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Archive for the ‘Computational Biology/Systems and Bioinformatics’ Category


Group of Researchers @ University of California, Riverside, the University of Chicago, the U.S. Department of Energy’s Argonne National Laboratory, and Northwestern University solve COVID-19 Structure and Map Potential Therapeutics

Reporters: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

 

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus virus was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019.

Image and Caption Credit: Alissa Eckert, MS; Dan Higgins, MAM available at https://phil.cdc.gov/Details.aspx?pid=23311

 

New coronavirus protein reveals drug target

Image of newly mapped coronavirus protein, called Nsp15, which helps the virus replicate.

Image Credit: Northwestern University

Image of newly mapped coronavirus protein, called Nsp15, which helps the virus replicate.

How UC is responding to the coronavirus (COVID-19)

The University of California is vigilantly monitoring and responding to new information about the coronavirus (COVID-19) outbreak, which has been declared a global health emergency.

Get UC news and updates on this evolving situation.

The 3-D structure of a potential drug target in a newly mapped protein of COVID-19, or coronavirus, has been solved by a team of researchers from the University of California, Riverside, the University of Chicago, the U.S. Department of Energy’s Argonne National Laboratory, and Northwestern University.

The scientists said their findings suggest drugs previously developed to treat the earlier SARS outbreak could now be developed as effective drugs against COVID-19.

The initial genome analysis and design of constructs for protein synthesis were performed by the bioinformatic group of Adam Godzik, a professor of biomedical sciences at the UC Riverside School of Medicine.

The protein Nsp15 from Severe Acute Respiratory Syndrome Coronavirus 2, or SARS-CoV-2, is 89% identical to the protein from the earlier outbreak of SARS-CoV. SARS-CoV-2 is responsible for the current outbreak of COVID-19. Studies published in 2010 on SARS-CoV revealed inhibition of Nsp15 can slow viral replication. This suggests drugs designed to target Nsp15 could be developed as effective drugs against COVID-19.

Adam Godzik
Adam Godzik, UC Riverside professor of biomedical sciences
Credit: Sanford Burnham Prebys Medical Discovery Institute

“While the SARS-CoV-19 virus is very similar to the SARS virus that caused epidemics in 2003, new structures shed light on the small, but potentially important differences between the two viruses that contribute to the different patterns in the spread and severity of the diseases they cause,” Godzik said.

The structure of Nsp15, which will be released to the scientific community on March 4, was solved by the group of Andrzej Joachimiak, a distinguished fellow at the Argonne National Laboratory, University of Chicago Professor, and Director of the Structural Biology Center at Argonne’s Advanced Photon Source, a Department of Energy Office of Science user facility.

“Nsp15 is conserved among coronaviruses and is essential in their lifecycle and virulence,” Joachimiak said. “Initially, Nsp15 was thought to directly participate in viral replication, but more recently, it was proposed to help the virus replicate possibly by interfering with the host’s immune response.”

Mapping a 3D protein structure of the virus, also called solving the structure, allows scientists to figure out how to interfere in the pathogen’s replication in human cells.

“The Nsp15 protein has been investigated in SARS as a novel target for new drug development, but that never went very far because the SARS epidemic went away, and all new drug development ended,” said Karla Satchell, a professor of microbiology-immunology at Northwestern, who leads the international team of scientists investigating the structure of the SARS CoV-2 virus to understand how to stop it from replicating. “Some inhibitors were identified but never developed into drugs. The inhibitors that were developed for SARS now could be tested against this protein.”

Rapid upsurge and proliferation of SARS-CoV-2 raised questions about how this virus could become so much more transmissible as compared to the SARS and MERS coronaviruses. The scientists are mapping the proteins to address this issue.

Over the past two months, COVID-19 infected more than 80,000 people and caused at least 2,700 deaths. Although currently mainly concentrated in China, the virus is spreading worldwide and has been found in 46 countries. Millions of people are being quarantined, and the epidemic has impacted the world economy. There is no existing drug for this disease, but various treatment options, such as utilizing medicines effective in other viral ailments, are being attempted.

