Abstract
Breast cancer is now the leading cause of cancer morbidity and mortality among women worldwide. Paclitaxel and anthracycline-based neoadjuvant chemotherapy is widely used for the treatment of breast cancer, but its sensitivity remains difficult to predict for clinical use. In our study, a LASSO logistic regression method was applied to develop a genomic classifier for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer. The predictive accuracy of the signature classifier was further evaluated using four other independent test sets. Also, functional enrichment analysis of genes in the signature was performed, and the correlations between the prediction score of the signature classifier and immune characteristics were explored. We found a 25-gene signature classifier through the modeling, which showed a strong ability to predict pCR to neoadjuvant chemotherapy in breast cancer. For T/FAC-based training and test sets, and a T/AC-based test set, the AUC of the signature classifier is 1.0, 0.9071, 0.9683, 0.9151, and 0.7350, respectively, indicating that it has good predictive ability for both T/FAC and T/AC schemes. The multivariate model showed that 25-gene signature was far superior to other clinical parameters as independent predictor. Functional enrichment analysis indicated that genes in the signature are mainly enriched in immune-related biological processes. The prediction score of the classifier was significantly positively correlated with the immune score. There were also significant differences in immune cell types between pCR and residual disease (RD) samples. Conclusively, we developed a 25-gene signature classifier that can effectively predict pCR to paclitaxel and anthracycline-based neoadjuvant chemotherapy in breast cancer. Our study also suggests that the immune ecosystem is actively involved in modulating clinical response to neoadjuvant chemotherapy and is beneficial to patient outcomes.
Highlights
Breast cancer is the leading cause of cancer incidence worldwide, with 2,261,419 new cases each year
On the other hand, compared to luminal subtype [2, 9], the triple-negative breast cancer (TNBC) and HER2positive subtypes are associated with more pathologic complete response (pCR) rates and higher sensitivity to neoadjuvant chemotherapy, suggesting that genetic characteristics may play an important role in chemotherapeutic sensitivity, so the characterization of gene expression profile can be used to predict treatment response and prognosis, guiding clinical practice
We explored the correlations of the expression values between genes in signature and immune checkpoints contained in the arrays, such as programmed cell death 1 (PD1/PDCD1), programmed cell death 1 ligand 2 (PDL2/PD1L2), cytotoxic Tlymphocyte antigen 4 (CTLA4), lymphocyte activation gene 3 protein (LAG3), Indoleamine 2,3-dioxygenase 1 (IDO1), etc
Summary
Breast cancer is the leading cause of cancer incidence worldwide, with 2,261,419 new cases each year. Patients who achieve pCR after neoadjuvant chemotherapy have better long-term disease-free survival than patients with residual disease (RD) [5] Histologic characteristics such as expressions of estrogen receptor (ER), progesterone receptor (PR), HER2, Ki67, and histological grade have been used as prognostic and predictive markers [6, 7]. On the other hand, compared to luminal subtype [2, 9], the triple-negative breast cancer (TNBC) and HER2positive subtypes are associated with more pCR rates and higher sensitivity to neoadjuvant chemotherapy, suggesting that genetic characteristics may play an important role in chemotherapeutic sensitivity, so the characterization of gene expression profile can be used to predict treatment response and prognosis, guiding clinical practice
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