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Gynecologic carcinosarcomas (CS) are biphasic neoplasms composed of carcinomatous (C) and sarcomatous (S) malignant components. Because of their rarity and histologic complexity, genetic and functional studies on CS are scarce and the mechanisms of initiation and development remain largely unknown. Whole-genome analysis of the C and S components reveals shared genomic alterations, thus emphasizing the clonal evolution of CS. Reconstructions of the evolutionary history of each tumor further reveal that C and S samples are composed of both ancestral cell populations and component-specific subclones, supporting a common origin followed by distinct evolutionary trajectories. However, while we do not find any recurrent genomic features associated with phenotypic divergence, transcriptomic and methylome analyses identify a common mechanism across the cohort, the epithelial-to-mesenchymal transition (EMT), suggesting a role for nongenetic factors in inflicting changes to cellular fate. Altogether, these data accredit the hypothesis that CS tumors are driven by both clonal evolution and transcriptomic reprogramming, essential for susceptibility to transdifferentiation upon encountering environmental cues, thus linking CS heterogeneity to genetic, transcriptomic, and epigenetic influences. Significance: We have provided a detailed characterization of the genomic landscape of CS and identified EMT as a common mechanism associated with phenotypic divergence, linking CS heterogeneity to genetic, transcriptomic, and epigenetic influences.

APCgermline pathogenic variants result in predisposition to familial adenomatous polyposis and extraintestinal tumours such as desmoid fibromatosis, medulloblastomas and thyroid cancers. They have also been recently involved in ovarian microcystic stromal tumours.APCinactivation has been described at the tumour level in epithelial ovarian cancers (EOCs). Here, we report the identification ofAPCgermline pathogenic variants in two patients diagnosed with premenopausal EOC in early 30s, with no other pathogenic variant detected in the known ovarian cancer predisposing genes. Subsequent tumour analysis showed neither a second hit ofAPCinactivation nor β-catenin activation. Both tumours did not have a homologous recombination (HR) deficiency, pointing towards the implication of other genes than those involved in HR.APCmay contribute to the carcinogenesis of EOC in a multifactorial context. Further studies are required to clarify the role ofAPCin predisposition to EOC.

The worldwide approval of the combination maintenance therapy of olaparib and bevacizumab in advanced high-grade serous ovarian cancer requires complex molecular diagnostic assays that are sufficiently robust for the routine detection of driver mutations in homologous recombination repair (HRR) genes and genomic instability (GI), employing formalin-fixed (FFPE) paraffin-embedded tumor samples without matched normal tissue. We therefore established a DNA-based hybrid capture NGS assay and an associated bioinformatic pipeline that fulfils our institution’s specific needs. The assay´s target regions cover the full exonic territory of relevant cancer-related genes and HRR genes and more than 20,000 evenly distributed single nucleotide polymorphism (SNP) loci to allow for the detection of genome-wide allele specific copy number alterations (CNA). To determine GI status, we implemented an %CNA score that is robust across a broad range of tumor cell content (25–85%) often found in routine FFPE samples. The assay was established using high-grade serous ovarian cancer samples for which BRCA1 and BRCA2 mutation status as well as Myriad MyChoice homologous repair deficiency (HRD) status was known. The NOGGO (Northeastern German Society for Gynecologic Oncology) GIS (GI-Score) v1 assay was clinically validated on more than 400 samples of the ENGOT PAOLA-1 clinical trial as part of the European Network for Gynaecological Oncological Trial groups (ENGOT) HRD European Initiative. The “NOGGO GIS v1 assay” performed using highly robust hazard ratios for progression-free survival (PFS) and overall survival (OS), as well a significantly lower dropout rate than the Myriad MyChoice clinical trial assay supporting the clinical utility of the assay. We also provide proof of a modular and scalable routine diagnostic method, that can be flexibly adapted and adjusted to meet future clinical needs, emerging biomarkers, and further tumor entities.

Heterogeneity of genomic rearrangements in carcinosarcoma.

Curated list of chromatin remodeling genes.

