JCO Clin Cancer Inform

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

La médecine de précision en oncologie : challenges, enjeux et nouveaux paradigmes

International audienceL’oncologie médicale a pris, depuis quelques années, un tournant substantiel en intégrant la dimension génomique dans la prise de décision thérapeutique. En raison de l’accès aux technologies de séquençage (exome complet, séquençage ciblé du génome, séquençage de l’ARN, ADN circulant…) facilité par la mise en place de plateformes de biologie moléculaire et la diminution des coûts par échantillon, la caractérisation moléculaire est devenue un outil supplémentaire à la disposition du clinicien, s’ajoutant au diagnostic histologique et immunohistochimique et aux données d’imagerie radiologique. Cette approche moléculaire a permis d’identifier de nouvelles formes nosologiques et permet, au-delà de l’aspect cognitif, de renseigner sur les altérations qui sont à prendre en compte dans les décisions thérapeutiques (biomarqueurs prédictifs, activation de voies spécifiques, mutations de résistance). C’est dans ce contexte de profond et rapide changement de pratique médicale et scientifique qu’il a été proposé de réfléchir collectivement aux nouveaux enjeux sous la forme d’un workshop à l’occasion de Biovision qui s’est tenu à Lyon, du 4 au 6 avril 2017. Biovision est un forum international dédié à la Santé et aux Sciences de la vie, qui réunit sur quelques jours des communautés scientifiques, académiques et industrielles dans le but d’accélérer l’innovation. Lors de la 12e édition en avril 2017, le CLARA, l’Institut Roche ainsi que le SIRIC de Lyon ont donc organisé un workshop autour de la médecine de précision (« Precision medicine in oncology: challenges, stakes and new paradigms »). Des experts ainsi que des participants de différents horizons ont échangé sur le sujet afin de proposer des mesures clé

Prediction of treatment (tx)-induced fatigue in breast cancer (BC) patients (pts) using machine learning on genome-wide association (GWAS) data in the prospective CANTO cohort

International audienceMany BC survivors report fatigue. The relevant genomic correlates of fatigue after BC are not well understood. We applied a previously validated machine learning methodology (Oh 2017) to GWAS data to identify biological correlates of fatigue induced after tx. Methods: We analyzed 3825 BC pts with GWAS data (Illumina InfiniumExome24 v 1.1) from the CANTO study (NCT01993498). The outcome of this study was post-tx fatigue 1 year after the end of primary chemotherapy/radiotherapy/surgery using the EORTC C30 fatigue subscale (overall fatigue) and the EORTC FA 12 fatigue domains (physical/emotional/cognitive). For each domain, we limited the study group to those with zero baseline fatigue and defined severe fatigue change as score increase above the third quartile. We tested univariate correlations between severe fatigue in each domain and 496539 SNPs as well as relevant clinical variables. The machine learning prediction model based on preconditioning random forest regression (PRFR) (Oh et al., 2017), was then built using the SNPs with ancestry adjusted univariate p-value < 0.001 and clinical variables with Bonferroni adjusted p-value < 0.05. The model was validated in a holdout subset of the cohort. Gene set enrichment analysis (GSEA) was performed using MetaCore to identify key biological correlates relevant to tx-induced fatigue. Results: Distinct results were found by fatigue domain (table). GSEA showed that the cognitive fatigue model SNPs included biomarkers for cognitive disorders (p = 1.6 x 10-12) and glutamatergic synaptic transmission (p = 1.6 x 10-8). Conclusions: A SNP based model had differential performance by fatigue domain, with a potential genetic role on risk and biology for tx induced cognitive fatigue. Further research to explore biomarkers of tx induced fatigue are needed

Prediction of treatment (tx)-induced fatigue in breast cancer (BC) patients (pts) using machine learning on genome-wide association (GWAS) data in the prospective CANTO cohort

