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We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.

Background: Preterm birth is associated with an increased risk of many complications, which is a main public health problem worldwide with social and economic consequences. Human milk from breast feeding has been proved to be the optimal nutrition strategy for preterm infants when available. However, the lack of human milk from mothers makes formula widely used in clinical practice. In recent years, donated breast milk has gained popularity as an alternative choice which can provide human milk oligosaccharides and other bioactive substances. Objective: We aimed to conduct a systematic review and meta-analysis to evaluate the nutritional effects of donated breast milk on preterm infants compared with formula. Method: In the present study, we searched Medline, Web of Science, Embase, clinicaltrials.gov, the China national knowledge infrastructure, and the Cochrane central register of controlled trials for candidate randomized controlled trials (RCTs). Results: A total of 1390 patients were enrolled in 11 RCTs and meta-analysis results showed that donated breast milk is also more advantageous in reducing the incidence of necrotizing enterocolitis (NEC, RR = 0.67, 95% CI = 0.48 to 0.93, p = 0.02), reducing the duration of parenteral nutrition (MD = −2.39, 95% CI = −3.66 to −1.13, p = 0.0002) and the time of full enteral feeding (MD = −0.33, 95% CI = −3.23 to 2.57, p = 0.0002). In comparison, formula significantly promotes the growth of premature infants, including their weight gain (MD = −3.45, 95% CI = −3.68 to −3.21, p < 0.00001), head growth (MD = −0.07, 95% CI = −0.08 to −0.06, p < 0.00001) and body length (MD = −0.13, 95% CI = −0.15 to −0.11, p < 0.00001), and reduces the time it takes for premature infants to regain birth weight (MD = 6.60, 95% CI = 6.11 to 7.08, p < 0.00001. Conclusion: Compared with formula, donated breast milk could significantly reduce the incidence of NEC, the duration of parenteral nutrition, and the time of full enteral feeding. Adding fortifiers in donated milk could make it as effective as formula in promoting the physical growth of premature infants.

To explore the efficacy and safety of brachytherapy combined with endocrine therapy (ET) and external beam radiotherapy (EBRT) in the treatment of patients with intermediate- and high-risk localized prostate cancer (PCa).A total of 128 patients with intermediate- and high-risk localized PCa treated in our hospital, were included, encompassing 64 cases undergoing brachytherapy combined with ET (control group), and 64 cases undergoing intensity-modulated EBRT on the above basis (combination group). The clinical efficacy, adverse reactions, the serum prostate specific antigen (PSA) level before and after treatment, maximum urinary flow rate (Qmax), and expanded prostate cancer index composite (EPIC) score were compared between the two groups. The overall survival (OS) of patients was analyzed using the Kaplan-Meier method and log-rank test.After treatment, the EPIC scores of urinary function, intestinal function, sexual function and hormone function declined significantly in both groups, and they were significantly higher in the combination group than in the control group. At 12 months after treatment, the combination group had an obviously lower serum PSA level, and obviously higher Qmax than the control group. All patients were followed up for 12-60 months. In the combination and control group, OS was 87.5% and 81.3%, disease-specific survival (DSS) was 89.1% and 78.1%, the biochemical progression-free survival (bPFS) was 76.6% and 60.9%, and distant metastasis free survival (DMFS) was 87.5% and 71.9%, respectively. Log-rank test showed no statistically significant differences in OS and DSS between the two groups, but both bPFS and DMFS in the combination group were remarkably superior compared with the control group.Brachytherapy combined with ET and EBRT has definite efficacy in intermediate- and high-risk localized PCa, which can significantly improve the physiological function, raise the quality of life of patients, and effectively control the disease progression.

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse., eLife digest Cells in the body remain healthy by tightly preventing and repairing random changes, or mutations, in their genetic material. In cancer cells, however, these mechanisms can break down. When these cells grow and multiply, they can then go on to accumulate many mutations. As a result, cancer cells in the same tumor can each contain a unique combination of genetic changes. This genetic heterogeneity has the potential to affect how cancer responds to treatment, and is increasingly becoming appreciated clinically. For example, if a drug only works against cancer cells carrying a specific mutation, any cells lacking this genetic change will keep growing and cause a relapse. However, it is still difficult to quantify and understand genetic heterogeneity in cancer. Copy number alterations (or CNAs) are a class of mutation where large and small sections of genetic material are gained or lost. This can result in cells that have an abnormal number of copies of the genes in these sections. Here, Baslan et al. set out to explore how CNAs might vary between individual cancer cells within the same tumor. To do so, thousands of individual cancer cells were isolated from human breast tumors, and a technique called single-cell genome sequencing used to screen the genetic information of each of them. These experiments confirmed that CNAs did differ – sometimes dramatically – between patients and among cells taken from the same tumor. For example, many of the cells carried extra copies of well-known cancer genes important for treatment, but the exact number of copies varied between cells. This heterogeneity existed for individual genes as well as larger stretches of DNA: this was the case, for instance, for an entire section of chromosome 8, a region often affected in breast and other tumors. The work by Baslan et al. captures the sheer extent of genetic heterogeneity in cancer and in doing so, highlights the power of single-cell genome sequencing. In the future, a finer understanding of the genetic changes present at the level of an individual cancer cell may help clinicians to manage the disease more effectively.

PURPOSE Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research. MATERIALS AND METHODS The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda. RESULTS Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization. CONCLUSION The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

DNA methylation has emerged as promising epigenetic markers for disease diagnosis. Both the differential mean (DM) and differential variability (DV) in methylation have been shown to contribute to transcriptional aberration and disease pathogenesis. The presence of confounding factors in large scale EWAS may affect the methylation values and hamper accurate marker discovery. In this paper, we propose a exible framework called methylDMV which allows for confounding factors adjustment and enables simultaneous characterization and identification of CpGs exhibiting DM only, DV only and both DM and DV. The proposed framework also allows for prioritization and selection of candidate features to be included in the prediction algorithm. We illustrate the utility of methylDMV in several TCGA datasets. An R package methylDMV implementing our proposed method is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#methylDMV.

SummaryLong non-coding RNAs (lncRNAs) represent the largest and most diverse class of non-coding RNAs, comprising almost 16,000 currently annotated transcripts in human and 10,000 in mouse. Here, we investigated the role of lncRNAs in mammary tumors by performing RNA-seq on tumor sections and organoids derived from MMTV-PyMT and MMTV-Neu-NDL mice. We identified several hundred lncRNAs that were overexpressed compared to normal mammary epithelium. Among these potentially oncogenic lncRNAs we prioritized a subset as Mammary Tumor Associated RNAs (MaTARs) and determined their human counterparts, hMaTARs. To functionally validate the role of MaTARs, we performed antisense knockdown and observed reduced cell proliferation, invasion, and/or organoid branching in a cancer-specific context. Assessing the expression of hMaTARs in human breast tumors revealed that 19 hMaTARs are significantly upregulated and many of these correlate with breast cancer subtype and/or hormone receptor status, indicating potential clinical relevance.