Cancer Res Treat.  2024 Jan;56(1):162-177. 10.4143/crt.2023.330.

Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy

• Kim D
• Yu Y
• Jung KS
• Kim YH
• Kim JJ

• KMID: 2550333
• DOI: http://doi.org/10.4143/crt.2023.330

Abstract

Purpose
Triple-negative breast cancer (TNBC) is a breast cancer subtype that has poor prognosis and exhibits a unique tumor microenvironment. Analysis of the tumor microbiome has indicated a relationship between the tumor microenvironment and treatment response. Therefore, we attempted to reveal the role of the tumor microbiome in patients with TNBC receiving neoadjuvant chemotherapy.
Materials and Methods
We collected TNBC patient RNA-sequencing samples from the Gene Expression Omnibus and extracted microbiome count data. Differential and relative abundance were estimated with linear discriminant analysis effect size. We calculated the immune cell fraction with CIBERSORTx and conducted survival analysis using the Cancer Genome Atlas patient data. Correlations between the microbiome and immune cell compositions were analyzed and a prediction model was constructed to estimate drug response.
Results
Among the pathological complete response group (pCR), the beta diversity varied considerably; consequently, 20 genera and 24 species were observed to express a significant differential and relative abundance. Pandoraea pulmonicola and Brucella melitensis were found to be important features in determining drug response. In correlation analysis, Geosporobacter ferrireducens, Streptococcus sanguinis, and resting natural killer cells were the most correlated factors in the pCR, whereas Nitrosospira briensis, Plantactinospora sp. BC1, and regulatory T cells were key features in the residual disease group.
Conclusion
Our study demonstrated that the microbiome analysis of tumor tissue can predict chemotherapy response of patients with TNBC. Further, the immunological tumor microenvironment may be impacted by the tumor microbiome, thereby affecting the corresponding survival and treatment response.

Keyword

Microbiota; Triple-negative breast neoplasms; Neoadjuvant therapy

Figure

• 

  Fig. 1. Flowchart of the analysis process in this study. GEO, Gene Expression Omnibus; RNA-seq, RNA-sequencing; ROC curve, receiver operating characteristic curve; TCGA, The Cancer Genome Atlas; TNBC, triple-negative breast cancer.

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  Fig. 2. Microbiome composition of tumors from triple-negative breast cancer patients from Gene Expression Omnibus (GEO) data stratified by drug response. pCR and RD each represent pathological complete response and residual disease, respectively. (A) Stacked bar chart of the relative abundance of the microbiome at phylum and genus level; 23 phyla for the entire group and the 30 most abundant genera for each response group were evaluated. (B) Alpha diversity for all patients. Chao1 index and Shannon index were used for alpha diversity analysis. ns, not significant. (C) Beta diversity for all patients (p < 0.05). (D) Differential abundance analysis at species level using linear discriminant analysis effect size (LEfSe) stratified by drug response (linear discriminant analysis [LDA] score > 1.5, p < 0.05). (E) Relative abundance comparison for each microbiome group. The microbiome selected was not affected by outliers.

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  Fig. 3. Classification model development by drug response. rf, random forest; svm, support vector machine; val, validation set. 01: dataset composed of the dominantly expressed microbiome in pathological complete response patients according to the linear discriminant analysis effect size (LEfSe) result; 03: dataset composed of the microbiome that is significantly different according to the LEfSe result. (A, B) Receiver operating characteristic (ROC) curve and corresponding area under the ROC curve (AUC) values for train-test and validation sets from the models classified with rf and svm, respectively. (C, D) Bar chart showing variance importance from the models classified with random forest (RF) and support vector machine (SVM), respectively.

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  Fig. 4. Correlations between microbiome and immune cell composition with a significant difference. (A, B) Heatmap plot indicating correlations between microbiome and immune cell composition in pathological complete response (pCR) and residual disease (RD) response patients, respectively. (C, D) network plot in the pCR and RD response, respectively. The phylum of each microbiome is marked with colors which are defined in the key. Round: immune cell, diamond: microbiome, round rectangle: microbiome dominantly expressed in the pCR patient group, hexagon: microbiome dominantly expressed in the RD patient group, dotted line: correlation coefficient (r) > 0.3, solid line: r > 0.4.

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  Fig. 5. The Kaplan-Meier plot for the 5-year survival analysis using the immune cell fraction data and microbiome count data from the Cancer Genome Atlas (TCGA; n=115) triple-negative breast cancer patients: (A) macrophages M2, (B) macrophages M1, (C) NK cells resting, (D) B cells memory, (E) T cells CD4 memory activated, (F) T cells regulatory Tregs, (G) macrophages M0, (H) natural killer (NK) cells activated, (I) mast cells activated, and (J) Brucella.

Reference

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