[2022 WHO classification of renal cell carcinomas: Focus on papillary renal cell carcinoma]. / Classification OMS 2022 des cancers du rein : focus sur le carcinome rénal papillaire.

Renal cell carcinomas (RCC) represent a group of heterogeneous tumors whose classification has greatly evolved since 1981. The latest update in 2022 classifies all renal cell carcinomas into six categories according to their morphology or the detection of specific molecular alterations. Molecular disassembly of renal cell carcinomas with papillary features has enabled the identification of new entities characterized by a specific molecular alteration, such as Fumarate Hydratase (FH) deficient RCC, TFE3-rearranged RCC or TFEB-altered RCC. This new classification allows for a more accurate diagnosis but requires a thorough knowledge of the genomic alterations to search for with immunohistochemical or molecular biology techniques. According to the new WHO 2022 classification, papillary renal cell carcinoma (PRC) type 1 or type 2 classification is no longer recommended. A classification based on nucleolar ISUP grade must be preferred: low-grade PRC (ISUP 1-2) or high-grade PRC (ISUP 3-4). The other prognostic factors remain the same: the pTNM stage, lymphovascular invasion, and the presence or absence of dedifferentiated areas referring to sarcomatoid or rhabdoid features. Of note, the presence of necrosis is not currently recognized as a poor prognostic element for this type of carcinoma. The diagnosis of high-grade PRC is from now on a diagnosis of exclusion. It can only be sustained after having ruled out TFE3-rearranged RCC, TFEB-altered RCC, and FH-deficient RCC. For clinicians, the diagnosis of PRC implies suggesting an oncogenetic consultation to screen for an associated genetic tumor syndrome regardless of the patient's age.

Clinical and histological features of histiocytoid Sweet syndrome associated with VEXAS syndrome.

BACKGROUND: "Vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic" (VEXAS) syndrome is caused by acquired somatic mutations in the ubiquitin-activating enzyme 1 (UBA1) gene. Sweet-syndrome-like skin disorders (and especially histiocytoid Sweet syndrome (HSS)) may be associated with VEXAS syndrome. OBJECTIVE: To characterize the clinical and histopathological features of HSS in patients with VEXAS syndrome. METHODS: The skin biopsies with a histological diagnosis of HSS had been collected at Rennes University Medical Center (Rennes, France) between October 2011 and January 2022. Sanger sequencing and digital PCR were used to screen skin, blood, and bone marrow samples for UBA1 variants, and thus classify patients as having VEXAS syndrome or not. We evaluated the clinical, histological, and molecular (UBA1) characteristics of patients with or without VEXAS syndrome. RESULTS: We compared 15 skin biopsies from seven patients found to have VEXAS syndrome and 19 skin biopsies from 15 patients without VEXAS syndrome. Persistent inflammatory syndrome, macrocytosis, anemia, and hematological malignancies were more prevalent in patients with VEXAS syndrome (86%, 86%, 100%, and 86%, respectively) than in patients without (36%, 40%, 53%, and 53%, respectively). These features sometimes appeared after the first skin manifestations, and a UBA1 mutation was found in the skin of five patients with VEXAS syndrome. Dermal infiltration by myeloperoxidase-positive, CD163-positive, reniform histiocytoid cells and a periadnexal distribution were more frequently observed in VEXAS syndrome biopsies (100% and 20% respectively, vs. 58% and 0% in non-VEXAS syndrome biopsies, respectively). CONCLUSION: Our findings might help the pathologist to consider a diagnosis of VEXAS syndrome and to initiate early genetic testing.

Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review.

CONTEXT: Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases. OBJECTIVE: To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC). EVIDENCE ACQUISITION: A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool. EVIDENCE SYNTHESIS: In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported. CONCLUSIONS: This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice. PATIENT SUMMARY: Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use.

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images.

BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sensitivity of high-grade ovarian cancer (HGOC) to poly(ADP-ribose) polymerase inhibitors. However, their testing by next-generation sequencing is costly and time-consuming and can be affected by various preanalytical factors. In this study, we present a deep learning classifier for BRCA mutational status prediction from hematoxylin-eosin-safran-stained whole slide images (WSI) of HGOC. We constituted the OvarIA cohort composed of 867 patients with HGOC with known BRCA somatic mutational status from 2 different pathology departments. We first developed a tumor segmentation model according to dynamic sampling and then trained a visual representation encoder with momentum contrastive learning on the predicted tumor tiles. We finally trained a BRCA classifier on more than a million tumor tiles in multiple instance learning with an attention-based mechanism. The tumor segmentation model trained on 8 WSI obtained a dice score of 0.915 and an intersection-over-union score of 0.847 on a test set of 50 WSI, while the BRCA classifier achieved the state-of-the-art area under the receiver operating characteristic curve of 0.739 in 5-fold cross-validation and 0.681 on the testing set. An additional multiscale approach indicates that the relevant information for predicting BRCA mutations is located more in the tumor context than in the cell morphology. Our results suggest that BRCA somatic mutations have a discernible phenotypic effect that could be detected by deep learning and could be used as a prescreening tool in the future.

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

Metastatic solid tumors to the testis: a clinicopathologic evaluation of 157 cases from an international collaboration.

To elucidate the spectrum of metastatic solid tumors to the testis and their clinicopathologic features. The databases and files of 26 pathology departments from 9 countries on 3 continents were surveyed to identify metastatic solid tumors to the testis and to characterize their clinicopathologic features in detail. We compiled a series of 157 cases of metastatic solid tumors that secondarily involved the testis. The mean patient age at diagnosis was 64 years (range, 12-93 years). Most patients (127/144; 88%) had clinical manifestation of the disease, with testicular mass/nodule (89/127; 70%) being the most common finding. The main mechanism of testicular involvement was metastasis in 154/157 (98%) cases. Bilateral testicular involvement was present in 12/157 (8%) patients. Concurrent or prior extratesticular metastases were present in 78/101 (77%) patients. The diagnosis was made mainly in orchiectomy specimens (150/157; 95%). Different types of carcinomas (138/157; 87%), most commonly adenocarcinoma (72/157; 46%), were the most common malignancies. The most common primary carcinomas included prostatic (51/149; 34%), renal (29/149; 20%), and colorectal (13/149; 9%). Intratubular growth was identified in 13/124 (11%) cases and paratesticular involvement was found in 73/152 (48%) cases. In patients with available follow-up (110/157; 70%), more than half (58/110; 53%) died of disease. In this largest series compiled to date, we found that most secondary tumors of the testis represent metastases from the genitourinary and gastrointestinal tract carcinomas and typically occur in the setting of disseminated disease.

Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.

BACKGROUND: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

Osteonecrosis of the jaw under palbociclib: A case series description.

INTRODUCTION: Cases of osteonecrosis of the jaw have been reported by dental surgeons to the pharmacovigilance center in Rennes, France, occurring among patients treated with palbociclib, a cyclin-dependent kinase 4/6 inhibitor. Although this event was not expected with the drug, a safety signal was raised. Describing a local case series, the aim of our study was to identify specific patterns that might suggest a triggering role for these drugs, and to discuss pathophysiological hypotheses. MATERIALS AND METHODS: A retrospective case series of patients exposed to cyclin-dependent kinase 4/6 inhibitors between 2016 and 2020 with a diagnosis of osteonecrosis of the jaw at the Rennes Dental Care Center was analyzed. The descriptive analysis was conducted on patient demographics, breast cancer characteristics, osteonecrosis of the jaw, biological data, and exposure to cyclin-dependent kinase 4/6 inhibitors. RESULTS: We identified eight cases, most of them at stages 0-1 (62.5%). Four patients were still exposed to palbociclib at the time of diagnosis and four had discontinued the treatment before the diagnosis. Chronological imputability could not be excluded given the drug's half-life and the variable intervals of dental monitoring from one patient to another. All patients had at least one dental osteonecrosis risk factor (including dental extraction, dentures, and denosumab exposure at the time of diagnosis). Neutropenia and mucositis were not systematically reported at the time of diagnosis. The anatomopathological characteristics were nonspecific. CONCLUSION: We did not identify a specific pattern that could suggest a triggering role of palbociclib in the development of ONJ.

Multicenter automatic detection of invasive carcinoma on breast whole slide images.

Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as "test reference dataset" and "test target dataset") and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1% and 96.3%, 95% and 87.8%, and 73.9% and 70.6%, respectively. At slide level, accuracy, recall, and precision were 97.6% and 92.0%, 90.9% and 100%, and 100% and 70.8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice.

[FH (Fumarate Hydratase)-deficient renal cell carcinoma: A case report]. / Carcinome à cellules rénales FH (fumarate hydratase)-déficient : à propos d'un cas.