International audienceMany BC survivors report fatigue. The relevant genomic correlates of fatigue after BC are not well understood. We applied a previously validated machine learning methodology (Oh 2017) to GWAS data to identify biological correlates of fatigue induced after tx. Methods: We analyzed 3825 BC pts with GWAS data (Illumina InfiniumExome24 v 1.1) from the CANTO study (NCT01993498). The outcome of this study was post-tx fatigue 1 year after the end of primary chemotherapy/radiotherapy/surgery using the EORTC C30 fatigue subscale (overall fatigue) and the EORTC FA 12 fatigue domains (physical/emotional/cognitive). For each domain, we limited the study group to those with zero baseline fatigue and defined severe fatigue change as score increase above the third quartile. We tested univariate correlations between severe fatigue in each domain and 496539 SNPs as well as relevant clinical variables. The machine learning prediction model based on preconditioning random forest regression (PRFR) (Oh et al., 2017), was then built using the SNPs with ancestry adjusted univariate p-value < 0.001 and clinical variables with Bonferroni adjusted p-value < 0.05. The model was validated in a holdout subset of the cohort. Gene set enrichment analysis (GSEA) was performed using MetaCore to identify key biological correlates relevant to tx-induced fatigue. Results: Distinct results were found by fatigue domain (table). GSEA showed that the cognitive fatigue model SNPs included biomarkers for cognitive disorders (p = 1.6 x 10-12) and glutamatergic synaptic transmission (p = 1.6 x 10-8). Conclusions: A SNP based model had differential performance by fatigue domain, with a potential genetic role on risk and biology for tx induced cognitive fatigue. Further research to explore biomarkers of tx induced fatigue are needed

Characterization of Depressive Symptoms Trajectories After Breast Cancer Diagnosis in Women in France

International audienceImportance: Breast cancer (BC) diagnosis and treatment expose patients to a 5-fold higher risk of depression compared with the general population, with an estimated prevalence of 10% to 25%. A depressive episode in patients with BC has implications for the tolerance of and adherence to treatment, impairing quality of life and reducing life expectancy.Objective: To identify and characterize distinct longitudinal patterns of depressive symptoms in patients with BC from diagnosis to 3 years after treatment.Design, settings, and participants: The CANTO-DEePRESS (Deeper in the Understanding and Prevention of Depression in Breast Cancer Patients) cohort study included women in the French multicenter CANTO (CANcer TOxicities) cohort study (conducted between March 20, 2012 and December 11, 2018), who were 18 years or older with invasive stage I to III BC and no previous BC treatment. The study aimed to characterize toxicities over a 5-year period following stage I to III primary BC treatment. Assessments of depressive symptoms were performed on a subset of patients with available data at diagnosis and at least 2 other time points. All data were extracted from the CANTO database on October 1, 2020.Main outcomes and measures: The primary outcome was the level of depressive symptoms at each assessment time point measured with the Hospital Anxiety and Depression Scale and depression subscale at BC diagnosis and at 3 to 6, 12, and 36 months after the end of treatment. The group-based trajectory modeling was used to identify trajectory groups, and multinomial logistic regression models were used to characterize the following factors associated with trajectory group affiliation: demographic, socioeconomic, clinical, lifestyle, and quality-of-life data.Results: A total of 4803 women (mean [SD] age, 56.2 [11.2] years; 2441 patients [50.8%] with stage I BC) were included in the study. Six trajectory groups that described the heterogeneity in the expression of depressive symptoms were identified: noncases with no expression of symptoms (n = 2634 [54.8%]), intermediate worsening (1076 [22.4%]), intermediate improvement (480 [10.0%]), remission (261 [5.4%]), delayed occurrence (200 [4.2%]), and stable depression (152 [3.2%]). HADS-D scores at diagnosis were consistently associated with the 5 depressive trajectory group affiliations, with an estimated higher probability per point increase of experiencing subthreshold or clinically significant depressive symptoms between diagnosis and the 3 years after the end of BC treatment. The higher probabilities ranged from 1.49 (95% CI, 1.43-1.54) for the intermediate worsening group to 10.53 (95% CI, 8.84-12.55) for the stable depression group. Trajectory groups with depressive symptoms differed from the noncases group without symptoms by demographic and clinical factors, such as having dependent children, lower household income, cancer stage, family history of BC, previous psychiatric hospitalizations, obesity, smoking status, higher levels of fatigue, and depression at diagnosis.Conclusions and relevance: In this cohort study, nearly a third of patients with BC experienced temporary or lasting significant depressive symptoms during and after treatment. Improving early identification of women at risk of developing long-term or delayed depression is therefore critical to increase quality of life and overall survival. Subjected to validation, this study is an important first step toward personalized care of patients with BC at risk of depression