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

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

ISEEK, a tool for high speed, concurrent, distributed forensic data acquisition

Electronic discovery (also written as e-discovery or eDiscovery) and digital forensics are processes in which electronic data is sought, located, secured, and processed with the expectation that it may be used as evidence in legal proceedings. Electronic evidence plays a fundamental role in many aspects of litigation (Stanfield, 2009). However, both eDiscovery and digital forensic approaches that rely on the creation of an index as part of their processing are struggling to cope with the huge increases in hard disk storage capacity. This paper introduces a novel technology that meets the existing and future data volume challenges faced by practitioners in these areas. The technology also addresses the concerns of those responsible for maintaining corporate networks as it does not require installation of ‘agents’ nor does it have any significant impact on network bandwidth during the search and collection process, even when this involves many computers. The technology is the embodiment of a patented process that opens the way for the development of new functionality, such as the detection of malware, compliance with corporate Information Technology (IT) policies and IT auditing. The technology introduced in this paper has been incorporated into a commercial tool called ISEEK that has already been successfully deployed in a variety of environments

The Advanced Data Acquisition Model (Adam): A Process Model for Digital Forensic Practice

As with other types of evidence, the courts make no presumption that digital evidence is reliable without some evidence of empirical testing in relation to the theories and techniques associated with its production. The issue of reliability means that courts pay close attention to the manner in which electronic evidence has been obtained and in particular the process in which the data is captured and stored. Previous process models have tended to focus on one particular area of digital forensic practice, such as law enforcement, and have not incorporated a formal description. We contend that this approach has prevented the establishment of generally-accepted standards and processes that are urgently needed in the domain of digital forensics. This paper presents a generic process model as a step towards developing such a generally-accepted standard for a fundamental digital forensic activity–the acquisition of digital evidence

The Role of Personalised Choice in Decision Support: A Randomized Controlled Trial of an Online Decision Aid for Prostate Cancer Screening.

IMPORTANCE: Decision support tools can assist people to apply population-based evidence on benefits and harms to individual health decisions. A key question is whether "personalising" choice within decisions aids leads to better decision quality. OBJECTIVE: To assess the effect of personalising the content of a decision aid for prostate cancer screening using the Prostate Specific Antigen (PSA) test. DESIGN: Randomized controlled trial. SETTING: Australia. PARTICIPANTS: 1,970 men aged 40-69 years were approached to participate in the trial. INTERVENTION: 1,447 men were randomly allocated to either a standard decision aid with a fixed set of five attributes or a personalised decision aid with choice over the inclusion of up to 10 attributes. OUTCOME MEASURES: To determine whether there was a difference between the two groups in terms of: 1) the emergent opinion (generated by the decision aid) to have a PSA test or not; 2) self-rated decision quality after completing the online decision aid; 3) their intention to undergo screening in the next 12 months. We also wanted to determine whether men in the personalised choice group made use of the extra decision attributes. RESULTS: 5% of men in the fixed attribute group scored 'Have a PSA test' as the opinion generated by the aid, as compared to 62% of men in the personalised choice group (χ2 = 569.38, 2df, p< 0001). Those men who used the personalised decision aid had slightly higher decision quality (t = 2.157, df = 1444, p = 0.031). The men in the personalised choice group made extensive use of the additional decision attributes. There was no difference between the two groups in terms of their stated intention to undergo screening in the next 12 months. CONCLUSIONS: Together, these findings suggest that personalised decision support systems could be an important development in shared decision-making and patient-centered care. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12612000723886