Fumarate hydratase deficient renal cell carcinoma (FH-RCC) is a rare malignant neoplasia caused by constitutive or somatic mutations in the FH gene whose diagnosis is primordial, requiring genetic counselling. Because of histological heterogeneity, such tumors have been in the past misclassified as "type 2 papillary carcinoma", "tubulo-cystic renal cell carcinoma" or "high grade papillary carcinoma". We report here a case of FH deficient renal cell carcinoma (FH-RCC) in a 69years old patient. Through this observation, we precise the epidemiological and histological aspects and diagnosis criteria of this rare tumor.

[Secondary tumors localized in testis]. / Localisations tumorales secondaires testiculaires.

Testis tumors are uncommon in oncology, and testicular metastasis from distant solid tumors are even rarer. We present two cases encountered in our department of pathology in CHU de Rennes, France. Moreover, we collected all reported cases in the Medline/PubMed databases of non-hematopoietic secondary testis tumors in adults, excluding autopsy studies, to propose an integrative study on this topic. In total, we report 98 cases of secondary testis lesions to prostate (n=38, 38.77 %), colorectal (n=19, 19.39%), gastric (n=12, 12.24%), kidney (n=7, 7.14%), lung (n=6, 6.12%) and other primary cancers. The median age at diagnosis was 66.5 years. We identified significantly more prostate adenocarcinoma (P<0.0001) when the primary tumor was known and significantly more colorectal adenocarcinoma (P=0.035) and pancreatic adenocarcinoma (P=0.002) when the primary tumor was unknown. The age at diagnosis was older when the primary tumor was known (P=0.007). We present the challenges for the diagnosis and propose some elements for diagnosis orientation. Finally, we discuss the possible ways of metastatic dissemination from primary site to testis, as illustrated by the two cases we present.

Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients.

BACKGROUND: Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC. OBJECTIVE: To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients. DESIGN, SETTING, AND PARTICIPANTS: We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples. INTERVENTION: Radical or partial nephrectomy on untreated ccRCC patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters. RESULTS AND LIMITATIONS: We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients. CONCLUSIONS: We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management. PATIENT SUMMARY: We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.

Can Molecular Classifications Help Tailor First-line Treatment of Metastatic Renal Cell Carcinoma? A Systematic Review of Available Models.

Context: The advent of immune check inhibitors (ICIs) has tremendously changed the prognosis of metastatic renal cell carcinoma (mRCC), adding an unseen substantial overall survival benefit. These agents could be administered alone or in combination with anti-vascular endothelial growth factor (anti-VEGF) therapies. So far, treatment allocation is based only on clinical stratification risk models. Objective: Herein, we aimed to report the different molecular classifications reported in the first-line treatment of mRCC and discuss the awaited clinical implications in terms of treatment selection. Evidence acquisition: Medline database as well as European Society for Medical Oncology (ESMO)/American Society of Clinical Oncology (ASCO) conference proceedings were searched to identify biomarker studies. Inclusion criteria comprised randomized and nonrandomized clinical trials that included patients treated in the first line of mRCC setting, patients treated with anti-VEGF therapies or ICIs, biological modeling, and available survival outcomes. Evidence synthesis: Four classification models were identified with subsequent clinical implications: Beuselinck model (34 gene signatures), IMmotion150, Hakimi, and JAVELIN 101 model. Tumor profiling shows distinct outcomes when treated with one or other combination. Patients are clustered into two gene signatures: angiogenic and proinflammatory (as per JAVELIN). The first is more likely to respond to therapy that includes anti-VEGF agents, while the best outcomes are obtained with an ICI combination with the second. Conclusions: The findings presented here were mostly derived from ancillary registered studies of new drugs in the setting of mRCC. Further validation is needed, which sets new paradigms for investigation in clinical research based on tumor biology for treatment allocation and not only on clinical stratification tools. Patient summary: First-line treatment of metastatic kidney includes immunotherapy alone or in combination with antiangiogenic therapy. However, clinical practice demonstrated that the "one treatment fits all" strategy might not be the best approach. In fact, recent studies showed that the addition of immunotherapy agents will not benefit all patients equally, and some still respond either equally to or better than anti-vascular endothelial growth factor alone. This review revealed biomarker modeling that impacts treatment selection. Recent tumor profiling into "angiogenic signature" more sensitive to angiogenic agents versus "immune signature" more likely to achieve the best response with immunotherapy should be validated. Tumor biology features might be more powerful than clinical classification for a tailored treatment approach.

Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic Review.

Mismatch repair deficiency (d-MMR)/microsatellite instability (MSI), KRAS, and BRAF mutational status are crucial for treating advanced colorectal cancer patients. Traditional methods like immunohistochemistry or polymerase chain reaction (PCR) can be challenged by artificial intelligence (AI) based on whole slide images (WSI) to predict tumor status. In this systematic review, we evaluated the role of AI in predicting MSI status, KRAS, and BRAF mutations in colorectal cancer. Studies published in PubMed up to June 2023 were included (n = 17), and we reported the risk of bias and the performance for each study. Some studies were impacted by the reduced number of slides included in the data set and the lack of external validation cohorts. Deep learning models for the d-MMR/MSI status showed a good performance in training cohorts (mean AUC = 0.89, [0.74-0.97]) but slightly less than expected in the validation cohort when available (mean AUC = 0.82, [0.63-0.98]). Contrary to the MSI status, the prediction of KRAS and BRAF mutations was less explored with a less robust methodology. The performance was lower, with a maximum of 0.77 in the training cohort, 0.58 in the validation cohort for KRAS, and 0.82 AUC in the training cohort for BRAF.

Comprehensive study of nine novel cases of TFEB-amplified renal cell carcinoma: an aggressive tumour with frequent PDL1 expression.

AIMS: First described in 2014, renal cell carcinoma (RCC) with TFEB amplification (6p21) is a rare molecular subgroup whose diagnosis is challenging. The prognosis and therapeutic implications remain unclear. METHODS: We report here the clinical, histological, immunohistochemical, and genetic features of nine novel cases. The pathological and immunohistochemical features were centrally reviewed by expert uropathologists. Fluorescence in situ hybridisation (FISH) confirmed the diagnosis and comparative genomic hybridisation (CGH) was performed to determine quantitative genomic alterations. We also performed an exhaustive review of the literature and compiled our data. RESULTS: TFEB-amplified RCC were locally advanced, with initial lymph node involvement in one case and liver metastasis in another case. They were high-grade eosinophilic tumours with papillary/pseudopapillary architecture, frequent positivity for melanocytic markers, and frequent PDL1 expression. FISH demonstrated high-level TFEB amplification in six cases. One case showed concomitant TFEB translocation. CGH analysis identified complex alterations with frequent losses of 1p, 2q, 3p, 6p, and frequent 6p and 8q gains. VEGFA coamplification was identified in all cases with a lower level than TFEB. The prognosis was poor, with five patients having lymph node or distant metastases. CONCLUSION: TFEB-amplified RCC is a rare molecular subgroup with variable morphology whose diagnosis is confirmed by FISH analysis. The complex alterations identified by CGH are consistent with an aggressive clinical behaviour. The coamplification of VEGFA and the expression of PDL1 could suggest a potential benefit from antiangiogenics and targeted immunotherapy in combination for these aggressive tumours.

Complete Transurethral Resection before Radical Cystectomy May Improve Oncological Outcomes.

OBJECTIVES: The objective of this study was to assess the impact of complete transurethral resection of bladder tumors (TURBTs) before radical cystectomy on pathological and oncological outcomes of patients with muscle-invasive bladder cancer (MIBC) and high-risk non-MIBC. MATERIALS AND METHODS: The charts of all patients who underwent radical cystectomy for bladder cancer in 2 academic departments of urology between 1996 and 2016 were retrospectively reviewed. Patients were divided into 2 groups according to the completeness of the last endoscopic resection before radical cystectomy: macroscopically complete transurethral resection (complete) or macroscopically incomplete transurethral resection (incomplete). The primary end point was the recurrence-free survival (RFS). Secondary end points included cancer-specific survival (CSS) and rates of pT0 and downstaging. RESULTS: Out of 486 patients included for analysis, the TURBT immediately preceding radical cystectomy was considered macroscopically complete in 253 patients (52.1%) and incomplete in 233 patients (47.9%). In multivariate analysis, macroscopically complete TURBT was the strongest predictor of both pT0 disease (OR = 3.1; p = 0.02) and downstaging (OR = 7.1; p < 0.0001). After a median follow-up of 41 months, macroscopically complete TURBT was associated with better RFS (5-year RFS: 57 vs. 37%; p < 0.0001) and CSS (5-year CSS: 70.8 vs. 54.5%; p = 0.002). In multivariate analysis adjusting for multifocality, weight of endoscopic resection specimen, cT4 stage on preoperative imaging, interval between endoscopic resection and radical cystectomy, neoadjuvant chemotherapy, pT stage, and associated carcinoma in situ, macroscopically complete endoscopic resection remained the main predictor of better RFS (HR = 0.4; p = 0.0003) and the only preoperative factor associated with CSS (HR = 0.5; p = 0.01). CONCLUSION: A macroscopically complete TURBT immediately preceding radical cystectomy may improve pathological and oncological outcomes in patients with MIBC and high-risk MIBC.

