Lorenzo Dreoni

The addition of aflibercept to FOLFIRI has been demonstrated to improve survival in patients with metastatic colorectal cancer (mCRC) who progressed after receiving a standard oxaliplatin-based regimen. In this retrospective, single-institution, observational study we collected clinical data from mCRC patients who received aflibercept in combination with FOLFIRI in routine clinical practice from October 2012 to March 2021 to describe feasibility and efficacy of this regimen in a real-world population. Forty-nine patients receiving aflibercept-FOLFIRI as second-line treatment were identified, 40.8% of whom were aged over 65 years. The majority of patients had multi-organ metastases (73.5%), and had previously received bevacizumab in combination with chemotherapy (CT) as first-line treatment (79.6%). Median overall survival (OS) and progression-free survival (PFS) were 13 and 6 months, respectively; overall response rate (ORR) and disease control rate (DCR) were 12.3% and 49.1%, respectively. Several factors were associated with survival in univariate analysis, including PFS in first-line therapy, number of metastatic sites, bone metastases and others. However, in multivariate analysis, only PFS in first-line CT over 12 months was significantly associated with better OS (HR 0.32; 95% CI 0.13–0.79; p = 0.01). Hypertension was the most commonly reported grade (G) 3–4 adverse event (AE), affecting 18.4% of the overall population. Thromboembolic events were observed in 16.3% of patients, hemorrhagic events in 10.2%, and proteinuria in 8.2%. Neutropenia was the most frequently observed hematological G3–4 AE with an incidence of 10.2%. Aflibercept-FOLFIRI has been confirmed as a feasible second-line treatment for mCRC in a re-al-life setting, and PFS in first-line therapy >12 months resulted as the only predictive marker of better survival.

The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naïve Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3–G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS.