Post-NAC LVI is a strong independent prognostic factor that: (i) should be systematically reported in pathology reports; (ii) should be used as stratification factor after NAC to propose inclusion in second-line trials or adjuvant treatment; (iii) should be included in post-NAC scoring systems.

Background To compare RCB (Residual Cancer Burden) and Neo-Bioscore in terms of prognostic performance and see if adding pathological variables improve these scores. Methods We analysed 750 female patients with invasive breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) at Institut Curie between 2002 and 2012. Scores were compared in global population and by BC subtype using Akaike information criterion (AIC), C-Index (concordance index), calibration curves and after adding lymphovascular invasion (LVI) and pre-/post-NAC TILs levels. Results RCB and Neo-Bioscore were significantly associated to disease-free and overall survival in global population and for triple-negative BC. RCB had the lowest AICs in every BC subtype, corresponding to a better prognostic performance. In global population, C-Index values were poor for RCB (0.66; CI [0.61–0.71]) and fair for Neo-Bioscore (0.70; CI [0.65–0.75]). Scores were well calibrated in global population, but RCB yielded better prognostic performances in each BC subtype. Concordance between the two scores was poor. Adding LVI and TILs improved the performance of both scores. Conclusions Although RCB and Neo-Bioscore had similar prognostic performances, RCB showed better performance in BC subtypes, especially in luminal and TNBC. By generating fewer prognostic categories, RCB enables an easier use in everyday clinical practice.

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.

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