponentially with the combination size and in most cases is intractable even for cutting-edge supercomputers. We propose a parameter-free heuristic approach that leverages the intrinsic topology of gene-patient mutations to identify carcinogenic combinations. The biological relevance of the identified combinations is measured by using them to predict the presence of tumor in previously unseen samples. The resulting classifiers for 16 cancer types perform on par with exhaustive search results, and score the average of 80.1% sensitivity and 91.6% specificity for the best choice of hit range per cancer type. Our approach is able to find higher-hit carcinogenic combinations targeting which would take years of computations using exhaustive search. |
| format | Article |
| id | doaj-art-c2dba821632e47cbb669f1eebd86002b |
| institution | OA Journals |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-c2dba821632e47cbb669f1eebd86002b2025-08-20T02:05:39ZengBMCBMC Bioinformatics1471-21052025-06-0126111810.1186/s12859-025-06043-1Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation dataVladyslav Oles0Sajal Dash1Ramu Anandakrishnan2Oak Ridge National Laboratory, National Center for Computational SciencesOak Ridge National Laboratory, National Center for Computational SciencesVirginia Tech, Edward Via College of Osteopathic MedicineAbstract Genome data from cancer patients represents relationships between the presence of a gene mutation and cancer occurrence in a patient. Different types of cancer in human are thought to be caused by combinations of two to nine gene mutations. Identifying these combinations through traditional exhaustive search requires the amount of computation that scales exponentially with the combination size and in most cases is intractable even for cutting-edge supercomputers. We propose a parameter-free heuristic approach that leverages the intrinsic topology of gene-patient mutations to identify carcinogenic combinations. The biological relevance of the identified combinations is measured by using them to predict the presence of tumor in previously unseen samples. The resulting classifiers for 16 cancer types perform on par with exhaustive search results, and score the average of 80.1% sensitivity and 91.6% specificity for the best choice of hit range per cancer type. Our approach is able to find higher-hit carcinogenic combinations targeting which would take years of computations using exhaustive search.https://doi.org/10.1186/s12859-025-06043-1Community detectionBinary classificationDriver mutationsTCGA |
| spellingShingle | Vladyslav Oles Sajal Dash Ramu Anandakrishnan Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data BMC Bioinformatics Community detection Binary classification Driver mutations TCGA |
| title | Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data |
| title_full | Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data |
| title_fullStr | Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data |
| title_full_unstemmed | Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data |
| title_short | Bigpicc: a graph-based approach to identifying carcinogenic gene combinations from mutation data |
| title_sort | bigpicc a graph based approach to identifying carcinogenic gene combinations from mutation data |
| topic | Community detection Binary classification Driver mutations TCGA |
| url | https://doi.org/10.1186/s12859-025-06043-1 |
| work_keys_str_mv | AT vladyslavoles bigpiccagraphbasedapproachtoidentifyingcarcinogenicgenecombinationsfrommutationdata AT sajaldash bigpiccagraphbasedapproachtoidentifyingcarcinogenicgenecombinationsfrommutationdata AT ramuanandakrishnan bigpiccagraphbasedapproachtoidentifyingcarcinogenicgenecombinationsfrommutationdata |