Dysthyroidism during immune checkpoint inhibitors is associated with improved overall survival in adult cancers: data mining of 1385 electronic patient records.

BACKGROUND: Dysthyroidism (DT) is a common toxicity of immune checkpoint inhibitors (ICIs) and prior work suggests that dysthyroidism (DT) might be associated with ICI efficacy. PATIENTS AND METHODS: ConSoRe, a new generation data mining solution, was used in this retrospective study, to extract data from electronic patient records of adult cancer patients treated with ICI at Institut Paoli-Calmettes (Marseille, France). Every DT was verified and only ICI-induced DT was retained. Survival analyses were performed by Kaplan-Meier method (log-rank test) and Cox model. To account for immortal time bias, a conditional landmark analysis was performed (2 months and 6 months), together with a time-varying Cox model. RESULTS: Data extraction identified 1385 patients treated with ICI between 2011 and 2021. DT was associated with improved overall survival (OS) (HR 0.46, (95% CI 0.33 to 0.65), p<0.001), with a median OS of 35.3 months in DT group vs 15.4 months in non-DT group (NDT). Survival impact of DT was consistent using a 6-month landmark analysis with a median OS of 36.7 months (95% CI 29.4 to not reported) in the DT group vs 25.5 months (95% CI 22.8 to 27.8) in the NDT group. In multivariate analysis, DT was independently associated with improved OS (HR 0.49, 95% CI 0.35 to 0.69, p=0.001). After adjustment in time-varying Cox model, this association remained significant (adjusted HR 0.64, 95% CI 0.45 to 0.90, p=0.010). Moreover, patients with DT and additional immune-related adverse event had increased OS compared with patients with isolated DT, with median OS of 38.8 months vs 21.4 months, respectively. CONCLUSION: Data mining identified a large number of patients with ICI-induced DT, which was associated with improved OS accounting for immortal time bias.

Corrigendum: Gastrointestinal Metastases From Primary Renal Cell Cancer: A Single Center Review.

[This corrects the article DOI: 10.3389/fonc.2021.644301.].

OSIRIS: A Minimum Data Set for Data Sharing and Interoperability in Oncology.

PURPOSE: Many institutions throughout the world have launched precision medicine initiatives in oncology, and a large amount of clinical and genomic data is being produced. Although there have been attempts at data sharing with the community, initiatives are still limited. In this context, a French task force composed of Integrated Cancer Research Sites (SIRICs), comprehensive cancer centers from the Unicancer network (one of Europe's largest cancer research organization), and university hospitals launched an initiative to improve and accelerate retrospective and prospective clinical and genomic data sharing in oncology. MATERIALS AND METHODS: For 5 years, the OSIRIS group has worked on structuring data and identifying technical solutions for collecting and sharing them. The group used a multidisciplinary approach that included weekly scientific and technical meetings over several months to foster a national consensus on a minimal data set. RESULTS: The resulting OSIRIS set and event-based data model, which is able to capture the disease course, was built with 67 clinical and 65 omics items. The group made it compatible with the HL7 Fast Healthcare Interoperability Resources (FHIR) format to maximize interoperability. The OSIRIS set was reviewed, approved by a National Plan Strategic Committee, and freely released to the community. A proof-of-concept study was carried out to put the OSIRIS set and Common Data Model into practice using a cohort of 300 patients. CONCLUSION: Using a national and bottom-up approach, the OSIRIS group has defined a model including a minimal set of clinical and genomic data that can be used to accelerate data sharing produced in oncology. The model relies on clear and formally defined terminologies and, as such, may also benefit the larger international community.