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• Real-world evidence in oncology is evolving rapidly with many particularities. • This guidance provides key recommendations for reporting real-world evidence studies in oncology. • Recommendations are based on a review of current evidence and the authors' collective expert opinion. • Authors are a multidisciplinary group of experts from different institutions and countries. • Guidance is provided for full article development, including title, introduction, methods, results, discussion, conclusion. [ABSTRACT FROM AUTHOR]

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice. • ML and DL (AI techniques) have been used to analyse big genomic and proteomic data in immuno-oncology. • This systematic review highlights the potential of AI in identifying new biomarkers that can personalise immunotherapy. • AI algorithms are promising in optimising treatment selection, and improving overall patient outcomes. • The article emphasises the need for further research and validation of the AI algorithm to switch from research to clinic. • Trustworthy explainable AI can be of great utility by providing explanations of the algorithms and discovery of biomarkers. [ABSTRACT FROM AUTHOR]

During recent years, the burden of bureaucracy in clinical research has increased dramatically, adversely impacting the activity of investigators and clinical research teams. Although compliance with the Declaration of Helsinki, the guidelines for Good Clinical Practice (GCP), and other applicable regulations remains unquestionable, their overinterpretation and substitution by the internal operating procedures of sponsors and Contract Research Organizations (CROs) have increased the administrative burden. A survey conducted by the European Society for Medical Oncology (ESMO) Clinical Research Observatory (ECRO) among 940 investigators confirmed that they considered that the administrative burden in clinical research is excessive; that administrative procedures could be reduced without affecting the safety and the rights of the patients and the quality of the data; and that bureaucracy represents an obstacle for clinical research. A panel of physicians with extensive experience in clinical research, composed by members of the ECRO and the ESMO Scientific Medical and Public Policy divisions, analyzed clinical trial procedures related to administrative workflow, pharmacovigilance, and medical care. The panel identified situations that generate debate between investigators and sponsors/CROs and selected real clinical scenarios that exemplify such situations. The panel discussed and proposed specific recommendations for those situations, based on GCP. This initiative aspires to streamline clinical research procedures and to become a platform for discussion among all clinical trial stakeholders, with the aim of promoting the sustainability of clinical research and the care of cancer patients. • The burden of bureaucracy in clinical research is overwhelming investigators and clinical research teams. • This ESMO manuscript proposes a framework of principles to improve sponsoring and monitoring of clinical trials. • We expect this awareness call to streamline clinical research and to promote the welfare of patients. [ABSTRACT FROM AUTHOR]

In 2021, the Food and Drug Administration Oncology Center of Excellence announced Project Optimus focusing on dose optimization for oncology drugs. The Methodology for the Development of Innovative Cancer Therapies (MDICT) Taskforce met to review and discuss the optimization of dosage for oncology trials and to develop a practical guide for oncology phase I trials. Defining a single recommended phase II dose based on toxicity may define doses that are neither the most effective nor the best tolerated. MDICT recommendations address the need for robust non-clinical data which are needed to inform trial design, as well as an expert team including statisticians and pharmacologists. The protocol must be flexible and adaptive, with clear definition of all endpoints. Health authorities should be consulted early and regularly. Strategies such as randomization, intrapatient dose escalation, and real-world eligibility criteria are encouraged whereas serial tumor sampling is discouraged in the absence of a strong rationale and appropriately validated assay. Endpoints should include consideration of all longitudinal toxicity. The phase I dose escalation trial should define the recommended dose range for later testing in randomized phase II trials, rather than a single recommended phase II dose, and consider scenarios where different populations may require different dosages. The adoption of these recommendations will improve dosage selection in early clinical trials of new anticancer treatments and ultimately, outcomes for patients. • Recent experience with targeted anticancer drugs and immunotherapies suggest the traditional approach of using the maximal tolerated dose (MTD) is not ideal. • Project Optimus recommends optimizing the dosage of oncology drugs to ensure they are both effective and tolerable. • MDICT developed practical recommendations for the design and conduct of phase I oncology trials. [ABSTRACT FROM AUTHOR]