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Cerebrospinal fluid liquid biopsies can enable the characterization and monitoring of medulloblastoma. The analysis of copy-number variations in circulating tumour DNA present in these samples can be used as a biomarker to determine the presence of measurable residual disease, and facilitate the optimal treatment and clinical management of patients with medulloblastoma.
A high tumour burden has long been associated with inferior outcomes on traditional cancer therapies and emerging evidence suggests that tumour burden is particularly relevant for patients receiving immune-checkpoint inhibitors. Here, the authors summarize the available clinical and preclinical evidence for the role of tumour burden in determining the outcomes of patients receiving immune-checkpoint inhibitors and highlight areas that are likely to be of future research interest in this emerging area.
In the past few years, advances in omics technologies have led to a better understanding of the heterogeneity of triple-negative breast cancers (TNBCs) and their microenvironment, supporting a view of this breast cancer subtype as an ecosystem that encompasses the intrinsic and extrinsic features of cancer cells. The authors of this Review describe the current and upcoming therapeutic landscape of TNBC and discuss how an integrated view of the TNBC ecosystem can provide improved opportunities for tailoring treatment.
Targeted therapies have improved the outcomes of many patients with cancer, although many more lack targetable alterations or do not derive clinical benefit for other reasons. Radiotherapy can also provide benefit to many patients, although radioresistance often limits the effectiveness of this intervention. Here, the authors describe the potential for radiotherapy to promote non-oncogene dependence on targetable signalling pathways, thus extending the benefits of both targeted therapy and radiotherapy to greater numbers of patients.
Prognostication of outcome across multiple cancers and prediction of response to various treatment modalities are among the next generation of challenges that artificial intelligence (AI) tools can solve using radiology images. The authors of this Perspective describe the evolution of AI-based approaches in oncology imaging and address the path to their adoption as decision-support tools in the clinic.
