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

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.

Automated identification of patient subgroups: A case-study on mortality of COVID-19 patients admitted to the ICU

“…[ [5] , [6] , [7] , [8] , [9] , [10] , [11] ], have been applied to the medical domain. Recent studies use subgroup discovery to identify subgroups of cancer patients [ 14 , 15 ], identify predictive factors for diabetic ketoacidosis [ 16 ], or discover subgroups of patients undergoing transcatheter aortic valve implantation with high model prediction error and their distribution over the centres [ 17 ]. Gamberger et al [ 11 ] demonstrated the applicability of SGD analysis for in brain ischaemia.…”

Updates on Breast Cancer

“…Despite such improvement, in a breast cancer scenario, triple-negative breast cancer (TNBC) remains a formidable clinical challenge due to its limited therapeutic options. Kholod O. et al [14] decode the immune-related gene signatures that hold the key to predicting chemoimmunotherapy outcomes in TNBC patients, analyzing a vast dataset encompassing 422 patients across 24 studies. Through an algorithmic approach, they categorize patients into 12 homogenous subgroups based on various parameters, including tumor mutational burden, relapse status, tumor cellularity, menopausal status, and tumor stage.…”