Clinical trials

This is multicenter, prospective, open label, non-interventional study that will collect real-world data (RWD) on the clinical efficacy and safety of NalIriFOx as a first-line therapy in metastatic pancreatic ductal adenocarcinoma (mPDAC).

The present study will assess real-world clinical outcomes and adverse events from treatment with endocrine therapy combined with abemaciclib in patients with HR-positive, HER2-negative advanced breast cancer.

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.