Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups. Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256). Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients. Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

Background The anti-SLAMF7 monoclonal antibody, elotuzumab (elo), plus lenalidomide (len) and dexamethasone (dex) is approved for relapsed/refractory MM in the U.S. and Europe. Recently, a small phase 2 study demonstrated an advantage in progression-free survival (PFS) for elo plus pomalidomide (pom)/dex compared to pom/dex alone and resulted in licensing of this novel triplet combination, but clinical experience is still limited. Purpose To analyze the efficacy and safety of elo/pom/dex in a “real world” cohort of patients with advanced MM, we queried the databases of the university hospitals of Würzburg and Vienna. Findings We identified 22 patients with a median number of five prior lines of therapy who received elo/pom/dex prior to licensing within an early access program. Patients received a median number of 5 four-week treatment cycles. Median PFS was 6.4 months with 12-month and 18-month PFS rates of 35% and 28%, respectively. The overall response rate was 50% and 64% of responding patients who achieved a longer PFS with elo/pom/dex compared to their most recent line of therapy. Objective responses were also seen in five patients who had been pretreated with pomalidomide. Low tumor burden was associated with improved PFS (13.5 months for patients with ISS stage I/II at study entry v 6.4 months for ISS III), although this difference did not reach statistical significance. No infusion-related reactions were reported. The most frequent grade 3/4 adverse events were neutropenia and pneumonia. Conclusion Elo/pom/dex is an active and well-tolerated regimen in highly advanced MM even after pretreatment with pomalidomide.

Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.