A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolu- tion of a given disease for the particular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to better estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of sam- ples available for a particular cancer type. To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been traditionally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of tradi- tional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset com- posed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a col- lection of specific breast cancer samples. This transfer-learning approach aims at combining supervised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Peroxisomes, lipid droplets, and endoplasmic reticulum "hitchhike" on motile early endosomes

This is the final version of the article. Available from the publisher via the DOI in this record.Intracellular transport is mediated by molecular motors that bind cargo to be transported along the cytoskeleton. Here, we report, for the first time, that peroxisomes (POs), lipid droplets (LDs), and the endoplasmic reticulum (ER) rely on early endosomes (EEs) for intracellular movement in a fungal model system. We show that POs undergo kinesin-3- and dynein-dependent transport along microtubules. Surprisingly, kinesin-3 does not colocalize with POs. Instead, the motor moves EEs that drag the POs through the cell. PO motility is abolished when EE motility is blocked in various mutants. Most LD and ER motility also depends on EE motility, whereas mitochondria move independently of EEs. Covisualization studies show that EE-mediated ER motility is not required for PO or LD movement, suggesting that the organelles interact with EEs independently. In the absence of EE motility, POs and LDs cluster at the growing tip, whereas ER is partially retracted to subapical regions. Collectively, our results show that moving EEs interact transiently with other organelles, thereby mediating their directed transport and distribution in the cell.This work was supported by the Portuguese Foundation for Science and Technology and FEDER/COMPETE (SFRH/BD/73532/2010 to S.C. Guimaraes) and CRUP/Treaty of Windsor (ACÇÕES INTEGRAD AS 2009, B-33/09 to G. Steinberg and M. Schuster). G. Steinberg acknowledges support from the Biotechnology and Biological Sciences Research Counc

Active diffusion and microtubule-based transport oppose myosin forces to position organelles in cells.

Even distribution of peroxisomes (POs) and lipid droplets (LDs) is critical to their role in lipid and reactive oxygen species homeostasis. How even distribution is achieved remains elusive, but diffusive motion and directed motility may play a role. Here we show that in the fungus Ustilago maydis ∼95% of POs and LDs undergo diffusive motions. These movements require ATP and involve bidirectional early endosome motility, indicating that microtubule-associated membrane trafficking enhances diffusion of organelles. When early endosome transport is abolished, POs and LDs drift slowly towards the growing cell end. This pole-ward drift is facilitated by anterograde delivery of secretory cargo to the cell tip by myosin-5. Modelling reveals that microtubule-based directed transport and active diffusion support distribution, mobility and mixing of POs. In mammalian COS-7 cells, microtubules and F-actin also counteract each other to distribute POs. This highlights the importance of opposing cytoskeletal forces in organelle positioning in eukaryotes.We thank Dr G. Dagdas, Dr S. Kilaru, Mr M. Schlick and Mrs T. Schrader for technical support. We thank Professor N.J. Talbot for helpful comments on the manuscript. This work was supported by the Biotechnology & Biological Sciences Research Council (BB/J009903/1 to G.S.). S.C.G. was supported by a fellowship from the Portuguese Foundation for Science and Technology (FCT) (SFRH/BD/73532/2010). J.M. is supported by a Wellcome Trust Institutional Strategic Support Award (WT097835MF)

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

Clipless management of the renal vein during hand-assist laparoscopic donor nephrectomy

BACKGROUND: Laparoscopic live donor nephrectomy has become the preferred method of donor nephrectomy at many transplant centers. The laparoscopic stapling device is commonly used for division of the renal vessels. Malfunction of the stapling device can occur, and is often due to interference from previously placed clips. We report our experience with a clipless technique in which no vascular clips are placed on tributaries of the renal vein at or near the renal hilum in order to avoid laparoscopic stapling device misfires. METHODS: From December 20, 2002 to April 12, 2005, 50 patients underwent hand-assisted laparoscopic left donor nephrectomy (LDN) at our institution. Clipless management of the renal vein tributaries was used in all patients, and these vessels were divided using either a laparoscopic stapling device or the LigaSureTM device (Valleylab, Boulder, CO). The medical and operative records of the donors and recipients were reviewed to evaluate patient outcomes. RESULTS: The mean follow-up time was 14 months. Of the 50 LDN procedures, there were no laparoscopic stapling device malfunctions and no vascular complications. All renal allografts were functioning at the time of follow-up. CONCLUSION: Laparoscopic stapling device failure due to deployment across previously placed surgical clips during laparoscopic live donor nephrectomy can be prevented by not placing clips on the tributaries of the renal vein. In our series, there were no vascular complications and no device misfires. We believe this clipless technique improves the safety of laparoscopic donor nephrectomy

