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Archive for the 'Genome Biology' Category
Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies
Posted in and Bioethics, Bio-Ethics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer Genomics, Childhood cancer, Genome Biology, Population genetics, Population Health Management, tagged AAAS, Cancer Genomics, health disparities, pediatric cancer, Population genetics, precision medicine on December 22, 2019| Leave a Comment »
Curator: Stephen J. Williams, PhD
From the POLICY FORUM ETHICS AND DIVERSITY Section of Science
Ethics of inclusion: Cultivate trust in precision medicine
Sandra Soo-Jin Lee1,
Stephanie M. Fullerton2,
Aliya Saperstein3,
Janet K. Shim4
See all authors and affiliations
Science 07 Jun 2019:
DOI: 10.1126/science.aaw8299
Precision medicine is at a crossroads. Progress toward its central goal, to address persistent health inequities, will depend on enrolling populations in research that have been historically underrepresented, thus eliminating longstanding exclusions from such research (1). Yet the history of ethical violations related to protocols for inclusion in biomedical research, as well as the continued misuse of research results (such as white nationalists looking to genetic ancestry to support claims of racial superiority), continue to engender mistrust among these populations (2). For precision medicine research (PMR) to achieve its goal, all people must believe that there is value in providing information about themselves and their families, and that their participation will translate into equitable distribution of benefits. This requires an ethics of inclusion that considers what constitutes inclusive practices in PMR, what goals and values are being furthered through efforts to enhance diversity, and who participates in adjudicating these questions. The early stages of PMR offer a critical window in which to intervene before research practices and their consequences become locked in (3).
Initiatives such as the All of Us program have set out to collect and analyze health information and biological samples from millions of people (1). At the same time, questions of trust in biomedical research persist. For example, although the recent assertions of white nationalists were eventually denounced by the American Society of Human Genetics (4), the misuse of ancestry testing may have already undermined public trust in genetic research.
There are also infamous failures in research that included historically underrepresented groups, including practices of deceit, as in the Tuskegee Syphilis Study, or the misuse of samples, as with the Havasupai tribe (5). Many people who are being asked to give their data and samples for PMR must not only reconcile such past research abuses, but also weigh future risks of potential misuse of their data.
To help assuage these concerns, ongoing PMR studies should open themselves up to research, conducted by social scientists and ethicists, that examines how their approaches enhance diversity and inclusion. Empirical studies are needed to account for how diversity is conceptualized and how goals of inclusion are operationalized throughout the life course of PMR studies. This is not limited to selection and recruitment of populations but extends to efforts to engage participants and communities, through data collection and measurement, and interpretations and applications of study findings. A commitment to transparency is an important step toward cultivating public trust in PMR's mission and practices.
From Inclusion to Inclusive
The lack of diverse representation in precision medicine and other biomedical research is a well-known problem. For example, rare genetic variants may be overlooked—or their association with common, complex diseases can be misinterpreted—as a result of sampling bias in genetics research (6). Concentrating research efforts on samples with largely European ancestry has limited the ability of scientists to make generalizable inferences about the relationships among genes, lifestyle, environmental exposures, and disease risks, and thereby threatens the equitable translation of PMR for broad public health benefit (7).
However, recruiting for diverse research participation alone is not enough. As with any push for "diversity," related questions arise about how to describe, define, measure, compare, and explain inferred similarities and differences among individuals and groups (8). In the face of ambivalence about how to represent population variation, there is ample evidence that researchers resort to using definitions of diversity that are heterogeneous, inconsistent, and sometimes competing (9). Varying approaches are not inherently problematic; depending on the scientific question, some measures may be more theoretically justified than others and, in many cases, a combination of measures can be leveraged to offer greater insight (10). For example, studies have shown that American adults who do not self-identify as white report better mental and physical health if they think others perceive them as white (11, 12).
The benefit of using multiple measures of race and ancestry also extends to genetic studies. In a study of hypertension in Puerto Rico, not only did classifications based on skin color and socioeconomic status better predict blood pressure than genetic ancestry, the inclusion of these sociocultural measures also revealed an association between a genetic polymorphism and hypertension that was otherwise hidden (13). Thus, practices that allow for a diversity of measurement approaches, when accompanied by a commitment to transparency about the rationales for chosen approaches, are likely to benefit PMR research more than striving for a single gold standard that would apply across all studies. These definitional and measurement issues are not merely semantic. They also are socially consequential to broader perceptions of PMR research and the potential to achieve its goals of inclusion.
Study Practices, Improve Outcomes
Given the uncertainty and complexities of the current, early phase of PMR, the time is ripe for empirical studies that enable assessment and modulation of research practices and scientific priorities in light of their social and ethical implications. Studying ongoing scientific practices in real time can help to anticipate unintended consequences that would limit researchers' ability to meet diversity recruitment goals, address both social and biological causes of health disparities, and distribute the benefits of PMR equitably. We suggest at least two areas for empirical attention and potential intervention.