Genomic landscape of CS. A, Tissue origin and histological subtypes for each tumor (from P01 to P15). B, Main component and detailed percentage content for both samples (a and b) derived from the same tumor. C, CNA analysis: tumor purity (%), ploidy, FGA, and CNA breakpoints number. D, SV number, classified as deletion, duplication, inversion, and interchromosomal translocation. E, SNV and small indel number. F, Proportion of each of four mutational signatures deciphered in the whole cohort. G, MSI phenotype: MSI score (representing the percentage of unstable microsatellite loci), MLH1 promoter methylation (beta-value), and mRNA expression level (TPM – transcripts per million). H, Kataegis number illustrating APOBEC error-prone DNA repair process. I, BRCAness phenotype: number of LST, (white stars highlight samples with LST number ≥20), BRCA1 promoter methylation level (beta-value), BRCA1/2 genes alterations and TDP score, collectively summarized by the HRD status track.

List of differentially methylated gene promoters (paired analysis of 5 selected tumors).

Key gene alterations in uterine and ovarian CS. Oncoprint of alterations identified in the 50 most altered genes from TCGA cancer pathways and custom lists of CRG. The type of genomic alteration (deletion, amplification, fusion, broken by SV, missense, truncated) is described in the legend. Number and proportions of alteration types are summarized by gene (horizontal bars on right) and by sample (vertical bars on top). The samples are classified into MSI and CN-high according to TCGA endometrial carcinoma classification.

Evolutionary histories of CS. Clonal lineage histories of P02 MSI phenotype tumor (A), P12 BRCAness-phenotype tumor (B), and P15 ovarian bilateral tumor (C). Left, Clonal lineage inference for the whole tumor: subclonal populations (Ci), main cancer gene alterations, genome doubling events, and representative mutational signature proportions are represented. Right, Subclonal population frequencies projection in each tumor sample. Clones colored in gray are found in both derived samples, pink (or green) clones are specific to sarcoma- (or carcinoma-) derived samples, respectively.

Differential expression and methylation analysis (C vs. S). A, Heatmap and pathway overrepresentation analysis of differentially expressed genes between C and S (or undifferentiated) components of the 10 samples derived from the five selected CS tumors. Gene clustering method: Ward's; distance: Spearman. FDR: false discovery rate. B, Heatmaps of EMT and stemness pathways scores (left) and methylation level of EMT-related miRNAs (right). mRNA level: scaled TPM. Pathway scores: scaled ssGSEA scores. Methylation level: scaled beta-values.

List of differentially expressed genes (paired analysis of 5 selected tumors).

Objective This study aimed to clinically validate the performance of a multi-feature AI-based solution on detection of invasive versus in situ carcinomas and in situ ductal carcinoma high grade from atypical ductal hyperplasia/low grade DCIS compared to rigorous ground truth (GT) established by multiple blinded expert pathologists in breast biopsies. Design Performance of the AI solution was prospectively tested on breast biopsies from two medical institutions in different geographies. AI results were compared against the ground truth (GT) established by consensus of two subspecialist breast pathologists. The study endpoints were detection of invasive carcinoma (IDC, ILC) and DCIS/ADH, including differentiating between different DCIS grades and ADH. ADH and DCIS Low Grade were pooled together because of similar clinical management when diagnosed on a biopsy. Results Six pathologists participated in the study and reported on 436 breast biopsies (841 H&E slides), including 156 invasive (including 31 rare subtypes), 135 DCIS/ADH and 145 benign diagnoses. The AI solution demonstrated high performance when compared with the GT with an AUC of 0.99 for the detection of invasive carcinoma (specificity and sensitivity of 93.6% and 95.5% respectively) and with AUC of 0.95 for the detection of DCIS/ADH. The AI solution differentiated well between subtypes/grades of invasive and in-situ cancers with an AUC of 0.97 for IDC vs. ILC and AUC of 0.92 for DCIS high grade vs. low grade/ADH, respectively. Only 11 (7%) cases had discrepancies on invasive diagnosis, 4 of these between invasive versus benign diagnosis encompassing one case on which the invasive component was only represented by rare lympho-vascular invasion, two cases of ILC (one with a diffuse pattern and the second in a case with granulomatous mastitis with multinucleated giant cells and hemosiderin-laden macrophages) and one rare case of tubular carcinoma surrounded by flat epithelial atypia and columnar cell lesions. Fifteen cases (11%) had discrepancies on DCIS/ADH diagnosis between the two specialist pathologists necessitating a third assessment by a specialist to establish GT. Six of these cases also necessitated a review on a multihead microscope to reach a consensus decision, since there was no majority even after 3 reviews. Conclusion This blinded multi-site study reports the successful clinical validation of a multi-feature AI-based solution in detecting and automatically imparting clinically relevant diagnostic parameters regarding invasive and in situ breast carcinoma, offering an important tool for computer-aided diagnosis in routine pathology practice. Citation Format: Anne Vincent-Salomon, Guillaume Bataillon, Alona Nudelman, Judith Sandbank, Anat Albrecht Shach, Lucie Thibault, Lilach Bien, Rachel Mikulinsky, Ira Krasnitsky, Ronen Heled, Chaim Linhart, Manuela Vecsler, Daphna Laifenfeld. A multi-feature AI-based solution for cancer diagnosis in breast biopsies: A prospective blinded multi-site clinical study [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-04.

