Quality of life and hormone use: new validation results of MRS scale

BACKGROUND: The Menopause Rating Scale is a health-related Quality of Life scale developed in the early 1990s and step-by-step validated since then. Recently the MRS scale was validated as outcomes measure for hormone therapy. The suspicion however was expressed that the data were too optimistic due to methodological problems of the study. A new study became available to check how founded this suspicion was. METHOD: An open post-marketing study of 3282 women with pre- and post- treatment data of the self-administered version of the MRS scale was analyzed to evaluate the capacity of the scale to detect hormone treatment related effects with the MRS scale. The main results were then compared with the old study where the interview-based version of the MRS scale was used. RESULTS: The hormone-therapy related improvement of complaints relative to the baseline score was about or less than 30% in total or domain scores, whereas it exceeded 30% improvement in the old study. Similarly, the relative improvement after therapy, stratified by the degree of severity at baseline, was lower in the new than in the old study, but had the same slope. Although we cannot exclude different treatment effects with the study method used, this supports our hypothesis that the individual MRS interviews performed by the physician biased the results towards over-estimation of the treatment effects. This hypothesis is underlined by the degree of concordance of physician's assessment and patient's perception of treatment success (MRS results): Sensitivity (correct prediction of the positive assessment by the treating physician) of the MRS and specificity (correct prediction of a negative assessment by the physician) were lower than the results obtained with the interview-based MRS scale in the previous publication. CONCLUSION: The study confirmed evidence for the capacity of the MRS scale to measure treatment effects on quality of life across the full range of severity of complaints before treatment. The difference of the relative improvement after therapy between the old and current study as well as the observed different sensitivity/specificity is – as a matter of probability – more likely to be caused by a bias introduced by the different application of the MRS scale than by real differences in the efficacy of the therapy. A randomized clinical trial would be needed to test the impact of the latter. The message for future studies is: The MRS scale should be only used as self-administered tool where the suggestive effect of questions raised by health professionals ("therapeutic optimism") can be largely excluded

Sensitivity as outcome measure of androgen replacement: the AMS scale

BACKGROUND: The capacity of the AMS scale as clinical utility and as outcome measure still needs validation. METHODS: An open post-marketing study was performed by office-based physicians in Germany in 2004. We analysed data of 1670 androgen-deficient males who were treated with testosterone gel. The AMS scale was applied prior to and after 3 months treatment. RESULTS: The improvement of complaints under treatment relative to the baseline score was 30.7% (total score), 27.3% (psychological domain), 30.5% (somatic domain), and 30.7% (sexual domain), respectively. Patients with little or no symptoms before therapy improved by 9%, those with mild complaints at entry by 24%, with moderate by 32%, and with severe symptoms by 39% – compared with the baseline score. We showed that the distribution of complaints of testosterone deficient men before therapy almost returned to norm values after 12 weeks of testosterone treatment. Age, BMI, and total testosterone level at baseline did not modify the positive effect of androgen therapy. We also demonstrated that the AMS results can predict the independent (physician's) opinion about the individual treatment effect. Both, sensitivity (correct prediction of a positive assessment by the physician) and specificity (correct prediction of a negative assessment by the physician) were over 70%, if about 22% improvement of the AMS total score was used as cut-off point. CONCLUSION: The AMS scale showed a convincing ability to measure treatment effects on quality of life across the full range of severity of complaints. Effect modification by other variables at baseline was not observed. In addition, results of the scale can predict the subjective clinical expert opinion on the treatment efficiency

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation