Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients

Abstract

Background: Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer.

Method: First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy.

Results: The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response.

Conclusion: These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

Keywords: Neoadjuvant chemotherapy; Non-invasive; Plasma cfDNA; Promoter profiles; Treatment efficacy.

Fig. 1

Study design. Our study mainly consisted of three stages, including discovery, validation by developing classifiers and promoter changes analysis in cfDNA during EC-T treatment. In the discovery stage, the genes with differential coverage in cfDNA of between pCR and non-pCR patients were identified. In the validation stage, different classifiers were developed by using the differential features. In the last stage, differential promoter profile changes due to EC-T treatment in cfDNA of between pCR and non-pCR patients were analyzed. cfDNA, cell-free DNA; EC-T, 3 or 4 cycles of epirubicinneoa/cyclophosphamide (EC) treatment and subsequent 3 or 4 cycles of docetaxel treatment before surgery; pCR, pathological complete response; non-pCR, non-pathological complete response

Fig. 2

Differential global chromatin (5 Mb windows) and sub-compartments in cfDNA of between pCR and non-pCR patients. a Genome-wide fragmentation profiles shown in 5 Mb bins in cfDNA of pCR and non-pCR patients. b Sub-compartments in cfDNA of pCR patients. c. Sub-compartments annotated in cfDNA of non-pCR patients. d The fold change and P-value of sub-compartments in cfDNA of pCR versus non-pCR patients. Sub-compartments of the human genome were annotated by the Hi-C data of GM12878. A1 and A2 regions are enriched regions. B1 consists of facultative heterochromatic regions. B2 is enriched at the nuclear lamina and NADs. B3 is also enriched at the nuclear lamina but not at NADs. cfDNA, cell-free DNA; pCR, pathological complete response; non-pCR, non-pathological complete response; cfDNA, cell-free DNA; NADs, nucleolus-associated domains

Fig. 3

Differential promoter profiles in cfDNA of between pCR and non-pCR patients. a Volcano plots of differential promoter profiles (P-value ≤ 0.05 and fold change ≥ 1.2). b GO enrichment analysis of the differential promoter profiles. c KEGG pathway analysis of the differential promoter profiles. d GSEA analysis of differential pathways from GO database. e GSEA analysis of differential pathways from KEGG database. cfDNA, cell-free DNA; pCR, pathological complete response; non-pCR, non-pathological complete response; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis

Fig. 4

Receiver operating characteristic (ROC) curves of classifiers for distinguishing pCR and non-pCR patients. a The classifier based on global chromatin (5 Mb windows) in Random Forest. b The classifier based on sub-compartments in Random Forest. c The classifier based on promoter profiles in Random Forest. d The classifier based on global chromatin (5 Mb windows) in Logistic Regression. e The classifier based on sub-compartments in Logistic Regression. f The classifier based on promoter profiles in Logistic Regression. g The classifier based on global chromatin (5 Mb windows) in Support Vector Machines. h The classifier based on sub-compartments in Support Vector Machines. i The classifier based on promoter profiles in Support Vector Machines. pCR, pathological complete response; non-pCR, non-pathological complete response; RF: Random Forest; LR: Logistic Regression; SVM, Support Vector Machines

Fig. 5

Disease-free survival (DFS) and relapse-free survival (RFS) for BAG2, TRIM35, TEAD4, TP53, GNAI2 and RUFY3. HR, hazard ratio; pCR, pathological complete response; non-pCR, non-pathological complete response

Fig. 6

Differential changes of promoter profiles in cfDNA of between pCR and non-pCR patients during EC-T neoadjuvant chemotherapy. a Heat map of the z-scores of cfDNA promoters with differential read coverage changes. b GO enrichment analysis of the differential promoter changes. c KEGG pathway analysis of the differential promoter changes. cfDNA, cell-free DNA; pCR, pathological complete response; non-pCR, non-pathological complete response; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes

Fig. 7

GO enrichment and KEGG pathway analysis for each cluster. a GO enrichment analysis for cluster 1; b KEGG pathway analysis for cluster 1; c GO enrichment analysis for cluster 2; d KEGG pathway analysis for cluster 2; e GO enrichment analysis for cluster 3; f KEGG pathway analysis for cluster 3; g GO enrichment analysis for cluster 4; h KEGG pathway analysis for cluster 4. Cluster 1, the coverage of promoter profiles was up-regulated due to EC treatment at the first stage in pCR group; cluster 2, the coverage of promoter profiles was up-regulated due to T treatment at the second stage in pCR group; cluster 3, the coverage of promoter profiles was down-regulated due to EC treatment at the first stage in pCR group; cluster 4, the coverage of promoter profiles was down-regulated due to T treatment at the second stage in pCR group; pCR, pathological complete response