ObjectiveOur study aimed to evaluate the association between timing of cytoreductive surgery and pattern of presentation of the first recurrence in patients with advanced ovarian cancer. We also aimed to assess the impact of the pattern of recurrence on post-relapse overall survival according to surgical timing.MethodsThis retrospective multicenter study evaluated patients with International Federation of Gynecology and Obstetrics (FIGO) stage IIIC-IV ovarian cancer. Patients had undergone either primary debulking surgery, early interval debulking surgery after 3–4 cycles of neoadjuvant chemotherapy, or delayed debulking surgery after 6 cycles, with minimal or no residual disease, between January 2008 and December 2015. Survival analyses were conducted using the Log-rank test and the Cox model. Cumulative incidences of the different patterns of recurrence were estimated using a competing risks methodology.ResultsA total of 549 patients were included: 175 (31.9%) patients had primary, 224 (40.8%) early interval, and 150 (27.3%) delayed debulking surgery. The cumulative incidence of peritoneal recurrences at 2 years was higher with increasing neoadjuvant cycles (24.4%, 30.9% and 39.2%; p=0.019). For pleural or pulmonary recurrences, it was higher after early interval surgery (9.9%, 13.0% and 4.1%; p=0.022). Median post-relapse overall survival was 33.5 months (95% confidence interval (CI) (24.3 to 44.2)), 26.8 months (95% CI (22.8 to 32.6)), and 24.5 months (95% CI (18.6 to 29.4)) for primary, early interval, and delayed debulking surgery groups, respectively (p=0.025). The pattern of recurrence in a lymph node (hazard ratio (HR) 0.42, 95% CI (0.27 to 0.64)), delayed surgery (HR 1.53, 95% CI (1.11 to 2.13)) and time to first recurrence (HR 0.95, 95% CI (0.93 to 0.96)) were associated with post-relapse overall survival. For primary and early interval surgery, lymph node recurrences were associated with significantly longer post-relapse overall survival.ConclusionsThe pattern of first recurrence was associated with timing of surgery, with peritoneal recurrences being more frequent with the increasing number of cycles of neoadjuvant chemotherapy. Lymph node recurrences were associated with better prognosis, having higher post-relapse overall survival. This improved prognosis of lymphatic recurrences was not observed in patients who underwent delayed surgery.

The use of conventional methods (immunohistochemistry, pentaplex PCR) for detecting microsatellite instability (MSI), a predictive biomarker of immunotherapy efficacy, is debated for cancers with low MSI prevalence, such as breast cancer (BC). We developed two multiplex drop-off droplet digital PCR (ddPCR) assays targeting four microsatellites, initially identified from public BC whole-genome sequencing dataset. Performances of the assays were investigated and 352 tumor DNA and 28 circulating cell-free DNA from BC patients, with unknown MSI status were blindly screened. Cross-validation of ddPCR MSI status with other MSI detection methods was performed. We then monitored circulating tumor DNA (ctDNA) dynamics before and during pembrolizumab immunotherapy in one patient with MSI-high (MSI-H) metastatic BC. The assays showed high analytical specificity and sensitivity (limit of detection = 0.16%). Among N = 380 samples, seven (1.8%) were found as MSI-H by ddPCR with six of them confirmed by next-generation sequencing (NGS). Specificity was 100% in N = 133 microsatellite stable BC submitted to NGS. In the patient with MSI-H metastatic BC, ctDNA monitoring revealed an early decrease of microsatellite mutant allelic frequencies during immunotherapy. These results demonstrated MSI detection by ddPCR, a non-invasive, fast and cost-effective approach, allowing for large pre-screening of BC patients who may benefit from immunotherapy.

