Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p \u3c 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine

Association of BRAFV600E Mutation and MicroRNA Expression with Central Lymph Node Metastases in Papillary Thyroid Cancer: A Prospective Study from Four Endocrine Surgery Centers

Background: Studies have demonstrated an association of the BRAFV600E mutation and microRNA (miR) expression with aggressive clinicopathologic features in papillary thyroid cancer (PTC). Analysis of BRAFV600E mutations with miR expression data may improve perioperative decision making for patients with PTC, specifically in identifying patients harboring central lymph node metastases (CLNM). Methods: Between January 2012 and June 2013, 237 consecutive patients underwent total thyroidectomy and prophylactic central lymph node dissection (CLND) at four endocrine surgery centers. All tumors were tested for the presence of the BRAFV600E mutation and miR-21, miR-146b-3p, miR-146b-5p, miR-204, miR-221, miR-222, and miR-375 expression. Bivariate and multivariable analyses were performed to examine associations between molecular markers and aggressive clinicopathologic features of PTC. Results: Multivariable logistic regression analysis of all clinicopathologic features found miR-146b-3p and miR-146b-5p to be independent predictors of CLNM, while the presence of BRAFV600E almost reached significance. Multivariable logistic regression analysis limited to only predictors available preoperatively (molecular markers, age, sex, and tumor size) found miR-146b-3p, miR-146b-5p, miR-222, and BRAFV600E mutation to predict CLNM independently. While BRAFV600E was found to be associated with CLNM (48% mutated in node-positive cases vs. 28% mutated in node-negative cases), its positive and negative predictive values (48% and 72%, respectively) limit its clinical utility as a stand-alone marker. In the subgroup analysis focusing on only classical variant of PTC cases (CVPTC), undergoing prophylactic lymph node dissection, multivariable logistic regression analysis found only miR-146b-5p and miR-222 to be independent predictors of CLNM, while BRAFV600E was not significantly associated with CLNM. Conclusion: In the patients undergoing prophylactic CLNDs, miR-146b-3p, miR-146b-5p, and miR-222 were found to be predictive of CLNM preoperatively. However, there was significant overlap in expression of these miRs in the two outcome groups. The BRAFV600E mutation, while being a marker of CLNM when considering only preoperative variables among all histological subtypes, is likely not a useful stand-alone marker clinically because the difference between node-positive and node-negative cases was small. Furthermore, it lost significance when examining only CVPTC. Overall, our results speak to the concept and interpretation of statistical significance versus actual applicability of molecular markers, raising questions about their clinical usefulness as individual prognostic markers.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140269/1/thy.2015.0378.pd

Monitoring of Serum DNA Methylation as an Early Independent Marker of Response and Survival in Metastatic Breast Cancer: TBCRC 005 Prospective Biomarker Study

Epigenetic alterations measured in blood may help guide breast cancer treatment. The multisite prospective study TBCRC 005 was conducted to examine the ability of a novel panel of cell-free DNA methylation markers to predict survival outcomes in metastatic breast cancer (MBC) using a new quantitative multiplex assay (cMethDNA)

Modeling precision treatment of breast cancer

Background: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified

Somatosensory System Deficits in Schizophrenia Revealed by MEG during a Median-Nerve Oddball Task

Although impairments related to somatosensory perception are common in schizophrenia, they have rarely been examined in functional imaging studies. In the present study, magnetoencephalography (MEG) was used to identify neural networks that support attention to somatosensory stimuli in healthy adults and abnormalities in these networks in patient with schizophrenia. A median-nerve oddball task was used to probe attention to somatosensory stimuli, and an advanced, high-resolution MEG source-imaging method was applied to assess activity throughout the brain. In nineteen healthy subjects, attention-related activation was seen in a sensorimotor network involving primary somatosensory (S1), secondary somatosensory (S2), primary motor (M1), pre-motor (PMA), and paracentral lobule (PCL) areas. A frontal–parietal–temporal “attention network”, containing dorsal- and ventral–lateral prefrontal cortex (DLPFC and VLPFC), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), superior parietal lobule (SPL), inferior parietal lobule (IPL)/supramarginal gyrus (SMG), and temporal lobe areas, was also activated. Seventeen individuals with schizophrenia showed early attention-related hyperactivations in S1 and M1 but hypo-activation in S1, S2, M1, and PMA at later latency in the sensorimotor network. Within this attention network, hypoactivation was found in SPL, DLPFC, orbitofrontal cortex, and the dorsal aspect of ACC. Hyperactivation was seen in SMG/IPL, frontal pole, and the ventral aspect of ACC in patients. These findings link attention-related somatosensory deficits to dysfunction in both sensorimotor and frontal–parietal–temporal networks in schizophrenia

