Bioinformatics Shared Resource

Collaborating with investigators to use genomics and informatics approaches in cancer research.

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.
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 (such as sample size, platform, sequencing depth, etc.), 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.

Publication Highlights

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

Services Provided

Bioinformatics and high-performance computing support, including study design, data analysis, visualization, and storage, and preparation of peer-reviewed publications and grant applications.
Genomic data analyses from traditional platforms (e.g., microarray, sequencing, CHIP-Seq) to the newest state-of-the-art genomics technologies, including 10X single-cell RNAseq, Visium Spatial gene expression, ATAC-seq, SPLiT-seq, and Nanopore long-reads sequencing.
Development of approaches to increase sensitivity for MRD (Minimal Residual Disease) detection, neoatnigen identification, and ULP-WGS (ultra-low pass – whole genome sequencing) liquid biopsy for early detection of recurrence in sarcomas.
In coordination with the Cancer Genomics, the Proteomics and Metabolomics and other Shared Resources, BISR integrates investigators’ analytic needs for genomics/proteomics/metabolomics for multi-omics projects.
Data support, management, and sharing for public omics databases such as TCGA, AACR GENIE, CCLE, as well as data from AHWFBCCC’s Precision Oncology Initiative.
Clinical data extraction and analytics services in coordination with the institutional Translational Data Warehouse (TDW) and the Cancer Registry.
Cost-effective computational and storage solutions for the researchers via the established Google Cloud.
Clinical and translational research support is provided through clinical trial matching using LCI Integrated Knowledge Data System (LInK) app, biospecimen data annotation utilizing the Specimen Pathology And Cancer Registry Research exploRer (SPAC3R), EHR interfaces, and other research tool development and implementation.
- Farhangfar CJ, Scarola GT, Morris VA et al. Impact of a Clinical Genomics Program on Trial Accrual for Targeted Treatments: Practical Approach Overcoming Barriers to Accrual for Underserved Patients. JCO Clinical Cancer Informatics. 2022; 6.
Machine Learning (ML) and Natural Language Processing (NLP) based model development and research for multi-modal data analytics on Real-World Data and registries.

Citation Resources

Cite the P30 CCSG

The authors wish to acknowledge the support of the Atrium Health Wake Forest Baptist Comprehensive Cancer Center Bioinformatics Shared Resource, supported by the National Cancer Institute’s Cancer Center Support Grant award number P30CA012197. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

NIHMS Guidelines for Authors

Authors should also make sure that all publications are processed by the National Institutes of Health Manuscript Submission (NIHMS) to obtain a PubMed Central reference number (PMCID), per NIH guidelines.

External Funding Application “Facility” Description