The Bioinformatics Shared Resource (BISR) collaborates with clinical, cancer control, and basic science investigators to use genomics and informatics approaches in cancer research.
We help researchers
- Better understand the complex processes involved in the development and progression of various types of cancer, especially tobacco-related cancers
- Use this knowledge to predict cancer recurrence, metastasis and response to therapies
- Assign patients to specific targeted therapies, such as immunotherapies, to improve survival and quality-of-life
Our objectives for improving patient outcomes include
- Identifying germline and somatic genetic and epigenetic alterations that are associated with the development and progression of cancer
- Identifying genetic and epigenetic events and mutation signatures in cancers caused by environmental factors, such as tobacco
- Identifying ethnicity-related genetic and epigenetic alterations that may be underlying factors in health disparities
- Utilizing genetic and epigenetic information for early detection of cancer (personalized risk prediction) and prediction of cancer recurrence and progression (personalized monitoring)
- Promoting precision medicine (personalized intervention)
- Understanding the functional mechanisms by which genetic and epigenetic findings have a direct effect on cancer
Areas of Focus and Goals
The primary goal of the BISR is to facilitate the peer-reviewed research and extramural grant application of Wake Forest Baptist Comprehensive Cancer Center members. We collaborate with members from all scientific programs throughout all phases of cancer-related research projects.
Major responsibilities are assumed for methodological-, computational-, and bioinformatics-related issues, including study design (including sample size, platform and sequencing depth), sampling, interim reviews, and final analysis and result delivery.
We focus on:
- Bioinformatics-based design and data analysis in support of grant development and publications
- Informatics services to Comprehensive Cancer Center investigators from all programs, including genomics data processing, bioinformatics analyses, data annotation and visualization/sharing to support basic, translational and clinical cancer research by leveraging the institutionally maintained Translational Data Warehouse
- Leadership, education, and training, including participating in graduate-level courses and T32 training grants
- Mentoring teams for K awardees and other young investigators
- Facilitating membership and leadership on committees responsible for scientific and administrative decisions for the Comprehensive Cancer Center
- Providing short courses on statistical or bioinformatics methods for Comprehensive Cancer Center members
1. Tang Z, Zhang T, Yang B, Su J, Song Q. spaCI: deciphering spatial cellular communications through adaptive graph model. Brief Bioinform 2023;24
2. Zheng N, Fang J, Xue G, Wang Z, Li X, Zhou M, et al. Induction of tumor cell autosis by myxoma virus-infected CAR-T and TCR-T cells to overcome primary and acquired resistance. Cancer Cell 2022;40:973-85 e7
3. Zhao H, Han K, Gao C, Madhira V, Topaloglu U, Lu Y, et al. VOC-alarm: mutation-based prediction of SARS-CoV-2 variants of concern. Bioinformatics 2022;38:3549-56
4. Yang HT, Crawford DC, Abazeed ME. Editorial: Translating clinical genomics and health informatics into precision oncology. Front Genet 2022;13:1029212
5. Yang HT, Chien MY, Chiang JH, Lin PC. Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer. Comput Struct Biotechnol J 2022;20:5287-95
6. Wolff DW, Deng Z, Bianchi-Smiraglia A, Foley CE, Han Z, Wang X, et al. Phosphorylation of guanosine monophosphate reductase triggers a GTP-dependent switch from pro- to anti-oncogenic function of EPHA4. Cell Chem Biol 2022;29:970-84 e6
7. Song Q, Zhu X, Jin L, Chen M, Zhang W, Su J. SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data. NAR Genom Bioinform 2022;4:lqac056
8. Song Q, Bates B, Shao YR, Hsu FC, Liu F, Madhira V, et al. Risk and Outcome of Breakthrough COVID-19 Infections in Vaccinated Patients With Cancer: Real-World Evidence From the National COVID Cohort Collaborative. J Clin Oncol 2022;40:1414-27
9. Lu Y, Xue G, Zheng N, Han K, Yang W, Wang RS, et al. hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy. Brief Bioinform 2022;23
10. Yang W, Jin G. Origin-independent analysis links SARS-CoV-2 local genomes with COVID-19 incidence and mortality. Brief Bioinform 2021;22:905-13
11. Xue G, Zheng N, Fang J, Jin G, Li X, Dotti G, et al. Adoptive cell therapy with tumor-specific Th9 cells induces viral mimicry to eliminate antigen-loss-variant tumor cells. Cancer Cell 2021;39:1610-22 e9
