Late-line options for patients with metastatic colorectal cancer: a review and evidence-based algorithm.

Over the past few years, several novel systemic treatments have emerged for patients with treatment-refractory metastatic colorectal cancer, thus making selection of the most effective later-line therapy a challenge for medical oncologists. Over the past decade, regorafenib and trifluridine-tipiracil were the only available drugs and often provided limited clinical benefit compared to best supportive care. Results from subsequent practice-changing trials opened several novel therapeutic avenues, both for unselected patients (such as trifluridine-tipiracil plus bevacizumab or fruquintinib) and for subgroups defined by the presence of actionable alterations in their tumours (such as HER2-targeted therapies or KRAS inhibitors) or with no acquired mechanisms of resistance to the previously received targeted agents in circulating tumour DNA (such as retreatment with anti-EGFR antibodies).

KRASG12D-Mutated Metastatic Colorectal Cancer: Clinical, Molecular, Immunologic, and Prognostic Features of a New Emerging Targeted Alteration.

Purpose: KRASG12D mutation (mut) occurs in about 10%-12% of metastatic colorectal cancer (mCRC). Recently, novel KRASG12D inhibitors have been developed and are currently under investigation in phase I/II clinical trials in solid tumors including mCRC. We aimed at performing a comprehensive characterization of clinical, molecular, immunologic, and prognostic features of KRASG12D-mutated mCRC to inform the design and the interpretation of future trials.

The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making.