The prognosis of patients treated with everolimus for advanced ER-positive, HER2-negative breast cancer is driven by molecular features.

Everolimus is widely used in patients with advanced ER-positive, HER2-negative breast cancer. We looked at alterations in the PIK3CA/AKT/mTOR pathway in a multicenter cohort as potential biomarkers of efficacy. Patients with advanced ER-positive, HER2-negative breast cancer treated with everolimus and endocrine therapy between 2012 and 2014 in two cancer centers were included. Targeted sequencing examined mutations in PIK3CA, ESR1, and AKT1 genes. An immunochemical analysis was conducted to evaluate expression of PTEN, INPP4B, STK11, p4EBP1, and pS6. We analyzed 71 patients (44 primary tumors; 27 metastatic tissues). Median age was 63 years [58-69]. All patients had heavily pretreated advanced disease. A mutation in the PIK3CA pathway was observed in 32 samples (PIK3CA exons 10 and 21 and AKT1 exon 4 in 15.5%, 24.0%, and 5.6% of samples), and in ESR1 in 5 samples (7.0%), respectively. Most samples showed cytoplasmic expression of the PIK3CA pathway proteins. Progression-free survival was longer in patients with a pS6 or p4EBP1 histoscore ≥ median value (6.6 versus 3.7 months, p = 0.037), and in patients with a PTEN histoscore ≤ median value (7.1 versus 5.3 months, p = 0.02). Overall survival was longer in patients with pS6 ≥ 3rd quartile (27.6 versus 19.3 months, p = 0.038) and in patients with any mutation in the PIK3CA/AKT/mTOR pathway (27.6 versus 19.3 months, p = 0.011). The prognosis of patients treated with everolimus for advanced ER-positive, HER2-negative breast cancer appears primarily driven by molecular features associated with the activation of the PIK3CA/AKT/mTOR pathway.

Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer.

Importance: The association of tumor-infiltrating lymphocyte (TIL) abundance in breast cancer tissue with cancer recurrence and death in patients with early-stage triple-negative breast cancer (TNBC) who are not treated with adjuvant or neoadjuvant chemotherapy is unclear. Objective: To study the association of TIL abundance in breast cancer tissue with survival among patients with early-stage TNBC who were treated with locoregional therapy but no chemotherapy. Design, Setting, and Participants: Retrospective pooled analysis of individual patient-level data from 13 participating centers in North America (Rochester, Minnesota; Vancouver, British Columbia, Canada), Europe (Paris, Lyon, and Villejuif, France; Amsterdam and Rotterdam, the Netherlands; Milan, Padova, and Genova, Italy; Gothenburg, Sweden), and Asia (Tokyo, Japan; Seoul, Korea), including 1966 participants diagnosed with TNBC between 1979 and 2017 (with follow-up until September 27, 2021) who received treatment with surgery with or without radiotherapy but no adjuvant or neoadjuvant chemotherapy. Exposure: TIL abundance in breast tissue from resected primary tumors. Main Outcomes and Measures: The primary outcome was invasive disease-free survival [iDFS]. Secondary outcomes were recurrence-free survival [RFS], survival free of distant recurrence [distant RFS, DRFS], and overall survival. Associations were assessed using a multivariable Cox model stratified by participating center. Results: This study included 1966 patients with TNBC (median age, 56 years [IQR, 39-71]; 55% had stage I TNBC). The median TIL level was 15% (IQR, 5%-40%). Four-hundred seventeen (21%) had a TIL level of 50% or more (median age, 41 years [IQR, 36-63]), and 1300 (66%) had a TIL level of less than 30% (median age, 59 years [IQR, 41-72]). Five-year DRFS for stage I TNBC was 94% (95% CI, 91%-96%) for patients with a TIL level of 50% or more, compared with 78% (95% CI, 75%-80%) for those with a TIL level of less than 30%; 5-year overall survival was 95% (95% CI, 92%-97%) for patients with a TIL level of 50% or more, compared with 82% (95% CI, 79%-84%) for those with a TIL level of less than 30%. At a median follow-up of 18 years, and after adjusting for age, tumor size, nodal status, histological grade, and receipt of radiotherapy, each 10% higher TIL increment was associated independently with improved iDFS (hazard ratio [HR], 0.92 [0.89-0.94]), RFS (HR, 0.90 [0.87-0.92]), DRFS (HR, 0.87 [0.84-0.90]), and overall survival (0.88 [0.85-0.91]) (likelihood ratio test, P < 10e-6). Conclusions and Relevance: In patients with early-stage TNBC who did not undergo adjuvant or neoadjuvant chemotherapy, breast cancer tissue with a higher abundance of TIL levels was associated with significantly better survival. These results suggest that breast tissue TIL abundance is a prognostic factor for patients with early-stage TNBC.

