Socioeconomic status and anxiety as predictors of antidepressant treatment response and suicidal ideation in older adults.

BACKGROUND: Separate reports from the maintenance treatment for late-life depression (MTLD) trials have shown that low socioeconomic status (SES) and anxiety symptoms at the time of treatment initiation predict lower levels of response to antidepressant treatment and higher levels of suicidal ideation in older adults. AIM: To determine whether SES and anxiety independently contribute to worse treatment outcomes, as indicated by persistence of depressive symptoms during treatment and the persistence of suicidal ideation. Consistent with prior evidence that sociodemographic factors and clinical history are both prognostic of depression treatment efficacy, we hypothesized that SES and pre-existing anxiety symptoms will both predict lower levels of response to treatment and higher levels of suicidal ideation. METHOD: Secondary analyses of data from the MTLD trials. RESULTS: Regression analyses which controlled for comorbid anxiety indicated that residents of middle- and high-income census tracts were more likely to respond to treatment (HR, 1.63; 95%CI, 1.08-2.46) and less likely to report suicidal ideation during treatment (OR, 0.51; 95%CI, 0.28-0.90) than residents of low income census tracts. The same regression models indicated that pre-existing anxiety symptoms were independently related to lower treatment response (HR, 0.73; 95%CI, 0.60-0.89) and higher risk of suicidal ideation (OR, 1.45; 95%CI, 0.98-2.14). CONCLUSION: These findings demonstrate the importance of treating anxiety symptoms during the course of treatment for late-life depression and, at the same time, addressing barriers to treatment response related to low SES

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 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

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

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

The CXCL12γ Chemokine Displays Unprecedented Structural and Functional Properties that Make It a Paradigm of Chemoattractant Proteins

The CXCL12γ chemokine arises by alternative splicing from Cxcl12, an essential gene during development. This protein binds CXCR4 and displays an exceptional degree of conservation (99%) in mammals. CXCL12γ is formed by a protein core shared by all CXCL12 isoforms, extended by a highly cationic carboxy-terminal (C-ter) domain that encompass four overlapped BBXB heparan sulfate (HS)-binding motifs. We hypothesize that this unusual domain could critically determine the biological properties of CXCL12γ through its interaction to, and regulation by extracellular glycosaminoglycans (GAG) and HS in particular. By both RT-PCR and immunohistochemistry, we mapped the localization of CXCL12γ both in mouse and human tissues, where it showed discrete differential expression. As an unprecedented feature among chemokines, the secreted CXCL12γ strongly interacted with cell membrane GAG, thus remaining mostly adsorbed on the plasmatic membrane upon secretion. Affinity chromatography and surface plasmon resonance allowed us to determine for CXCL12γ one of the higher affinity for HS (Kd = 0.9 nM) ever reported for a protein. This property relies in the presence of four canonical HS-binding sites located at the C-ter domain but requires the collaboration of a HS-binding site located in the core of the protein. Interestingly, and despite reduced agonist potency on CXCR4, the sustained binding of CXCL12γ to HS enabled it to promote in vivo intraperitoneal leukocyte accumulation and angiogenesis in matrigel plugs with much higher efficiency than CXCL12α. In good agreement, mutant CXCL12γ chemokines selectively devoid of HS-binding capacity failed to promote in vivo significant cell recruitment. We conclude that CXCL12γ features unique structural and functional properties among chemokines which rely on the presence of a distinctive C-ter domain. The unsurpassed capacity to bind to HS on the extracellular matrix would make CXCL12γ the paradigm of haptotactic proteins, which regulate essential homeostatic functions by promoting directional migration and selective tissue homing of cells

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

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility