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Updated: Aug 18, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer
Olha Kholod1, William I Basket1, Jonathan B Mitchem1,2,3,4
1MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA.
Abstract:
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients' outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.
Insights
Researchers identified immune gene signatures in triple-negative breast cancer (TNBC) to predict response to anti-PD-L1 plus chemotherapy. Specific T-cell populations, CD4-Tn-LEF1 and CD4-CXCL13, were linked to partial remission, aiding treatment selection.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Triple-negative breast cancer (TNBC) is aggressive with limited treatment options.
- Immunotherapy shows promise but modest response rates in TNBC patients.
- Combination therapy with immunotherapy and chemotherapy requires further investigation.
Purpose of the Study:
- Identify immune-related gene signatures in TNBC patients.
- Predict patient outcomes following anti-PD-L1 plus chemotherapy treatment.
- Uncover mechanisms of chemoimmunotherapy response and resistance.
Main Methods:
- Exploratory subgroup discovery on a TNBC dataset (422 patients, 24 studies).
- Analysis of twelve subgroups based on tumor mutational burden, relapse status, cellularity, menopausal status, and tumor stage.
- Multinomial regression to identify genotypic patterns linked to partial remission.
- Single-cell resolution analysis of immune cell populations.
Main Results:
- Identified distinct immune cell populations associated with treatment outcomes.
- Discovered CD4-Tn-LEF1 and CD4-CXCL13 T-cells linked to partial remission.
- Uncovered immune-related gene signatures potentially explaining differential patient responses.
Conclusions:
- An informatics pipeline can help select better responders to TNBC chemoimmunotherapy.
- Identified biomarkers may guide personalized treatment strategies for TNBC.
- Findings provide insights into mechanisms of drug resistance at a single-cell level.