Multiple metastatic clones assessed by an integrative multiomics strategy in clear cell renal carcinoma: a case study.

The dynamics of metastatic evolution in clear cell renal cell carcinoma (ccRCC) are complex. We report a case study where tumour heterogeneity resulting from clonal evolution is a frequent feature and could play a role in metastatic dissemination.We used an integrative multiomics strategy combining genomic and transcriptomic data to classify fourteen specimens from spatially different areas of a kidney tumour and three non-primary sites including a vein thrombus and two adrenal metastases.All sites were heterogeneous and polyclonal, each tumour site containing two different aggressive subclonal populations, with differentially expressed genes implicated in distinct biological functions. These are rare primary metastatic samples prior to any medical treatment, where we showed a multiple metastatic seeding of two subclonal populations.Multiple interdependent lineages could be the source of metastatic heterogeneity in ccRCC. By sampling metastases, patients with resistance to therapies could benefit a combination of targeted therapies based on more than one aggressive clone.

Positive Association Between Location of Melanoma, Ultraviolet Signature, Tumor Mutational Burden, and Response to Anti-PD-1 Therapy.

Emerging evidence suggests a correlation between the tumor mutational burden (TMB) and the response to programmed cell death-1 protein (PD-1) monotherapy across multiple cancer types. In skin cancers, as high TMB is mostly because of ultraviolet (UV) exposure, we hypothesized a correlation between the primary melanoma cutaneous location according to sun exposure and response to anti-PD-1 monotherapy. METHODS: The aim of this study was to analyze, in advanced melanoma, the relationship between TMB, locations according to sun exposure, and response to PD-1 inhibitors. We conducted a prospective multicentric analysis, by sequencing the most recent metastatic sample before PD-1 inhibitors using FoundationOne assay. RESULTS: One hundred two patients were included, with TMB available for 94 cases. In univariate and multivariate linear regression, TMB was significantly associated with sun-exposed areas of the primary melanoma location and with age (coefficients of the association with log-TMB: non-UV location, -1.05; chronic sun-exposed area, 1.12; P value for the location, < 10-5; age, 0.021 per year, P value for age, .002). Molecular UV signature present on the metastatic site was associated with higher TMB (P = .003). Melanomas bearing a high TMB had a higher probability of response to PD-1 inhibitors compared with melanomas with a low TMB, with a dose-dependent effect following an exponential curve and a negative odds ratio of 0.40 (95% CI, 0.20 to 0.72, P = .004) between log-TMB and 6-month progression. CONCLUSION: Cumulative sun exposure related to skin location and molecular UV signature present on the metastatic site appear to be relevant biomarkers directly linked to TMB. Because TMB is not yet available to all for routine clinical use, the location of the primary melanoma in a sun-exposed area may play an important role in clinical decisions regarding therapeutic choice.

[Renal carcinoma with leiomyomatous stroma and osseous metaplasia from a patient diagnosed with a tuberous sclerosis complex]. / Carcinome rénal à stroma léiomyomateux avec métaplasie osseuse chez un patient porteur d'une sclérose tubéreuse de Bourneville.

Renal cell carcinoma with leiomyomatous stroma is a rare and poorly described histopathological entity. Here we report a unique case with osseous metaplasia, in a 31-year-old man recently diagnosed with a tuberous sclerosis complex (TSC2 gene mutation). Partial nephrectomy was performed. Histologically, the epithelial component was made up of papillary and alveolar structures with clear to eosinophilic cytoplasm, and basally located nuclei. The cells are surrounded by an abundant smooth muscle stroma with focally osseous metaplasia. The tumor was positive for carbonic anhydrase IX, cytokeratin 7, cytokeratin 20, and CD10, and negative for TFE3. This emerging entity is highly correlated to tuberous sclerosis complex, which justifies a screening for the syndrome when this diagnosis is made.