Stabilizing AqdC, a Pseudomonas Quinolone Signal-Cleaving Dioxygenase from Mycobacteria, by FRESCO-Based Protein Engineering

The mycobacterial PQS dioxygenase AqdC, a cofactor-less protein with an α/β-hydrolase fold, inactivates the virulence-associated quorum-sensing signal molecule 2-heptyl-3-hydroxy-4(1H)-quinolone (PQS) produced by the opportunistic pathogen Pseudomonas aeruginosa and is therefore a potential anti-virulence tool. We have used computational library design to predict stabilizing amino acid replacements in AqdC. While 57 out of 91 tested single substitutions throughout the protein led to stabilization, as judged by increases in (Formula presented.) of >2 °C, they all impaired catalytic activity. Combining substitutions, the proteins AqdC-G40K-A134L-G220D-Y238W and AqdC-G40K-G220D-Y238W showed extended half-lives and the best trade-off between stability and activity, with increases in (Formula presented.) of 11.8 and 6.1 °C and relative activities of 22 and 72 %, respectively, compared to AqdC. Molecular dynamics simulations and principal component analysis suggested that stabilized proteins are less flexible than AqdC, and the loss of catalytic activity likely correlates with an inability to effectively open the entrance to the active site

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

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

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

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

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

Antimicrobial peptides of the Cecropin-family show potent antitumor activity against bladder cancer cells

<p>Abstract</p> <p>Background</p> <p>This study evaluated the cytotoxic and antiproliferative efficacy of two well-characterized members of the Cecropin-family of antimicrobial peptides against bladder tumor cells and benign fibroblasts.</p> <p>Methods</p> <p>The antiproliferative and cytotoxic potential of the Cecropins A and B was quantified by colorimetric WST-1-, BrdU- and LDH-assays in four bladder cancer cell lines as well as in murine and human fibroblast cell lines. IC₅₀values were assessed by logarithmic extrapolation, representing the concentration at which cell viability was reduced by 50%. Scanning electron microscopy (SEM) was performed to visualize the morphological changes induced by Cecropin A and B in bladder tumor cells and fibroblasts.</p> <p>Results</p> <p>Cecropin A and B inhibit bladder cancer cell proliferation and viability in a dose-dependent fashion. The average IC₅₀values of Cecropin A and B against all bladder cancer cell lines ranged between 73.29 μg/ml and 220.05 μg/ml. In contrast, benign fibroblasts were significantly less or not at all susceptible to Cecropin A and B. Both Cecropins induced an increase in LDH release from bladder tumor cells whereas benign fibroblasts were not affected. SEM demonstrated lethal membrane disruption in bladder cancer cells as opposed to fibroblasts.</p> <p>Conclusion</p> <p>Cecropin A and B exert selective cytotoxic and antiproliferative efficacy in bladder cancer cells while sparing targets of benign murine or human fibroblast origin. Both peptides may offer novel therapeutic strategies for the treatment of bladder cancer with limited cytotoxic effects on benign cells.</p

Multi-center assessment of lymph-node density and nodal-stage to predict disease-specific survival in patients with bladder cancer treated by radical cystectomy

BACKGROUND:Prognostic tools in pathological-node (pN) patients after radical cystectomy (RC) are needed.OBJECTIVES:To evaluate the prognostic impact of lymph node (LN)-density on disease-specific survival (DSS) in patients with bladder cancer (BC) undergoing RC with pelvic lymph node dissection.METHODS:We analyzed a multi-institutional cohort of 1169 patients treated with upfront RC for cT1-4aN0M0 urothelial BCat nine centers. LN-densitywas calculated as the ratio of the number of positive LNs×100% to the number of LNs removed. The optimal LN-density cut-off value was defined by creating a time-dependent receiver operating characteristic (ROC) curve in pN patients. Univariable and multivariable Cox’ regression analyses were used to assess the effect of conventional Tumor Nodes Metastasis (TNM) nodal staging system, LN-density and other LN-related variables on DSS in the pN-positive cohort.RESULTS:Of the 1169 patients, 463 (39.6%) patients had LN-involvement. The area under the ROC curve was 0.60 and the cut-off for LN-density was set at 20%, 223 of the pN-positive patients (48.2%) had a LN-density ≥ 20%. In multivariable models, the number of LN-metastases (HR 1.03, p = 0.005) and LN-density, either as continuous (HR 1.01, p = 0.013) or as categorical variable (HR 1.37, p = 0.014), were independently associated with worse DSS, whereas pN-stage was not.CONCLUSIONS:LN-density ≥ 20% was an independent predictor of worse DSS in BC patients with LN-involvement at RC. The integration of LN-density and other LN-parameters rather than only conventional pN-stage may contribute to a more refined risk-stratification in BC patients with nodal involvement.Development and application of statistical models for medical scientific researc

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

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