A genome-wide association study of contralateral breast cancer in the Women's Environmental Cancer and Radiation Epidemiology Study.

BACKGROUND: Contralateral breast cancer (CBC) is the most common second primary cancer diagnosed in breast cancer survivors, yet the understanding of the genetic susceptibility of CBC, particularly with respect to common variants, remains incomplete. This study aimed to investigate the genetic basis of CBC to better understand this malignancy. FINDINGS: We performed a genome-wide association analysis in the Women's Environmental Cancer and Radiation Epidemiology (WECARE) Study of women with first breast cancer diagnosed at age < 55 years including 1161 with CBC who served as cases and 1668 with unilateral breast cancer (UBC) who served as controls. We observed two loci (rs59657211, 9q32, SLC31A2/FAM225A and rs3815096, 6p22.1, TRIM31) with suggestive genome-wide significant associations (P < 1 × 10-6). We also found an increased risk of CBC associated with a breast cancer-specific polygenic risk score (PRS) comprised of 239 known breast cancer susceptibility single nucleotide polymorphisms (SNPs) (rate ratio per 1-SD change: 1.25; 95% confidence interval 1.14-1.36, P < 0.0001). The protective effect of chemotherapy on CBC risk was statistically significant only among patients with an elevated PRS (Pheterogeneity = 0.04). The AUC that included the PRS and known breast cancer risk factors was significantly elevated. CONCLUSIONS: The present GWAS identified two previously unreported loci with suggestive genome-wide significance. We also confirm that an elevated risk of CBC is associated with a comprehensive breast cancer susceptibility PRS that is independent of known breast cancer risk factors. These findings advance our understanding of genetic risk factors involved in CBC etiology.

Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.

Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, current diagnostic methods often subject patients to unnecessary surgical burden. In this study, we developed and validated an automated, highly accurate multi-study-derived diagnostic model for PTCs using personalized biological pathways coupled with a sophisticated machine learning algorithm. Surprisingly, the algorithm achieved near-perfect performance in discriminating PTCs from non-tumoral thyroid samples with an overall cross-study-validated area under the receiver operating characteristic curve (AUROC) of 0.999 (95% confidence interval [CI]: 0.995-1) and a Brier score of 0.013 on three independent development cohorts. In addition, the algorithm showed excellent generalizability and transferability on two large-scale external blind PTC cohorts consisting of The Cancer Genome Atlas (TCGA), which is the largest genomic PTC cohort studied to date, and the post-Chernobyl cohort, which includes PTCs reported after exposure to radiation from the Chernobyl accident. When applied to the TCGA cohort, the model yielded an AUROC of 0.969 (95% CI: 0.950-0.987) and a Brier score of 0.109. On the post-Chernobyl cohort, it yielded an AUROC of 0.962 (95% CI: 0.918-1) and a Brier score of 0.073. This algorithm also is robust against other various types of clinical scenarios, discriminating malignant from benign lesions as well as clinically aggressive thyroid cancer with poor prognosis from indolent ones. Furthermore, we discovered novel pathway alterations and prognostic signatures for PTC, which can provide directions for follow-up studies.

PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma.

MOTIVATION: Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly. RESULTS: To address these issues, we propose a novel method, called PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at: https://github.com/mskspi/PathCNN.

Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".

In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing, speech recognition, and natural language processing, and now rapidly becoming a dominant tool in biomedicine [...].

Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods.

The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.

Pathway-based deep clustering for molecular subtyping of cancer.

