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Background: Cancer‐related cognitive impairment (CRCI) and anxiety co‐occur in patients with cancer. Little is known about mechanisms for the co‐occurrence of these two symptoms. The purposes of this secondary analysis were to evaluate for perturbed pathways associated with the co‐occurrence of self‐reported CRCI and anxiety in patients with low versus high levels of these two symptoms and to identify potential mechanisms for the co‐occurrence of CRCI and anxiety using biological processes common across any perturbed neurodegenerative disease pathways. Methods: Patients completed the Attentional Function Index and the Spielberger State‐Trait Anxiety Inventory six times over two cycles of chemotherapy. Based on findings from a previous latent profile analysis, patients were grouped into none versus both high levels of these symptoms. Gene expression was quantified, and pathway impact analyses were performed. Signaling pathways for evaluation were defined with the Kyoto Encyclopedia of Genes and Genomes database. Results: A total of 451 patients had data available for analysis. Approximately 85.0% of patients were in the none class and 15.0% were in the both high class. Pathway impact analyses identified five perturbed pathways related to neurodegenerative diseases (i.e., amyotrophic lateral sclerosis, Huntington disease, Parkinson disease, prion disease, and pathways of neurodegeneration–multiple diseases). Apoptosis, mitochondrial dysfunction, oxidative stress, and endoplasmic reticulum stress were common biological processes across these pathways. Conclusions: This study is the first to describe perturbations in neurodegenerative disease pathways associated with CRCI and anxiety in patients receiving chemotherapy. These findings provide new insights into potential targets for the development of mechanistically based interventions. This study provides new information on associations between the co‐occurrence of cancer‐related cognitive impairment and anxiety and perturbations in neurodegenerative disease pathways. These findings suggest that biological processes associated with neurodegeneration may be associated with cancer‐related cognitive impairment and anxiety in patients with cancer receiving chemotherapy. [ABSTRACT FROM AUTHOR]

Purpose: One plausible mechanistic hypothesis is the potential contribution of inflammatory mechanisms to shortness of breath. This study was aimed to evaluate for associations between the occurrence of shortness of breath and perturbations in inflammatory pathways. Methods: Patients with cancer reported the occurrence of shortness of breath six times over two cycles of chemotherapy. Latent class analysis was used to identify subgroups of patients with distinct shortness of breath occurrence profiles (i.e., none (70.5%), decreasing (8.2%), increasing (7.8%), high (13.5%)). Using an extreme phenotype approach, whole transcriptome differential gene expression and pathway impact analyses were performed to evaluate for perturbed signaling pathways associated with shortness of breath between the none and high classes. Two independent samples (RNA-sequencing (n = 293) and microarray (n = 295) methodologies) were evaluated. Fisher’s combined probability method was used to combine these results to obtain a global test of the null hypothesis. In addition, an unweighted knowledge network was created using the specific pathway maps to evaluate for interconnections among these pathways. Results: Twenty-nine Kyoto Encyclopedia of Genes and Genomes inflammatory signaling pathways were perturbed. The mitogen-activated protein kinase signaling pathway node had the highest closeness, betweenness, and degree scores. In addition, five common respiratory disease-related pathways, that may share mechanisms with cancer-related shortness of breath, were perturbed. Conclusions: Findings provide preliminary support for the hypothesis that inflammation contribute to the occurrence of shortness of breath in patients with cancer. In addition, the mechanisms that underlie shortness of breath in oncology patients may be similar to other respiratory diseases. [ABSTRACT FROM AUTHOR]

Background: Moderate to severe fatigue occurs in up to 94% of patients with cancer. Recent evidence suggests that morning and evening fatigue are distinct dimensions of physical fatigue. The purposes of this study were to evaluate the transcriptome for common and distinct perturbed inflammatory pathways in patients receiving chemotherapy who reported low versus high levels of morning or low versus high levels of evening cancer‐related fatigue. Methods: Patients completed questionnaires during the week prior to their chemotherapy treatment. Severity of morning and evening fatigue was evaluated using the Lee Fatigue Scale. Gene expression and pathway impact analyses (PIA) were performed in two independent samples using RNA‐sequencing (n = 357) and microarray (n = 360). Patterns of interactions between and among these perturbed pathways were evaluated using a knowledge network (KN). Results: Across the PIA, nine perturbed pathways (FDR < 0.025) were common to both morning and evening fatigue, six were distinct for morning fatigue, and four were distinct for evening fatigue. KN (19 nodes, 39 edges) identified the phosphatidylinositol 3‐kinase (PI3K)‐Akt pathway node (perturbed in evening fatigue) with the highest betweenness (0.255) and closeness (0.255) centrality indices. The next highest betweenness centrality indices were seen in pathways perturbed in evening fatigue (i.e., nuclear factor kappa B: 0.200, natural killer cell‐mediated cytotoxicity: 0.178, mitogen‐activated protein kinase: 0.175). Conclusions: This study describes perturbations in common and distinct inflammatory pathways associated with morning and/or evening fatigue. PI3K‐Akt was identified as a bottleneck pathway. The analysis identified potential targets for therapeutic interventions for this common and devastating clinical problem. [ABSTRACT FROM AUTHOR]

There are currently no clinical strategies utilizing tumor gene expression to inform therapeutic selection for patients with head and neck squamous cell carcinoma (HNSCC). One of the challenges in developing predictive biomarkers is the limited characterization of preclinical HNSCC models. Patient-derived xenografts (PDXs) are increasingly recognized as translationally relevant preclinical avatars for human tumors; however, the overall transcriptomic concordance of HNSCC PDXs with primary human HNSCC is understudied, especially in human papillomavirus-associated (HPV+) disease. Here, we characterized 64 HNSCC PDXs (16 HPV+ and 48 HPV-) at the transcriptomic level using RNA-sequencing. The range of human-specific reads per PDX varied from 64.6%-96.5%, with a comparison of the most differentially expressed genes before and after removal of mouse transcripts revealing no significant benefit to filtering out mouse mRNA reads in this cohort. We demonstrate that four previously established HNSCC molecular subtypes found in The Cancer Genome Atlas (TCGA) are also clearly recapitulated in HNSCC PDXs. Unsupervised hierarchical clustering yielded a striking natural division of HNSCC PDXs by HPV status, with C19orf57 (BRME1), a gene previously correlated with positive response to cisplatin in cervical cancer, among the most significantly differentially expressed genes between HPV+ and HPV- PDXs. In vivo experiments demonstrated a possible relationship between increased C19orf57 expression and superior anti-tumor responses of PDXs to cisplatin, which should be investigated further. These findings highlight the value of PDXs as models for HPV+ and HPV- HNSCC, providing a resource for future discovery of predictive biomarkers to guide treatment selection in HNSCC. [ABSTRACT FROM AUTHOR]

