Moving from correlative science to predictive oncology

Hierarchical Bayesian clustering design of multiple biomarker subgroups (HCOMBS)

Given the Food and Drug Administration's (FDA's) acceptance of master protocol designs in recent guidance documents, the oncology field is rapidly moving to address the paradigm shift to molecular subtype focused studies. Identifying new "marker-based" treatments requires new methodologies to address the growing demand to conduct clinical trials in smaller molecular subpopulations, identify effective treatment and marker interactions, and control for false positives. We introduce our methodology, Hierarchical Bayesian Clustering Design of Multiple Biomarker Subgroups (HCOMBS), a two-stage umbrella Phase II design with effect size clustering and information borrowing across multiple biomarker-treatment pairs. HCOMBS was designed to reduce required sample size, differentiate between varying effect sizes, and control for operating characteristics in the multi-arm setting. When compared to independently applied Simon's Optimal two-stage design, we showed through simulations that HCOMBS required less participants per treatment arm with a well-controlled family-wise error rate and desirable marginal power. Additionally, HCOMBS features a statistical approach that simultaneously conducts clustering and hypothesis testing in one step. We also applied the proposed design on the alliance brain metastases umbrella trial.

Sustainable medical research by effective and comprehensive medical skills: overcoming the frontiers by predictive, preventive and personalized medicine

Background

Clinical research and practice require affordable objectives, sustainable tools, rewarding training strategies and meaningful collaboration.

Method

Our unit delivers courses on project design and management promoting ideas, useful skills, teaching and exploring implementation of networks and existing collaborations. We investigated the effectiveness of a sustainable approach of comprehensive diagnosis and care and its usefulness within concrete models of research project teaching methodology.

Results

The model of predictive, preventive and personalized medicine (PPPM) of adolescent hypertension, developed since 1976 and still active, was displayed. This is a paradigm of comprehensive PPPM aimed at the management of a recognized, but actually neglected, societal and clinical problem. The second model was addressed to the analysis of performance of an outpatient diagnostic and therapy unit and its relationship with the emergency department. Part of the patients, 4,057 cancer patients presenting at the emergency care, were addressed to the outpatient diagnostic and therapy unit for further assessment, treatment and follow-up. The stay in DH was 6.3 ± 2.1 non-consecutive days, with shortage of costs, vs. in-hospital stays. Research planning courses, based on these models, ensued in an increase of competitive project submission and successful funding.

Discussion

Active promotion of interdisciplinary knowledge and skills is warranted. Misleading messages and information are detrimental not only to healthy and sick people but, equally, to all health professionals: efforts for basing on evidence by research any statement are needed. The actual pre-requisite of personalized medicine is the coherent and articulated promotion of the professional quality of staff. Health professionals should and can be skilled in sustainable non-invasive diagnostic procedures, in non-pharmacological intervention, in translational research (from epidemiology to personalized therapy) and in timely dissemination of the information.

Conclusion

Recommendations are provided according to PPPM: proposed models are based on financial sustainability and patient's satisfaction criteria and are addressed to research projects and dissemination also by e-learning. The guidelines of the EU calls in personalized medicine are able to provide a critical added value by accurate planning, transparency of assessment and unbiased reports, dissemination and exploitation.

Circulating microRNA-21 as noninvasive predictive biomarker for response in cancer immunotherapy

A pre-existing T cell-inflamed tumor microenvironment is predictive of clinical outcome to immunotherapy, but the mechanisms of immune effector cells infiltration of tumors are not clear. MicroRNAs are a class of small non-coding RNAs that regulate gene expression at the posttranscriptional level. Additionally, circulating miRNAs might be useful as noninvasive biomarkers of disease and therapy response. Previous studies indicate that STAT3 activity in tumor cells affects immune cells recruitment, which is prerequisite for effective T cell therapy. MiRNA-21 is one of the cell-free miRNAs that has recently been identified as a potential regulator of STAT3. Meanwhile, miRNA-21 is an oncogenic miRNA that could be detected in various tumors. Therefore, we get the hypotheses that circulating miRNA-21 is a potential predictive biomarker for response in cancer immunotherapy and so a novel therapeutic target.