Intraepithelial tumor-infiltrating lymphocytes shape loco-regional PET/CT spread of locally advanced cervical cancer.

BACKGROUND: Data suggest an association between positron emission tomography/CT (PET/CT) metabolic metrics and tumor microenvironment in several malignancies, and a potential role of PET/CT to monitor response to immunotherapy. OBJECTIVE: To evaluate the correlation between tumor loco-regional extension and tumor-infiltrating lymphocyte infiltration in locally advanced cervical cancer prior to concurrent chemo-radiotherapy.The secondary objective was to assess the association between tumor-infiltrating lymphocytes and PET/CT metabolic metrics. METHODS: Patients with locally advanced cervical cancer and negative para-aortic extensions on PET/CT were included. Two senior nuclear medicine physicians specializing in gynecologic oncology reviewed all PET/CT exams, and extracted tumor maximum standardized uptake value, metabolic tumor volume, and total lesion glycolysis, as well as pelvic lymph node involvement. One senior gynecologic oncology pathologist assessed intraepithelial tumor-infiltrating lymphocytes and stromal tumor-infiltrating lymphocytes. Intraepithelial tumor-infiltrating lymphocytes were categorized following previous studies as <1% and >1%. The cut-off for stromal tumor-infiltrating lymphocytes was chosen empirically: intermediate <60% and high >60%. RESULTS: 86 patients were included. Intraepithelial tumor-infiltrating lymphocytes were not significantly associated with tumor metabolic metrics. Intraepithelial tumor-infiltrating lymphocytes were not significantly associated with maximum standard uptake value (p=0.16), or metabolic tumor volume (p=0.19). Tumors with <1% intraepithelial tumor-infiltrating lymphocytes score were associated with a higher MRI tumor size (≥ median) (63.3% vs 39.3%, p=0.04). Patients with pelvic lymph node uptake were significantly more frequent in patients with high stromal tumor-infiltrating lymphocytes score (≥60%) (61.5% vs 31.7%, p=0.009). CONCLUSIONS: Poor or absent intraepithelial tumor-infiltrating lymphocytes were associated with more advanced disease at diagnosis and larger tumor size. Tumor-infiltrating lymphocytes were not associated with tumor metabolic activity. Intraepithelial and stroma tumor-infiltrating lymphocytes are not redundant and should be assessed separately. Further work is needed to evaluate the association between tumor metabolic profile and immune populations, including different T-cell subtypes for patient selection for immunotherapy strategies.

Gynecological sarcomas, surgical management: primary, metastatic, and recurrent disease.

Adult-type gynecological soft tissue and visceral sarcomas are rare tumors, with an estimated incidence of 13% of all sarcomas and 4% of all gynecological malignancies. They most often develop in the uterus (83%), followed by the ovaries (8%), vulva and vagina (5%), and other gynecological organs (2%). The objective of this review is to provide an overview of the current management of gynecological sarcomas, according to international guidelines. The management of gynecological sarcomas should follow the recommendations for the management of soft tissue and visceral sarcomas. Centralizing cases in expert centers improves patient survival, both for the diagnostic phase and for multidisciplinary therapeutic management. In the case of pelvic soft tissue sarcomas, a radiological biopsy is essential before any surgical decision is taken. In the case of a myometrial tumour which may correspond to a sarcoma, if conservative surgery such as myomectomy or morcellation is planned, an ultrasound-guided biopsy with pathological analysis including comparative genomic hybridization analysis must be carried out. In all cases, en bloc surgery, without rupture, is mandatory. Many rare histological subtypes require specific surgical management.

Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images.

Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However, a frequent drawback of AI models is their propension to make decisions based rather on bias in training dataset than on concrete biological features, thus weakening pathologists' trust in these tools. Technically, it is well known that microscopic images are altered by tissue processing and staining procedures, being one of the main sources of bias in machine learning for digital pathology. So as to deal with it, many teams have written about color normalization and augmentation methods. However, only a few of them have monitored their effects on bias reduction and model generalizability. In our study, two methods for stain augmentation (AugmentHE) and fast normalization (HEnorm) have been created and their effect on bias reduction has been monitored. Actually, they have also been compared to previously described strategies. To that end, a multicenter dataset created for breast cancer histological grading has been used. Thanks to it, classification models have been trained in a single center before assessing its performance in other centers images. This setting led to extensively monitor bias reduction while providing accurate insight of both augmentation and normalization methods. AugmentHE provided an 81% increase in color dispersion compared to geometric augmentations only. In addition, every classification model that involved AugmentHE presented a significant increase in the area under receiving operator characteristic curve (AUC) over the widely used RGB shift. More precisely, AugmentHE-based models showed at least 0.14 AUC increase over RGB shift-based models. Regarding normalization, HEnorm appeared to be up to 78x faster than conventional methods. It also provided satisfying results in terms of bias reduction. Altogether, our pipeline composed of AugmentHE and HEnorm improved AUC on biased data by up to 21.7% compared to usual augmentations. Conventional normalization methods coupled with AugmentHE yielded similar results while being much slower. In conclusion, we have validated an open-source tool that can be used in any deep learning-based digital pathology project on H&E whole slide images (WSI) that efficiently reduces stain-induced bias and later on might help increase pathologists' confidence when using AI-based products.