Background: Methods for inferring the three-dimensional (3D) configuration of chromatin from conformation capture assays that provide strictly pairwise interactions, notably Hi-C, utilize the attendant contact matrix as input. More recent assays, in particular split-pool recognition of interactions by tag extension (SPRITE), capture multi-way interactions instead of solely pairwise contacts. These assays yield contacts that straddle appreciably greater genomic distances than Hi-C, in addition to instances of exceptionally high-order chromatin interaction. Such attributes are anticipated to be consequential with respect to 3D genome reconstruction, a task yet to be undertaken with multi-way contact data. However, performing such 3D reconstruction using distance-based reconstruction techniques requires framing multi-way contacts as (pairwise) distances. Comparing approaches for so doing, and assessing the resultant impact of long-range and multi-way contacts, are the objectives of this study. Results: We obtained 3D reconstructions via multi-dimensional scaling under a variety of weighting schemes for mapping SPRITE multi-way contacts to pairwise distances. Resultant configurations were compared following Procrustes alignment and relationships were assessed between associated Procrustes root mean square errors and key features such as the extent of multi-way and/or long-range contacts. We found that these features had surprisingly limited influence on 3D reconstruction, a finding we attribute to their influence being diminished by the preponderance of pairwise contacts. Conclusion: Distance-based 3D genome reconstruction using SPRITE multi-way contact data is not appreciably affected by the weighting scheme used to convert multi-way interactions to pairwise distances. [ABSTRACT FROM AUTHOR]

Objectives: While the gastrointestinal symptom cluster (GISC) is common in patients receiving chemotherapy, limited information is available on its underlying mechanism(s). Emerging evidence suggests a role for inflammatory processes through the actions of the nuclear factor kappa B (NF-κB) signaling pathway. This study evaluated for associations between a GISC and levels of DNA methylation for genes within this pathway. Methods: Prior to their second or third cycle of chemotherapy, 1071 outpatients reported symptom occurrence using the Memorial Symptom Assessment Scale. A GISC was identified using exploratory factor analysis. Differential methylation analyses were performed in two independent samples using EPIC (n = 925) and 450K (n = 146) microarrays. Trans expression-associated CpG (eCpG) loci for 56 NF-κB signaling pathway genes were evaluated. Loci significance were assessed using an exploratory false discovery rate (FDR) of 25% for the EPIC sample. For the validation assessment using the 450K sample, significance was assessed at an unadjusted p -value of 0.05. Results: For the EPIC sample, the GISC was associated with increased expression of lymphotoxin beta (LTB) at one differentially methylated trans eCpG locus (cg03171795; FDR = 0.168). This association was not validated in the 450K sample. Conclusions: This study is the first to identify an association between a GISC and epigenetic regulation of a gene that is involved in the initiation of gastrointestinal immune responses. Findings suggest that increased LTB expression by hypermethylation of a trans eCpG locus is involved in the occurrence of this cluster in patients receiving chemotherapy. LTB may be a potential therapeutic target for this common cluster. [ABSTRACT FROM AUTHOR]

271 Background: Many studies have shown that fatigue is associated with poor outcomes such as quality of life and physical function in breast cancer survivors.Early treatment of symptoms that result in persistent fatigue may be a strategy to mitigate long-term fatigue.As a result, we studied symptoms and developed risk estimates for patients that are most likely to develop fatigue by pre-surgery and follow up among patients treated on the I-SPY 2 neoadjuvant trial. I-SPY 2 is a phase-II adaptive trial for high-risk early-stage breast cancer patients. Methods: In this retrospective cohort study of 306 women undergoing neoadjuvant chemotherapy +/- targeted therapy, patients complete electronic surveys of symptoms via 34 PRO-CTCAE items and QOL via 9 PROMIS instruments. Fatigue is collected as part of the PROMIS survey and scored using Health Measures. Study timepoints are screening (SC), weekly study treatment (ST), 12 weeks (12w), pre-surgery (PS) and at 1, 6, 12 and 24 months (FU1-FU4) following surgery. Correlation between symptoms and fatigue at equal timepoints was quantified using spearman correlations and BH-adjusted p-values. For symptom odds analysis, symptoms were binarized such that those that had an average grade of mild or above (severity), rarely or above (frequency) or a little bit or above (interference) between ST weeks 1-6 were considered present. Clinically significant (CS) increase in fatigue was defined as an increase >=0.5 standard deviation (SD) in mean from baseline. Logistic regression was used to determine odds ratios of factors associated with fatigue. Results: Baseline PROMIS fatigue mean score was 47.14 (SD 9.31) with a median of 47.45. A CS increase in fatigue from baseline was seen in 55% of patients at 12w, and 61% of patients pre-surgery. Of the patients that had a CS increase in fatigue at any point prior to surgery, 45.7% had persistent fatigue through FU1-4. Symptoms associated with Fatigue at 12w were shortness of breath, headache, and nausea (P< 0.03). At PS, fatigue was significantly correlated with GI symptoms, neuropathy, and shortness of breath (P< 0.03). Risk estimates for patients that filled out symptom surveys at early timepoints (weeks 1-6) and also had fatigue were evaluated. Patients with early GU (genitourinary) symptoms had higher risk of fatigue at IR (OR=9.33, p=0.02, 88.9% in cases vs 33.3% in controls). Patients with early GI symptoms at PS (OR=8.56, p=0.03, 90.1% vs 50%), or FU2-4 (OR= 5.88 and 7.36, p= 0.04 and 0.02, 80% vs 39.1% and 81.8% vs 36.3%) also had a higher risk of developing fatigue. Conclusions: A CS increase in fatigue during treatment and follow up is seen in the majority of patients and is associated with a large number of persistent symptoms. [ABSTRACT FROM AUTHOR]