12. Xue G, Wang Z, Zheng N, Fang J, Mao C, Li X, et al. Elimination of acquired resistance to PD-1 blockade via the concurrent depletion of tumour cells and immunosuppressive cells. Nat Biomed Eng 2021;5:1306-19
13. Song Q, Su J, Zhang W. scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics. Nat Commun 2021;12:3826
14. Song Q, Su J. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Brief Bioinform 2021;22
15. Liu L, Ahmed T, Petty WJ, Grant S, Ruiz J, Lycan TW, et al. SMARCA4 mutations in KRAS-mutant lung adenocarcinoma: a multi-cohort analysis. Mol Oncol 2021;15:462-72
16. Lipchick BC, Utley A, Han Z, Moparthy S, Yun DH, Bianchi-Smiraglia A, et al. The fatty acid elongase ELOVL6 regulates bortezomib resistance in multiple myeloma. Blood Adv 2021;5:1933-46
17. Gao C, Jin G, Forbes E, Mangala LS, Wang Y, Rodriguez-Aguayo C, et al. Inactivating Mutations of the IK Gene Weaken Ku80/Ku70-Mediated DNA Repair and Sensitize Endometrial Cancer to Chemotherapy. Cancers (Basel) 2021;13
18. Chang A, Liu L, Ashby JM, Wu D, Chen Y, O'Neill SS, et al. Recruitment of KMT2C/MLL3 to DNA Damage Sites Mediates DNA Damage Responses and Regulates PARP Inhibitor Sensitivity in Cancer. Cancer Res 2021;81:3358-73
19. Salcedo A, Tarabichi M, Espiritu SMG, Deshwar AG, David M, Wilson NM, et al. A community effort to create standards for evaluating tumor subclonal reconstruction. Nat Biotechnol 2020;38:97-107
2. Zheng N, Fang J, Xue G, Wang Z, Li X, Zhou M, et al. Induction of tumor cell autosis by myxoma virus-infected CAR-T and TCR-T cells to overcome primary and acquired resistance. Cancer Cell 2022;40:973-85 e7
3. Zhao H, Han K, Gao C, Madhira V, Topaloglu U, Lu Y, et al. VOC-alarm: mutation-based prediction of SARS-CoV-2 variants of concern. Bioinformatics 2022;38:3549-56
4. Yang HT, Crawford DC, Abazeed ME. Editorial: Translating clinical genomics and health informatics into precision oncology. Front Genet 2022;13:1029212
5. Yang HT, Chien MY, Chiang JH, Lin PC. Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer. Comput Struct Biotechnol J 2022;20:5287-95
6. Wolff DW, Deng Z, Bianchi-Smiraglia A, Foley CE, Han Z, Wang X, et al. Phosphorylation of guanosine monophosphate reductase triggers a GTP-dependent switch from pro- to anti-oncogenic function of EPHA4. Cell Chem Biol 2022;29:970-84 e6
7. Song Q, Zhu X, Jin L, Chen M, Zhang W, Su J. SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data. NAR Genom Bioinform 2022;4:lqac056
8. Song Q, Bates B, Shao YR, Hsu FC, Liu F, Madhira V, et al. Risk and Outcome of Breakthrough COVID-19 Infections in Vaccinated Patients With Cancer: Real-World Evidence From the National COVID Cohort Collaborative. J Clin Oncol 2022;40:1414-27
9. Lu Y, Xue G, Zheng N, Han K, Yang W, Wang RS, et al. hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy. Brief Bioinform 2022;23
10. Yang W, Jin G. Origin-independent analysis links SARS-CoV-2 local genomes with COVID-19 incidence and mortality. Brief Bioinform 2021;22:905-13
11. Xue G, Zheng N, Fang J, Jin G, Li X, Dotti G, et al. Adoptive cell therapy with tumor-specific Th9 cells induces viral mimicry to eliminate antigen-loss-variant tumor cells. Cancer Cell 2021;39:1610-22 e9
12. Xue G, Wang Z, Zheng N, Fang J, Mao C, Li X, et al. Elimination of acquired resistance to PD-1 blockade via the concurrent depletion of tumour cells and immunosuppressive cells. Nat Biomed Eng 2021;5:1306-19
13. Song Q, Su J, Zhang W. scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics. Nat Commun 2021;12:3826
14. Song Q, Su J. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Brief Bioinform 2021;22
15. Liu L, Ahmed T, Petty WJ, Grant S, Ruiz J, Lycan TW, et al. SMARCA4 mutations in KRAS-mutant lung adenocarcinoma: a multi-cohort analysis. Mol Oncol 2021;15:462-72
16. Lipchick BC, Utley A, Han Z, Moparthy S, Yun DH, Bianchi-Smiraglia A, et al. The fatty acid elongase ELOVL6 regulates bortezomib resistance in multiple myeloma. Blood Adv 2021;5:1933-46
17. Gao C, Jin G, Forbes E, Mangala LS, Wang Y, Rodriguez-Aguayo C, et al. Inactivating Mutations of the IK Gene Weaken Ku80/Ku70-Mediated DNA Repair and Sensitize Endometrial Cancer to Chemotherapy. Cancers (Basel) 2021;13
18. Chang A, Liu L, Ashby JM, Wu D, Chen Y, O'Neill SS, et al. Recruitment of KMT2C/MLL3 to DNA Damage Sites Mediates DNA Damage Responses and Regulates PARP Inhibitor Sensitivity in Cancer. Cancer Res 2021;81:3358-73
19. Salcedo A, Tarabichi M, Espiritu SMG, Deshwar AG, David M, Wilson NM, et al. A community effort to create standards for evaluating tumor subclonal reconstruction. Nat Biotechnol 2020;38:97-107
