TCTP regulates genotoxic stress and tumorigenicity via intercellular vesicular signaling.

Oncogenic intercellular signaling is regulated by extracellular vesicles (EVs), but the underlying mechanisms remain mostly unclear. Since TCTP (translationally controlled tumor protein) is an EV component, we investigated whether it has a role in genotoxic stress signaling and malignant transformation. By generating a Tctp-inducible knockout mouse model (Tctp-/f-), we report that Tctp is required for genotoxic stress-induced apoptosis signaling via small EVs (sEVs). Human breast cancer cells knocked-down for TCTP show impaired spontaneous EV secretion, thereby reducing sEV-dependent malignant growth. Since Trp53-/- mice are prone to tumor formation, we derived tumor cells from Trp53-/-;Tctp-/f- double mutant mice and describe a drastic decrease in tumori-genicity with concomitant decrease in sEV secretion and content. Remarkably, Trp53-/-;Tctp-/f- mice show highly prolonged survival. Treatment of Trp53-/- mice with sertraline, which inhibits TCTP function, increases their survival. Mechanistically, TCTP binds DDX3, recruiting RNAs, including miRNAs, to sEVs. Our findings establish TCTP as an essential protagonist in the regulation of sEV-signaling in the context of apoptosis and tumorigenicity.

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.

FACILITATE: A real-world, multicenter, prospective study investigating the utility of a rapid, fully automated real-time PCR assay versus local reference methods for detecting epidermal growth factor receptor variants in NSCLC.

Accurate testing for epidermal growth factor receptor (EGFR) variants is essential for informing treatment decisions in non-small cell lung cancer (NSCLC). Automated diagnostic workflows may allow more streamlined initiation of targeted treatments, where appropriate, while comprehensive variant analysis is ongoing. FACILITATE, a real-world, prospective, multicenter, European study, evaluated performance and analytical turnaround time of the Idylla™ EGFR Mutation Test compared with local reference methods. Sixteen sites obtained formalin-fixed paraffin-embedded biopsy samples with ≥ 10% neoplastic cells from patients with NSCLC. Consecutive 5 µm sections from patient samples were tested for clinically relevant NSCLC-associated EGFR variants using the Idylla™ EGFR Mutation Test and local reference methods; performance (concordance) and analytical turnaround time were compared. Between January 2019 and November 2020, 1,474 parallel analyses were conducted. Overall percentage agreement was 97.7% [n = 1,418; 95% confidence interval (CI): 96.8-98.3], positive agreement, 87.4% (n = 182; 95% CI: 81.8-91.4) and negative agreement, 99.2% (n = 1,236; 95% CI: 98.5-99.6). There were 38 (2.6%) discordant cases. Ninety percent of results were returned with an analytical turnaround time of within 1 week using the Idylla™ EGFR Mutation Test versus ∼22 days using reference methods. The Idylla™ EGFR Mutation Test performed well versus local methods and had shorter analytical turnaround time. The Idylla™ EGFR Mutation Test can thus support application of personalized medicine in NSCLC.

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.

MiR-31-3p do not predict anti-EGFR efficacy in first-line therapy of RAS wild-type metastatic right-sided colon cancer.

BACKGROUND: Low miR-31-3p expression was identified as predictive of anti-EGFR efficacy in RAS-wt mCRC. Primary tumor side was also proposed as a predictive factor of anti-EGFR benefit. This retrospective multicentric study evaluated the predictive role of miR-31-3p in right-sided RAS-wt mCRC patients treated with first-line CT+anti-EGFR or CT+bevacizumab (Beva). METHODS: Seventy-two right-sided RAS-wt mCRC patients treated in first-line with CT+anti-EGFR (n = 43) or Beva (n = 29) were included. Overall survival (OS), progression-free survival (PFS) and response rate (RR) were analyzed and stratified according to tumor miR-31-3p expression level and targeted therapy (TT). RESULTS: BRAF V600E mutation was more frequent in high vs low miR-31-3p expressers (60.6% vs 15.4%, P < 0.001). PFS was significantly longer with CT+Beva than with CT+anti-EGFR (13 vs 7 months; P = 0.024). Among low miR-31-3p expressers, PFS, OS and RR were not significantly different between the two groups, while in high miR-31-3p expressers, only PFS was longer in the CT+Beva group (11 vs 6 months; P = 0.03). In patients treated with CT+anti-EGFR, low miR-31-3p expressers had a significantly longer OS (20 vs 13 months; P = 0.02) than high miR-31-3p expressers. ORR was not significantly different between the two groups of treatment, in both low and high miR-31-3p expressers. MiR-31-3p expression status was statistically correlated between primary tumors and corresponding metastases. CONCLUSION: In this study, miR-31-3p couldn't identify a subgroup of patients with right-sided RAS-wt mCRC who might benefit from anti-EGFR and suggest that Beva is the TT of choice in first-line treatment of these patients.