Introduction: COVID-19 has caused tremendous death and suffering since it first emerged in 2019. Soon after its emergence, models were developed to help predict the course of various disease metrics, and these models have been relied upon to help guide public health policy. Methods: Here we present a method called COVIDNearTerm to "forecast" hospitalizations in the short term, two to four weeks from the time of prediction. COVIDNearTerm is based on an autoregressive model and utilizes a parametric bootstrap approach to make predictions. It is easy to use as it requires only previous hospitalization data, and there is an open-source R package that implements the algorithm. We evaluated COVIDNearTerm on San Francisco Bay Area hospitalizations and compared it to models from the California COVID Assessment Tool (CalCAT). Results: We found that COVIDNearTerm predictions were more accurate than the CalCAT ensemble predictions for all comparisons and any CalCAT component for a majority of comparisons. For instance, at the county level our 14-day hospitalization median absolute percentage errors ranged from 16 to 36%. For those same comparisons, the CalCAT ensemble errors were between 30 and 59%. Conclusion: COVIDNearTerm is a simple and useful tool for predicting near-term COVID-19 hospitalizations. [ABSTRACT FROM AUTHOR]

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. Thirdhand smoke (THS) is the residual tobacco contamination that remains after the smoke clears. We investigated the effects of THS exposure in utero and during early life in a transgenic Cdkn2a knockout mouse model that is vulnerable to the development of leukemia/lymphoma. Female mice, and their offspring, were exposed from the first day of pregnancy to weaning. Plasma cytokines, body weight and hematologic parameters were measured in the offspring. To investigate THS exposure effects on the development of leukemia/lymphoma, bone marrow (BM) was collected from control and THS-exposed mice and transplanted into BM-ablated recipient mice, which were followed for tumor development for 1 year. We found that in utero and early-life THS exposure caused significant changes in plasma cytokine concentrations and in immune cell populations; changes appeared more pronounced in male mice. Spleen (SP) and BM B-cell populations were significantly lower in THS-exposed mice. We furthermore observed that THS exposure increased the leukemia/lymphoma-free survival in BM transplantation recipient mice, potentially caused by THS-induced B-cell toxicity. A trend towards increased solid tumors in irradiated mice reconstituted with THS-exposed BM stimulates the hypothesis that the immunosuppressive effects of in utero and early-life THS exposure might contribute to carcinogenesis by lowering the host defense to other toxic exposures. Our study adds to expanding evidence that THS exposure alters the immune system and that in utero and early-life developmental periods represent vulnerable windows of susceptibility for these effects. [ABSTRACT FROM AUTHOR]

Many complex diseases are caused by a variety of both genetic and environmental factors acting in conjunction. To help understand these relationships, nonparametric methods that use aggregate learning have been developed such as random forests and conditional forests. Molinaro et al. (2010) described a powerful, single model approach called partDSA that has the advantage of producing interpretable models. We propose two extensions to the partDSA algorithm called bagged partDSA and boosted partDSA. These algorithms achieve higher prediction accuracies than individual partDSA objects through aggregating over a set of partDSA objects. Further, by using partDSA objects in the ensemble, each base learner creates decision rules using both “and” and “or” statements, which allows for natural logical constructs. We also provide four variable ranking techniques that aid in identifying the most important individual factors in the models. In the regression context, we compared bagged partDSA and boosted partDSA to random forests and conditional forests. Using simulated and real data, we found that bagged partDSA had lower prediction error than the other methods if the data were generated by a simple logic model, and that it performed similarly for other generating mechanisms. We also found that boosted partDSA was effective for a particularly complex case. Taken together these results suggest that the new methods are useful additions to the ensemble learning toolbox. We implement these algorithms as part of the partDSA R package. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative disorder of childhood caused by mutations in the Ras pathway. Outcomes in JMML vary markedly from spontaneous resolution to rapid relapse after hematopoietic stem cell transplantation. Here, we hypothesized that DNA methylation patterns would help predict disease outcome and therefore performed genome-wide DNA methylation profiling in a cohort of 39 patients. Unsupervised hierarchical clustering identifies three clusters of patients. Importantly, these clusters differ significantly in terms of 4-year event-free survival, with the lowest methylation cluster having the highest rates of survival. These findings were validated in an independent cohort of 40 patients. Notably, all but one of 14 patients experiencing spontaneous resolution cluster together and closer to 22 healthy controls than to other JMML cases. Thus, we show that DNA methylation patterns in JMML are predictive of outcome and can identify the patients most likely to experience spontaneous resolution. [ABSTRACT FROM AUTHOR]

Common environmental contaminants such as bisphenols and phthalates and persistent contaminants such as polychlorinated biphenyls are thought to influence tissue homeostasis and carcinogenesis by acting as disrupters of endocrine function. In this study we investigated the direct effects of exposure to bisphenol A (BPA), mono-n-butyl phthalate (Pht), and polychlorinated biphenyl 153 (PCB153) on the proteome of primary organotypic cultures of the mouse mammary gland. At low-nanomolar doses each of these agents induced distinct effects on the proteomes of these cultures. Although BPA treatment produced effects that were similar to those induced by estradiol, there were some notable differences, including a reduction in the abundance of retinoblastomaassociated protein and increases in the Rho GTPases Ras-related C3 botulinum toxin substrate 1 (Rac1) and cell division cycle protein CDC42. Both Pht and PCB153 induced changes that were distinct from those induced by estrogen, including decreased levels of the transcriptional corepressor C-terminal binding protein 1. Interestingly, the three chemicals appeared to alter the abundance of distinct splice forms of many proteins as well as the abundance of several proteins that regulate RNA splicing. Our combined results indicate that the three classes of chemical have distinct effects on the proteome of normal mouse mammary cultures, some estrogen-like but most estrogen independent, that influence diverse biological processes including apoptosis, cell adhesion, and proliferation. [ABSTRACT FROM AUTHOR]

Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64–79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome. [ABSTRACT FROM AUTHOR]

Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative neoplasm (MPN) of childhood with a poor prognosis. Mutations in NF1, NRAS, KRAS, PTPN11 or CBL occur in 85% of patients, yet there are currently no risk stratification algorithms capable of predicting which patients will be refractory to conventional treatment and could therefore be candidates for experimental therapies. In addition, few molecular pathways aside from the RAS-MAPK pathway have been identified that could serve as the basis for such novel therapeutic strategies. We therefore sought to genomically characterize serial samples from patients at diagnosis through relapse and transformation to acute myeloid leukemia to expand knowledge of the mutational spectrum in JMML. We identified recurrent mutations in genes involved in signal transduction, splicing, Polycomb repressive complex 2 (PRC2) and transcription. Notably, the number of somatic alterations present at diagnosis appears to be the major determinant of outcome. [ABSTRACT FROM AUTHOR]

Desmoplastic melanoma is an uncommon variant of melanoma with sarcomatous histology, distinct clinical behavior and unknown pathogenesis. We performed low-coverage genome and high-coverage exome sequencing of 20 desmoplastic melanomas, followed by targeted sequencing of 293 genes in a validation cohort of 42 cases. A high mutation burden (median of 62 mutations/Mb) ranked desmoplastic melanoma among the most highly mutated cancers. Mutation patterns strongly implicate ultraviolet radiation as the dominant mutagen, indicating a superficially located cell of origin. Newly identified alterations included recurrent promoter mutations of NFKBIE, encoding NF-κB inhibitor ɛ (IκBɛ), in 14.5% of samples. Common oncogenic mutations in melanomas, in particular in BRAF (encoding p.Val600Glu) and NRAS (encoding p.Gln61Lys or p.Gln61Arg), were absent. Instead, other genetic alterations known to activate the MAPK and PI3K signaling cascades were identified in 73% of samples, affecting NF1, CBL, ERBB2, MAP2K1, MAP3K1, BRAF, EGFR, PTPN11, MET, RAC1, SOS2, NRAS and PIK3CA, some of which are candidates for targeted therapies. [ABSTRACT FROM AUTHOR]

Individual bacteria and shifts in the composition of the microbiome have been associated with human diseases including cancer. To investigate changes in the microbiome associated with oral cancers, we profiled cancers and anatomically matched contralateral normal tissue from the same patient by sequencing 16S rDNA hypervariable region amplicons. In cancer samples from both a discovery and a subsequent confirmation cohort, abundance of Firmicutes (especially Streptococcus) and Actinobacteria (especially Rothia) was significantly decreased relative to contralateral normal samples from the same patient. Significant decreases in abundance of these phyla were observed for pre-cancers, but not when comparing samples from contralateral sites (tongue and floor of mouth) from healthy individuals. Weighted UniFrac principal coordinates analysis based on 12 taxa separated most cancers from other samples with greatest separation of node positive cases. These studies begin to develop a framework for exploiting the oral microbiome for monitoring oral cancer development, progression and recurrence. [ABSTRACT FROM AUTHOR]

Motivation: The translational landscape of diverse cellular systems remains largely uncharacterized. A detailed understanding of the control of gene expression at the level of messenger RNA translation is vital to elucidating a systems-level view of complex molecular programs in the cell. Establishing the degree to which such post-transcriptional regulation can mediate specific phenotypes is similarly critical to elucidating the molecular pathogenesis of diseases such as cancer. Recently, methods for massively parallel sequencing of ribosome-bound fragments of messenger RNA have begun to uncover genome-wide translational control at codon resolution. Despite its promise for deeply characterizing mammalian proteomes, few analytical methods exist for the comprehensive analysis of this paired RNA and ribosome data.Results: We describe the Babel framework, an analytical methodology for assessing the significance of changes in translational regulation within cells and between conditions. This approach facilitates the analysis of translation genome-wide while allowing statistically principled gene-level inference. Babel is based on an errors-in-variables regression model that uses the negative binomial distribution and draws inference using a parametric bootstrap approach. We demonstrate the operating characteristics of Babel on simulated data and use its gene-level inference to extend prior analyses significantly, discovering new translationally regulated modules under mammalian target of rapamycin (mTOR) pathway signaling control.Availability: The Babel framework is freely available as source code at http://taylorlab.ucsf.edu/software_data.html.Contact: barry.taylor@ucsf.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease. [ABSTRACT FROM AUTHOR]

Large-scale integrated cancer genome characterization efforts including the cancer genome atlas and the cancer cell line encyclopedia have created unprecedented opportunities to study cancer biology in the context of knowing the entire catalog of genetic alterations. A clinically important challenge is to discover cancer subtypes and their molecular drivers in a comprehensive genetic context. Curtis et al. [Nature (2012) 486(7403):346-352] has recently shown that integrative clustering of copy number and gene expression in 2,000 breast tumors reveals novel subgroups beyond the classic expression subtypes that show distinct clinical outcomes. To extend the scope of integrative analysis for the inclusion of somatic mutation data by massively parallel sequencing, we propose a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling. The core idea is motivated by the hypothesis that diverse molecular phenotypes can be predicted by a set of orthogonal latent variables that represent distinct molecular drivers, and thus can reveal tumor subgroups of biological and clinical importance. Using the cancer cell line encyclopedia dataset, we demonstrate our method can accurately group cell lines by their cell-of-origin for several cancer types, and precisely pinpoint their known and potential cancer driver genes. Our integrative analysis also demonstrates the power for revealing subgroups that are not lineage-dependent, but consist of different cancer types driven by a common genetic alteration. Application of the cancer genome atlas colorectal cancer data reveals distinct integrated tumor subtypes, suggesting different genetic pathways in colon cancer progression. [ABSTRACT FROM AUTHOR]

Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery. [ABSTRACT FROM AUTHOR]

Motivation: High-throughput techniques facilitate the simultaneous measurement of DNA copy number at hundreds of thousands of sites on a genome. Older techniques allow measurement only of total copy number, the sum of the copy number contributions from the two parental chromosomes. Newer single nucleotide polymorphism (SNP) techniques can in addition enable quantifying parent-specific copy number (PSCN). The raw data from such experiments are two-dimensional, but are unphased. Consequently, inference based on them necessitates development of new analytic methods.Methods: We have adapted and enhanced the circular binary segmentation (CBS) algorithm for this purpose with focus on paired test and reference samples. The essence of paired parent-specific CBS (Paired PSCBS) is to utilize the original CBS algorithm to identify regions of equal total copy number and then to further segment these regions where there have been changes in PSCN. For the final set of regions, calls are made of equal parental copy number and loss of heterozygosity (LOH). PSCN estimates are computed both before and after calling.Results: The methodology is evaluated by simulation and on glioblastoma data. In the simulation, PSCBS compares favorably to established methods. On the glioblastoma data, PSCBS identifies interesting genomic regions, such as copy-neutral LOH.Availability: The Paired PSCBS method is implemented in an open-source R package named PSCBS, available on CRAN (http://cran.r-project.org/).Contact: olshena@biostat.ucsf.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Pancreatic ductal adenocarcinoma (PDA) is a lethal disease. Overall survival is typically 6 months from diagnosis. Numerous phase 3 trials of agents effective in other malignancies have failed to benefit unselected PDA populations, although patients do occasionally respond. Studies in other solid tumors have shown that heterogeneity in response is determined, in part, by molecular differences between tumors. Furthermore, treatment outcomes are improved by targeting drugs to tumor subtypes in which they are selectively effective, with breast and lung cancers providing recent examples. Identification of PDA molecular subtypes has been frustrated by a paucity of tumor specimens available for study. We have overcome this problem by combined analysis of transcriptional profiles of primary PDA samples from several studies, along with human and mouse PDA cell lines. We define three PDA subtypes: classical, quasimesenchymal and exocrine-like, and we present evidence for clinical outcome and therapeutic response differences between them. We further define gene signatures for these subtypes that may have utility in stratifying patients for treatment and present preclinical model systems that may be used to identify new subtype specific therapies. [ABSTRACT FROM AUTHOR]

When a cancer patient develops a new tumor it is necessary to determine if it is a recurrence (metastasis) of the original cancer, or an entirely new occurrence of the disease. This is accomplished by assessing the histo-pathology of the lesions. However, there are many clinical scenarios in which this pathological diagnosis is difficult. Since each tumor is characterized by a distinct pattern of somatic mutations, a more definitive diagnosis is possible in principle in these difficult clinical scenarios by comparing the two patterns. In this article we develop and evaluate a statistical strategy for this comparison when the data are derived from array copy number data, designed to identify all of the somatic allelic gains and losses across the genome. First a segmentation algorithm is used to estimate the regions of allelic gain and loss. The correlation in these patterns between the two tumors is assessed, and this is complemented with more precise quantitative comparisons of each plausibly clonal mutation within individual chromosome arms. The results are combined to determine a likelihood ratio to distinguish clonal tumor pairs (metastases) from independent second primaries. Our data analyses show that in many cases a strong clonal signal emerges. Sensitivity analyses show that most of the diagnoses are robust when the data are of high quality. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: The high mortality rate associated with epithelial ovarian carcinoma (EOC) reflects diagnosis commonly at an advanced stage, but improved early detection is hindered by uncertainty as to the histologic origin and early natural history of this malignancy. Methodology/Principal Findings: Here we report combined molecular genetic and morphologic analyses of normal human ovarian tissues and early stage cancers, from both BRCA mutation carriers and the general population, indicating that EOCs frequently arise from dysplastic precursor lesions within epithelial inclusion cysts. In pathologically normal ovaries, molecular evidence of oncogenic stress was observed specifically within epithelial inclusion cysts. To further explore potential very early events in ovarian tumorigenesis, ovarian tissues from women not known to be at high risk for ovarian cancer were subjected to laser catapult microdissection and gene expression profiling. These studies revealed a quasineoplastic expression signature in benign ovarian cystic inclusion epithelium compared to surface epithelium, specifically with respect to genes affecting signal transduction, cell cycle control, and mitotic spindle formation. Consistent with this gene expression profile, a significantly higher cell proliferation index (increased cell proliferation and decreased apoptosis) was observed in histopathologically normal ovarian cystic compared to surface epithelium. Furthermore, aneuploidy was frequently identified in normal ovarian cystic epithelium but not in surface epithelium. Conclusions/Significance: Together, these data indicate that EOC frequently arises in ovarian cystic inclusions, is preceded by an identifiable dysplastic precursor lesion, and that increased cell proliferation, decreased apoptosis, and aneuploidy are likely to represent very early aberrations in ovarian tumorigenesis. [ABSTRACT FROM AUTHOR]

Background: The goal of our study was to investigate the molecular underpinnings associated with the relatively aggressive clinical behavior of prostate cancer (PCa) in African American (AA) compared to Caucasian American (CA) patients using a genome-wide approach. Methods: AA and CA patients treated with radical prostatectomy (RP) were frequency matched for age at RP, Gleason grade, and tumor stage. Array-CGH (BAC SpectralChip2600) was used to identify genomic regions with significantly different DNA copy number between the groups. Gene expression profiling of the same set of tumors was also evaluated using Affymetrix HG-U133 Plus 2.0 arrays. Concordance between copy number alteration and gene expression was examined. A second aCGH analysis was performed in a larger validation cohort using an oligo-based platform (Agilent 244K). Results: BAC-based array identified 27 chromosomal regions with significantly different copy number changes between the AA and CA tumors in the first cohort (Fisher's exact test, P < 0.05). Copy number alterations in these 27 regions were also significantly associated with gene expression changes. aCGH performed in a larger, independent cohort of AA and CA tumors validated 4 of the 27 (15%) most significantly altered regions from the initial analysis (3q26, 5p15-p14, 14q32, and 16p11). Functional annotation of overlapping genes within the 4 validated regions of AA/CA DNA copy number changes revealed significant enrichment of genes related to immune response. Conclusions: Our data reveal molecular alterations at the level of gene expression and DNA copy number that are specific to African American and Caucasian prostate cancer and may be related to underlying differences in immune response. [ABSTRACT FROM AUTHOR]