Cancer is a genetic disease comprising multiple subtypes that have distinct molecular characteristics and clinical features. Cancer subtyping helps in improving personalized treatment and making decision, as different cancer subtypes respond differently to the treatment. The increasing availability of cancer related genomic data provides the opportunity to identify molecular subtypes. Several unsupervised machine learning techniques have been applied on molecular data of the tumor samples to identify cancer subtypes that are genetically and clinically distinct. However, most clustering methods often fail to efficiently cluster patients due to the challenges imposed by high-throughput genomic data and its non-linearity. In this paper, we propose a pathway-based deep clustering method (PACL) for molecular subtyping of cancer, which incorporates gene expression and biological pathway database to group patients into cancer subtypes. The main contribution of our model is to discover high-level representations of biological data by learning complex hierarchical and nonlinear effects of pathways. We compared the performance of our model with a number of benchmark clustering methods that recently have been proposed in cancer subtypes. We assessed the hypothesis that clusters (subtypes) may be associated to different survivals by logrank tests. PACL showed the lowest p-value of the logrank test against the benchmark methods. It demonstrates the patient groups clustered by PACL may correspond to subtypes which are significantly associated with distinct survival distributions. Moreover, PACL provides a solution to comprehensively identify subtypes and interpret the model in the biological pathway level. The open-source software of PACL in PyTorch is publicly available at https://github.com/tmallava/PACL.

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.

Quantitative in vivo proton MR spectroscopic assessment of lipid metabolism: Value for breast cancer diagnosis and prognosis.

BACKGROUND: Breast magnetic resonance spectroscopy (1 H-MRS) has been largely based on choline metabolites; however, other relevant metabolites can be detected and monitored. PURPOSE: To investigate whether lipid metabolite concentrations detected with 1 H-MRS can be used for the noninvasive differentiation of benign and malignant breast tumors, differentiation among molecular breast cancer subtypes, and prediction of long-term survival outcomes. STUDY TYPE: Retrospective. SUBJECTS: In all, 168 women, aged ≥18 years. FIELD STRENGTH/SEQUENCE: Dynamic contrast-enhanced MRI at 1.5 T: sagittal 3D spoiled gradient recalled sequence with fat saturation, flip angle = 10°, repetition time / echo time (TR/TE) = 7.4/4.2 msec, slice thickness = 3.0 mm, field of view (FOV) = 20 cm, and matrix size = 256 × 192. 1 H-MRS: PRESS with TR/TE = 2000/135 msec, water suppression, and 128 scan averages, in addition to 16 reference scans without water suppression. ASSESSMENT: MRS quantitative analysis of lipid resonances using the LCModel was performed. Histopathology was the reference standard. STATISTICAL TESTS: Categorical data were described using absolute numbers and percentages. For metric data, means (plus 95% confidence interval [CI]) and standard deviations as well as median, minimum, and maximum were calculated. Due to skewed data, the latter were more adequate; unpaired Mann-Whitney U-tests were performed to compare groups without and with Bonferroni correction. ROC analyses were also performed. RESULTS: There were 111 malignant and 57 benign lesions. Mean voxel size was 4.4 ± 4.6 cm3 . Six lipid metabolite peaks were quantified: L09, L13 + L16, L21 + L23, L28, L41 + L43, and L52 + L53. Malignant lesions showed lower L09, L21 + L23, and L52 + L53 than benign lesions (P = 0.022, 0.027, and 0.0006). Similar results were observed for Luminal A or Luminal A/B vs. other molecular subtypes. At follow-up, patients were split into two groups based on median values for the six peaks; recurrence-free survival was significantly different between groups for L09, L21 + L23, and L28 (P = 0.0173, 0.0024, and 0.0045). DATA CONCLUSION: Quantitative in vivo 1 H-MRS assessment of lipid metabolism may provide an additional noninvasive imaging biomarker to guide therapeutic decisions in breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:239-249.

A literature mining-based approach for identification of cellular pathways associated with chemoresistance in cancer.

Chemoresistance is a major obstacle to the successful treatment of many human cancer types. Increasing evidence has revealed that chemoresistance involves many genes and multiple complex biological mechanisms including cancer stem cells, drug efflux mechanism, autophagy and epithelial-mesenchymal transition. Many studies have been conducted to investigate the possible molecular mechanisms of chemoresistance. However, understanding of the biological mechanisms in chemoresistance still remains limited. We surveyed the literature on chemoresistance-related genes and pathways of multiple cancer types. We then used a curated pathway database to investigate significant chemoresistance-related biological pathways. In addition, to investigate the importance of chemoresistance-related markers in protein-protein interaction networks identified using the curated database, we used a gene-ranking algorithm designed based on a graph-based scoring function in our previous study. Our comprehensive survey and analysis provide a systems biology-based overview of the underlying mechanisms of chemoresistance.

Pretreatment dynamic contrast-enhanced MRI biomarkers correlate with progression-free survival in primary central nervous system lymphoma.

PURPOSE: Prediction of clinical outcomes in patients with primary central nervous system lymphoma (PCNSL) is important for optimization of treatment planning. Quantitative imaging biomarkers for PCNSL have not yet been established. This study evaluated the prognostic value of pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging for progression-free survival (PFS) in patients with PCNSL. METHODS: Pretreatment dynamic contrast-enhanced MRI and diffusion-weighted imaging were retrospectively analyzed in 18 immunocompetent patients with PCNSL. Volumes of interest encompassing the tumors were assessed for measurements of blood plasma volume (Vp), volume transfer constant (Ktrans), and apparent diffusion coefficient. Patients were divided into short and long PFS groups based on median PFS. Imaging and clinical variables were correlated with PFS. RESULTS: Median PFS was 19.6 months. Lower Vpmean and Ktransmean values increased risk for rapid progression (< 19.6 months). Receiver operating characteristic curve analysis demonstrated an optimal Vpmean cutoff value of 2.29 (area under the curve [AUC] = 0.74, sensitivity and specificity = 0.78, p = 0.023) for separating patients with short and long PFS. The optimal Ktransmean cutoff was 0.08 (AUC = 0.74, sensitivity = 0.67, specificity = 0.78, p = 0.025). Kaplan-Meier survival analysis with log-rank test demonstrated significantly (p = 0.015) increased risk of rapid progression for patients with Vpmean < 2.29. Vpmean was significantly (p = 0.03) associated with PFS on univariate Cox analysis. Apparent diffusion coefficient values and clinical factors did not influence PFS. CONCLUSIONS: Pretreatment Vp and Ktrans derived from dynamic contrast-enhanced MRI may be novel prognostic quantitative imaging biomarkers of progression-free survival in patients with PCNSL. These data should be prospectively validated in larger patient cohorts.

Intravoxel incoherent motion diffusion-weighted MRI during chemoradiation therapy to characterize and monitor treatment response in human papillomavirus head and neck squamous cell carcinoma.

PURPOSE: Characterize and monitor treatment response in human papillomavirus (HPV) head and neck squamous cell carcinoma (HNSCC) using intra-treatment (intra-TX) imaging metrics derived from intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (DW-MRI). MATERIALS AND METHODS: Thirty-four (30 HPV positive [+] and 4 HPV negative [-]) HNSCC patients underwent a total of 136 MRI including multi-b value DW-MRI (pretreatment [pre-TX] and intra-TX weeks 1, 2, and 3) at 3.0 Tesla. All patients were treated with chemo-radiation therapy. Monoexponential (yielding apparent diffusion coefficient [ADC]) and bi-exponential (yielding perfusion fraction [f], diffusion [D], and pseudo-diffusion [D*] coefficients) fits were performed on a region of interest and voxel-by-voxel basis, on metastatic neck nodes. Response was assessed using RECISTv1.1. The relative percentage change in D, f, and D* between the pre- and intra-TX weeks were used for hierarchical clustering. A Wilcoxon rank-sum test was performed to assess the difference in metrics within and between the complete response (CR) and non-CR groups. RESULTS: The delta (Δ) change in volume (V)1wk-0wk for the CR group differed significantly (P = 0.016) from the non-CR group, while not for V2wk-0wk and V3wk-0wk (P > 0.05). The mean increase in ΔD3wk-0wk for the CR group was significantly higher (P = 0.017) than the non-CR group. ADC and D showed an increasing trend at each intra-TX week when compared with pre-TX in CR group (P < 0.003). Hierarchical clustering demonstrated the existence of clusters in HPV + patients. CONCLUSION: After appropriate validation in a larger population, these IVIM imaging metrics may be useful for individualized treatment in HNSCC patients. LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:1013-1023.

Divergence of Drosophila melanogaster repeatomes in response to a sharp microclimate contrast in Evolution Canyon, Israel.

Repeat sequences, especially mobile elements, make up large portions of most eukaryotic genomes and provide enormous, albeit commonly underappreciated, evolutionary potential. We analyzed repeatomes of Drosophila melanogaster that have been diverging in response to a microclimate contrast in Evolution Canyon (Mount Carmel, Israel), a natural evolutionary laboratory with two abutting slopes at an average distance of only 200 m, which pose a constant ecological challenge to their local biotas. Flies inhabiting the colder and more humid north-facing slope carried about 6% more transposable elements than those from the hot and dry south-facing slope, in parallel to a suite of other genetic and phenotypic differences between the two populations. Nearly 50% of all mobile element insertions were slope unique, with many of them disrupting coding sequences of genes critical for cognition, olfaction, and thermotolerance, consistent with the observed patterns of thermotolerance differences and assortative mating.

Second-opinion interpretations of neuroimaging studies by oncologic neuroradiologists can help reduce errors in cancer care.

BACKGROUND: The purpose of this study was to investigate the utility and clinical impact of second-opinion interpretations of outside neuroimaging studies by oncologic neuroradiologists at a National Cancer Institute-designated cancer center. METHODS: We performed a retrospective analysis of initial outside and second-opinion radiology reports from 300 computed tomography and magnetic resonance imaging studies and identified cases with discrepancies between the two reports. An adult neuro-oncologist, pediatric neuro-oncologist, and head and neck surgeon reviewed each pair of discrepant reports based on their area of expertise, patient age, and the type of study performed. The clinicians were blinded to the origin of each report and recorded whether the differences in the reports would have led to a change in patient management and/or disease staging. Histopathologic analysis, clinical assessment, and/or minimum 3-month imaging follow-up served as the reference standards to establish which of the 2 reports was correct. RESULTS: Among the 283 cases that met our study criteria, there were 55 neuroimaging studies with disagreements (19%) between the initial outside report and second-opinion interpretation. Patient management and/or disease stage would have been altered in 42 of 283 cases (15%) based on report differences as determined by the 2 neuro-oncologists and the surgeon participating in the study. Sufficient follow-up was available in 35 of 42 cases (83%). The second-opinion interpretation was correct 100% of the time (35/35). CONCLUSION: Second-opinion interpretations of neuroimaging studies by subspecialized oncologic neuroradiologists provide added value by reducing error and optimizing the care of cancer patients. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2708-2714. © 2016 American Cancer Society.

Breast cancer molecular subtype classifier that incorporates MRI features.

PURPOSE: To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. MATERIALS AND METHODS: This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test. RESULTS: Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR-/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR-/HER2+), and 81.0% (TN). CONCLUSION: We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129.

Predictors of acute toxicities during definitive chemoradiation using intensity-modulated radiotherapy for anal squamous cell carcinoma.

PURPOSE: To identify clinical and dosimetric factors associated with acute hematologic and gastrointestinal (GI) toxicities during definitive therapy using intensity-modulated radiotherapy (IMRT) for anal squamous cell carcinoma (ASCC). MATERIALS AND METHODS: We retrospectively analyzed 108 ASCC patients treated with IMRT. Clinical information included age, gender, stage, concurrent chemotherapy, mitomycin (MMC) chemotherapy and weekly hematologic and GI toxicity during IMRT. From contours of the bony pelvis and bowel, dose-volume parameters were extracted. Logistic regression models were used to test associations between toxicities and clinical or dosimetric predictors. RESULTS: The median age was 59 years, 81 patients were women and 84 patients received concurrent MMC and 5-fluorouracil (5FU). On multivariate analysis (MVA), the model most predictive of Grade 2 + anemia included the maximum bony pelvis dose (Dmax), female gender, and T stage [p = 0.035, cross validation area under the curve (cvAUC) = 0.66]. The strongest model of Grade 2 + leukopenia included V10 (percentage of pelvic bone volume receiving ≥ 10 Gy) and number of MMC cycles (p = 0.276, cvAUC = 0.57). The model including MMC cycle number and T stage correlated best with Grade 2 + neutropenia (p = 0.306, cvAUC = 0.57). The model predictive of combined Grade 2 + hematologic toxicity (HT) included V10 and T stage (p = 0.016, cvAUC = 0.66). A model including VA45 (absolute bowel volume receiving ≥ 45 Gy) and MOH5 (mean dose to hottest 5% of bowel volume) best predicted diarrhea (p = 0.517, cvAUC = 0.56). CONCLUSION: Dosimetric constraints to the pelvic bones should be integrated into IMRT planning to reduce toxicity, potentially reducing treatment interruptions and improving disease outcomes in ASCC. Specifically, our results indicate that Dmax should be confined to ≤ 57 Gy to minimize anemia and that V10 should be restricted to ≤ 87% to reduce incidence of all HT.

