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Background: Erdafitinib is indicated for the treatment of adults with locally advanced/metastatic urothelial carcinoma and susceptible FGFR3/2 alterations progressing on/after one or more lines of prior platinum-based chemotherapy. Objective: To better understand the frequency and management of select treatment-emergent adverse events (TEAEs) to enable optimal fibroblast growth factor receptor inhibitor (FGFRi) treatment. Design, setting, and participants: Longer-term efficacy and safety results of the BLC2001 (NCT02365597) trial in patients with locally advanced and unresectable or metastatic urothelial carcinoma were studied. Intervention: Erdafitinib schedule of 8 mg/d continuous in 28-d cycles, with uptitration to 9 mg/d if serum phosphate level was

BackgroundManaging the inflammatory response to SARS-Cov-2 could prevent respiratory insufficiency. Cytokine profiles could identify cases at risk of severe disease.MethodsWe designed a randomized phase II clinical trial to determine whether the combination of ruxolitinib (5 mg twice a day for 7 days followed by 10 mg BID for 7 days) plus simvastatin (40 mg once a day for 14 days), could reduce the incidence of respiratory insufficiency in COVID-19. 48 cytokines were correlated with clinical outcome.ParticipantsPatients admitted due to COVID-19 infection with mild disease.ResultsUp to 92 were included. Mean age was 64 ± 17, and 28 (30%) were female. 11 (22%) patients in the control arm and 6 (12%) in the experimental arm reached an OSCI grade of 5 or higher (p = 0.29). Unsupervised analysis of cytokines detected two clusters (CL-1 and CL-2). CL-1 presented a higher risk of clinical deterioration vs CL-2 (13 [33%] vs 2 [6%] cases, p = 0.009) and death (5 [11%] vs 0 cases, p = 0.059). Supervised Machine Learning (ML) analysis led to a model that predicted patient deterioration 48h before occurrence with a 85% accuracy.ConclusionsRuxolitinib plus simvastatin did not impact the outcome of COVID-19. Cytokine profiling identified patients at risk of severe COVID-19 and predicted clinical deterioration.Trial registrationhttps://clinicaltrials.gov/, identifier NCT04348695.

Abstract Background The efficacy and safety of rucaparib maintenance treatment in ARIEL3 were evaluated in subgroups based on best response to most recent platinum‐based chemotherapy and baseline disease. Methods Patients were randomized 2:1 to receive either oral rucaparib at a dosage of 600 mg twice daily or placebo. Investigator‐assessed PFS was assessed in prespecified, nested cohorts: BRCA‐mutated, homologous recombination deficient (HRD; BRCA mutated or wild‐type BRCA/high loss of heterozygosity), and the intent‐to‐treat (ITT) population. Results Median PFS for patients in the ITT population with a complete response to most recent platinum‐based chemotherapy was 11.1 months in the rucaparib arm (126 patients) versus 5.6 months in the placebo arm (64 patients) (HR, 0.33 [95% CI, 0.23–0.48]), and in patients with a partial response (249 vs. 125), it was 9.0 versus 5.3 months (HR, 0.38 [0.30–0.49]). In subgroups of the ITT population based on baseline disease, median PFS was 8.2 versus 5.3 months (HR, 0.40 [0.28–0.57]) in patients with measurable disease (141 rucaparib vs. 66 placebo), 10.4 versus 4.5 months (HR, 0.31 [0.20–0.48]) in those with nonmeasurable but evaluable disease (104 vs. 56), and 14.1 versus 7.3 months (HR, 0.35 [0.24–0.51]) in those with no residual disease (130 vs. 67). Across subgroups, significantly longer median PFS was observed with rucaparib versus placebo in the BRCA‐mutated and HRD cohorts. Objective responses were reported in patients with measurable disease and in patients with nonmeasurable but evaluable baseline disease. Safety was consistent across subgroups. Conclusion Rucaparib maintenance treatment provided clinically meaningful efficacy benefits across subgroups based on response to last platinum‐based chemotherapy or baseline disease.

