[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.

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

PEC-PRO: A new prognostic score from a series of 87 patients with localized perivascular epithelioid cell neoplasms (PEComas) treated with curative intent.

BACKGROUND: Perivascular epithelioid cell neoplasms (PEComas) encompass a heterogeneous family of mesenchymal tumors. Previously described clinicopathologic features aimed at distinguishing benign from malignant variants but lacked prognostic value. METHODS: This retrospective analysis examined clinicopathologic data from patients who had localized PEComa across French Sarcoma Network centers. The authors analyzed 12 clinicopathologic features in a Cox proportional hazard framework to derive a multivariate prognostic risk model for event-free survival (EFS). They built the PEComa prognostic score (PEC-PRO), in which scores ranged from 0 to 5, based on the coefficients of the multivariate model. Three groups were identified: low risk (score = 0), intermediate risk (score = 1), and high risk (score ≥ 2). RESULTS: Analyzing 87 patients who had a median 46-month follow-up (interquartile range, 20-74 months), the median EFS was 96.5 months (95% confidence interval [CI], 47.1 months to not applicable), with 2-year and 5-year EFS rates of 64.7% and 58%, respectively. The median overall survival was unreached, with 2-year and 5-year overall survival rates of 82.3% and 69.3%, respectively. The simplified Folpe classification did not correlate with EFS. Multivariate analysis identified three factors affecting EFS: positive surgical margins (hazard ratio [HR], 5.17; 95% CI, 1.65-16.24; p = .008), necrosis (HR, 3.94; 95% CI, 1.16-13.43; p = .030), and male sex (HR, 3.13; 95% CI, 1.19-8.27; p = 0.023). Four variables were retained in the prognostic model. Patients with low-risk PEC-PRO scores had a 2-year EFS rate of 93.7% (95% CI, 83.8%-100.0%), those with intermediate-risk PEC-PRO scores had a 2-year EFS rate of 67.4% (95% CI, 53.9%-80.9%), and those with high-risk PEC-PRO scores had a 2-year EFS rate of 2.3% (95% CI, 0.0%-18.3%). CONCLUSIONS: The PEC-PRO score reliably predicts the risk of postoperative recurrence in patients with localized PEComa. It has the potential to improve follow-up strategies but requires validation in a prospective trial.

Pathogenic alterations in PIK3CA and KMT2C are frequent and independent prognostic factors in anal squamous cell carcinoma treated with salvage abdominoperineal resection.

The management of anal squamous cell carcinoma (ASCC) has yet to experience the transformative impact of precision medicine. Conducting genomic analyses may uncover novel prognostic biomarkers and offer potential directions for the development of targeted therapies. To that end, we assessed the prognostic and theragnostic implications of pathogenic variants identified in 571 cancer-related genes from surgical samples collected from a homogeneous, multicentric French cohort of 158 ASCC patients who underwent abdominoperineal resection treatment. Alterations in PI3K/AKT/mTOR, chromatin remodeling, and Notch pathways were frequent in HPV-positive tumors, while HPV-negative tumors often harbored variants in cell cycle regulation and genome integrity maintenance genes (e.g., frequent TP53 and TERT promoter mutations). In patients with HPV-positive tumors, KMT2C and PIK3CA exon 9/20 pathogenic variants were associated with worse overall survival in multivariate analysis (Hazard ratio (HR)KMT2C = 2.54, 95%CI = [1.25,5.17], P value = .010; HRPIK3CA = 2.43, 95%CI = [1.3,4.56], P value = .006). Alterations with theragnostic value in another cancer type was detected in 43% of patients. These results suggest that PIK3CA and KMT2C pathogenic variants are independent prognostic factors in patients with ASCC with HPV-positive tumors treated by abdominoperineal resection. And, importantly, the high prevalence of alterations bearing potential theragnostic value strongly supports the use of genomic profiling to allow patient enrollment in precision medicine clinical trials.

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.

Comprehensive analyses of immune tumor microenvironment in papillary renal cell carcinoma.

BACKGROUND: Papillary renal cell carcinoma (pRCC) is the most common non-clear cell RCC, and associated with poor outcomes in the metastatic setting. In this study, we aimed to comprehensively evaluate the immune tumor microenvironment (TME), largely unknown, of patients with metastatic pRCC and identify potential therapeutic targets. METHODS: We performed quantitative gene expression analysis of TME using Microenvironment Cell Populations-counter (MCP-counter) methodology, on two independent cohorts of localized pRCC (n=271 and n=98). We then characterized the TME, using immunohistochemistry (n=38) and RNA-sequencing (RNA-seq) (n=30) on metastatic pRCC from the prospective AXIPAP trial cohort. RESULTS: Unsupervised clustering identified two "TME subtypes", in each of the cohorts: the "immune-enriched" and the "immune-low". Within AXIPAP trial cohort, the "immune-enriched" cluster was significantly associated with a worse prognosis according to the median overall survival to 8 months (95% CI, 6 to 29) versus 37 months (95% CI, 20 to NA, p=0.001). The two immune signatures, Teff and JAVELIN Renal 101 Immuno signature, predictive of response to immune checkpoint inhibitors (CPI) in clear cell RCC, were significantly higher in the "immune-enriched" group (adjusted p<0.05). Finally, five differentially overexpressed genes were identified, corresponding mainly to B lymphocyte populations. CONCLUSION: For the first time, using RNA-seq and immunohistochemistry, we have highlighted a specific immune TME subtype of metastatic pRCC, significantly more infiltrated with T and B immune population. This "immune-enriched" group appears to have a worse prognosis and could have a potential predictive value for response to immunotherapy, justifying the confirmation of these results in a cohort of metastatic pRCC treated with CPI and in combination with targeted therapies. TRIAL REGISTRATION NUMBER: NCT02489695.

Hypothermic machine perfusion for uterus transplantation.

OBJECTIVE: To describe the feasibility of hypothermic machine perfusion (HMP) in uterus transplantation (UT) to potentially improve the preservation of the uterus and enhance graft preservation in the donation after brainstem death (DBD) context. Uterus transplantation is a new surgical approach to treating absolute uterine infertility; it can be performed after living donation or after DBD. In the DBD context, the uterus is typically the last organ removed after other vital organs, with the exception of the Baylor team, which removes the uterus first. This key aspect imposes an unavoidable mild temperature ischemia for >1 hour on the uterus during the removal of the vital abdominal and chest organs. In renal transplantation, the perfusion machine reduces the risk of delayed graft function; thus, we hypothesized that machine perfusion could result in a reduction of uterus graft dysfunction. The uterus graft dysfunction could be expressed by a low embryo implantation rate, pregnancy loss, or vascular pregnancy diseases such as preeclampsia or fetal growth restriction." To date, static cold storage of the uterus is the only standard method for preservation before transplantation. HMP is an emerging method that could potentially improve the preservation of the uterus to enhance graft preservation in the DBD context. DESIGN: This video article shows all the technical details of using the HMP for uterine transplantation. SETTING: University. ANIMALS: Porcine model. INTERVENTION: Porcine uterus was retrieved from a DBD domestic animal model and flushed with KPS MP (Bridge To Life Ltd in UK) at 4 °C. After vascular preparation on the back table, the uterus was perfused using KPS MP through a cannula in the aorta using the VitaSmart device (Bridge To Life Ltd in UK) for 18 hours. Then, the uterus was transplanted to the porcine recipient. MAIN OUTCOME MEASURES: The macroscopic appearance of the uterus at the end of HMP and the assessment of the uterus vascularization after transplantation in the recipient compared with the native uterus. RESULTS: This video shows the cannulation of the iliac vessels, cooling and removal of the uterus on a porcine model, uterus preservation using HMP during 18 hours, and then UT in a new recipient pig with the reperfusion of the transplanted uterus next to the native, intact uterus of the recipient. The macroscopic appearance of the uterus at the end of HMP appeared viable and was perfectly flushed. The assessment of the uterus vascularization after transplantation in the recipient was similar to that of the native uterus. To our knowledge, we describe here for the first time the UT procedure in DBD context on an animal model and the use of HMP for uterus preservation in UT programs; this could increase the number of uterine grafts available for a greater number of female recipients. CONCLUSION: Hypothermic machine perfusion could allow the duration of cold ischemia to be prolonged without altering the uterine graft. Nevertheless, this assertion has to be validated in a human context.