Genome differentiation of Drosophila melanogaster from a microclimate contrast in Evolution Canyon, Israel.

The opposite slopes of "Evolution Canyon" in Israel have served as a natural model system of adaptation to a microclimate contrast. Long-term studies of Drosophila melanogaster populations inhabiting the canyon have exhibited significant interslope divergence in thermal and drought stress resistance, candidate genes, mobile elements, habitat choice, mating discrimination, and wing-shape variation, all despite close physical proximity of the contrasting habitats, as well as substantial interslope migration. To examine patterns of genetic differentiation at the genome-wide level, we used high coverage sequencing of the flies' genomes. A total of 572 genes were significantly different in allele frequency between the slopes, 106 out of which were associated with 74 significantly overrepresented gene ontology (GO) terms, particularly so with response to stimulus and developmental and reproductive processes, thus corroborating previous observations of interslope divergence in stress response, life history, and mating functions. There were at least 37 chromosomal "islands" of interslope divergence and low sequence polymorphism, plausible signatures of selective sweeps, more abundant in flies derived from one (north-facing) of the slopes. Positive correlation between local recombination rate and the level of nucleotide polymorphism was also found.

Nucleolar dominance and maternal control of 45S rDNA expression.

Using a system of interspecies hybrids, trihybrids, and recombinants with varying proportions of genomes from three distinct Xenopus species, we provide evidence for de novo epigenetic silencing of paternal 45 S ribosomal ribonucleic acid (rRNA) genes and their species-dependent expression dominance that escapes transcriptional inactivation after homologous recombination. The same pattern of imprinting is maintained in the offspring from mothers being genetic males (ZZ) sex-reversed to females, indicating that maternal control of ribosomal deoxyribonucleic acid (rDNA) expression is not sex-chromosome linked. Nucleolar dominance (nucleolus underdevelopment) in Xenopus hybrids appears to be associated with a major non-Mendelian reduction in the number of 45 S rDNA gene copies rather than a specific pattern of their expression. The loss of rRNA gene copies in F1 hybrids was non-random with respect to the parental species, with the transcriptionally dominant variant preferentially removed from hybrid zygotes. This dramatic disruption in the structure and function of 45 S rDNA impacts transcriptome patterns of small nucleolar RNAs and messenger RNAs, with genes from the ribosome and oxidative stress pathways being among the most affected. Unorthodoxies of rDNA inheritance and expression may be interpreted as hallmarks of genetic conflicts between parental genomes, as well as defensive epigenetic mechanisms employed to restore genome integrity.

Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay.

PURPOSE: To investigate the association between a validated, gene-expression-based, aggressiveness assay, Oncotype Dx RS, and morphological and texture-based image features extracted from magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective study received Internal Review Board approval and need for informed consent was waived. Between 2006-2012, we identified breast cancer patients with: 1) ER+, PR+, and HER2- invasive ductal carcinoma (IDC); 2) preoperative breast MRI; and 3) Oncotype Dx RS test results. Extracted features included morphological, histogram, and gray-scale correlation matrix (GLCM)-based texture features computed from tumors contoured on pre- and three postcontrast MR images. Linear regression analysis was performed to investigate the association between Oncotype Dx RS and different clinical, pathologic, and imaging features. P < 0.05 was considered statistically significant. RESULTS: Ninety-five patients with IDC were included with a median Oncotype Dx RS of 16 (range: 0-45). Using stepwise multiple linear regression modeling, two MR-derived image features, kurtosis in the first and third postcontrast images and histologic nuclear grade, were found to be significantly correlated with the Oncotype Dx RS with P = 0.0056, 0.0005, and 0.0105, respectively. The overall model resulted in statistically significant correlation with Oncotype Dx RS with an R-squared value of 0.23 (adjusted R-squared = 0.20; P = 0.0002) and a Spearman's rank correlation coefficient of 0.49 (P < 0.0001). CONCLUSION: A model for IDC using imaging and pathology information correlates with Oncotype Dx RS scores, suggesting that image-based features could also predict the likelihood of recurrence and magnitude of chemotherapy benefit.