Ovarian cancer (OC) is the second most common gynecological malignancy and the gynecological tumor with the worst prognosis. To try to improve this situation, Data Science technologies could be a useful tool to help clinicians to know more about the disease. In our case, we are interested in exploring OC data to discover relationships between clinical and genetic factors and the disease progression. For it, we propose an analysis framework for simple and univariate statistical descriptions of features of different types, based on bootstrap resampling. Foremost, we define the framework for metric, categorical, and dates variables and determine what are the advantages and disadvantages of using different bootstrap resampling strategies, based on their statistical basis. Then, we use it to perform a univariate analysis over an OC dataset that allows to explore how is the disease progression, having platinum-free interval as indicator, in relation to clinical and genetic features of different types. Also, it provides a first set of variables possibly relevant for survival prediction. Results obtained show that some features have led to individual differences between both platinum resistant (6 months) groups. It can be concluded that this could be an indicator that the database could be discriminatory for the hypotheses studied, though it is convenient to make multivariate analyses to check how relationships among features are influenced.

Developments of richer integrative analysis methods for oncological studies are needed for efficiently leveraging the amount of clinical and genetic data available to provide the clinicians with better information. However, analyses of this nature often require mixing data of different types, which are not immediate to address jointly with classical methods. In this work, our aim is to find relationships between clinical and genetic features of different types (metric, categorical, and text) and the ovarian cancer (OC) disease progression. To this end, we first propose a univariate statistical method for text type applying bootstrap resampling to Bag of Words and Latent Dirichlet Allocation in order to include as features the free-text fields of the health recordings. Secondly, we extend bootstrap resampling for metric and categorical feature extraction with Principal Component Analysis (PCA) and Multiple Correspondence Analysis (MCA), respectively. We subsequently formulate a novel and integrative method for jointly considering metric, categorical, and text features. Results obtained in text analysis indicate individual differences in some words between two OC patients groups categorised according to their sensitivity to platinum drugs. These results indicate separability between both groups for text features. Also, regarding the multivariate analysis, clinical data results showed separability patterns for the three methods analysed according to the platinum-sensitivity degree. The use of these analytical tools in our OC cohort has allowed us to demonstrate their strengths by confirming the predictive and prognostic role of widely-known clinical and genetic variables (BRCA status, value of adjuvant therapy and optimal resection, or family history) and demonstrating significant associations in other variables whose role in OC development has been studied to a lesser extent (such as PMS1, GPC3, and SLX4 genes). These results highlight the value of implementing these approaches for the identification of novel biomarkers in the context of OC.

Clear-cell renal cell carcinomas (ccRCC) can be divided into four transcriptomic subtypes, two of which have a favorable and two an unfavorable prognosis. To assess mechanisms driving these subtypes, we investigated their miRNA expression patterns. miRNAs are master regulators of mRNAs, that are widely deregulated in cancer. Unsupervised clustering in our dataset (n = 128) and The Cancer Genome Atlas (TCGA) validation set identified two distinct miRNA clusters that overlapped with the transcriptomic subtypes, underscoring the validity of these subtypes on a multi-omics level and suggesting a driving role for miRNAs. Discriminatory miRNAs for the favorable subtypes repressed epithelial-to-mesenchymal transition, based on gene set enrichment analysis and target-mRNA expression levels. Strikingly, throughout the entire dataset, miRNAs associated with favorable subtypes were also associated with longer overall survival after diagnosis, and miRNAs associated with unfavorable subtypes with shorter overall survival (Pearson r = -0.54, p

To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell-carcinoma (MRCC) and to evaluate in vitro their mechanism of action in sunitinib resistance.We screened 673 microRNAs using TaqMan Low-density-Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n = 41). The most relevant differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenotypes selected from an independent cohort (n = 101). In vitro experiments were conducted to study the role of miR-942 in sunitinib resistance.TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p = 0.0074). High expression of miR-942, miR-628-5p, miR-133a, and miR-484 was significantly associated with decreased time to progression and overall survival. These microRNAs were also overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive cell line. MiR-942 overexpression in Caki-2 up-regulates MMP-9 and VEGF secretion which, in turn, promote HBMEC endothelial migration and sunitinib resistance.We identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity or resistance to sunitinib. MiR-942 was the best predictor of efficacy. We describe a novel paracrine mechanism through which high miR-942 levels in MRCC cells up-regulates MMP-9 and VEGF secretion to enhance endothelial migration and sunitinib resistance. Our results support further validation of these miRNA in clinical confirmatory studies.