Intratumoral microbiota-aided fusion radiomics model for predicting tumor response to neoadjuvant chemoimmunotherapy in triple-negative breast cancer.

Abstract

Background: Neoadjuvant chemoimmunotherapy (NACI) has emerged as the standard treatment for early-stage triple-negative breast cancer (TNBC). However, reliable biomarkers for identifying patients who are likely to benefit from NACI are lacking. This study aims to develop an intratumoral microbiota-aided radiomics model for predicting pathological complete response (pCR) in patients with TNBC.

Methods: Intratumoral microbiota are characterized by 16S rDNA sequencing and quantified through experimental assays. Single-cell RNA sequencing is performed to analyze the tumor microenvironment of tumors with various responses to NACI. Radiomics features are extracted from tumor regions on longitudinal magnetic resonance images (MRIs) scanned before and after NACI in the training set. On the basis of treatment response (pCR or non-pCR) and intratumoral microbiota scoring, we select key radiomics features and construct a fusion model integrating multi-timepoint (pre-NACI and post-NACI) MRI to predict the efficacy of immunotherapy, followed by independent external validation.

Results: A total of 124 patients are enrolled, with 88 in the training set and 36 in the validation set. Tumors from patients who achieves pCR present a significantly greater intratumoral microbiota load than tumors from patients who achieve non-pCR (p < 0.05). Additionally, tumors in non-pCR group exhibit greater infiltration of tumor-associated SPP1⁺ macrophages, which is negatively correlated with the microbiota load. On the basis of intratumoral microbiota scoring, we select 17 radiomics features and use them to construct the fusion radiomics model. The fusion model achieves the highest AUC of 0.945 in the training set, outperforming pre-NACI (AUC = 0.875) and post-NACI (AUC = 0.917) models. In the validation set, this model maintains a superior AUC of 0.873, surpassing those of pre-NACI (AUC = 0.769) and post-NACI (AUC = 0.802) models. Clinically, the fusion model distinguishes patients who achieve pCR from those who do not with an accuracy of 77.8%. Decision curve analysis demonstrates the superior net clinical benefit of this model across varying risk thresholds.

Conclusions: Our intratumoral microbiota-aided radiomics model could serve as a powerful and noninvasive tool for predicting the response of patients with early-stage TNBC to NACI.