Real Time Molecular Analysis of Breast Cancer Receiving Neo-adjuvant Chemotherapy — NEO-R  

Breast Cancer Female Clinical Trial

Official title: Real Time Molecular Analysis of Breast Cancer Receiving Neo-adjuvant Chemotherapy: Identification of Preclinical Models Predictive for Therapeutic Resistance

Status Recruiting

Clinical Trial Summary

The present project aims at identifying robust candidates for drug resistance in BC patients eligible for NAC. Its originality lies upon the combination of three different and complementary prospective approaches: from the molecular analyses of biopsies sampled before and after NAC, from in vitro BC Patient-Derived Organoids (PDO) mimicking patient's response to NAC, and from Circulating Tumor Cells (CTCs) isolated before/during/after NAC.

Clinical Trial Description

Breast cancer (BC) is a major public health problem in France, with an increasing incidence, estimated at ~60,000 new cases in 2017 and 12,000 deaths. Despite survival improvement in the recent years, resistance to chemotherapy (CT) remains a paramount challenge in BC: the molecular mechanisms remain poorly known and the current interventions are inadequate to target chemoresistance. Neo-adjuvant chimiotherapy (NAC) is now used increasingly in women with operable but aggressive BC such as triple-negative (TN) or HER2+ tumor (Pusztai et al, Lancet Oncol 2019). It provides at least three advantages: i) tumor debulking authorizing conservative surgery, ii) tailoring of adjuvant systemic therapy according to the pathological response, with delivery of capecitabine in TN patients (Masuda N. et al, N Engl J Med. 2017) and T-DM1 in HER2+ patients (von Minckwitz G et al., N Engl J Med. 2019) in the absence of pathological complete response (pCR), and iii) providing resources for investigating the molecular mechanisms of chemoresistance. The current standard NAC regimen is based on a sequential association of anthracycline and taxane (and trastuzumab if HER2+) (Huober & von Minckwitz, Breast Care 2011), and the achievement of pCR, defined as the absence of residual invasive cancer on pathological evaluation of the operative specimen (resected breast specimen and sampled ipsilateral lymph nodes (i.e., ypT0/Tis ypN0 in the AJCC staging system), is a good-prognosis feature; the patients without pCR being at high relapse risk (≈50% of the TN and HER2+ subtypes).

The major bottleneck in improving treatment efficiency for these patients is the identification of candidates effectively involved in the resistance to NAC, their validation in relevant and predictive models, and their detection from surrogate markers. Of course, the search for molecular alterations differentially represented in the pre-NAC samples between the resistant (no pCR) versus sensitive (pCR) patients, or between the paired post- versus pre-NAC samples may reveal candidates involved in resistance ("innate" and "acquired stable" respectively), but ignores the alterations that occur during the NAC that may be reversible ("acquired reversible") and not identified in the resected specimen (Echeverria GV, Sci Transl Med 2019), but only identifiable by analysis of serial samples during NAC regimen, difficult and traumatic in clinical practice.

The present project aims at identifying robust candidates for drug resistance in BC patients eligible for NAC. Its originality lies upon the combination of three different and complementary prospective approaches: from the molecular analyses of biopsies sampled before and after NAC, from in vitro BC Patient-Derived Organoids (PDO) mimicking patient's response to NAC, and from Circulating Tumor Cells (CTCs) isolated before/during/after NAC. This project will combine the most advanced technologies (RNA and DNA-sequencing from small amount of biologic material, rare subsets/single-cell isolation, and multi-omics analyses) with innovative models (drug testing on breast PDO, identification of reversible drug-induced changes) and clever combination of robust markers involved in drug resistance that have all been established and are running in the laboratory (cf Part B). The last two points have only recently become feasible thanks to two recent technological advances: breast cancer PDO cultures (Sachs N, et al. Cell. 2018) and CTCs isolation and characterization (Gkountela S, et al. Cell. 2019). Briefly, the investigators will use serial tumor biopsies and blood samples from a cohort of BC patients treated with NAC (Figure 1). Tumor biopsies will be collected before and after NAC. They will be analyzed by RNA-seq and targeted-NGS (exome) approaches to capture the differences resulting from NAC in patients with different pathological response. They will also be used to generate BC PDO models. The PDO models predictive for NAC response will be exploited as relevant models to investigate NAC resistance. Specifically, investigators will use these models to look at in vitro "acquired reversible" drug-induced resistance mechanisms (i.e only detectable during the drug exposure). Blood samples will be collected before, during, and after NAC to analyze CTCs, and aim at identifying "acquired reversible" drug-induced resistance mechanisms ex vivo, which the investigators might fail to detect with the before/after NAC samples only.

To our knowledge, this is the first time that these three innovative approaches will be used together to investigate in depth the topic of "resistance" in patients receiving NAC. The strength of this combination is to anticipate different - not mutually exclusive - situations that might occur ("innate", "acquired stable" and/or "reversible" drug-induced resistance). Each approach will provide original, precise and specific information that will contribute to build a more complete answer to this question than the scattered and incomplete data currently available in the literature. ;

Related Conditions & MeSH terms

• Breast Cancer Female
• Breast Neoplasms

• NCT number: NCT04504747
• Study type: Observational
• Source: Institut Paoli-Calmettes
• Contact: Dominique GENRE, Dr
• Phone: 0491223778
• Email: drci.up@ipc.unicancer.fr
• Status: Recruiting
• Start date: November 3, 2022
• Completion date: January 2030