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.

Artificial intelligence to improve cytology performance in urothelial carcinoma diagnosis: results from validation phase of the French, multicenter, prospective VISIOCYT1 trial.

PURPOSE: Cytology and cystoscopy, the current gold standard for diagnosing urothelial carcinomas, have limits: cytology has high interobserver variability with moderate or not optimal sensitivity (particularly for low-grade tumors); while cystoscopy is expensive, invasive, and operator dependent. The VISIOCYT1 study assessed the benefit of VisioCyt® for diagnosing urothelial carcinoma. METHODS: VISIOCYT1 was a French prospective clinical trial conducted in 14 centers. The trial enrolled adults undergoing endoscopy for suspected bladder cancer or to explore the lower urinary tract. Participants were allocated either Group 1: with bladder cancer, i.e., with positive cystoscopy or with negative cystoscopy but positive cytology, or Group 2: without bladder cancer. Before cystoscopy and histopathology, slides were prepared for cytology and the VisioCyt® test from urine samples. The diagnostic performance of VisioCyt® was assessed using sensitivity (primary objective, 70% lower-bound threshold) and specificity (75% lower-bound threshold). Sensitivity was also assessed by tumor grade and T-staging. VisioCyt® and cytology performance were evaluated relative to the histopathological assessments. RESULTS: Between October 2017 and December 2019, 391 participants (170 in Group 1 and 149 in Group 2) were enrolled. VisioCyt®'s sensitivity was 80.9% (95% CI 73.9-86.4%) and specificity was 61.8% (95% CI 53.4-69.5%). In high-grade tumors, the sensitivity was 93.7% (95% CI 86.0-97.3%) and in low-grade tumors 66.7% (95% CI 55.2-76.5%). Sensitivity by T-staging, compared to the overall sensitivity, was higher in high-grade tumors and lower in low-grade tumors. CONCLUSION: VisioCyt® is a promising diagnostic tool for urothelial cancers with improved sensitivities for high-grade tumors and notably for low-grade tumors.

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.

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.

[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.

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.

Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma.

BACKGROUND: Predictive tools can be useful for adapting surveillance or including patients in adjuvant trials after surgical resection of nonmetastatic renal cell carcinoma (RCC). Current models have been built using traditional statistical modelling and prespecified variables, which limits their performance. OBJECTIVE: To investigate the performance of machine learning (ML) framework to predict recurrence after RCC surgery and compare them with current validated models. DESIGN, SETTING, AND PARTICIPANTS: In this observational study, we derived and tested several ML-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XG boost]) to predict recurrence of patients who underwent radical or partial nephrectomy for a nonmetastatic RCC, between 2013 and 2020, at 21 French medical centres. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary end point was disease-free survival. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using the Brier score. ML models were compared with four conventional prognostic models, using decision curve analysis (DCA). RESULTS AND LIMITATIONS: A total of 4067 patients were included in this study (3253 in the development cohort and 814 in the validation cohort). Most tumours (69%) were clear cell RCC, 40% were of high grade (nuclear International Society of Urological Pathology grade 3 or 4), and 24% had necrosis. Of the patients, 4% had nodal involvement. After a median follow-up of 57 mo (interquartile range 29-76), 523 (13%) patients recurred. ML models obtained higher c-index values than conventional models. The RSF yielded the highest c-index values (0.794), followed by S-SVM (c-index 0.784) and XG boost (c-index 0.782). In addition, all models showed good calibration with low integrated Brier scores (all integrated brier scores <0.1). However, we found calibration drift over time for all models, albeit with a smaller magnitude for ML models. Finally, DCA showed an incremental net benefit from all ML models compared with conventional models currently used in practice. CONCLUSIONS: Applying ML approaches to predict recurrence following surgical resection of RCC resulted in better prediction than that of current validated models available in clinical practice. However, there is still room for improvement, which may come from the integration of novel biological and/or imaging biomarkers. PATIENT SUMMARY: We found that artificial intelligence algorithms could better predict the risk of recurrence after surgery for a localised kidney cancer. These algorithms may help better select patients who will benefit from medical treatment after surgery.

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.