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Exploring the epigenetic regulation of telomerase reverse transcriptase (TERT) in human cancer cell lines

Telomerase regulation, including TERT promoter methylation, has been of long‐standing interest to cancer biologists. Rowland et al. have now vastly expanded their ongoing characterization of TERT promoter methylation in cancer cells, analyzing the methylation patterns of 833 cell lines from 23 human cancers. They document a highly conserved pattern of hypomethylation around the proximal promoter, as well as a more heterogeneous region of hypermethylation further upstream, both associated with active TERT expression in cancer cells. They further describe the interplay between activating TERT promoter mutations and allelic methylation and transcription patterns. This valuable dataset represents the most extensive characterization of TERT promoter methylation in cancer cells to date and will help guide the future study of transcriptional regulation of telomerase. Comment on: https://doi.org/10.1002/1878‐0261.1278

Preoperative Molecular Markers in Thyroid Nodules

The need for distinguishing benign from malignant thyroid nodules has led to the pursuit of differentiating molecular markers. The most common molecular tests in clinical use are Afirma® Gene Expression Classifier (GEC) and Thyroseq® V2. Despite the rapidly developing field of molecular markers, several limitations exist. These challenges include the recent introduction of the histopathological diagnosis “Non-Invasive Follicular Thyroid neoplasm with Papillary-like nuclear features”, the correlation of genetic mutations within both benign and malignant pathologic diagnoses, the lack of follow-up of molecular marker negative nodules, and the cost-effectiveness of molecular markers. In this manuscript, we review the current published literature surrounding the diagnostic value of Afirma® GEC and Thyroseq® V2. Among Afirma® GEC studies, sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) ranged from 75 to 100%, 5 to 53%, 13 to 100%, and 20 to 100%, respectively. Among Thyroseq® V2 studies, Se, Sp, PPV, and NPV ranged from 40 to 100%, 56 to 93%, 13 to 90%, and 48 to 97%, respectively. We also discuss current challenges to Afirma® GEC and Thyroseq® V2 utility and clinical application, and preview the future directions of these rapidly developing technologies

Telomerase activity as a measure for monitoring radiocurability of tumor cells

Radiotherapy plays a key role in the treatment of many tumors. It is difficult to determine what fraction of tumor cells survives after treatment with ionizing radiation. A convenient and sensitive biochemical assay could be efficacious in determining the potential success of radiotherapy. Since telomerase activity is frequently associated with the malignant phenotype, we sought to determine whether a correlation existed between ionizing radiation-induced cell killing and telomerase activity. We evaluated telomerase activity in two telomerase-positive and one telomerase-negative human cell line exposed to ionizing radiation. Telomerase activity was determined using a PCR-based telomeric repeat amplification protocol coupled with ELISA. We found ionizing radiation treatment to decrease the telomerase activity (in plateau phase cells of RKO, HeLa; and growing cells of RKO) in a dose-dependent manner, which correlated with cell death in in vitro tests as well as during tumor regression in nude mice. In contrast, growing HeLa cells after 24 h postradiation treatment showed an increase in telomerase activity, but there was no increase in the levels of mRNA of hTERT. To assess the sensitivity of the telomerase activity assay, we performed mixing experiments of HeLa and AG1522 cell extracts. These studies showed that telomerase activity could be detected in lysate equal to a single HeLa cell when mixed with 10,000 AG1522 cells. Our results indicate that even a few surviving neoplastic cells can be detected by telomerase activity assay. Therefore, detection of telomerase activity may be a useful monitor of radiotherapeutic efficacy and an early predictor of outcome