Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery

Olha Kholod; William I Basket; Jonathan B Mitchem; Jussuf T Kaifi; Richard D Hammer; Christos N Papageorgiou; Chi-Ren Shyu

doi:10.3390/cancers14235806

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery

Cancers (Basel). 2022 Nov 25;14(23):5806. doi: 10.3390/cancers14235806.

Authors

Olha Kholod¹, William I Basket¹, Jonathan B Mitchem^{1

2

3

4}, Jussuf T Kaifi^{1

2

3

4}, Richard D Hammer^{1

4

5

6}, Christos N Papageorgiou^{4

6}, Chi-Ren Shyu^{1

7}

Affiliations

¹ MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65212, USA.
² Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA.
³ Harry S. Truman Memorial Veterans' Hospital, Columbia, MO 65201, USA.
⁴ Ellis Fischel Cancer Center, University of Missouri, Columbia, MO 65211, USA.
⁵ Department of Pathology & Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, USA.
⁶ Department of Medicine, School of Medicine, University of Missouri, Columbia, MO 65212, USA.
⁷ Department of Electrical Engineering & Computer Science, 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.

Keywords: chemoimmunotherapy; exploratory subgroup discovery; triple-negative breast cancer.

Grants and funding

This research study was funded by University of Missouri Institute for Data Science and Informatics to O.K and W.I.B. and Data-Driven and Artificial Intelligence Initiatives to C.-R.S., O.K. and C.-R.S. were funded by Shumaker Endowment for Bioinformatics. J.B.M. received funding from the Department of Veteran’s Affairs (K2BX004346-01A1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans’ Affairs. The funding bodies had no role in the study design; in the collection, analysis, interpretation of data; or in the writing of the manuscript.