Background: Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively. Results: We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the DGV and several other variables, including segment's length, height, closeness to a telomere or centromere and occurrence in other patients. The models are fitted on data from glioblastoma and their corresponding normal samples that were collected as part of The Cancer Genome Atlas project and hybridized to Agilent 244 K arrays. Conclusions: Using the DGV alone CNVs in the test set can be correctly identified with about 85% accuracy if the outliers are removed before segmentation and with 72% accuracy if the outliers are included, and additional variables improve the prediction by about 2-3% and 12%, respectively. Final models applied to data from ovarian tumors have about 90% accuracy with all the variables and 86% accuracy with the DGV alone. [ABSTRACT FROM AUTHOR]

Motivation: The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment. [ABSTRACT FROM PUBLISHER]

The principal curative agent in the front-line treatment of patients with diffuse large B-cell lymphoma (DLBCL) is the anthracycline, doxorubicin. To define pathways that may have a functional role in the response of DLBCL in vivo to doxorubicin-based therapies, seven DLBCL cell lines were treated with doxorubicin and the cellular response evaluated. Expression profiling of responses revealed changes in levels of genes consistent with discrete pathway activation that were confirmed functionally. The two most sensitive cell lines (Ly3 and Ly10) displayed activation of the TP53 pathway but not in the remaining five (Ly1, Ly2, Ly4, Ly7 and Ly8), where TP53 mutations were identified. In this latter group, a G2/M delay was invoked. NF-κB pathway activation was evident in Ly1 which with Ly4 displayed the most chemoresistant response. Treatment of Ly1 after doxorubicin with the proteasomic inhibitor, bortezomib, additively increased the cytotoxic effect of doxorubicin. Chemoresistance of Ly4 was associated with loss of chromosome 2 (0-9 Mbp) that in vivo was highly correlated with adverse outcome. Thus, the response of DLBCL in vivo and in vitro is defined by several distinct molecular and genetic pathways which is, perhaps, not surprising given the heterogeneous clinical, morphologic and genetic nature of DLBCL. [ABSTRACT FROM AUTHOR]

We performed a three-phase genome-wide association study (GWAS) using cases and controls from a genetically isolated population, Ashkenazi Jews (AJ), to identify loci associated with breast cancer risk. In the first phase, we compared allele frequencies of 150,080 SNPs in 249 high-risk, BRCA1/2 mutation-negative AJ familial cases and 299 cancer-free AJ controls using χ² and the Cochran-Armitage trend tests. In the second phase, we genotyped 343 SNPs from 123 regions most significantly associated from stage 1, including 4 SNPs from the FGFR2 region, in 950 consecutive AJ breast cancer cases and 979 age-matched AJ controls. We replicated major associations in a third independent set of 243 AJ cases and 187 controls. We obtained a significant allele P value of association with AJ breast cancer in the FGFR2 region (P = 1.5 × 10-5, odds ratio (OR) 1.26, 95% confidence interval (Cl) 1.13-1.40 at rs1078806 for all phases combined). In addition, we found a risk locus in a region of chromosome 6q22.33 (P = 2.9 × 10-8, OR 1.41, 95% Cl 1.25-1.59 at rs2180341). Using several SNPs at each implicated locus, we were able to verify associations and impute haplotypes. The major haplotype at the 6q22.33 locus conferred protection from disease, whereas the minor haplotype conferred risk. Candidate genes in the 6q22.33 region include ECHDC1, which encodes a protein involved in mitochondrial fatty acid oxidation, and also RNF146, which encodes a ubiquitin protein ligase, both known pathways in breast cancer pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Genetic isolates such as the Ashkenazi Jews (AJ) potentially offer advantages in mapping novel loci in whole genome disease association studies. To analyze patterns of genetic variation in AJ, genotypes of 101 healthy individuals were determined using the Affymetrix EAv3 500 K SNP array and compared to 60 CEPH-derived HapMap (CEU) individuals. 435,632 SNPs overlapped and met annotation criteria in the two groups. Results: A small but significant global difference in allele frequencies between AJ and CEU was demonstrated by a mean FST of 0.009 (P < 0.001); large regions that differed were found on chromosomes 2 and 6. Haplotype blocks inferred from pairwise linkage disequilibrium (LD) statistics (Haploview) as well as by expectation-maximization haplotype phase inference (HAP) showed a greater number of haplotype blocks in AJ compared to CEU by Haploview (50,397 vs. 44,169) or by HAP (59,269 vs. 54,457). Average haplotype blocks were smaller in AJ compared to CEU (e.g., 36.8 kb vs. 40.5 kb HAP). Analysis of global patterns of local LD decay for closely-spaced SNPs in CEU demonstrated more LD, while for SNPs further apart, LD was slightly greater in the AJ. A likelihood ratio approach showed that runs of homozygous SNPs were approximately 20% longer in AJ. A principal components analysis was sufficient to completely resolve the CEU from the AJ. Conclusion: LD in the AJ versus was lower than expected by some measures and higher by others. Any putative advantage in whole genome association mapping using the AJ population will be highly dependent on regional LD structure. [ABSTRACT FROM AUTHOR]

