Patients with stage II-III Triple negative breast cancer (TNBC) candidates to receive neoadjuvant chemotherapy (NACT) +/- immune checkpoint inhibitor (ICI) will be included. Several samples from different tissues will be analyzed through different omics to establish predictive biomarkers of response to the treatment. Multiple algorithms will then be used to look for an integrative predictive algorithm that incorporates multi-parameter inputs in order to develop a clinical tool to assist clinicians in the process of treatment decision-making in TNBC.

The combination of pembrolizumab, an immune checkpoint inhibitor (ICI), with neoadjuvant chemotherapy (NACT) increases pathologic complete response (pCR) and event-free survival (EFS) in patients with early triple negative breast cancer (eTNBC). However, not all patients equally benefit from a treatment that may have relevant adverse events (AEs).

Objectives: (1) To establish predictive biomarkers of response to NACT + ICI in eTNBC by correlating data coming from different layers of omics performed in different tissues, together with imaging, with pCR, EFS, and overall survival (OS). (2) To integrate data generated from (1), and clinical data, and explore multivariate predictive models of response to NACT + ICI.

Methods: Patients with stage II-III TNBC candidates to receive NACT +/- ICI will be included. Collected samples and type of analysis: (1) Tumor tissue (baseline and from residual disease after NACT): whole genome sequencing (WGS) and RNA-Seq will be performed (Hartwig sequencing platform and analytical pipeline), tissue immune phenotyping (PD-L1, T and B infiltrating lymphocytes, among others), and microbiome analysis (16S rRNA); (2) Blood (before and during NACT): circulating tumor DNA (ctDNA) analysis (targeted gene panel and shallow WGS), T-cell receptor beta (TCR-β) repertoire sequencing and analysis (ImmunoSeq hsTCRβ kit and immunoSEQ), and peripheral blood mononuclear cells (PBMCs) phenotyping; (3) Stools and saliva (before and during NACT): microbiome analysis (16S rRNA); (4) Breast MRI imaging (before and after NACT): radiomics analysis. Multiple algorithms including Multiple Kernel Learning, Multi-Omics Factor Analysis (MOFA) and Method for the Functional Integration of Spatial and Temporal Omics data (MEFISTO) will then be used to look for an integrative predictive algorithm that incorporates multi-parameter inputs. The aim is to provide more personalized treatment efficacy and risk for relapse estimates.

Expected outcome: To develop a clinical tool to assist clinicians in the process of treatment decision-making in eTNBC, in order to maximize patient's benefit and quality of life, while minimizing AEs and financial burden to the health system.

Inclusion Criteria:

• Histologically documented TNBC (negative human epidermal growth factor receptor 2 [HER2], estrogen receptor [ER], and progesterone receptor [PgR] status)
• Stage 2 - 3 defined by the American Joint Committee of Cancer (AJCC) staging criteria 8th edition for breast cancer as assessed by the investigator based on radiological and/or clinical assessment
• Patient is a candidate to receive NACT with or without ICI as assessed by the investigator
• Patient is ≥ 18 years old at the time of consent to participate in this trial

Exclusion Criteria:

• Metastatic disease on imaging (stage 4)

Patients with stage II-III TNBC candidates to receive NACT +/- ICI will be included