Proactive assessment of patient reported outcomes in ovarian cancer studies: a systematic review and call for action in future studies.

Objective: This systematic review aims to evaluate the proactive or real-time assessment of patient reported outcomes in studies involving patients with ovarian cancer undergoing systemic therapy.

Methods: PubMed, Embase, and Cochrane databases were searched (from database inception until February 2022), and prospective ovarian cancer studies (experimental or observational) that incorporated patient reported outcomes, including quality of life, were included. The primary objective was to assess the ratio of studies incorporating real-time use of patient reported outcomes among those studies performing patient reported outcomes.

Predicting Severe Haematological Toxicity in Gastrointestinal Cancer Patients Undergoing 5-FU-Based Chemotherapy: A Bayesian Network Approach.

Purpose: Severe toxicity is reported in about 30% of gastrointestinal cancer patients receiving 5-Fluorouracil (5-FU)-based chemotherapy. To date, limited tools exist to identify at risk patients in this setting. The objective of this study was to address this need by designing a predictive model using a Bayesian network, a probabilistic graphical model offering robust, explainable predictions.

Identification of HLA class I-restricted immunogenic neoantigens in triple negative breast cancer.

Immune checkpoint inhibitor (ICI)-based immunotherapy in triple negative breast cancer (TNBC) is achieving limited therapeutic results, requiring the development of more potent strategies. Combination of ICI with vaccination strategies would enhance antitumor immunity and response rates to ICI in patients having poorly infiltrated tumors. In heavily mutated tumors, neoantigens (neoAgs) resulting from tumor mutations have induced potent responses when used as vaccines.