Metastatic Breast Cancer
Conditions
Keywords
mBC, biomarkers, ADCs, immunotherapy, PARPi, Breast Cancer
Brief summary
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.
Interventions
None listed
Sponsors
Hellenic Cooperative Oncology Group
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Progression-free survival (PFS) | Through study completion, 5 years | Correlation of genetic and molecular/circulating biomarkers with PFS, with PFS defined as the time from enrollment to disease progression or death |
| Overall Survival (OS) | Through study completion, 5 years | Correlation of genetic and molecular/circulating biomarkers with OS, with OS defined as the time from date of metastatic diagnosis to last follow-up (36 months, post enrollment) or death |
| Objective Response Rate (ORR) | Through study completion, 5 years | Correlation of genetic and molecular biomarkers with response to treatment |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Evaluation of AI-predictive algorithm | Through study completion, 5 years | Evaluation of the efficiency of the AI-predictive algorithm, by determining key metrics such as sensitivity and specificity |
Countries
Greece
Contacts
Primary ContactElectra Sofou, PhD
Backup ContactElena Fountzilas, MD, PhD
Outcome results
None listed