Human embryonal carcinoma (EC) cell lines exhibit considerable heterogeneity in their levels of pluripotency. Thus, NT2/D1 cells differentiate into neural lineages upon exposure to all-trans retinoic acid (ATRA) and non-neural epithelial lineages upon exposure to bone morphogenetic protein-2 (BMP-2). In contrast, 27X-1 cells differentiate into extra-embryonic endodermal (ExE) cells upon treatment with either morphogen. To understand the molecular basis for the differential responses of the two cell lines, we performed gene expression profiling at the undifferentiated EC cell line state to identify constitutive differences in gene expression. NT2/D1 cells preferentially expressed transcripts associated with neurectodermal development, whereas 27X-1 cells expressed high levels of transcripts associated with mesendodermal characteristics. We then determined temporal expression profiles of 27X-1 cells during ExE differentiation upon treatment with ATRA and BMP-2 and compared the data with changes in gene expression observed during BMP-2- and ATRA-induced differentiation of NT2/D1 cells. ATRA and BMP-2 induced distinct sets of transcription factors and phenotypic markers in the two EC cell lines, underlying distinct lineage choices. Although 27X-1 differentiation yielded comprehensive gene expression profiles of parietal endodermal lineages, we were able to use the combined analysis of 27X-1 data with data derived from yolk sac tumors for the identification of transcripts associated with visceral endoderm formation. Our results demonstrate constitutive differences in the levels of pluripotency between NT2/D1 and 27X-1 cells that correlate with lineage potential. This study also demonstrates that EC cells can serve as robust models to investigate early lineage choices during both embryonic and extra-embryonic human development. [ABSTRACT FROM AUTHOR]

By means of in vivo selection, transcriptomic analysis, functional verification and clinical validation, here we identify a set of genes that marks and mediates breast cancer metastasis to the lungs. Some of these genes serve dual functions, providing growth advantages both in the primary tumour and in the lung microenvironment. Others contribute to aggressive growth selectively in the lung. Many encode extracellular proteins and are of previously unknown relevance to cancer metastasis. [ABSTRACT FROM AUTHOR]

Male adult germ cell tumors (GCTs) comprise two major histologic groups: seminomas and nonseminomas. Nonseminomatous GCTs (NSGCTs) can be further divided into embryonal carcinoma (EC), teratoma (T), yolk sac tumor (YS), and choriocarcinoma (CC) on the basis of the lineage differentiation that they exhibit. NSGCTs frequently present as mixed tumors consisting of two or more histological subtypes, often limiting correlative studies of clinical and molecular features to histology. We sought to develop a molecular classifier that could predict the predominant histologic subtype within mixed NSGCT tumor samples. The expression profiles of 84 NSGCTs (42 pure and 42 mixed) and normal age-matched testes were obtained using Affymetrix microarrays. Using prediction analysis for microarrays, we identified 146 transcripts that classified the histology of pure NSGCTs samples with 93% accuracy. When applied to mixed NSGCTs, the classifier predicted a histology that was consistent with one of the reported components in 93% of cases. Among the predictive transcripts were CGB (high in CC), LCN2 (high in T), BMP2 (high in YS), and POU5F1 (high in EC). Thus, the expression-based classifier accurately assigned a single predominant histology to mixed NSGCTs, and identified transcripts differentially expressed between histologic components with relevance to NSGCT differentiation.Oncogene (2005) 24, 5101–5107. doi:10.1038/sj.onc.1208694; published online 2 May 2005 [ABSTRACT FROM AUTHOR]

Pluripotent human embryonal carcinoma NTera2/cloneD1 (NT2/D1) cells respond to multiple vertebrate patterning factors and offer a unique model system to investigate the signaling events associated with lineage determination and cell differentiation. Here, we define the temporal changes in global gene expression patterns in NT2/D1 cells upon treatment with bone morphogenetic protein-2 (BMP-2). Exposure to BMP-2 rapidly induced the expression of several transcription factors involved in establishing non-neural ectodermal fate followed by the appearance of epithelial-specific markers. Subsequent loss of stem cell markers was coupled to gene expression changes associated with decreased proliferative activity. Temporal clustering of gene expression patterns revealed a concurrent down-regulation of multiple transcripts involved in neurogenesis, neurite outgrowth, and axonal guidance, suggesting that the BMP-mediated differentiation process involves pro-epithelial as well as anti-neurogenic mechanisms. In addition, increased expression of smooth muscle markers both by gene expression and immunohistochemistry was detected. Several neural crest markers were induced preceding such a differentiation, compatible with a neural crest origin of NT2/D1-derived smooth muscle cells. Comparison of changes in transcript expression between BMP-2-induced epithelial versus all-trans-retinoic acid (ATRA)-induced neural differentiation revealed potential candidates for regulation of BMP-2 signaling and suppression of neural fate by BMP-2. This study suggests that BMP-2-induced differentiation of NT2/D1 cells provides a powerful assay to study early human epithelial and smooth muscle development. [ABSTRACT FROM AUTHOR]

The BRCA1 tumor suppressor has been implicated in the maintenance of chromosomal stability through homology-directed repair of DNA double-strand breaks. Much of the BRCA1 in cells forms a heterodimeric complex with a structurally related protein BARD1. We report that expression of truncated mouse or human BARD1 peptides capable of interacting with Brcal results in a homologous-repair deficiency. Repair is mildly reduced in Brcal wild-type cells and severely reduced in cells that harbor a Brcal splice product deleted for exon 11. Nuclear localization of the Brca1 or BARD1 peptides is not compromised, implying that the repair deficiency is caused by a more direct effect on repair. The tumor suppressor activity of BRCA1 may require the participation of BARD1 to maintain chromosome integrity through the homologous-repair pathway. [ABSTRACT FROM AUTHOR]