A question of scale: Human migrations writ large and small

Several recent papers illustrate the importance of migration and gene flow in molding the patterns of genetic variation observed in humans today. We place the varied demographic processes covered by these terms into a more general framework, and discuss some of the challenges facing attempts to reconstruct past human mobility and determine its influence on our genetic heritage

Systematic evaluation of library preparation methods and sequencing platforms for high-throughput whole genome bisulfite sequencing

Whole genome bisulfte sequencing (WGBS), with its ability to interrogate methylation status at single CpG site resolution epigenome-wide, is a powerful technique for use in molecular experiments. Here, we aim to advance strategies for accurate and efcient WGBS for application in future largescale epidemiological studies. We systematically compared the performance of three WGBS library preparation methods with low DNA input requirement (Swift Biosciences Accel-NGS, Illumina TruSeq and QIAGEN QIAseq) on two state-of-the-art sequencing platforms (Illumina NovaSeq and HiSeq X), and also assessed concordance between data generated by WGBS and methylation arrays. Swift achieved the highest proportion of CpG sites assayed and efective coverage at 26x(P<0.001). TruSeq sufered from the highest proportion of PCR duplicates, while QIAseq failed to deliver across all quality metrics. There was little diference in performance between NovaSeq and HiSeq X, with the exception of higher read duplication rate on the NovaSeq (P<0.05), likely attributable to the higher cluster densities on its fow cells. Systematic biases exist between WGBS and methylation arrays, with lower precision observed for WGBS across the range of depths investigated. To achieve a level of precision broadly comparable to the methylation array, a minimum coverage of 100x is recommended

Targeting tumour re-wiring by triple blockade of mTORC1, epidermal growth factor, and oestrogen receptor signalling pathways in endocrine-resistant breast cancer

Background Endocrine therapies are the mainstay of treatment for oestrogen receptor (ER)-positive (ER+) breast cancer (BC). However, resistance remains problematic largely due to enhanced cross-talk between ER and growth factor pathways, circumventing the need for steroid hormones. Previously, we reported the anti-proliferative effect of everolimus (RAD001-mTORC1 inhibitor) with endocrine therapy in resistance models; however, potential routes of escape from treatment via ERBB2/3 signalling were observed. We hypothesised that combined targeting of three cellular nodes (ER, ERBB, and mTORC1) may provide enhanced long-term clinical utility. Methods A panel of ER+ BC cell lines adapted to long-term oestrogen deprivation (LTED) and expressing ESR1wt or ESR1Y537S, modelling acquired resistance to an aromatase-inhibitor (AI), were treated in vitro with a combination of RAD001 and neratinib (pan-ERBB inhibitor) in the presence or absence of oestradiol (E2), tamoxifen (4-OHT), or fulvestrant (ICI182780). End points included proliferation, cell signalling, cell cycle, and effect on ER-mediated transactivation. An in-vivo model of AI resistance was treated with monotherapies and combinations to assess the efficacy in delaying tumour progression. RNA-seq analysis was performed to identify changes in global gene expression as a result of the indicated therapies. Results Here, we show RAD001 and neratinib (pan-ERBB inhibitor) caused a concentration-dependent decrease in proliferation, irrespective of the ESR1 mutation status. The combination of either agent with endocrine therapy further reduced proliferation but the maximum effect was observed with a triple combination of RAD001, neratinib, and endocrine therapy. In the absence of oestrogen, RAD001 caused a reduction in ER-mediated transcription in the majority of the cell lines, which associated with a decrease in recruitment of ER to an oestrogen-response element on the TFF1 promoter. Contrastingly, neratinib increased both ER-mediated transactivation and ER recruitment, an effect reduced by the addition of RAD001. In-vivo analysis of an LTED model showed the triple combination of RAD001, neratinib, and fulvestrant was most effective at reducing tumour volume. Gene set enrichment analysis revealed that the addition of neratinib negated the epidermal growth factor (EGF)/EGF receptor feedback loops associated with RAD001. Conclusions Our data support the combination of therapies targeting ERBB2/3 and mTORC1 signalling, together with fulvestrant, in patients who relapse on endocrine therapy and retain a functional ER