Homologous Recombination DNA-repair deficiency (HRD) is a well-recognized marker of platinum-salt and PARP inhibitor chemotherapies in ovarian and breast cancers (BC). Causing high genomic instability, HRD is currently determined by BRCA1/2 sequencing or by genomic signatures, but its morphological manifestation is not well understood. Deep Learning (DL) is a powerful machine learning technique that has been recently shown to be capable of predicting genomic signatures from stained tissue slides. However, DL is known to be sensitive to dataset biases and lacks interpretability. Here, we present and evaluate a strategy to control for biases in retrospective cohorts. We train a deep-learning model to predict the HRD in a controlled cohort with unprecedented accuracy (AUC: 0.86) and we develop a new visualization technique that allows for automatic extraction of new morphological features related to HRD. We analyze in detail the extracted morphological patterns that open new hypotheses on the phenotypic impact of HRD.

Uterine sarcomas represent a clinical challenge because of their difficult diagnosis and the poor prognosis of certain subtypes. The aim of this study was to evaluate the expression of the special AT-rich sequence-binding protein 2 (SATB2) in endometrial stromal sarcoma (ESS) and other types of uterine sarcoma by immunohistochemistry. We studied the expression of SATB2 on 71 full tissue sections of endometrial stromal nodule, low-grade ESS, uterine leiomyomas and leiomyosarcoma, undifferentiated uterine sarcoma, adenosarcoma, and carcinosarcoma samples. Nuclear SATB2 expression was then evaluated in an extended sample set using a tissue microarray, including 78 additional uterine tumor samples. Overall, with a cut-off of ≥10% of tumor cell staining as positive, the nuclear SATB2 score was negative in all endometrial stromal nodule samples (n=10) and positive in 83% of low-grade ESS samples (n=29/35), 40% of undifferentiated uterine sarcoma (n=4/10), 13% of leiomyosarcoma (n=2/16), 14% of adenosarcoma (n=3/22), and 8% carcinosarcoma (n=2/25) samples. Furthermore, in ESS patients, direct comparison of nuclear SATB2 scores with clinicopathologic parameters and other reported biomarkers such as progesterone receptor and estrogen receptor showed that nuclear SATB2 was associated with PR expression and a decreased risk of disease-specific death (odds ratio=0.06, 95% confidence interval=0.04-0.81, P=0.04). Our data suggest that SATB2 could be a marker with relative sensitivity (83%) for distinguishing between endometrial stromal nodule and ESS with potential prognostic value.

Background: The prognostic value of stromal tumor-infiltrating lymphocytes (TILs) as a biomarker for triple-negative breast cancer (TNBC) has been extensively demonstrated in patients (pts) receiving (neo)adjuvant systemic therapy. In addition, several small studies suggest that a subset of pts with early-stage TNBC and high TILs have excellent long-term outcomes, even in the absence of systemic therapy [1-3]. However, data on the absolute risk of TNBC recurrence according to TIL levels in the absence of systemic therapy are limited and critical to inform the design of future systemic therapy de-escalation clinical trials. Methods: We conducted an individual patient data pooled analysis of 12 international cohorts of pts with TNBC treated with locoregional therapy but no systemic therapy. TNBC was defined as tumors with estrogen and progesterone receptor of < 1% and HER2 negative (IHC 0, 1+ or IHC 2+ and FISH negative) per local evaluation. TILs were locally assessed in hematoxylin & eosin-stained slides according to the International Immuno-Oncology Biomarker Working Group guidelines (www.tilsinbreastcancer.org). We used the Kaplan-Meier method to assess survival outcomes according to prespecified TIL thresholds: 30% and 50%. Confidence intervals (CI) for survival probabilities were calculated using a percentile bootstrap method. The primary endpoint was invasive disease-free survival (iDFS, STEEP 2.0 definition). Key secondary outcomes included recurrence-free survival (RFS), distant disease-free survival (DDFS) and overall survival (OS). Results: 1,835 pts diagnosed with TNBC between 1982 and 2017 who did not receive systemic therapy were included. The median age at diagnosis was 56 (IQR 38-71). Menopausal status was known in 1,184 women, of whom 78% were post-menopausal. The median tumor size was 2.0 cm (IQR 1.2-2.6). Most pts (87%) had no axillary lymph node involvement (N0). Most tumors were invasive ductal carcinoma (74%) and grade 3 (70%). The median level of TILs was 15% (IQR 5-40). The median duration of follow-up was 30.4 years (95% CI 29.9, 31.1). A total of 950 (52%) iDFS, 828 (45%) RFS, 767 (42%) DDFS events, and 604 (33%) deaths were observed. In multivariable analyses, higher TILs were independently associated with improved iDFS, RFS, DDFS, and OS beyond clinicopathological factors (likelihood ratio p< 10e-6). Each 10% increment in stromal TILs was associated with an 8% (95% CI: 6-11), 10% (95% CI: 7-13), and 13% (95% CI: 10-15) reduction in the risk of experiencing an iDFS, RFS or DDFS event, and with a 12% (95% CI: 9-15) reduction in the risk of death. iDFS, RFS, DDFS and OS rates according to different TIL thresholds and nodal status are shown in the Table. Of note, the RFS estimates (which exclude second non-breast primaries and contralateral breast cancers) were consistently higher than the iDFS counterparts (which include both), consistent with a high rate of contralateral breast cancers and second primary tumors in this cohort. Notably, patients with node-negative—and especially stage I—TNBC with high TILs had excellent survival rates at 10-year follow-up. Conclusion: TILs are highly prognostic in pts with systemically untreated early-stage TNBC. Pts with pN0 (and especially stage I) TNBC with high TILs exhibited very favorable long-term outcomes even in the absence of systemic therapy. These data define the natural history of TIL-rich TNBC pts and are crucial to identifying the optimal patient population for future chemotherapy and immunotherapy de-escalation clinical trials. References: [1] Leon-Ferre et al, 2017, PMID: 28913760 [2] Park et al, 2019, PMID: 31566659 [3] de Jong et al, 2022, PMID: 35353548 Table 5 and 10-year survival endpoints according TIL level, nodal status, and stage Citation Format: Roberto A. Leon-Ferre, Sarah Flora Jonas, Roberto Salgado, Sherene Loi, Vincent De Jong, Jodi M. Carter, Torsten Nielson, Samuel Leung, Nazia Riaz, Giuseppe Curigliano, Carmen Criscitiello, Vincent Cockenpot, Matteo Lambertini, Vera Suman, Barbro Linderholm, John WM Martens, Carolien HM van Deurzen, Mieke Timmermans, Tatsunori Shimoi, Shu Yazaki, Masayuki Yoshida, Sung-Bae Kim, Hee Jin Lee, Maria Vittoria Dieci, Guillaume Bataillon, Anne Salomon, Fabrice Andre, Marleen Kok, Sabine Linn, Matthew P. Goetz, Stefan Michiels. Stromal tumor-infiltrating lymphocytes identify early-stage triple-negative breast cancer patients with favorable outcomes at 10-year follow-up in the absence of systemic therapy: a pooled analysis of 1835 patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD9-05.

The automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets.In this article, we propose a modification of the standard nested cross-validation procedure for hyper-parameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.