[Diagnosis of uterine sarcomas and rare uterine mesenchymal tumours with malignant potential. Guidelines of the French Sarcoma Group and Rare Gynaecological Tumours]. / Diagnostic des sarcomes utérins et tumeurs mésenchymateuses utérines rares à potentiel de malignité. Référentiels du Groupe Sarcome Français et des Tumeurs Rares Gynécologiques.

The landscape of uterine sarcomas is becoming more complex with the description of new entities associated with recurrent driver molecular alterations. Uterine sarcomas, in analogy with soft tissue sarcomas, are distinguished into complex genomic and simple genomic sarcomas. Leiomyosarcomas and undifferentiated uterine sarcomas belong to complex genomic sarcomas group. Low-grade and high-grade endometrial stromal sarcomas, other rare tumors associated with fusion transcripts (such as NTRK, PDGFB, ALK, RET ROS1) and SMARCA4-deficient uterine sarcoma are considered simple genomic sarcomas. The most common uterine sarcoma are first leiomyosarcoma and secondly endometrial stromal sarcomas. Three different histological subtypes of leiomyosarcoma (fusiform, myxoid, epithelioid) are identified, myxoid and epithelioid leiomyosarcoma being more aggressive than fusiform leiomyosarcoma. The distinction between low-grade and high-grade endometrial stromal sarcoma is primarily morphological and immunohistochemical and the detection of fusion transcripts can help the diagnosis. Uterine PEComa is a rare tumor, which is distinguished into borderline and malignant, according to a risk assessment algorithm. Embryonal rhabdomyosarcoma of the uterine cervix is more common in children but can also occur in adult women. Embryonal rhabdomyosarcoma of the uterine cervix is almost always DICER1 mutated, unlike that of the vagina which is wild-type DICER1, and adenosarcoma which can be DICER1 mutated but with less frequency. Among the emerging entities, sarcomas associated with fusion transcripts involving the NTRK, ALK, PDGFB genes benefit from targeted therapy. The integration of molecular data with histology and clinical data allows better identification of uterine sarcomas in order to better treat them.

Shallow whole genome sequencing approach to detect Homologous Recombination Deficiency in the PAOLA-1/ENGOT-OV25 phase-III trial.

The bevacizumab (bev)/olaparib (ola) maintenance regimen was approved for BRCA1/2-mutated (BRCAmut) and Homologous Recombination Deficient (HRD) high-grade Advanced Ovarian Cancer (AOC) first line setting, based on a significantly improved progression-free survival (PFS) compared to bev alone in the PAOLA-1/ENGOT-ov25 trial (NCT02477644), where HRD was detected by MyChoice CDx PLUS test. The academic shallowHRDv2 test was developed based on shallow whole-genome sequencing as an alternative to MyChoice. Analytical and clinical validities of shallowHRDv2 as compared to MyChoice on 449 PAOLA-1 tumor samples are presented. The overall agreement between shallowHRDv2 and MyChoice was 94% (369/394). Less non-contributive tests were observed with shallowHRDv2 (15/449; 3%) than with MyChoice (51/449; 11%). Patients with HRD tumors according to shallowHRDv2 (including BRCAmut) showed a significantly prolonged PFS with bev+ola versus bev (median PFS: 65.7 versus 20.3 months, hazard ratio (HR): 0.36 [95% CI: 0.24-0.53]). This benefit was significant also for BRCA1/2 wild-type tumors (40.8 versus 19.5 months, HR: 0.45 [95% CI: 0.26-0.76]). ShallowHRDv2 is a performant, clinically validated, and cost-effective test for HRD detection.

Development of the NOGGO GIS v1 Assay, a Comprehensive Hybrid-Capture-Based NGS Assay for Therapeutic Stratification of Homologous Repair Deficiency Driven Tumors and Clinical Validation.

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.

Dissecting the Origin of Heterogeneity in Uterine and Ovarian Carcinosarcomas.

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.

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

APC germline pathogenic variants and epithelial ovarian cancer: causal or coincidental findings?

APC germline 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. APC inactivation has been described at the tumour level in epithelial ovarian cancers (EOCs). Here, we report the identification of APC germline 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 of APC inactivation nor ß-catenin activation. Both tumours did not have a homologous recombination (HR) deficiency, pointing towards the implication of other genes than those involved in HR. APC may contribute to the carcinogenesis of EOC in a multifactorial context. Further studies are required to clarify the role of APC in predisposition to EOC.

Impact of pattern of recurrence on post-relapse survival according to surgical timing in patients with advanced ovarian cancer.

OBJECTIVE: Our 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. METHODS: This 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. RESULTS: A 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. CONCLUSIONS: The 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.

Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies.

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)-based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.

Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images.

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.

Microsatellite instability detection in breast cancer using drop-off droplet digital PCR.

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.

A catalog of numerical centrosome defects in epithelial ovarian cancers.

Centrosome amplification, the presence of more than two centrosomes in a cell is a common feature of most human cancer cell lines. However, little is known about centrosome numbers in human cancers and whether amplification or other numerical aberrations are frequently present. To address this question, we have analyzed a large cohort of primary human epithelial ovarian cancers (EOCs) from 100 patients. We found that rigorous quantitation of centrosome number in tumor samples was extremely challenging due to tumor heterogeneity and extensive tissue disorganization. Interestingly, even if centrosome clusters could be identified, the incidence of centrosome amplification was not comparable to what has been described in cultured cancer cells. Surprisingly, centrosome loss events where a few or many nuclei were not associated with centrosomes were clearly noticed and overall more frequent than centrosome amplification. Our findings highlight the difficulty of characterizing centrosome numbers in human tumors, while revealing a novel paradigm of centrosome number defects in EOCs.

Therapeutic Challenges in Patients with Gynecologic Carcinosarcomas: Analysis of a Multicenter National Cohort Study from the French Prospective TMRG Network.

BACKGROUND: Gynecological carcinosarcomas are rare and aggressive diseases, with a poor prognosis. The rarity of these tumors explains the lack of robust and specific data available in the literature. The objective of this study was to investigate the impact of initial adjuvant treatment and recurrent therapeutic strategies. PATIENTS AND METHODS: A multicentric cohort study within the French national prospective Rare Malignant Gynecological Tumors (TMRG) network was conducted. Data from all included carcinosarcomas diagnosed between 2011 and 2018 were retrospectively collected. RESULTS: 425 cases of uterine and ovarian carcinosarcomas (n = 313 and n = 112, respectively) were collected and analyzed from 12 participating centers. At diagnosis, 140 patients (48%) had a FIGO stage III-IV uterine carcinosarcoma (UCS) and 88 patients (83%) had an advanced ovarian carcinosarcoma (OCS) (FIGO stage ≥ III). Two hundred sixty-seven patients (63%) received adjuvant chemotherapy, most preferably carboplatin-paclitaxel regimen (n = 227, 86%). After a median follow-up of 47.4 months, the median progression-free survival (mPFS) was 15.1 months (95% CI 12.3-20.6) and 14.8 months (95% CI 13.1-17.1) for OCS and UCS, respectively. The median overall survival for OCS and UCS was 37.1 months (95% CI 22.2-49.2) and 30.6 months (95% CI 24.1-40.9), respectively. With adjuvant chemotherapy followed by radiotherapy, mPFS was 41.0 months (95% CI 17.0-NR) and 18.9 months (95% CI 14.0-45.6) for UCS stages I-II and stages III-IV, respectively. In the early stage UCS subgroup (i.e., stage IA, n = 86, 30%), mPFS for patients treated with adjuvant chemotherapy (n = 24) was not reached (95% CI 22.2-NR), while mPFS for untreated patients (n = 62) was 19.9 months (95% IC 13.9-72.9) (HR 0.44 (0.20-0.95) p = 0.03). At the first relapse, median PFS for all patients was 4.2 months (95% CI 3.5-5.3). In the first relapse, mPFS was 6.7 months (95% CI 5.1-8.5) and 2.2 months (95% CI 1.9-2.9) with a combination of chemotherapy or monotherapy, respectively (p < 0.001). CONCLUSIONS: Interestingly, this vast prospective cohort of gynecological carcinosarcoma patients from the French national Rare Malignant Gynecological Tumors network (i) highlights the positive impact of adjuvant CT on survival in all localized stages (including FIGO IA uterine carcinosarcomas), (ii) confirms the importance of platinum-based combination as an option for relapse setting, and (iii) reports median PFS for various therapeutic strategies in the relapse setting.

[The first data challenge of the french society of pathology: An international competition in 2020, a research tool in A.I. for the future?] / Le premier data challenge organisé par la Société Française de Pathologie : une compétition internationale en 2020, un outil de recherche en intelligence artificielle pour l'avenir ?

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.