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(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

Background: The PI3K/AKT/mTORC1 axis is implicated in hormone receptor-positive HER2-negative metastatic breast cancer (HR+ HER2− mBC) resistance to anti-estrogen treatments. Based on results of the BOLERO-2 trial, the mTORC1 inhibitor everolimus in combination with the steroidal aromatase inhibitor (AI) exemestane has become a standard treatment for patients with HR+ HER2− mBC resistant to prior non-steroidal AI therapy. In the recent SOLAR-1 trial, the inhibitor of the PI3K alpha subunit (p110α) alpelisib in combination with fulvestrant prolonged progression-free survival (PFS) when compared to fulvestrant alone in patients with PIK3CA-mutated HR+ HER2− mBC that progressed after/on previous AI treatment. Therefore, two different molecules targeting the PI3K/AKT/ mTORC1 axis, namely everolimus and alpelisib, are available for patients progressing on/after previous AI treatment, but it is unclear how to optimize their use in the clinical practice. Main body of the abstract: Here, we reviewed the available clinical evidence deriving from the BOLERO-2 and SOLAR-1 trials to compare efficacy and safety profiles of everolimus and alpelisib in advanced HR+ HER2− BC treatment. Adding either compound to standard endocrine therapy provided similar absolute and relative PFS advantage. In the SOLAR-1 trial, a 76% incidence of grade (G) 3 or 4 (G3/G4) adverse events was reported, while G3/G4 toxicities occurred in 42% of patients in the BOLERO-2 trial. While alpelisib was only effective in patients with PIK3CA-mutated neoplasms, retrospective analyses indicate that everolimus improves exemestane efficacy independently of PIK3CA mutational status. (Continued on next page)Conclusions: Based on the available efficacy and safety data, the "new" alpelisib may be burdened by higher incidence of severe adverse events, higher costs, and anticancer efficacy that is limited to PIK3CA-mutated tumors when compared to the "old" everolimus. Therefore, the everolimus-exemestane combination remains an effective and reasonably well-tolerated therapeutic option for HR+ HER2− mBC patients progressing after/on previous AI treatment, independently of PIK3CA mutational status. BackgroundEndocrine therapy (ET) is the mainstay of treatment for patients with hormone receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2−) metastatic breast cancer (mBC) [1]. However, tumors initially responding to ET, including the most recent ET-Cyclin-Dependent Kinase 4/6 (CDK4/6) inhibitor combinations, almost invariably develop resistance [2][3][4]. Hence, the identification of targeted therapies that are able to revert or delay endocrine resistance is a clinically relevant issue.Aberrant signaling through the phosphatidylinositol 3kinase/protein kinase B (AKT)/mechanistic target of rapamycin complex 1 (PI3K/AKT/mTORC1) cascade is clearly implicated in endocrine resistance, thus providing the rationale for combining inhibitors of this pathway with currently available ET [5][6][7]. Based on the results of the BOLERO-2 trial, the mTORC1 inh...

(1) Background. The onset of a drug–drug interaction (DDI) may affect treatment efficacy and toxicity of advanced non-small-cell lung cancer (aNSCLC) patients during epidermal growth factor receptor (EGFR) tyrosine-kinase inhibitor (TKI) use. Here we present the use of Drug-PIN® (Personalized Interactions Network) software to detect DDIs in aNSCLC patients undergoing EGFR-TKIs. (2) Methods. We enrolled patients with Stage IV aNSCLC already treated with or candidates to receive EGFR-TKIs, in any line; ECOG PS 0–2; taking at least one concomitant drug. Cancer treatments, concomitant drugs, and clinical and laboratory data were collected and inserted in Drug-PIN®. (3) Results. Ninety-two patients, median age of 68.5 years (range 43–89), were included. In total, 20 clinically relevant DDIs needing medical intervention in a total of 14 patients were identified; the 14 major DDIs were related to a high-grade interaction between TKIs and SSRIs, antipsychotics, antiepileptics, H2-receptor antagonist and calcium antagonists. A negative association between statin intake and PFS was identified (p = 0.02; HR 0.281, 95% CI 0.096–0.825). (4) Conclusions. This is the first retrospective study assessing the prevalence of DDIs, the clinical need for medical intervention and the impact of concomitant drugs on EGFR-TKIs survival in aNSCLC.

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Exocrine pancreatic neoplasms represent up to 95% of pancreatic cancers (PCs) and are widely recognized among the most lethal solid cancers, with a very poor 5-year survival rate of 5%-10%. The remaining < 5% of PCs are neuroendocrine tumors that are usually characterized by a better prognosis, with a median overall survival of 3.6 years. The most common type of PC is pancreatic ductal adenocarcinoma (PDAC), which accounts for roughly 85% of all exocrine PCs. However up to 10% of exocrine PCs have rare histotypes, which are still poorly understood. These subtypes can be distinguished from PDAC in terms of pathology, imaging, clinical presentation and prognosis. Additionally, due to their rarity, any knowledge regarding these specific histotypes is mostly based on case reports and a small series of retrospective analyses. Therefore, treatment strategies are generally deduced from those used for PDAC, even if these patients are often excluded or not clearly represented in clinical trials for PDAC. For these reasons, it is essential to collect as much information as possible on the management of PC, as assimilating it with PDAC may lead to the potential mistreatment of these patients. Here, we report the most significant literature regarding the epidemiology, typical presentation, possible treatment strategies, and prognosis of the most relevant histotypes among rare PCs.