Profiling the coding and long non-coding transcriptome to identify novel genetic mediators of breast tumor immune suppression

Aim/goals:

  1. Retrospective and prospective enrollment, collection and analysis of breast cancer patient tumor samples from treatment naïve-patients, inclusive of “gold standard” analysis.

  2. In addition to protein coding genes, use the RNAaeq data to quantify lncRNA expression for over 12000 lncRNA genes.

  3. Perform transcriptome deconvolution analysis of the tumor RNAseq data to estimate the percentages of all the major cell types of the tumor.

  4. Identify genes associated with low and high immune cell infiltration and fibroblast cells based on the transcriptome deconvolution.

  5. Perform high parameter multiplex immunofluorescence on the formalin fixed paraffin embedded matched tumor sections to confirm the transcriptome deconvolution data.

  6. Analyze associated genomic data, clinical data, pathological features for correlations with the transcriptome deconvolution findings.

  7. Identify genes which could be biomarkers for immunotherapy response or targeted for improved response to immunotherapies.

  8. Improve access to genomic precision medicine for all patients in Nova Scotia. 

Summary:

Immunotherapy pembrolizumab is starting to be used for the treatment of some forms of breast cancer. This drug triggers the patient’s own immune cells to destroy the cancer cells. Although pembrolizumab is effective for some patients, it does not trigger the desired immune response in many patients leading to ineffective treatment results. If physicians could accurately predict which patients would most benefit from pembrolizumab or which complimentary treatments were required to limit resistance, the efficacy of pembrolizumab would increase significantly and the overall health of patients would improve. Our research will be a critical enabler of this type of personalized approach for the treatment of breast cancer. We will use genomic data to profile the immune cell composition of breast tumors, which could be used to accurately predict which patients will respond to treatment. 

Key Researcher