Godzik, Satchell, and Joachimiak — along with the entire center team — will map the structure of some of the 28 proteins in the virus in order to see where drugs can throw a chemical monkey wrench into its machinery. The proteins are folded globular structures with precisely defined functions and their “active sites” can be targeted with chemical compounds.
The first step is to clone and express the genes of the virus proteins and grow them as protein crystals in miniature ice cube-like trays. The consortium includes nine labs across eight institutions that will participate in this effort.

Above is a modified version of the Northwestern University news release written by Marla Paul.

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Bioinformatic Tools for RNASeq: A Curation

Curator: Stephen J. Williams, Ph.D. 

 

Note:  This will be an ongoing curation as new information and tools become available.

RNASeq is a powerful tool for the analysis of the transcriptome profile and has been used to determine the transcriptional changes occurring upon stimuli such as drug treatment or detecting transcript differences between biological sample cohorts such as tumor versus normal tissue.  Unlike its genomic companion, whole genome and whole exome sequencing, which analyzes the primary sequence of the genomic DNA, RNASeq analyzes the mRNA transcripts, thereby more closely resembling the ultimate translated proteome. In addition, RNASeq and transcriptome profiling can determine if splicing variants occur as well as determining the nonexomic sequences, such as miRNA and lncRNA species, all of which have shown pertinence in the etiology of many diseases, including cancer.

However, RNASeq, like other omic technologies, generates enormous big data sets, which requires multiple types of bioinformatic tools in order to correctly analyze the sequence reads, and to visualize and interpret the output data.  This post represents a curation by the RNA-Seq blog of such tools useful for RNASeq studies and lists and reviews published literature using these curated tools.

 

From the RNA-Seq Blog

List of RNA-Seq bioinformatics tools

Posted by: RNA-Seq Blog in Data Analysis, Web Tools September 16, 2015 6,251 Views

from: https://en.wiki2.org/wiki/List_of_RNA-Seq_bioinformatics_tools

A review of some of the literature using some of the aforementioned curated tools are discussed below:

 

A.   Tools Useful for Single Cell RNA-Seq Analysis

 

B.  Tools for RNA-Seq Analysis of the Sliceasome

 

C.  Tools Useful for RNA-Seq read assembly visualization

 

Other articles on RNA and Transcriptomics in this Open Access Journal Include:

NIH to Award Up to $12M to Fund DNA, RNA Sequencing Research: single-cell genomics, sample preparation, transcriptomics and epigenomics, and genome-wide functional analysis.

Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute

Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line

Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

First challenge to make use of the new NCI Cloud Pilots – Somatic Mutation Challenge – RNA: Best algorithms for detecting all of the abnormal RNA molecules in a cancer cell

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

 

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Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

 

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

 

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

  • millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
  • In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
  • we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
  • Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

  • fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
  • Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
  • 1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
  • refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

  • a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
  • developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

  • shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
  • building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

  • To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
  • Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

  • In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
  • enhanced capabilities to perform functions that are not feasible now.
  • AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
  • the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

  • Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

  • finding new ways to share clinical data that are as open as possible and as closed as necessary.
  • moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
  • working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
  • deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

  • genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
  • Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
  • Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
  • AI will help guide the development of individually matched
  • genetic patient screenings
  • the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

  • the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
  • But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

  • we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
  • This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
  • The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

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Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

Curator & Reporter: Aviva Lev-Ari, PhD, RN

 

Subjects:

The Scientific Frontier is presented in Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promotersNat Biotechnol (2019) doi:10.1038/s41587-019-0315-8

Abstract

How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.

The Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis is presented in the following Table

 

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9 Hughes, T. R. & de Boer, C. G. Mapping yeast transcriptional networks. Genetics 195, 9–36 (2013).
10 Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).
19 Kosuri, S. et al. Composability of regulatory sequences controlling transcription and translation in Escherichia coli. Proc. Natl Acad. Sci. USA 110, 14024–14029 (2013).
7 Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).
18 de Boer, C. G. & Hughes, T. R. YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities. Nucleic Acids Res. 40, D169–D179 (2012).
56 Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
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11 Nutiu, R. et al. Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument. Nat. Biotechnol. 29, 659–664 (2011).
26 Zhang, Z. et al. A packing mechanism for nucleosome organization reconstituted across a eukaryotic genome. Science 332, 977–980 (2011).
30 Ganapathi, M. et al. Extensive role of the general regulatory factors, Abf1 and Rap1, in determining genome-wide chromatin structure in budding yeast. Nucleic Acids Res. 39, 2032–2044 (2011).
52 Erb, I. & van Nimwegen, E. Transcription factor binding site positioning in yeast: proximal promoter motifs characterize TATA-less promoters. PloS One 6, e24279 (2011).
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8 Gertz, J., Siggia, E. D. & Cohen, B. A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215–218 (2009).
16 Wunderlich, Z. & Mirny, L. A. Different gene regulation strategies revealed by analysis of binding motifs. Trends Genet. 25, 434–440 (2009).
27 Hesselberth, J. R. et al. Global mapping of protein–DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).
29 Hartley, P. D. & Madhani, H. D. Mechanisms that specify promoter nucleosome location and identity. Cell 137, 445–458 (2009).
51 Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
58 Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nat. Rev. Genet. 10, 443–456 (2009).
2 Yuan, Y., Guo, L., Shen, L. & Liu, J. S. Predicting gene expression from sequence: a reexamination. PLoS Comput. Biol. 3, e243 (2007).
46 Hibbs, M. A. et al. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 23, 2692–2699 (2007).
25 Liu, X., Lee, C. K., Granek, J. A., Clarke, N. D. & Lieb, J. D. Whole-genome comparison of Leu3 binding in vitro and in vivo reveals the importance of nucleosome occupancy in target site selection. Genome Res. 16, 1517–1528 (2006).
34 Roberts, G. G. & Hudson, A. P. Transcriptome profiling of Saccharomyces cerevisiae during a transition from fermentative to glycerol-based respiratory growth reveals extensive metabolic and structural remodeling. Mol. Genet. Genomics 276, 170–186 (2006).
48 Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Gen. Res. 16, 962–972 (2006).
53 Tong, A. H. & Boone, C. Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol. Biol. 313, 171–192 (2006).
57 Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
62 Chua, G. et al. Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl Acad. Sci. USA 103, 12045–12050 (2006).
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45 Harbison, C. T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004).
60 Kent, N. A., Eibert, S. M. & Mellor, J. Cbf1p is required for chromatin remodeling at promoter-proximal CACGTG motifs in yeast. J. Biol. Chem. 279, 27116–27123 (2004).
22 Kulkarni, M. M. & Arnosti, D. N. Information display by transcriptional enhancers. Development 130, 6569–6575 (2003).
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38 Sato, T. et al. TheE-box DNA binding protein Sgc1p suppresses the gcr2 mutation, which is involved in transcriptional activation of glycolytic genes in Saccharomyces cerevisiae. FEBS Lett. 463, 307–311 (1999).
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To access each reference as a live link, go to the number in the first column in the Table and look it up in the List of References in the Link, below

https://www.nature.com/articles/s41587-019-0315-8

Author information

C.G.D. and A.R. drafted the manuscript, with all authors contributing. C.G.D. analyzed the data. C.G.D., E.D.V., E.L.A. and R.S. performed the experiments. A.R. and N.F. supervised the research.

Correspondence to Carl G. de Boer or Aviv Regev.

