Mechanisms of Response and Resistance to Innovative Treatments in Patients With Locally Advanced or Metastatic Breast Cancer

Overview

Ample evidence has highlighted the significant clinical benefit of novel therapies for many patients with advanced breast cancer (aBC). The use of CDK inhibitors, antibody-drug conjugates (ADCs), immune checkpoint inhibitors (ICIs), and PARP inhibitors as first-line or subsequent treatments has improved progression-free survival (PFS) rates compared to conventional therapies. In selected cases, these treatments have also increased overall survival (OS), reshaping the therapeutic landscape for advanced breast cancer. However, several key questions remain unanswered. For example, what should be the first-line treatment when multiple effective options are available? Determining the optimal sequence of drugs in successive lines of therapy is another major challenge. Furthermore, the development of resistance to treatment and the occurrence of severe adverse events that may lead to early discontinuation or fatal outcomes are pressing concerns. That said, identifying robust predictive biomarkers of response or resistance is crucial for ensuring that patients receive the most effective treatment while avoiding unnecessary exposure to therapies that could cause harm without benefit. Additionally, when multiple effective options exist, selecting the optimal treatment algorithm for each patient based on clinical, pathological, and molecular biomarkers is essential. We herein, aim at employing high throughput methodologies, such as Whole Exome Sequencing, circulating tumour DNA (ctDNA) analysis, digital pathology and radiomics analyses, as well as real-world data obtained both from patients records for the training of a ML-based algorithm that can predict response or resistance to a specific treatment, based on the genetic make-up of the patient and the molecular profile of the tumour.

Primary outcome measures

• Progression-free survival (PFS) [Time frame: Through study completion, 5 years]
• Overall Survival (OS) [Time frame: Through study completion, 5 years]
• Objective Response Rate (ORR) [Time frame: Through study completion, 5 years]

Secondary outcome measures (1)

• Evaluation of AI-predictive algorithm [Time frame: Through study completion, 5 years]

Eligibility criteria

Inclusion criteria

• Eligible patients will be 18 years of age and older
• Histologically confirmed, advanced breast cancer.
• Diagnosis of i) hormone receptor positive and/or ii) HER2-positive or -low or iii) triple negative breast cancer (TNBC).
• Patients will be included in the analysis after receiving at least one treatment cycle.

Exclusion criteria

• Diagnosis of early breast cancer at time of enrollment
• Unwillingness to provide informed consent
• Unwillingness to provide biological specimen
• Lack of comprehensive clinical data

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

Greece · 5 centers

• University General Hospital "ATTIKON", 2nd propedeutic dept. of Internal Medicine — Athens
• "MITERA" Hospital, Dept. of Medical Oncology — Athens
• Metropolitan Hospital, 2nd Dept. of Medical Oncology — Athens
• Metropolitan Hospital, 4th Dept. of Medical Oncology — Athens
• St. Luke's Hospital, Dept. of Medical Oncology — Thessaloniki

Publications

• Schwartzberg L, Kim ES, Liu D, Schrag D. Precision Oncology: Who, How, What, When, and When Not? Am Soc Clin Oncol Educ Book. 2017;37:160-169. doi: 10.1200/EDBK_174176. PMID 28561651
• D'Arienzo A, Verrazzo A, Pagliuca M, Napolitano F, Parola S, Viggiani M, Caputo R, Puglisi F, Giuliano M, Del Mastro L, Arpino G, De Laurentiis M, Montemurro F. Toxicity profile of antibody-drug conjugates in breast cancer: practical considerations. EClinicalMedicine. 2023 Jul 27;62:102113. doi: 10.1016/j.eclinm.2023.102113. eCollection 2023 Aug. PMID 37554126
• Guidi L, Pellizzari G, Tarantino P, Valenza C, Curigliano G. Resistance to Antibody-Drug Conjugates Targeting HER2 in Breast Cancer: Molecular Landscape and Future Challenges. Cancers (Basel). 2023 Feb 10;15(4):1130. doi: 10.3390/cancers15041130. PMID 36831473
• Fenton MA, Tarantino P, Graff SL. Sequencing Antibody Drug Conjugates in Breast Cancer: Exploring Future Roles. Curr Oncol. 2023 Nov 29;30(12):10211-10223. doi: 10.3390/curroncol30120743. PMID 38132377
• Litton JK, Rugo HS, Ettl J, Hurvitz SA, Goncalves A, Lee KH, Fehrenbacher L, Yerushalmi R, Mina LA, Martin M, Roche H, Im YH, Quek RGW, Markova D, Tudor IC, Hannah AL, Eiermann W, Blum JL. Talazoparib in Patients with Advanced Breast Cancer and a Germline BRCA Mutation. N Engl J Med. 2018 Aug 23;379(8):753-763. doi: 10.1056/NEJMoa1802905. Epub 2018 Aug 15. PMID 30110579
• Robson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, Delaloge S, Li W, Tung N, Armstrong A, Wu W, Goessl C, Runswick S, Conte P. Olaparib for Metastatic Breast Cancer in Patients with a Germline BRCA Mutation. N Engl J Med. 2017 Aug 10;377(6):523-533. doi: 10.1056/NEJMoa1706450. Epub 2017 Jun 4. PMID 28578601
• Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, Dieras V, Hegg R, Im SA, Shaw Wright G, Henschel V, Molinero L, Chui SY, Funke R, Husain A, Winer EP, Loi S, Emens LA; IMpassion130 Trial Investigators. Atezolizumab and Nab-Paclitaxel in Advanced Triple-Negative Breast Cancer. N Engl J Med. 2018 Nov 29;379(22):2108-2121. doi: 10.1056/NEJMoa1809615. Epub 2018 Oct 20. PMID 30345906
• Cortes J, Rugo HS, Cescon DW, Im SA, Yusof MM, Gallardo C, Lipatov O, Barrios CH, Perez-Garcia J, Iwata H, Masuda N, Torregroza Otero M, Gokmen E, Loi S, Guo Z, Zhou X, Karantza V, Pan W, Schmid P; KEYNOTE-355 Investigators. Pembrolizumab plus Chemotherapy in Advanced Triple-Negative Breast Cancer. N Engl J Med. 2022 Jul 21;387(3):217-226. doi: 10.1056/NEJMoa2202809. PMID 35857659

Identifiers

NCT: NCT07066917 · HE_11PDR/24

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