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Benefits of functional assays in personalized cancer medicine: more than just a proof-of-concept.

ABSTRACT: As complex and heterogeneous diseases, cancers require a more tailored therapeutic management than most pathologies. Recent advances in anticancer drug development, including the immuno-oncology revolution, have been too often plagued by unsatisfying patient response rates and survivals. In reaction to this, cancer care has fully transitioned to the "personalized medicine" concept. Numerous tools are now available tools to better adapt treatments to the profile of each patient. They encompass a large array of diagnostic assays, based on biomarkers relevant to targetable molecular pathways. As a subfamily of such so-called companion diagnostics, chemosensitivity and resistance assays represent an attractive, yet insufficiently understood, approach to individualize treatments. They rely on the assessment of a composite biomarker, the ex vivo functional response of cancer cells to drugs, to predict a patient's outcome. Systemic treatments, such as chemotherapies, as well as targeted treatments, whose efficacy cannot be fully predicted yet by other diagnostic tests, may be assessed through these means. The results can provide helpful information to assist clinicians in their decision-making process. We explore here the most advanced functional assays across oncology indications, with an emphasis on tests already displaying a convincing clinical demonstration. We then recapitulate the main technical obstacles faced by researchers and clinicians to produce more accurate, and thus more predictive, models and the recent advances that have been developed to circumvent them. Finally, we summarize the regulatory and quality frameworks surrounding functional assays to ensure their safe and performant clinical implementation. Functional assays are valuable in vitro diagnostic tools that already stand beyond the "proof-of-concept" stage. Clinical studies show they have a major role to play by themselves but also in conjunction with molecular diagnostics. They now need a final lift to fully integrate the common armament used against cancers, and thus make their way into the clinical routine.

SUBMITTER: Morand du Puch CB

PROVIDER: S-EPMC8490527 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants. METHODS:We used published literature to estimate the numbers of patients who might be eligible to receive treatments approved for other indications based on molecular profiles. We then estimated the sample size and number of variables that would be required for a successful system to detect exceptional responses with sufficient power. We generated an appropriately sized, simulated dataset (n = 5000) and used an established HE algorithm to detect exceptional responses and calculate total drug exposure, while the data remained encrypted. RESULTS:Our results demonstrated the feasibility of using an HE-based system to identify exceptional responders and perform calculations on patient data during a hypothetical 3-year study. Although homomorphically encrypted computations are time consuming, the required basic computations (i.e., addition) do not pose a critical bottleneck to the analysis. CONCLUSION:In this proof-of-concept study, based on simulated data, we demonstrate that identifying exceptional responders to targeted cancer treatments represents a valuable and feasible use case. Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable data. Because of its privacy protections, we believe that an HE-based learning system for real-world cancer treatment would entice thousands more patients to voluntarily contribute data through participation in research studies beyond the currently available secondary data populated from hospital electronic health records and administrative claims. Forming collaborations between technical experts, physicians, patient advocates, payers, and researchers, and testing the system on existing RWD are critical next steps to making HE-based learning a reality in healthcare.

Project description:OBJECTIVES:Cognitive behavioural therapy (CBT) can improve symptoms of anxiety and depression, but also reduces the risk of future relapse after therapy completion. However, current CBT relapse prevention methods are resource-intensive and can be limited in clinical practice. This paper investigates a personalized means of reducing relapse using smart-messaging in two settings: research and routine care. DESIGN:Study 1 presents a cohort study comparing a cohort of smart-messaging users versus non-users. Study 2 presents time series follow-up data from a case series of smart-messaging users from clinical practice. METHODS:Fifteen of 56 CBT completers who participated in a trial for the treatment of health anxiety wrote advice they would want if in future they were doing well, experiencing early warning signs of relapse, or experiencing full relapse. Following CBT, participants received weekly text-message requests to rate their well-being. Dependent upon their response, participants received tailored advice they had written, appropriate to the well-being level reported after recovery from health anxiety. Smart-messaging was also trialled in a routine practice sample of 14 CBT completers with anxiety and depression. RESULTS:Across a 12-month follow-up, participants receiving smart-messaging showed greater health improvements than those who did not. Well-being scores showed stability between CBT completion and 6-month follow-up among routine care patients. CONCLUSIONS:These findings suggest that a low-intensity, personalized relapse prevention method can have a clinical benefit following CBT for common mental health problems. PRACTITIONER POINTS:Post-treatment outcomes may be improved using personalized smart-messaging to prevent relapse following cognitive behavioural therapy (CBT) for health anxiety. In clinical practice, post-treatment smart-messaging can be well-used by patients and may help maintain stable well-being in the 6 months after CBT ends. This evidence supports the clinical utility of a brief tailored digital intervention, which can be integrated within routine clinical practice with minimal therapist input. Overall, longer-term post-CBT outcomes may be improved by integrating a smart-messaging intervention at the end of therapy.

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Benefits of functional assays in personalized cancer medicine: more than just a proof-of-concept.

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