Id proteins bind basic helix-loop-helix transcription factors and function as dominant negative inhibitors of gene expression. Id1 and Id3 are required for the recruitment of bone marrow-derived endothelial cell precursors and tumors transplanted into Id-deficient mice demonstrate impaired angiogenesis. Mouse mammary tumor virus-neu mice were bred with ld1[SUP-/-]ld[SUP3+1-] mice to ascertain the role of ld1 and ld3 in mammary tumorigenesis in a more physiologically relevant model. In mammary tumors from these mice, Id1 and Id3 expression was restricted to the vascular endothelium. Id1 and Id3 deficiency did not prevent or delay tumor formation but did alter tumor phenotype. The tumors that developed in the Id-deficient mice were larger and cystic with a viable rim of tumor cells surrounding a nonviable core of cellular debris. The Hsp90 chaperone protein is required for cellular survival under condition of environmental stress and for the stability of the neu oncogene. 17-Allylamino-17-demethoxygeldanamycin, an Hsp90 inhibitor, was used to treat these mice. Whereas 17-allylamino-17demethoxygeldanamycin only modestly delayed the growth of established mammary tumors in WT mice for Id, tumor suppression was dramatically more effective in an Id1- or Id3-deficient background. These data suggest that tumorigenesis can occur in a background of defective angiogenesis but that tumors developing in such an environment may be especially sensitive to inhibitors of neu and stress-activated survival pathways. Thus angiogenesis inhibitors in combination with inhibitors of Hspg0 function should be evaluated for the treatment of advanced breast cancer. [ABSTRACT FROM AUTHOR]

Summary. Malignant plasma cells generally grow within the bone marrow microenvironment; however, they can also grow at extramedullary sites. To identify the tumour-specific alterations required for extramedullary growth, we analysed the expression profiles of a series of plasma cell neoplasms including primary multiple myeloma (MM), plasma cell leukaemia (PCL) and extramedullary plasmacytoma (EPC). Hierarchical clustering analysis segregated the EPCs from the remaining samples, and revealed an expression pattern associated with angiogenesis in the EPCs, involving higher expression of the genes TIE2, NOTCH3 , CD31 and endoglin. Direct comparison of EPC samples with the MM samples identified 156 genes significantly upregulated and 85 genes significantly downregulated (P < 0·005, t -test) in the EPCs, including several genes involved in angiogenesis and adhesion that were upregulated (including angiopoietin 1, SPARC, Notch3 and fibronectin 1). Immunohistochemical staining demonstrated CD31 and endoglin protein expression in the EPC tumour cells, which are both angiogenesis related and could confer malignant plasma cells with the ability to grow outside the normal bone marrow environment. Defining how malignant plasma cell growth is regulated in the bone marrow versus at extramedullary sites will help to delineate the mechanisms underlying the dependence of tumour cell growth on angiogenesis and cell adhesion. [ABSTRACT FROM AUTHOR]

Camouflaged deconvolution arises when the kernel in a simple convolution model is not completely specified. We consider a situation in which the same fixed signal is repeatedly measured by separate convolutions with imprecisely known kernels. We develop a regularization methodology for application to these problems. The method involves simultaneous estimation of the target signal and the unknown parameters of the convolution kernels. Cross-validation is used to determine the degree of smoothness of the solution. We use simulation studies matched to the application to evaluate statistical performance. These simulations find that the convergence of the regularization estimator is largely unatfected by the lack of information about the convolution kernels. We illustrate the methodology by application to a blood curve modeling problem arising in the context of positron emission tomography (PET) studies with flurodeoxyglucose (FDG), a commonly used glucose tracer. The results show promise towards the goal of obtaining reliable metabolic information with much more limited blood sampling. [ABSTRACT FROM AUTHOR]

Background: Comprehensive molecular profiling has revealed somatic variations in cancer at genomic, epigenomic, transcriptomic, and proteomic levels. The accumulating data has shown clearly that molecular phenotypes of cancer are complex and influenced by a multitude of factors. Conventional unsupervised clustering applied to a large patient population is inevitably driven by the dominant variation from major factors such as cell-of-origin or histology. Translation of these data into clinical relevance requires more effective extraction of information directly associated with patient outcome. Methods: Drawing from ideas in supervised text classification, we developed survClust, an outcome-weighted clustering algorithm for integrative molecular stratification focusing on patient survival. survClust was performed on 18 cancer types across multiple data modalities including somatic mutation, DNA copy number, DNA methylation, and mRNA, miRNA, and protein expression from the Cancer Genome Atlas study to identify novel prognostic subtypes. Results: Our analysis identified the prognostic role of high tumor mutation burden with concurrently high CD8 T cell immune marker expression and the aggressive clinical behavior associated with CDKN2A deletion across cancer types. Visualization of somatic alterations, at a genome-wide scale (total mutation burden, mutational signature, fraction genome altered) and at the individual gene level, using circomap further revealed indolent versus aggressive subgroups in a pan-cancer setting. Conclusions: Our analysis has revealed prognostic molecular subtypes not previously identified by unsupervised clustering. The algorithm and tools we developed have direct utility toward patient stratification based on tumor genomics to inform clinical decision-making. The survClust software tool is available at https://github.com/arorarshi/survClust. [ABSTRACT FROM AUTHOR]

Summary: If a cancer patient develops multiple tumors, it is sometimes impossible to determine whether these tumors are independent or clonal based solely on pathological characteristics. Investigators have studied how to improve this diagnostic challenge by comparing the presence of loss of heterozygosity (LOH) at selected genetic locations of tumor samples, or by comparing genomewide copy number array profiles. We have previously developed statistical methodology to compare such genomic profiles for an evidence of clonality. We assembled the software for these tests in a new R package called ‘Clonality’. For LOH profiles, the package contains significance tests. The analysis of copy number profiles includes a likelihood ratio statistic and reference distribution, as well as an option to produce various plots that summarize the results.Availability: Bioconductor (http://bioconductor.org/packages/release/bioc/html/Clonality.html) and http://www.mskcc.org/mskcc/html/13287.cfm.Contact: ostrovni@mskcc.org [ABSTRACT FROM AUTHOR]

A correction to the article "Integrative Clustering of Multiple Genomic Data Types Using a Joint Latent Variable Model With Application to Breast and Lung Cancer Subtype Analysis," by Ronglai Shen, Adam B. Olshen and Marc Ladanyi, is presented.