Neurophysiology

Contains research objectives and summary of research on ten research projects.National Institutes of Health (Grant 5 R01 EY01149-02)National Institutes of Health (Grant 1 T01 EY00090-01)Bell Telephone Laboratories, Inc. (Grant)National Institutes of Health (Grant 5 TO1 GM00778-19)National Institutes of Health (Grant 5 TO1 GM01555-08

Neurophysiology

Contains research objectives and summary of research on seventeen research projects and reports on four research projects.National Institutes of Health (Grant 5 TOl EY00090-02)Bell Telephone Laboratories, Inc. (Grant)National Institutes of Health (Grant 5 ROI EY01149-03)National Institutes of Health (Grant NS 12307-01)National Institutes of Health (Grant 1 K04 NS00010

Neurophysiology

Contains reports on twenty research projects.Bell Laboratories (Grant)National Institutes of Health (Grant 5 R01 EY01149-03S2)National Institutes of Health (Grant 5 TO1 EY00090-04)National Institutes of Health (Grant 5 RO1 NS12307-03)National Institutes of Health (Grant K04 NS00010)National Multiple Sclerosis Society (Grant RG-1133-A-1)Health Sciences Fund (Grant 78-10

Plasmas and Controlled Nuclear Fusion

Contains reports on thirteen research projects split into three sections.National Science Foundation (Grant GK-2581

Neurophysiology

Contains research objectives and summary of research on sixteen research projects.National Institutes of Health (Grant 5 TO1 EY00090-03)National Institutes of Health (Grant 3 RO1 EY01149-03S1)Bell Laboratories (Grant)National Institutes of Health (Grant 5 RO1 NS12307-02)National Institutes of Health (Grant K04 NS00010

Plasmas and Controlled Nuclear Fusion

Contains research objectives and reports on three research projects.National Science Foundation (Grant GK-1165

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