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PURPOSE To compare the computability of Observational Medical Outcomes Partnership (OMOP)–based queries related to prescreening of patients using two versions of the OMOP common data model (CDM; v5.3 and v5.4) and to assess the performance of the Greater Paris University Hospital (APHP) prescreening tool. MATERIALS AND METHODS We identified the prescreening information items being relevant for prescreening of patients with cancer. We randomly selected 15 academic and industry-sponsored urology phase I-IV clinical trials (CTs) launched at APHP between 2016 and 2021. The computability of the related prescreening criteria (PC) was defined by their translation rate in OMOP-compliant queries and by their execution rate on the APHP clinical data warehouse (CDW) containing data of 205,977 patients with cancer. The overall performance of the prescreening tool was assessed by the rate of true- and false-positive cases of three randomly selected CTs. RESULTS We defined a list of 15 minimal information items being relevant for patients' prescreening. We identified 83 PC of the 534 eligibility criteria from the 15 CTs. We translated 33 and 62 PC in queries on the basis of OMOP CDM v5.3 and v5.4, respectively (translation rates of 40% and 75%, respectively). Of the 33 PC translated in the v5.3 of the OMOP CDM, 19 could be executed on the APHP CDW (execution rate of 58%). Of 83 PC, the computability rate on the APHP CDW reached 23%. On the basis of three CTs, we identified 17, 32, and 63 patients as being potentially eligible for inclusion in those CTs, resulting in positive predictive values of 53%, 41%, and 21%, respectively. CONCLUSION We showed that PC could be formalized according to the OMOP CDM and that the oncology extension increased their translation rate through better representation of cancer natural history.

Significant rational is available for specific targeting of PI3K/AKT/mTOR pathway in the treatment of non-small cell lung cancer (NSCLC). However, almost all clinical trials that have evaluated Pi3K pathway-based monotherapies/combinations did not observe an improvement of patient's outcome. The aim of our study was therefore to define combination of treatment based on the determination of predictive markers of resistance to the mTORC1 inhibitor RAD001/Everolimus. An in vivo study showed high efficacy of RAD001 in NSCLC Patient-Derived Xenografts (PDXs). When looking at biomarkers of resistance by RT-PCR study, three genes were found to be highly expressed in resistant tumors, i.e., PLK1, CXCR4, and AXL. We have then focused our study on the combination of RAD001 + Volasertib, a PLK1 inhibitor, and observed a high antitumor activity of the combination in comparison to each monotherapy; similarly, a clear synergistic effect between the two compounds was found in an in vitro study. Pharmacodynamics study demonstrated that this synergy was due to (1) tumor vascularization decrease, increase of the HIF1 protein expression and decrease of the intracellular pH, and (2) decrease of the Carbonic Anhydrase 9 (CAIX) protein that could not correct intracellular acidosis. In conclusion, all these preclinical data strongly suggest that the inhibition of mTORC1 and PLK1 proteins may be a promising therapeutic approach for NSCLC patients.

Count data is becoming more and more ubiquitous in a wide range of applications, with datasets growing both in size and in dimension. In this context, an increasing amount of work is dedicated to the construction of statistical models directly accounting for the discrete nature of the data. Moreover, it has been shown that integrating dimension reduction to clustering can drastically improve performance and stability. In this paper, we rely on the mixture of multinomial PCA, a mixture model for the clustering of count data, also known as the probabilistic clustering-projection model in the literature. Related to the latent Dirichlet allocation model, it offers the flexibility of topic modeling while being able to assign each observation to a unique cluster. We introduce a greedy clustering algorithm, where inference and clustering are jointly done by mixing a classification variational expectation maximization algorithm, with a branch & bound like strategy on a variational lower bound. An integrated classification likelihood criterion is derived for model selection, and a thorough study with numerical experiments is proposed to assess both the performance and robustness of the method. Finally, we illustrate the qualitative interest of the latter in a real-world application, for the clustering of anatomopathological medical reports, in partnership with expert practitioners from the Institut Curie hospital., 34 pages, 11 figures, published in : Computational Statistics

ObjectiveDescribe characteristics, treatment patterns and clinical outcomes of patients with small-cell lung cancer (SCLC).DesignRetrospective chart review study defining several cohorts: (1) limited-stage disease (LD) SCLC initiating 1L therapy (1 L LD-SCLC), (2) extensive-stage disease (ED) SCLC initiating 1L therapy (1L ED-SCLC) and (3) patients initiating 2L therapy.Setting39 physicians (medical oncologists, thoracic oncologists and/or pulmonologists) from France, Italy and the UK.ParticipantsPatients >18 years of age with a confirmed diagnosis of LD-SCLC or ED-SCLC and a full oncology medical history. Patients included initiated a 1L (2013–2015) or 2L (2013–2016) treatment (chemotherapy and/or radiotherapy—RT).Primary and secondary outcome measuresOverall survival (OS) and progression-free survival (PFS).Results231 patients in 1L LD-SCLC, 308 in 1L ED-SCLC and 225 with relapse/refractory SCLC initiating 2L treatment were included. The proportion of men was higher across all groups (56.8% to 68.5%) and mean age at time of diagnosis was 66.0 and 65.4 years in 1L LD-SCLC and 2L ED-SCLC cohorts. The majority of patients in LD-SCLC 1L group received chemotherapy with RT (76.2%). Patients initiating 2L therapy predominantly received chemotherapy alone (79.6%).Median OS in 1 L patients was 17.3 months in LD-SCLC and 8.8 months in ED-SCLC. Median PFS was 11.6 months in LD-SCLC and 6.1 months in ED-SCLC patients. Median OS in patients initiating 2L treatment was 6.6 months. OS from start of 2L treatment was lower in patients initially diagnosed with ED (5.1 months) than in patients initially diagnosed with LD (9.3 months) (pConclusionsDespite the availability of a high number of treatments and combinations, the prognosis of SCLC is still unsatisfactory, especially for those patients diagnosed with ED-SCLC, indicating high unmet need in this patient population.

1AbstractTriple-Negative Breast Cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options compared to other breast cancers. The current standard of care in non-metastatic settings is neoadjuvant chemotherapy (NACT), with the goal of breast-conserving surgery and for an in vivo assessment of chemosensitivity. However, the efficacy of this treatment varies significantly across patients, and this histological response heterogeneity is still poorly understood partly due to the paucity of available curated TNBC data. Motivated by this problem, we investigate the use of machine learning (ML) to predict at diagnosis the histological response to NACT for early TNBC patients. To overcome the known biases of related small scale studies while respecting data privacy, we conduct, for the first time, a TNBC study in a multi-centric fashion behind hospitals’ firewalls using collaborative Federated Learning (FL). Thereby allowing access to enough TNBC data to sustain a complete response heterogeneity investigation. We show evidence that local ML models relying on Whole-Slide Images (WSIs) at diagnosis are able to predict the histological response to NACT as accurately as current clinical approaches, which rely on time-consuming expert annotations. We demonstrate that collaborative training further improves performance over single-center training outperforming clinical methods. Our ML model is interpretable by design, and we show that it is sensitive to specific histological patterns. While we identify known predictive biomarkers among them, this proof of concept for real-world collaborative FL paves the way for future biomarker discovery using unprecedently large datasets.

Significant rational is available for specific targeting of PI3K/AKT/mTOR pathway in the treatment of non-small cell lung cancer (NSCLC). However, almost all clinical trials that have evaluated Pi3K pathway-based monotherapies/combinations did not observe an improvement of patient’s outcome. The aim of our study was therefore to define combination of treatment based on the determination of predictive markers of resistance to the mTORC1 inhibitor RAD001/Everolimus. An in vivo study showed high efficacy of RAD001 in NSCLC Patient-Derived Xenografts (PDXs). When looking at biomarkers of resistance by RT-PCR study, three genes were found to be highly expressed in resistant tumors, i.e., PLK1, CXCR4, and AXL. We have then focused our study on the combination of RAD001 + Volasertib, a PLK1 inhibitor, and observed a high antitumor activity of the combination in comparison to each monotherapy; similarly, a clear synergistic effect between the two compounds was found in an in vitro study. Pharmacodynamics study demonstrated that this synergy was due to (1) tumor vascularization decrease, increase of the HIF1 protein expression and decrease of the intracellular pH, and (2) decrease of the Carbonic Anhydrase 9 (CAIX) protein that could not correct intracellular acidosis. In conclusion, all these preclinical data strongly suggest that the inhibition of mTORC1 and PLK1 proteins may be a promising therapeutic approach for NSCLC patients.

International audience; Cancers of unknown primary (CUP) are metastatic cancers for which the primary tumor is not found despite thorough diagnostic investigations. Multiple molecular assays have been proposed to identify the tissue of origin (TOO) and inform clinical care; however, none has been able to combine accuracy, interpretability, and easy access for routine use. We developed a classifier tool based on the training of a variational autoencoder to predict tissue of origin based on RNA-sequencing data. We used as training data 20,918 samples corresponding to 94 different categories, including 39 cancer types and 55 normal tissues. The TransCUPtomics classifier was applied to a retrospective cohort of 37 CUP patients and 11 prospective patients. TransCUPtomics exhibited an overall accuracy of 96% on reference data for TOO prediction. The TOO could be identified in 38 (79%) of 48 CUP patients. Eight of 11 prospective CUP patients (73%) could receive first-line therapy guided by TransCUPtomics prediction, with responses observed in most patients. The variational autoencoder added further utility by enabling prediction interpretability, and diagnostic predictions could be matched to detection of gene fusions and expressed variants. TransCUPtomics confidently predicted TOO for CUP and enabled tailored treatments leading to significant clinical responses. The interpretability of our approach is a powerful addition to improve the management of CUP patients. Copyright

590 Background: Triple-Negative Breast Cancer (TNBC) is characterized by high metastatic potential and poor prognosis with limited treatment options. Neoadjuvant chemotherapy (NACT) is the standard of care in non-metastastic setting due to the ability to assess pathologic responses providing important prognostic information and guidance in adjuvant therapy decisions. However, the histological response heterogeneity is still poorly understood. We investigate the use of Machine Learning (ML) to predict from diagnosis Whole-Slide Images (WSI) of early TNBC the positive histological Complete Response (pCR) to NACT on surgical specimens. To overcome the known biases of small scale studies while respecting data privacy, we conduct a study in a multi-centric fashion behind hospitals’ firewalls using collaborative Federated Learning (FL). Thereby allowing access to enough TNBC data to sustain a complete response heterogeneity investigation. Methods: We collected in both comprehensive cancer centers: Centre Léon Bérard (A)(n=99) and Institut Curie (B) (n=420), WSI of biopsies performed at diagnosis and relevant clinical variables. We use traditional Multiple Instance Learning pipelines by tiling the matter on each WSI with a pre-trained Neural Network (NN). We train a second NN to predict the NACT pCR using the mean feature of each WSI. ML trainings are performed using either one cohort in isolation (NN Local) or both cohorts using FL. We compare the performance of this federated WSI based model to the best clinical model (Clin.) simulating clinical practice (using grade and Tumor-Infiltrating Lymphocytes (TILs) percentage) on both centers. Results: Performance of models to predict NACT pCR (AUC). All results are evaluated in 5 repeated 4-folds cross validations. Conclusions: The final ML model, that was trained in a privacy preserving fashion on both hospitals, provides better prediction of NACT pCR than current clinical standards. This study shows that 1. Not all relevant information is routinely extracted from WSI and 2. Non simulated FL is possible in Healthcare and gives better results than siloed studies on open medical questions. Additional interpretability results of the model show that it has re-discovered known biomarkers such as TILs and apocrine tumor cells without any tile-level annotation, and hints at potential new biomarkers. [Table: see text]