Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman’s rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets

A giant planet undergoing extreme-ultraviolet irradiation by its hot massive-star host

The amount of ultraviolet irradiation and ablation experienced by a planet depends strongly on the temperature of its host star. Of the thousands of extrasolar planets now known, only six have been found that transit hot, A-type stars (with temperatures of 7,300-10,000 kelvin), and no planets are known to transit the even hotter B-type stars. For example, WASP-33 is an A-type star with a temperature of about 7,430 kelvin, which hosts the hottest known transiting planet, WASP-33b (ref. 1); the planet is itself as hot as a red dwarf star of type M (ref. 2). WASP-33b displays a large heat differential between its dayside and nightside, and is highly inflated-traits that have been linked to high insolation. However, even at the temperature of its dayside, its atmosphere probably resembles the molecule-dominated atmospheres of other planets and, given the level of ultraviolet irradiation it experiences, its atmosphere is unlikely to be substantially ablated over the lifetime of its star. Here we report observations of the bright star HD 195689 (also known as KELT-9), which reveal a close-in (orbital period of about 1.48 days) transiting giant planet, KELT-9b. At approximately 10,170 kelvin, the host star is at the dividing line between stars of type A and B, and we measure the dayside temperature of KELT-9b to be about 4,600 kelvin. This is as hot as stars of stellar type K4 (ref. 5). The molecules in K stars are entirely dissociated, and so the primary sources of opacity in the dayside atmosphere of KELT-9b are probably atomic metals. Furthermore, KELT-9b receives 700 times more extreme-ultraviolet radiation (that is, with wavelengths shorter than 91.2 nanometres) than WASP-33b, leading to a predicted range of mass-loss rates that could leave the planet largely stripped of its envelope during the main-sequence lifetime of the host star

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

Multi-disciplinary efforts to evaluate the therapeutic potential of CDK11, a novel transcription associated cyclin dependent kinase.

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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

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

Ovarian cancer risk factors by tumor aggressiveness : An analysis from the Ovarian Cancer Cohort Consortium

Ovarian cancer risk factors differ by histotype; however, within subtype, there is substantial variability in outcomes. We hypothesized that risk factor profiles may influence tumor aggressiveness, defined by time between diagnosis and death, independent of histology. Among 1.3 million women from 21 prospective cohorts, 4,584 invasive epithelial ovarian cancers were identified and classified as highly aggressive (death in = 35 vs. 20 to <25 kg/m(2), 1.93 [1.46-2.56] and current smoking (vs. never, 1.30 [1.07-1.57]) were associated with increased risk of highly aggressive disease. Results were similar within histotypes. Ovarian cancer risk factors may be directly associated with subtypes defined by tumor aggressiveness, rather than through differential effects on histology. Studies to assess biological pathways are warranted.Peer reviewe

Evaluation of Candidate Stromal Epithelial Cross-Talk Genes Identifies Association between Risk of Serous Ovarian Cancer and TERT, a Cancer Susceptibility “Hot-Spot”

We hypothesized that variants in genes expressed as a consequence of interactions between ovarian cancer cells and the host micro-environment could contribute to cancer susceptibility. We therefore used a two-stage approach to evaluate common single nucleotide polymorphisms (SNPs) in 173 genes involved in stromal epithelial interactions in the Ovarian Cancer Association Consortium (OCAC). In the discovery stage, cases with epithelial ovarian cancer (n = 675) and controls (n = 1,162) were genotyped at 1,536 SNPs using an Illumina GoldenGate assay. Based on Positive Predictive Value estimates, three SNPs—PODXL rs1013368, ITGA6 rs13027811, and MMP3 rs522616—were selected for replication using TaqMan genotyping in up to 3,059 serous invasive cases and 8,905 controls from 16 OCAC case-control studies. An additional 18 SNPs with Pper-allele<0.05 in the discovery stage were selected for replication in a subset of five OCAC studies (n = 1,233 serous invasive cases; n = 3,364 controls). The discovery stage associations in PODXL, ITGA6, and MMP3 were attenuated in the larger replication set (adj. Pper-allele≥0.5). However genotypes at TERT rs7726159 were associated with ovarian cancer risk in the smaller, five-study replication study (Pper-allele = 0.03). Combined analysis of the discovery and replication sets for this TERT SNP showed an increased risk of serous ovarian cancer among non-Hispanic whites [adj. ORper-allele 1.14 (1.04–1.24) p = 0.003]. Our study adds to the growing evidence that, like the 8q24 locus, the telomerase reverse transcriptase locus at 5p15.33, is a general cancer susceptibility locus

Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci

Background: Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors (TFs) critical to somatic tumorigenesis. Methods: All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21 693 controls; independent from discovery), and combined (9627 cases/30 845 controls; including additional individuals). Results: The PAX8-target gene set was ranked 1/615 in the discovery (PGSEA&lt;0.001; FDR=0.21), 7/615 in the replication (PGSEA=0.004; FDR=0.37), and 1/615 in the combined (PGSEA&lt;0.001; FDR=0.21) studies. Adding other genes reported to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P&lt;10−5 (including six with P&lt;5 × 10−8). The pathway was also associated with differential gene expression after shRNA-mediated silencing of PAX8 in HeyA8 (PGSEA=0.025) and IGROV1 (PGSEA=0.004) SOC cells and several PAX8 targets near SOC risk loci demonstrated in vitro transcriptomic perturbation. Conclusions: Putative PAX8 target genes are enriched for common SOC risk variants. This finding from our agnostic evaluation is of particular interest given that PAX8 is well-established as a specific marker for the cell of origin of SOC