Ethics declarations

Competing interests

A.R. is an SAB member of Thermo Fisher Scientific, Neogene Therapeutics, Asimov, and Syros Pharmaceuticals, an equity holder of Immunitas, and a founder of and equity holder in Celsius Therapeutics. All other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Cite this article

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat Biotechnol (2019) doi:10.1038/s41587-019-0315-8

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Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis

Reporter: Aviva Lev-Ari, PhD, RN

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Yu Fu1, Alexander W Jung1, Ramon Viñas Torne1, Santiago Gonzalez1,2, Harald Vöhringer1, Mercedes Jimenez-Linan3, Luiza Moore3,4, and Moritz Gerstung#1,5 # to whom correspondence should be addressed 1) European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK. 2) Current affiliation: Institute for Research in Biomedicine (IRB Barcelona), Parc Científic de Barcelona, Barcelona, Spain. 3) Department of Pathology, Addenbrooke’s Hospital, Cambridge, UK. 4) Wellcome Sanger Institute, Hinxton, UK 5) European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.

Correspondence:

Dr Moritz Gerstung European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton, CB10 1SA UK. Tel: +44 (0) 1223 494636 E-mail: moritz.gerstung@ebi.ac.uk

Abstract

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Here we use deep transfer learning to quantify histopathological patterns across 17,396 H&E stained histopathology image slides from 28 cancer types and correlate these with underlying genomic and transcriptomic data. Pan-cancer computational histopathology (PC-CHiP) classifies the tissue origin across organ sites and provides highly accurate, spatially resolved tumor and normal distinction within a given slide. The learned computational histopathological features correlate with a large range of recurrent genetic aberrations, including whole genome duplications (WGDs), arm-level copy number gains and losses, focal amplifications and deletions as well as driver gene mutations within a range of cancer types. WGDs can be predicted in 25/27 cancer types (mean AUC=0.79) including those that were not part of model training. Similarly, we observe associations with 25% of mRNA transcript levels, which enables to learn and localise histopathological patterns of molecularly defined cell types on each slide. Lastly, we find that computational histopathology provides prognostic information augmenting histopathological subtyping and grading in the majority of cancers assessed, which pinpoints prognostically relevant areas such as necrosis or infiltrating lymphocytes on each tumour section. Taken together, these findings highlight the large potential of PC-CHiP to discover new molecular and prognostic associations, which can augment diagnostic workflows and lay out a rationale for integrating molecular and histopathological data.

SOURCE

https://www.biorxiv.org/content/10.1101/813543v1

Key points

● Pan-cancer computational histopathology analysis with deep learning extracts histopathological patterns and accurately discriminates 28 cancer and 14 normal tissue types

● Computational histopathology predicts whole genome duplications, focal amplifications and deletions, as well as driver gene mutations

● Wide-spread correlations with gene expression indicative of immune infiltration and proliferation

● Prognostic information augments conventional grading and histopathology subtyping in the majority of cancers

 