Radiation Dose to the Penile Structures and Patient-Reported Sexual Dysfunction in Long-Term Prostate Cancer Survivors.

AIM: The involvement of various penile structures in radiotherapy (RT)-induced sexual dysfunction among prostate cancer survivors remains unclear and domains beyond erectile dysfunction such as orgasm, and pain have typically not been considered. The purpose of this study was to investigate sexual dysfunction post-RT for localized prostate cancer and to examine whether radiation dose to different penile structures can explain these symptoms. METHODS: We investigated sexual dysfunction in two treated prostate cancer cohorts and in one non-pelvic-irradiated cohort, 328 sexually active men part of an unselected, population-based study conducted in 2008. The treated subjects were prescribed primary/salvage external-beam RT to 70 Gy@2.0 Gy/fraction. Absorbed RT doses (Dmean and Dmax ) of the corpora cavernosa (CC), the penile bulb (PB), and the total penile structure (CC + PB) were related to 13 patient-reported symptoms on sexual dysfunction by means of factor analysis (FA) and logistic regression. RESULTS: Three distinct symptom domains were identified across all cohorts: "erectile dysfunction" (ED, two to five symptoms), "orgasmic dysfunction" (OD, two to four symptoms), and "pain" (two to three symptoms). The strongest predictor for ED symptoms was CC + PB Dmax (P = 0.001-0.03), CC and PB Dmean predicted OD symptoms equally well (P = 0.03 and 0.02-0.05, respectively), and the strongest predictor for pain symptoms was CC + PB Dmean (P = 0.02-0.03). CONCLUSION: Sexual dysfunction following RT was separated into three main domains with symptoms related to erectile dysfunction, orgasmic dysfunction, and pain. Chances for intact sexual functionality may be increased if dose to the total penile structure can be restricted for these domains in the planning of RT .

Predicting radiation-induced valvular heart damage.

PURPOSE: To develop a predictive multivariate normal tissue complication probability (NTCP) model for radiation-induced heart valvular damage (RVD). The influence of combined heart-lung irradiation on RVD development was included. MATERIAL AND METHODS: Multivariate logistic regression modeling with the least absolute shrinkage and selection operator (LASSO) was used to build an NTCP model to predict RVD based on a cohort of 90 Hodgkin lymphoma patients treated with sequential chemo-radiation therapy. In addition to heart irradiation factors, clinical variables, along with left and right lung dose-volume histogram statistics, were included in the analysis. To avoid overfitting, 10-fold cross-validation (CV) was used for LASSO logistic regression modeling, with 50 reshuffled cycles. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and Spearman's correlation coefficient (Rs). RESULTS: At a median follow-up time of 55 months (range 12-92 months) after the end of radiation treatment, 27 of 90 patients (30%) manifested at least one kind of RVD (mild or moderate), with a higher incidence of left-sided valve defects (64%). Fourteen prognostic factors were frequently selected (more than 100/500 model fits) by LASSO, which included mainly heart and left lung dosimetric variables along with their volume variables. The averaged cross-validated performance was AUC-CV = 0.685 and Rs = 0.293. The overall performance of a final NTCP model for RVD obtained applying LASSO logistic regression to the full dataset was satisfactory (AUC = 0.84, Rs = 0.55, p < 0.001). CONCLUSION: LASSO proved to be an improved and flexible modeling method for variable selection. Applying LASSO, we showed, for the first time, the importance of jointly considering left lung irradiation and left lung volume size in the prediction of subclinical radiation-related heart disease resulting in RVD.