Baseline circulating unswitched memory B cells and B-cell related soluble factors are associated with overall survival in patients with clear cell renal cell carcinoma treated with nivolumab within the NIVOREN GETUG-AFU 26 study.

BACKGROUND: The phase II NIVOREN GETUG-AFU 26 study reported safety and efficacy of nivolumab in patients with metastatic clear cell renal cell carcinoma (m-ccRCC) in a 'real-world setting'. We conducted a translational-research program to determine whether specific circulating immune-cell populations and/or soluble factors at baseline were predictive of clinical outcomes in patients with m-ccRCC treated with nivolumab within the NIVOREN study. METHODS: Absolute numbers of 106 circulating immune-cell populations were prospectively analyzed in patients treated at a single institution within the NIVOREN trial with available fresh-whole-blood, using dry formulation panels for multicolor flow cytometry. In addition, a panel of 14 predefined soluble factors was quantified for each baseline plasma sample using the Meso-Scale-Discovery immunoassay. The remaining patients with available plasma sample were used as a validation cohort for the soluble factor quantification analysis. Tumor immune microenvironment characterization of all patients included in the translational program of the study was available. The association of blood and tissue-based biomarkers, with overall survival (OS), progression-free survival (PFS) and response was analyzed. RESULTS: Among the 44 patients, baseline unswitched memory B cells (NSwM B cells) were enriched in responders (p=0.006) and associated with improved OS (HR=0.08, p=0.002) and PFS (HR=0.54, p=0.048). Responders were enriched in circulating T follicular helper (Tfh) (p=0.027) and tertiary lymphoid structures (TLS) (p=0.043). Circulating NSwM B cells positively correlated with Tfh (r=0.70, p<0.001). Circulating NSwM B cells correlated positively with TLS and CD20 +B cells at the tumor center (r=0.59, p=0.044, and r=0.52, p=0.033) and inversely correlated with BCA-1/CXCL13 and BAFF (r=-0.55 and r=-0.42, p<0.001). Tfh cells also inversely correlated with BCA-1/CXCL13 (r=-0.61, p<0.001). IL-6, BCA-1/CXCL13 and BAFF significantly associated with worse OS in the discovery (n=40) and validation cohorts (n=313). CONCLUSION: We report the first fresh blood immune-monitoring of patients with m-ccRCC treated with nivolumab. Baseline blood concentration of NSwM B cells was associated to response, PFS and OS in patients with m-ccRCC treated with nivolumab. BCA-1/CXCL13 and BAFF, inversely correlated to NSwM B cells, were both associated with worse OS in discovery and validation cohorts. Our data confirms a role for B cell subsets in the response to immune checkpoint blockade therapy in patients with m-ccRCC. Further studies are needed to confirm these findings.

Prognostic Value of Fusobacterium nucleatum after Abdominoperineal Resection for Anal Squamous Cell Carcinoma.

Main prognostic factors of anal squamous cell carcinoma (ASCC) are tumor size, differentiation, lymph node involvement, and male gender. However, they are insufficient to predict relapses after exclusive radiotherapy (RT) or chemoradiotherapy (CRT). Fusobacterium nucleatum has been associated with poor prognosis in several digestive cancers. In this study, we assessed the association between intratumoral F. nucleatum load and clinico-pathological features, relapse, and survival in patients with ASCC who underwent abdominoperineal resection (APR) after RT/CRT. We retrospectively analyzed surgical samples from a cohort of 166 patients with ASCC who underwent APR. F. nucleatum 16S rRNA gene sequences were quantified using real-time quantitative PCR. We associated F. nucleatum load with classical clinicopathological features, overall survival (OS), disease-free survival (DFS), and metastasis-free survival (MFS) using Cox regression univariate and multivariate analyses. Tumors harboring high loads of F. nucleatum (highest tercile) showed longer OS and DFS (median: not reached vs. 50.1 months, p = 0.01, and median: not reached vs. 18.3 months, p = 0.007, respectively). High F. nucleatum load was a predictor of longer OS (HR = 0.55, p = 0.04) and DFS (HR = 0.50, p = 0.02) in multivariate analysis. High F. nucleatum load is an independent favorable prognostic factor in patients with ASCC who underwent APR.