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

Purpose: Almost all cervical cancers are caused by human papillomavirus (HPV) and patients with advanced stage are at high risk for relapse. Circulating HPV DNA (HPV ctDNA) may serve as a residual tumor marker at the end of chemoradiation or to predict relapse during the follow-up period. Experimental Design: We analyzed serum samples from 94 HPV16- or HPV18-related CCs from the BioRAIDs prospective cohort. Samples were collected before and after treatment and during an 18-month follow-up period. Using digital droplet PCR (ddPCR), we assessed the relevance of circulating HPV E7 gene as a marker for residual disease compared to HPV integration site and PIK3CA mutations. Finally, the prognostic impact of circulating HPV E7 gene was assessed with its prediction value of relapse. Results: HPV E7 gene was the most sensitive tumor marker, superior to both HPV integration sites and PIK3CA mutations in serum. Circulating HPV DNA (HPV ctDNA) was detected in 63% (59/94) of patients, before treatment. HPV ctDNA detection in serum sample was associated with high FIGO stage (P = 0.02) and para-aortic lymph node involvement (P = 0.01). The level of HPV ctDNA was positively correlated with HPV copy number in the tumor (R = 0.39, P < 0.001). Complete clearance of HPV ctDNA by the end of treatment was significantly associated with a longer PFS (P < 0.0001). Patients with persistent HPV ctDNA in serum relapsed with a median time of 10 months (range, 2–15) from HPV ctDNA detection. Conclusions: HPV ctDNA detection is a useful marker to predict relapse in cervical cancer. See related commentary by Wentzensen and Clarke, p. 5733

Homologous recombination DNA-repair deficiency (HRD) is becoming a well-recognized marker of platinum salt and polyADP-ribose polymerase inhibitor chemotherapies in ovarian and breast cancers. While large-scale screening for HRD using genomic markers is logistically and economically challenging, stained tissue slides are routinely acquired in clinical practice. With the objectives of providing a robust deep-learning method for HRD prediction from tissue slides and identifying related morphological phenotypes, we first show that digital pathology workflows are sensitive to potential biases in the training set, then we propose a method to overcome the influence of these biases, and we develop an interpretation method capable of identifying complex phenotypes. Application to our carefully curated in-house dataset allows us to predict HRD with high accuracy (area under the receiver-operator characteristics curve 0.86) and to identify morphological phenotypes related to HRD. In particular, the presence of laminated fibrosis and clear tumor cells associated with HRD open new hypotheses regarding its phenotypic impact.

Objective Tumors harboring a POLE pathogenic variant, associated with high tumor mutational burden, are good candidates for immunotherapy. However, POLE pathogenic variants are not currently screened in routine clinical practice. Can these tumors be identified by means of an already available test? Methods We describe seven tumors harboring a POLE pathogenic variant, among eight patients with tumors harboring multiple BRCA1/2 variants (from 4 to 20). All patients were managed at Institut Curie, Paris. Five patients were selected because of unexpected tumor BRCA testing results with multiple variants and another three patients were selected because of a POLE pathogenic variant detected by large tumor testing. We looked for other tumor variants by Next-Generation Sequencing in tumors harboring multiple BRCA1/2 variants, and for multiple BRCA1/2 variants in tumors harboring a POLE pathogenic variant. Results Four of the five tumors selected because of multiple BRCA1/2 variants exhibited a POLE pathogenic variant, and all three tumors selected for POLE pathogenic variants exhibited multiple BRCA1/2 variants. Conclusions Tumor BRCA testing could be a way to detect tumors harboring a highly mutagenic POLE pathogenic variant.

The french society of pathology (SFP) organized in 2020 its first data challenge with the help of Health Data Hub (HDH). The organisation of this event first consisted in recruiting almost 5000 slides of uterus cervical biopsies obtained in 20 pathology centers. After having made sure that patients did not refuse to include their slides in the project, the slides were anonymised, digitized and annotated by expert pathologists, and were finally uploaded on a data challenge platform for competitors all around the world. Competitors teams had to develop algorithms that could distinguish among four diagnostic classes in epithelial lesions of uterine cervix. Among many submissions by competitors, the best algorithms obtained an overall score close to 95%. The best 3 teams shared 25k€ prizes during a special session organised during the national congress of the SFP. The final part of the competition lasted only 6 weeks and the goal of SFP and HDH is now to allow for the collection to be published in open access. This final step will allow data scientists and pathologists to further develop artificial intelligence algorithms in this medical area.