Reporting and Assessing the Quality of Diagnostic Accuracy Studies for Cervical Cancer Screening and Management.

ObjectiveWe adapted the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool for studies of cervical cancer screening and management and used the adapted tool to evaluate the quality of studies included in a systematic review supporting the 2019 Risk-Based Management Consensus Guidelines.MethodsWe evaluated the quality of all studies included in our systematic review for postcolposcopy (n = 5) and posttreatment (n = 23) surveillance using QUADAS-2 criteria. Subsequently, we adapted signaling questions to indications of cervical cancer screening and management. An iterative process was carried out to evaluate interrater agreement between 2 study authors (M.A.C. and N.W.). Discrepant ratings were discussed, and criteria were adapted accordingly. We also evaluated the influence of study quality on risk estimates and between study variation using stratified subgroup meta-analyses.ResultsTwelve signaling questions for bias assessment that were adapted to or newly developed for cervical cancer screening and management are described here. Interrater agreement on bias assessment increased from 70% to 83% during the adaptation process. Detailed assessment of bias and applicability showed that all studies on postcolposcopy management and 90% of studies on posttreatment management had high risk of bias in at least 1 domain. Most commonly, high risk of bias was observed for the patient selection domain, indicating the heterogeneity of study designs and clinical practice in reported studies.ConclusionsThe adapted QUADAS-2 will have broad application for researchers, evidence evaluators, and journals who are interested in designing, conducting, evaluating, and publishing studies for cervical cancer screening and management

Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images

In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. Image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to classify the segments and the whole epithelium. The experimental data consists of 65 images. The proposed method accuracy is 77.25% compared to 75.75% using the previous method for the same dataset

Human Papillomavirus 16 E5 Induces Bi-Nucleated Cell Formation By Cell-Cell Fusion

Human Papillomaviruses (HPV) 16 is a DNA virus encoding three oncogenes – E5, E6, and E7. The E6 and E7 proteins have well-established roles as inhibitors of tumor suppression, but the contribution of E5 to malignant transformation is controversial. Using spontaneously immortalized human keratinocytes (HaCaT cells), we demonstrate that expression of HPV16 E5 is necessary and sufficient for the formation of bi-nucleated cells, a common characteristic of precancerous cervical lesions. Expression of E5 from non-carcinogenic HPV6b does not produce bi-nucleate cells. Video microscopy and biochemical analyses reveal that bi-nucleates arise through cell-cell fusion. Although most E5-induced bi-nucleates fail to propagate, co-expression of HPV16 E6/E7 enhances the proliferation of these cells. Expression of HPV16 E6/E7 also increases bi-nucleated cell colony formation. These findings identify a new role for HPV16 E5 and support a model in which complementary roles of the HPV16 oncogenes lead to the induction of carcinogenesis

Evaluation of TypeSeq, a Novel High-Throughput, Low-Cost, Next-Generation Sequencing-Based Assay for Detection of 51 Human Papillomavirus Genotypes.

BackgroundHuman papillomaviruses (HPV) cause over 500 000 cervical cancers each year, most of which occur in low-resource settings. Human papillomavirus genotyping is important to study natural history and vaccine efficacy. We evaluated TypeSeq, a novel, next-generation, sequencing-based assay that detects 51 HPV genotypes, in 2 large international epidemiologic studies.MethodsTypeSeq was evaluated in 2804 cervical specimens from the Study to Understand Cervical Cancer Endpoints and Early Determinants (SUCCEED) and in 2357 specimens from the Costa Rica Vaccine Trial (CVT). Positive agreement and risks of precancer for individual genotypes were calculated for TypeSeq in comparison to Linear Array (SUCCEED). In CVT, positive agreement and vaccine efficacy were calculated for TypeSeq and SPF10-LiPA.ResultsWe observed high overall and positive agreement for most genotypes between TypeSeq and Linear Array in SUCCEED and SPF10-LiPA in CVT. There was no significant difference in risk of precancer between TypeSeq and Linear Array in SUCCEED or in estimates of vaccine efficacy between TypeSeq and SPF10-LiPA in CVT.ConclusionsThe agreement of TypeSeq with Linear Array and SPF10-LiPA, 2 well established standards for HPV genotyping, demonstrates its high accuracy. TypeSeq provides high-throughput, affordable HPV genotyping for world-wide studies of cervical precancer risk and of HPV vaccine efficacy

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

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