A three-gene expression score for predicting clinical benefit to anti-PD-1 blockade in advanced renal cell carcinoma.

In the advanced renal cell carcinoma (RCC) scenario, there are no consistent biomarkers to predict the clinical benefit patients derived from immune checkpoint blockade (ICB). Taking this into consideration, herein, we conducted a retrospective study in order to develop and validate a gene expression score for predicting clinical benefit to the anti-PD-1 antibody nivolumab in the context of patients diagnosed with advanced clear cell RCC enrolled in the CheckMate-009, CheckMate-010, and CheckMate-025 clinical trials. First, a three-gene expression score (3GES) with prognostic value for overall survival integrating HMGA1, NUP62, and ARHGAP42 transcripts was developed in a cohort of patients treated with nivolumab. Its prognostic value was then validated in the TCGA-KIRC cohort. Second, the predictive value for nivolumab was confirmed in a set of patients from the CheckMate-025 phase 3 clinical trial. Lastly, we explored the correlation of our 3GES with different clinical, molecular, and immune tumor characteristics. If the results of this study are definitively validated in other retrospective and large-scale, prospective studies, the 3GES will represent a valuable tool for guiding the design of ICB-based clinical trials in the aRCC scenario in the near future.

Ascites-Derived Organoids to Depict Platinum Resistance in Gynaecological Serous Carcinomas.

Gynaecological serous carcinomas (GSCs) constitute a distinctive entity among female tumours characterised by a very poor prognosis. In addition to late-stage diagnosis and a high rate of recurrent disease associated with massive peritoneal carcinomatosis, the systematic acquisition of resistance to first-line chemotherapy based on platinum determines the unfavourable outcome of GSC patients. To explore the molecular mechanisms associated with platinum resistance, we generated patient-derived organoids (PDOs) from liquid biopsies of GSC patients. PDOs are emerging as a relevant preclinical model system to assist in clinical decision making, mainly from tumoural tissue and particularly for personalised therapeutic options. To approach platinum resistance in a GSC context, proficient PDOs were generated from the ascitic fluid of ovarian, primary peritoneal and uterine serous carcinoma patients in platinum-sensitive and platinum-resistant clinical settings from the uterine aspirate of a uterine serous carcinoma patient, and we also induced platinum resistance in vitro in a representative platinum-sensitive PDO. Histological and immunofluorescent characterisation of these ascites-derived organoids showed resemblance to the corresponding original tumours, and assessment of platinum sensitivity in these preclinical models replicated the clinical setting of the corresponding GSC patients. Differential gene expression profiling of a panel of 770 genes representing major canonical cancer pathways, comparing platinum-sensitive and platinum-resistant PDOs, revealed cellular response to DNA damage stimulus as the principal biological process associated with the acquisition of resistance to the first-line therapy for GSC. Additionally, candidate genes involved in regulation of cell adhesion, cell cycles, and transcription emerged from this proof-of-concept study. In conclusion, we describe the generation of PDOs from liquid biopsies in the context of gynaecological serous carcinomas to explore the molecular determinants of platinum resistance.

MSIMEP: Predicting microsatellite instability from microarray DNA methylation tumor profiles.

Deficiency in DNA MMR activity results in tumors with a hypermutator phenotype, termed microsatellite instability (MSI). Beyond its utility in Lynch syndrome screening algorithms, today MSI has gained importance as predictive biomarker for various anti-PD-1 therapies across many different tumor types. Over the past years, many computational methods have emerged to infer MSI using either DNA- or RNA-based approaches. Considering this together with the fact that MSI-high tumors frequently exhibit a hypermethylated phenotype, herein we developed and validated MSIMEP, a computational tool for predicting MSI status from microarray DNA methylation tumor profiles of colorectal cancer samples. We demonstrated that MSIMEP optimized and reduced models have high performance in predicting MSI in different colorectal cancer cohorts. Moreover, we tested its consistency in other tumor types with high prevalence of MSI such as gastric and endometrial cancers. Finally, we demonstrated better performance of both MSIMEP models vis-à-vis a MLH1 promoter methylation-based one in colorectal cancer.