Cancer Innovation最新文献

Breast cancer (BC) is the most common malignant tumor and the main cause of death in women worldwide. With increased knowledge regarding tumor escape mechanisms and advances in immunology, many new antitumor strategies such as nonspecific immunotherapies, monoclonal antibodies, anticancer vaccines, and oncolytic viruses, among others, make immunotherapy a promising approach for the treatment of BC. However, these approaches still require meticulous assessment and readjustment as resistance and modest response rates remain important barriers. In this article, we aim to summarize the most recent data available in BC immunotherapy to include the results of ongoing clinical trials and approved therapies used as monotherapies or in combination with conventional treatments.

Liquid–liquid phase separation (LLPS) has emerged as a pivotal biological phenomenon involved in various cellular processes, including the formation of membrane-less organelles and the regulation of biomolecular condensates through precise spatiotemporal coordination of signaling pathways in cells. Dysregulation of LLPSs results in aberrant biomolecular condensates, which are widely implicated in tumorigenesis and cancer progression. Here, we comprehensively summarize the multifaceted roles of LLPS in tumor biology from the perspective of cancer hallmarks, including genomic stability, metabolic reprogramming progression, ferroptosis, and metastasis, to unveil the intricate mechanisms by which LLPS occurs in tumorigenesis. We discuss current discoveries related to therapeutic involvement and potential clinical applications of LLPS in cancer treatment, highlighting the potential of targeting LLPS-driven processes as novel therapeutic strategies. Additionally, we discuss the challenges associated with new approaches for cancer treatment based on LLPS. This in-depth discussion of the impact of LLPS on fundamental aspects of tumor biology provides new insights into overcoming cancer.

Patient-centered care (PCC) is an innovative approach to the diagnosis and treatment of malignancy that aims to improve patients' experience during the management of their disease. However, despite growing interest, the concept and specifics of PCC remain unclear. This consensus document addresses this gap by providing a literature review and a clear definition of PCC and outlines its main components as observed in real-world practice. These components include daytime diagnostic and treatment procedures, in-hospital and community-based infusion centers, home-based diagnostic and treatment services, smart healthcare solutions, and integration of traditional Chinese medicine. This document delves into the implementation of PCC and explores its potential benefits.

With the advances in artificial intelligence (AI), data-driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision-making by such algorithms is not trustworthy for clinicians and is considered a black-box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer-reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model-agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree-based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree-based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI-enabled health systems and medical devices and, ultimately, the quality of care and outcomes.