Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients

Abstract

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.

Identification of a Gene Panel Predictive of Triple-Negative Breast Cancer Response to Neoadjuvant Chemotherapy Employing Transcriptomic and Functional Validation

“…In particular, we identified an 87 gene signature that predicted better OS, RFS, and DMFS in basal and HER2+ breast cancer. Our data are consistent with recently published reports [14,15] and with our recently published work on single-cell analysis of TNBC in the context of TNBC response to NAC [7]. This interesting observation raises a key question about whether TNBC tumors with heavy immune infiltration represent different molecular subtype with different sensitivity to NAC compared to tumors lacking immune infiltration, which warrants further investigation.…”

“…RNA-Seq data were retrieved from the PRJNA688066 [14] (discovery) and GSE192341 [15] (validation cohort): Clinical characteristics for patients from both cohorts are shown in Table 1.…”

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

“…This yielded, as expected, high accuracy for both PathExt genes (AUROC of 0.76) as well as DEG genes (AUROC of 0.75). However, when, we tested the performance of the aforementioned model (trained and tested using GSE41998 dataset) on an additional independent dataset GSE163882 57 and observed that PathExt identified genes’ expression discriminates responders and non-responders with high AUROC of 0.72 and AUPRC of 0.79 [ Figure 7 A] compared to DEGs, which achieved an AUROC of 0.65 and AUPRC of 0.75 [ Figure 7 B]. All the training and testing were done using only the samples classified as TNBC.…”

The genomic signature of resistance to platinum-containing neoadjuvant therapy based on single-cell data

“…Some patients are unable to undergo surgery due to disease progression during treatment [30]. For example, clinical research revealed that Neoadjuvant chemotherapy might exacerbate postoperative complications, which led to a negative prognostic impact [31][32][33][34][35]. It is therefore essential and necessary to predict the response to NAT in order to optimize treatment planning.…”