Discussion

Here we presented PC-CHiP, a pan-cancer transfer learning approach to extract computational histopathological features across 42 cancer and normal tissue types and their genomic, molecular and prognostic associations. Histopathological features, originally derived to classify different tissues, contained rich histologic and morphological signals predictive of a range of genomic and transcriptomic changes as well as survival. This shows that computer vision not only has the capacity to highly accurately reproduce predefined tissue labels, but also that this quantifies diverse histological patterns, which are predictive of a broad range of genomic and molecular traits, which were not part of the original training task. As the predictions are exclusively based on standard H&E-stained tissue sections, our analysis highlights the high potential of computational histopathology to digitally augment existing histopathological workflows. The strongest genomic associations were found for whole genome duplications, which can in part be explained by nuclear enlargement and increased nuclear intensities, but seemingly also stems from tumour grade and other histomorphological patterns contained in the high-dimensional computational histopathological features. Further, we observed associations with a range of chromosomal gains and losses, focal deletions and amplifications as well as driver gene mutations across a number of cancer types. These data demonstrate that genomic alterations change the morphology of cancer cells, as in the case of WGD, but possibly also that certain aberrations preferentially occur in distinct cell types, reflected by the tumor histology. Whatever is the cause or consequence in this equation, these associations lay out a route towards genomically defined histopathology subtypes, which will enhance and refine conventional assessment. Further, a broad range of transcriptomic correlations was observed reflecting both immune cell infiltration and cell proliferation that leads to higher tumor densities. These examples illustrated the remarkable property that machine learning does not only establish novel molecular associations from pre-computed histopathological feature sets but also allows the localisation of these traits within a larger image. While this exemplifies the power of a large scale data analysis to detect and localise recurrent patterns, it is probably not superior to spatially annotated training data. Yet such data can, by definition, only be generated for associations which are known beforehand. This appears straightforward, albeit laborious, for existing histopathology classifications, but more challenging for molecular readouts. Yet novel spatial transcriptomic44,45 and sequencing technologies46 bring within reach spatially matched molecular and histopathological data, which would serve as a gold standard in combining imaging and molecular patterns. Across cancer types, computational histopathological features showed a good level of prognostic relevance, substantially improving prognostic accuracy over conventional grading and histopathological subtyping in the majority of cancers. It is this very remarkable that such predictive It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543. The copyright holder for this preprint signals can be learned in a fully automated fashion. Still, at least at the current resolution, the improvement over a full molecular and clinical workup was relatively small. This might be a consequence of the far-ranging relations between histopathology and molecular phenotypes described here, implying that histopathology is a reflection of the underlying molecular alterations rather than an independent trait. Yet it probably also highlights the challenges of unambiguously quantifying histopathological signals in – and combining signals from – individual areas, which requires very large training datasets for each tumour entity. From a methodological point of view, the prediction of molecular traits can clearly be improved. In this analysis, we adopted – for the reason of simplicity and to avoid overfitting – a transfer learning approach in which an existing deep convolutional neural network, developed for classification of everyday objects, was fine tuned to predict cancer and normal tissue types. The implicit imaging feature representation was then used to predict molecular traits and outcomes. Instead of employing this two-step procedure, which risks missing patterns irrelevant for the initial classification task, one might directly employ either training on the molecular trait of interest, or ideally multi-objective learning. Further improvement may also be related to the choice of the CNN architecture. Everyday images have no defined scale due to a variable z-dimension; therefore, the algorithms need to be able to detect the same object at different sizes. This clearly is not the case for histopathology slides, in which one pixel corresponds to a defined physical size at a given magnification. Therefore, possibly less complex CNN architectures may be sufficient for quantitative histopathology analyses, and also show better generalisation. Here, in our proof-of-concept analysis, we observed a considerable dependence of the feature representation on known and possibly unknown properties of our training data, including the image compression algorithm and its parameters. Some of these issues could be overcome by amending and retraining the network to isolate the effect of confounding factors and additional data augmentation. Still, given the flexibility of deep learning algorithms and the associated risk of overfitting, one should generally be cautious about the generalisation properties and critically assess whether a new image is appropriately represented. Looking forward, our analyses revealed the enormous potential of using computer vision alongside molecular profiling. While the eye of a trained human may still constitute the gold standard for recognising clinically relevant histopathological patterns, computers have the capacity to augment this process by sifting through millions of images to retrieve similar patterns and establish associations with known and novel traits. As our analysis showed this helps to detect histopathology patterns associated with a range of genomic alterations, transcriptional signatures and prognosis – and highlight areas indicative of these traits on each given slide. It is therefore not too difficult to foresee how this may be utilised in a computationally augmented histopathology workflow enabling more precise and faster diagnosis and prognosis. Further, the ability to quantify a rich set of histopathology patterns lays out a path to define integrated histopathology and molecular cancer subtypes, as recently demonstrated for colorectal cancers47 .

Lastly, our analyses provide It is made available under a CC-BY-NC 4.0 International license. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

bioRxiv preprint first posted online Oct. 25, 2019; doi: http://dx.doi.org/10.1101/813543.

The copyright holder for this preprint proof-of-concept for these principles and we expect them to be greatly refined in the future based on larger training corpora and further algorithmic refinements.

