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.

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 .

Background

Gene expression may be regulated by the DNA methylation of regulatory elements in cis, distal, and trans regions. One method to evaluate the relationship between DNA methylation and gene expression is the mapping of expression quantitative trait methylation (eQTM) loci (also called expression associated CpG loci, eCpG). However, no open-source tools are available to provide eQTM mapping. In addition, eQTM mapping can involve a large number of comparisons which may prevent the analyses due to limitations of computational resources. Here, we describe Torch-eCpG, an open-source tool to perform eQTM mapping that includes an optimized implementation that can use the graphical processing unit (GPU) to reduce runtime.

Results

We demonstrate the analyses using the tool are reproducible, up to 18 × faster using the GPU, and scale linearly with increasing methylation loci.

Conclusions

Torch-eCpG is a fast, reliable, and scalable tool to perform eQTM mapping. Source code for Torch-eCpG is available at https://github.com/kordk/torch-ecpg .

Gene regulation during cell-cycle progression is an intricately choreographed process, ensuring accurate DNA replication and division. However, the translational landscape of gene expression underlying cell-cycle progression remains largely unknown. Employing genome-wide ribosome profiling, we uncover widespread translational regulation of hundreds of mRNAs serving as an unexpected mechanism for gene regulation underlying cell-cycle progression. A striking example is the S phase translational regulation of RICTOR, which is associated with cell cycle-dependent activation of mammalian target of rapamycin complex 2 (mTORC2) signaling and accurate cell-cycle progression. We further identified unappreciated coordination in translational control of mRNAs within molecular complexes dedicated to cell-cycle progression, lipid metabolism, and genome integrity. This includes the majority of mRNAs comprising the cohesin and condensin complexes responsible for maintaining genome organization, which are coordinately translated during specific cell cycle phases via their 5' UTRs. Our findings illuminate the prevalence and dynamic nature of translational regulation underlying the mammalian cell cycle.

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.

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 retinoblastoma-associated 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.

Autophagy promotes protein degradation, and therefore has been proposed to maintain amino acid pools to sustain protein synthesis during metabolic stress. To date, how autophagy influences the protein synthesis landscape in mammalian cells remains unclear. Here, we utilize ribosome profiling to delineate the effects of genetic ablation of the autophagy regulator, ATG12, on translational control. In mammalian cells, genetic loss of autophagy does not impact global rates of cap dependent translation, even under starvation conditions. Instead, autophagy supports the translation of a subset of mRNAs enriched for cell cycle control and DNA damage repair. In particular, we demonstrate that autophagy enables the translation of the DNA damage repair protein BRCA2, which is functionally required to attenuate DNA damage and promote cell survival in response to PARP inhibition. Overall, our findings illuminate that autophagy impacts protein translation and shapes the protein landscape.

HER2 and HER3 play key driving functions in the pathophysiology of HER2-amplified breast cancers, but this function is less well characterized in other cancers driven by HER2 amplification. This study aimed to explore the role of HER2 and HER3 signaling in other types of HER2-amplified cancer. The expression and signaling activity of HER2, HER3, and downstream pathway proteins were studied in cell panels representing HER2-amplified cancers of the breast, bladder, colon and rectal, stomach, esophagus, lung, tongue, and endometrium along with controls lacking HER2 amplification. We report that HER2-amplified cancers are addicted to HER2 across different cancer types and the depth of addiction is best linked with the expression level of HER2, but not with HER3 expression. We report that the expression and constitutive phosphorylation of HER3 are ubiquitous in HER2-amplified breast cancer cell lines, but much more variable in HER2-amplified cancer cells from other tissues. We observed the lapatinib-induced compensatory upregulation of HER3 signaling in many types of HER2-amplified cancers, although with much variability. We find that HER3 expression is essential for in vivo tumorigenic growth in some HER2-amplified tumors but not others. Importantly HER3 expression level does not correlate well with its functional importance. More biomarkers will be needed to guide the optimal use of HER3 inhibitors in HER2-amplified cancers from non-breast origin. Unlike oncogenes activated through mutational events, the activation of HER2 through overexpression represents a gradient of activities and depth of addiction and the response to inhibitors follows a similar gradient.

Background

Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time.

Purpose

To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches.

Methods

Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches.

Results

Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors.

Conclusion

This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict evening fatigue severity.