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.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Standardisation of pathogenicity classification for somatic alterations in solid tumours and haematologic malignancies.

BACKGROUND: The difficulty in interpreting somatic alterations is correlated with the increase in sequencing panel size. To correctly guide the clinical management of patients with cancer, there needs to be accurate classification of pathogenicity followed by actionability assessment. Here, we describe a specific detailed workflow for the classification of the pathogenicity of somatic variants in cancer into five categories: benign, likely benign, unknown significance, likely pathogenic and pathogenic. METHODS: Classification is obtained by combining a set of eight relevant criteria in favour of either a pathogenic or a benign effect (pathogenic stand-alone, pathogenic very strong, pathogenic strong, pathogenic moderate, pathogenic supporting, benign supporting, benign strong and benign stand-alone). RESULTS: Our guide is concordant with the ACMG/AMP 2015 guidelines for germline variants. Interpretation of somatic variants requires considering specific criteria, such as the disease and therapeutic context, co-occurring genomic events in the tumour when available and the use of cancer-specific variant databases. In addition, the gene role in tumorigenesis (oncogene or tumour suppressor gene) also needs to be taken into consideration. CONCLUSION: Our classification could contribute to homogenize best practices on somatic variant pathogenicity interpretation and improve interpretation consistency both within and between laboratories.

Association of AXL and PD-L1 Expression with Clinical Outcomes in Patients with Advanced Renal Cell Carcinoma Treated with PD-1 Blockade.

PURPOSE: A minority of patients currently respond to single-agent immune-checkpoint blockade (ICB), and strategies to increase response rates are urgently needed. AXL is a receptor tyrosine kinase commonly associated with drug resistance and poor prognosis in many cancer types, including in clear-cell renal cell carcinoma (ccRCC). Recent experimental cues in breast, pancreatic, and lung cancer models have linked AXL with immune suppression and resistance to antitumor immunity. However, its role in intrinsic and acquired resistance to ICB remains largely unexplored. EXPERIMENTAL DESIGN: In this study, tumoral expression of AXL was examined in ccRCC specimens from 316 patients who were metastatic receiving the PD-1 inhibitor nivolumab in the GETUG AFU 26 NIVOREN trial after failure of antiangiogenic therapy. We assessed associations between AXL and patient outcomes following PD-1 blockade, as well as the relationship with various markers, including PD-L1; VEGFA; the immune markers CD3, CD8, CD163, and CD20; and the mutational status of the tumor-suppressor gene von Hippel-Lindau (VHL). RESULTS: Our results show that high AXL-expression level in tumor cells is associated with lower response rates and a trend to shorter progression-free survival following anti-PD-1 treatment. AXL expression was strongly associated with tumor-PD-L1 expression, especially in tumors with VHL inactivation. Moreover, patients with tumors displaying concomitant PD-L1 expression and high AXL expression had the worst overall survival. CONCLUSIONS: Our findings propose AXL as candidate factor of resistance to PD-1 blockade, and provide compelling support for screening both AXL and PD-L1 expression in the management of advanced ccRCC.See related commentary by Hahn et al., p. 6619.

Circulating tumor DNA is detectable in canine histiocytic sarcoma, oral malignant melanoma, and multicentric lymphoma.