SOURCE

https://www.biorxiv.org/content/biorxiv/early/2019/10/25/813543.full.pdf

 

Other related articles published in this Open Access Online Scientific Journal include the following: 

 

CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to anyone on Earth

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/07/28/cancerbase-org-the-global-hub-for-diagnoses-genomes-pathology-images-a-real-time-diagnosis-and-therapy-mapping-service-for-cancer-patients-anonymized-medical-records-accessible-to/

 

631 articles had in their Title the keyword “Pathology”

https://pharmaceuticalintelligence.com/?s=Pathology

 

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Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Pancreatic cancer is a significant cause of cancer mortality; therefore, the development of early diagnostic strategies and effective treatment is essential. Improvements in imaging technology, as well as use of biomarkers are changing the way that pancreas cancer is diagnosed and staged. Although progress in treatment for pancreas cancer has been incremental, development of combination therapies involving both chemotherapeutic and biologic agents is ongoing.

 

Cancer is an evolutionary disease, containing the hallmarks of an asexually reproducing unicellular organism subject to evolutionary paradigms. Pancreatic ductal adenocarcinoma (PDAC) is a particularly robust example of this phenomenon. Genomic features indicate that pancreatic cancer cells are selected for fitness advantages when encountering the geographic and resource-depleted constraints of the microenvironment. Phenotypic adaptations to these pressures help disseminated cells to survive in secondary sites, a major clinical problem for patients with this disease.

 

The immune system varies in cell types, states, and locations. The complex networks, interactions, and responses of immune cells produce diverse cellular ecosystems composed of multiple cell types, accompanied by genetic diversity in antigen receptors. Within this ecosystem, innate and adaptive immune cells maintain and protect tissue function, integrity, and homeostasis upon changes in functional demands and diverse insults. Characterizing this inherent complexity requires studies at single-cell resolution. Recent advances such as massively parallel single-cell RNA sequencing and sophisticated computational methods are catalyzing a revolution in our understanding of immunology.

 

PDAC is the most common type of pancreatic cancer featured with high intra-tumoral heterogeneity and poor prognosis. In the present study to comprehensively delineate the PDAC intra-tumoral heterogeneity and the underlying mechanism for PDAC progression, single-cell RNA-seq (scRNA-seq) was employed to acquire the transcriptomic atlas of 57,530 individual pancreatic cells from primary PDAC tumors and control pancreases. The diverse malignant and stromal cell types, including two ductal subtypes with abnormal and malignant gene expression profiles respectively, were identified in PDAC.

 

The researchers found that the heterogenous malignant subtype was composed of several subpopulations with differential proliferative and migratory potentials. Cell trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. Furthermore, it was found a subset of ductal cells with unique proliferative features were associated with an inactivation state in tumor-infiltrating T cells, providing novel markers for the prediction of antitumor immune response. Together, the findings provided a valuable resource for deciphering the intra-tumoral heterogeneity in PDAC and uncover a connection between tumor intrinsic transcriptional state and T cell activation, suggesting potential biomarkers for anticancer treatment such as targeted therapy and immunotherapy.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/31273297

 

https://www.ncbi.nlm.nih.gov/pubmed/21491194

 

https://www.ncbi.nlm.nih.gov/pubmed/27444064

 

https://www.ncbi.nlm.nih.gov/pubmed/28983043

 

https://www.ncbi.nlm.nih.gov/pubmed/24976721

 

https://www.ncbi.nlm.nih.gov/pubmed/27693023

 

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scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.

 

One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.

 

Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.

 

The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.

 

This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.

 

Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.

 

References:

 

https://www.sciencedirect.com/science/article/pii/S2405471219301887

 

https://www.tandfonline.com/doi/abs/10.1080/23307706.2017.1397554

 

https://ieeexplore.ieee.org/abstract/document/4031383

 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y

 

https://www.sciencedirect.com/science/article/pii/S2405471216302666

 

 

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