Circulating tumor DNA (ctDNA) has become an attractive biomarker in human oncology, and its use may be informative in canine cancer. Thus, we used droplet digital PCR or PCR for antigen receptor rearrangement, to explore tumor-specific point mutations, copy number alterations, and chromosomal rearrangements in the plasma of cancer-affected dogs. We detected ctDNA in 21/23 (91.3%) of histiocytic sarcoma (HS), 2/8 (25%) of oral melanoma, and 12/13 (92.3%) of lymphoma cases. The utility of ctDNA in diagnosing HS was explored in 133 dogs, including 49 with HS, and the screening of recurrent PTPN11 mutations in plasma had a specificity of 98.8% and a sensitivity between 42.8 and 77% according to the clinical presentation of HS. Sensitivity was greater in visceral forms and especially related to pulmonary location. Follow-up of four dogs by targeting lymphoma-specific antigen receptor rearrangement in plasma showed that minimal residual disease detection was concordant with clinical evaluation and treatment response. Thus, our study shows that ctDNA is detectable in the plasma of cancer-affected dogs and is a promising biomarker for diagnosis and clinical follow-up. ctDNA detection appears to be useful in comparative oncology research due to growing interest in the study of natural canine tumors and exploration of new therapies.

Implementation of a molecular tumor board at a regional level to improve access to targeted therapy.

BACKGROUND: With the development of precision oncology, Molecular Tumor Boards (MTB) are developing in many institutions. However, the implementation of MTB in routine clinical practice has still not been thoroughly studied. MATERIAL AND METHODS: Since the first drugs approved for targeted therapies, patient tumor samples were centralized to genomic testing platforms. In our institution, all tumor samples have been analyzed since 2014 by Next Generation Sequencing (NGS). In 2015, we established a regional MTB to discuss patient cases with 1 or more alterations identified by NGS, in genes different from those related to drug approval. We conducted a retrospective comparative analysis to study whether our MTB increased the prescriptions of Molecular Targeted Therapies (MTT) and the inclusions of patients in clinical trials with MTT, in comparison with patients with available NGS data but no MTB discussion. RESULTS: In 2014, 86 patients had UGA, but the results were not available to clinicians and not discussed in MTB. During the years 2015 and 2016, 113 patients with an UGA (unreferenced genomic alteration) were discussed in MTB. No patients with an UGA were included in 2014 in a clinical trial, versus 2 (2%) in 2015-2016. 13 patients with an UGA (12%) were treated in 2015-2016 with a MTT whereas in 2014, no patient (p = 0.001). CONCLUSIONS: In this retrospective analysis, we showed that the association of large-scale genomic testing and MTB was feasible, and could increase the prescription of MTT. However, in routine clinical practice, the majority of patients with UGA still do not have access to MTT.

Depicting the genetic architecture of pediatric cancers through an integrative gene network approach.

The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.

Metastatic Clear-cell Renal Cell Carcinoma With a Long-term Response to Sunitinib: A Distinct Phenotype Independently Associated With Low PD-L1 Expression.

BACKGROUND: Long-term responders (LTRs) are defined by at least 18 months of response to sunitinib in metastatic clear-cell renal cell carcinoma (ccRCC). Well-described by clinical studies, the phenotype of these tumors has never been explored. PATIENTS AND METHODS: In a retrospective and multicenter study, 90 ccRCCs of patients with metastatic disease were analyzed. Immunohistochemistry (carbonic anhydrase IX, vascular endothelial growth factor, c-MET, programmed death-ligand 1 [PD-L1], and PD-1) and VHL status were performed. Progression-free survival and overall survival were calculated from sunitinib introduction and from progression. LTRs and their corresponding tumors were compared with others using univariate and multivariate analysis. RESULTS: Twenty-eight patients were LTRs. They had a median progression-free survival of 28 months versus 4 months for other patients (P < .001). Similarly, LTRs had a median overall survival of 49 months versus 14 months (P < .001), even from progression (median, 21 vs. 7 months; P = .029). They were associated with a favorable or intermediate risk (International Metastatic Renal Cell Carcinoma Database Consortium model) (P = .007) and less liver metastasis (P = .036). They experienced more frequent complete or partial responses at the first radiologic evaluation (P = .035). The corresponding ccRCCs were associated with less nucleolar International Society for Urological Pathology grade 4 (P = .037) and hilar fat infiltration (P = .006). They were also associated with low PD-L1 expression (P = .02). Only the International Metastatic Renal Cell Carcinoma Database Consortium model and PD-L1 expression remained significant after multivariate analysis (P = .014 and P = .029, respectively). CONCLUSION: Primary tumor characteristics of LTRs were studied for the first time and demonstrated a different phenotype. Interestingly, they were characterized by low expression of PD-L1, suggesting a potentially lower impact of targeted immunotherapy in these patients.

Biomarqueurs prédictifs de réponse aux inhibiteurs de points de contrôle immuns.

PREDICTIVE BIOMARKERS OF RESPONSE TO IMMUNE CHECKPOINT INHIBITORS: PD-1 checkpoint inhibitors are becoming the reference treatment for several types of cancers. Many patients show remarkable efficacy and low toxicity. However, some patients have a better outcome than others with PD-1 checkpoint inhibitors. So, it is crucial to identify biomarkers of response. We review here the available data of several potential biomarkers of efficacy. The expression of PD-L1, detected by immunohistochemistry on tumor cells and immune cells is a good predictive biomarker of response for some cancers; however, this method is not standardized, and there are different antibodies, different cut-off values, and different targets (tumor or microenvironment). Moreover, the expression of PD-L1 is dynamic and heterogeneous within the tumor: expression is discordant between primary tumor and metastasis or between biopsy and surgical specimen. Peripheral blood lymphocytes also can be informative, especially the baseline neutrophil to lymphocyte ratio which is easy to measure in daily practice. High rate of neoantigens is also associated with improved response. Therefore, mutation burden can be predictive of response and this explains why tumors with microsatellites instability have an enhanced response. Similarly, genetic signatures are linked with resistance or response to treatment. Gut microbiota is associated with improved antitumor immune response although the underlying mechanism is not well understood so far. Lastly, it seems that cytokines, mediators of immunity may play a role in the response to immunotherapy and so, constitute an interesting biomarker. Several potential biomarkers are identified but none is prospectively validated so far.

Mutational Landscape of DDR2 Gene in Lung Squamous Cell Carcinoma Using Next-generation Sequencing.

BACKGROUND: Lung cancer represents the leading cause of cancer-related death worldwide. Despite great advances in lung cancer management with the recent emergence of molecular targeted therapies for non-squamous non-small-cell lung cancer, no dramatic improvements have been achieved in lung squamous cell carcinoma (SCC). Mutations in discoidin domain receptor 2 (DDR2) gene were recently identified as promising molecular targets in this histology. The aim of this study is to describe the DDR2 mutational landscape of lung SCC and investigate the associated clinical factors. METHODS: Next-generation sequencing of the DDR2 gene was performed on 271 samples of lung SCC. Patients followed in our institution from January 2011 to August 2014 were retrospectively selected for data collection. Other driver gene alterations (EGFR, KRAS, BRAF, HER2, and PI3KCA) were analyzed using pyrosequencing. RESULTS: A total of 11 patients harboring a DDR2 mutation was detected among the 271 sequenced lung SCC samples (4%). We describe 10 unreported mutations, comprising a novel DDR2 exon 7 splice mutant. DDR2 mutations were not mutually exclusive with other driver gene alterations. One hundred thirty-six patients were included for clinical comparison and logistic regression analysis. No difference was detected between DDR2-mutant and DDR2 wild-type lung SCC regarding clinical characteristics or survival. CONCLUSION: DDR2 mutations were observed in 4% of cases of lung SCC of European descent. DDR2-mutated tumors can exhibit other driver gene alterations. No clinical characteristics were significantly associated with DDR2 mutation.

Hilar fat infiltration: A new prognostic factor in metastatic clear cell renal cell carcinoma with first-line sunitinib treatment.

INTRODUCTION: The selection of patients with metastatic clear cell renal cell carcinoma (ccRCC) who may benefit from targeted tyrosine kinase inhibitors has been a challenge, even more so now with the advent of new therapies. Hilar fat infiltration (HFI) is a validated prognostic factor in nonmetastatic ccRCC (TNM 2009 staging system) but has never been studied in metastatic patients. We aimed to assess its phenotype and prognostic effect in patients with metastatic ccRCC treated with first-line sunitinib. MATERIALS AND METHODS: In a multicentric study, we retrospectively included 90 patients and studied the corresponding ccRCC at the pathological, immunohistochemical, and molecular levels. Patient and tumor characteristics were compared using univariate and multivariate analysis. All the features were then studied by Cox models for prognostic effect. RESULTS: HFI was found in 42 patients (46.7%), who had worse prognosis (Heng criteria) (P = 0.003), liver metastases (P = 0.036), and progressive diseases at first radiological evaluation (P = 0.024). The corresponding ccRCC was associated with poor pathological prognostic factors that are well known in nonmetastatic ccRCC. For these patients, median progression-free survival was 4 months vs. 13 months (P = 0.02), and median overall survival was 14 months vs. 29 months (P = 0.006). In a multivariate Cox model integrating all the variables, only poor prognosis, according to the Heng criteria and HFI, remained independently associated with both progression-free survival and overall survival. CONCLUSION: HFI was demonstrated for the first time to be an independent poor prognostic factor. Its potential role in predicting resistance to antiangiogenic therapy warrants further investigation.

Correlation of c-MET Expression with PD-L1 Expression in Metastatic Clear Cell Renal Cell Carcinoma Treated by Sunitinib First-Line Therapy.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is highly metastatic. Cabozantinib, an anti-angiogenic tyrosine kinase inhibitor that targets c-MET, provided interesting results in metastatic ccRCC treatment. OBJECTIVE: To understand better the role of c-MET in ccRCC, we assessed its status in a population of patients with metastatic ccRCC. PATIENTS AND METHODS: For this purpose, tumor samples were analyzed for c-MET expression by immunohistochemistry (IHC), for c-MET copy number alterations by fluorescence in situ hybridization (FISH), and for c-MET mutations by next generation sequencing (NGS) in a retrospective cohort of 90 primary ccRCC of patients with metastases treated by first-line sunitinib. The expression of c-MET was correlated with pathological, immunohistochemical (VEGFA, CAIX, PD-L1), clinical, and molecular criteria (VHL status) by univariate and multivariate analyses and to clinical outcome using Kaplan-Meier curves compared by log-rank test. RESULTS: Of ccRCC, 31.1% had low c-MET expression (absent to weak intensity by IHC) versus 68.9% with high expression (moderate to strong intensity). High expression of c-MET was associated with a gain in FISH analysis (p=0.0284) without amplification. No mutations were detected in NGS. Moreover, high c-MET expression was associated with lymph node metastases (p=0.004), sarcomatoid component (p=0.029), VEGFA (p=0.037), and PD-L1 (p=0.001) overexpression, the only factor that remained independently associated (p<0.001) after logistic regression. No difference was observed in clinical outcomes. CONCLUSION: This study is the first to analyse c-MET status in metastatic ccRCC. The high expression of c-MET in the majority of ccRCC and its independent association with PD-L1 expression, may suggest a potential benefit from combining c-MET inhibitors and targeted immunotherapy.

Clear cell renal cell carcinoma: a comparative study of histological and chromosomal characteristics between primary tumors and their corresponding metastases.

Clear cell renal cell carcinoma (ccRCC) has a poor prognosis with a 50% risk of metastases. Little is known about the phenotypic and molecular profiles of metastases regarding their corresponding primary tumors. This study aimed to screen phenotypic and genotypic differences between metastases and their corresponding primary tumors. We selected four cases with available frozen material. The histological, immunohistochemical (VEGFA, CD31, SMA, Ki67, p53, PAR-3), FISH (VHL gene), next-generation sequencing (VHL and c-MET genes), multiplex ligation-dependent probe amplification, and array-(comparative genomic hybridization) CGH analyses were realized. Metastases were nodal, hepatic (synchronous), adrenal, and pulmonary (metachronous). High-grade tumor cells were significantly more frequent in metastases (p = 0.019). Metastases and high-grade zones of primary tumors shared similar characteristics compared to low-grade zones: a lower microscopic vascular density (43.5 vs 382.5 vessels/mm2; p = 0.0027), a higher expression of VEGF (73 vs 10%, p = 0.045), Ki67 (37.6 vs 8.3%; p = 0.011), and p53 (54 vs 10.6%; p = 0.081), and a cytoplasmic and membranous PAR-3 staining. Metastases exhibited more chromosomal imbalances than primary tumors in total (18.75 ± 6.8; p = 0.044) with more genomic gains (13.5 ± 7; p = 0.013). The loss of chromosome 9 and gain of Xq were found in both primary tumors and metastases but gains of loci or chromosomes 2p, 3q, 5, 8q, 12, and 20 were only found in metastases. The VHL gene status was similar in each tumor couple. Although metastases and primary tumors share common histological features, this study highlights chromosomal differences specific to metastases which could be involved in ccRCC metastatic evolution.