First, we need to understand how "upstream" decisions about how to characterize study populations and exposures influence "downstream" research findings of what are deemed causal factors. For example, when precision medicine researchers rely on self-identification with U.S. Census categories to characterize race and ethnicity, this tends to circumscribe their investigation of potential gene-environment interactions that may affect health. The convenience and routine nature of Census categories seemed to lead scientists to infer that the reasons for differences among groups were self-evident and required no additional exploration (9). The ripple effects of initial study design decisions go beyond issues of recruitment to shape other facets of research across the life course of a project, from community engagement and the return of results to the interpretation of study findings for human health.
Second, PMR studies are situated within an ecosystem of funding agencies, regulatory bodies, disciplines, and other scholars. This partly explains the use of varied terminology, different conceptual understandings and interpretations of research questions, and heterogeneous goals for inclusion. It also makes it important to explore how expectations related to funding and regulation influence research definitions of diversity and benchmarks for inclusion.
For example, who defines a diverse study population, and how might those definitions vary across different institutional actors? Who determines the metrics that constitute successful inclusion, and why? Within a research consortium, how are expectations for data sharing and harmonization reconciled with individual studies' goals for recruitment and analysis? In complex research fields that include multiple investigators, organizations, and agendas, how are heterogeneous, perhaps even competing, priorities negotiated? To date, no studies have addressed these questions or investigated how decisions facilitate, or compromise, goals of diversity and inclusion.
The life course of individual studies and the ecosystems in which they reside cannot be easily separated and therefore must be studied in parallel to understand how meanings of diversity are shaped and how goals of inclusion are pursued. Empirically "studying the studies" will also be instrumental in creating mechanisms for transparency about how PMR is conducted and how trade-offs among competing goals are resolved. Establishing open lines of inquiry that study upstream practices may allow researchers to anticipate and address downstream decisions about how results can be interpreted and should be communicated, with a particular eye toward the consequences for communities recruited to augment diversity. Understanding how scientists negotiate the challenges and barriers to achieving diversity that go beyond fulfilling recruitment numbers is a critical step toward promoting meaningful inclusion in PMR.
Transparent Reflection, Cultivation of Trust
Emerging research on public perceptions of PMR suggests that although there is general support, questions of trust loom large. What we learn from studies that examine on-the-ground approaches aimed at enhancing diversity and inclusion, and how the research community reflects and responds with improvements in practices as needed, will play a key role in building a culture of openness that is critical for cultivating public trust.
Cultivating long-term, trusting relationships with participants underrepresented in biomedical research has been linked to a broad range of research practices. Some of these include the willingness of researchers to (i) address the effect of history and experience on marginalized groups' trust in researchers and clinicians; (ii) engage concerns about potential group harms and risks of stigmatization and discrimination; (iii) develop relationships with participants and communities that are characterized by transparency, clear communication, and mutual commitment; and (iv) integrate participants' values and expectations of responsible oversight beyond initial informed consent (14). These findings underscore the importance of multidisciplinary teams that include social scientists, ethicists, and policy-makers, who can identify and help to implement practices that respect the histories and concerns of diverse publics.
A commitment to an ethics of inclusion begins with a recognition that risks from the misuse of genetic and biomedical research are unevenly distributed. History makes plain that a multitude of research practices ranging from unnecessarily limited study populations and taken-for-granted data collection procedures to analytic and interpretive missteps can unintentionally bolster claims of racial superiority or inferiority and provoke group harm (15). Sustained commitment to transparency about the goals, limits, and potential uses of research is key to further cultivating trust and building long-term research relationships with populations underrepresented in biomedical studies.
As calls for increasing diversity and inclusion in PMR grow, funding and organizational pathways must be developed that integrate empirical studies of scientific practices and their rationales to determine how goals of inclusion and equity are being addressed and to identify where reform is required. In-depth, multidisciplinary empirical investigations of how diversity is defined, operationalized, and implemented can provide important insights and lessons learned for guiding emerging science, and in so doing, meet our ethical obligations to ensure transparency and meaningful inclusion.
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Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine
Posted in Advanced Computing Platform, AI, Big Data, BioBanking, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer and Current Therapeutics, CAST - Alternative to CRISPR/Cas9, Computational Biology/Systems and Bioinformatics, CRISPR/Cas9 & Gene Editing, Diagnostic Immunology, Digital HealthCare – biotech & internet joint ventures, Disease Biology, DNA repair, Drug Delivery Platform Technology, Gene Editing Impact on Longevity, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Health Care System by Country, Health Economics and Outcomes Research, HealthCare IT, Human Immune System in Health and in Disease, Immuno-Oncology & Genomics, Immunodiagnostics, Liquid Biopsy: Circulating Tumor Cells in Urine and Blood, Microfuidics, Single Cell Genomics, Variation in human protein-coding regions on December 9, 2019| Leave a Comment »
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.
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
Posted in Advanced Computing Platform, Big Data, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biological Networks, Gene Regulation and Evolution, Computational Biology/Systems and Bioinformatics, Gene Regulation, Gene Regulation and Evolution, Genome Biology, Intelligent Information Systems, Single Cell Genomics, Single-cell sequencing, Variation in human protein-coding regions on December 8, 2019| Leave a Comment »
Curator & Reporter: Aviva Lev-Ari, PhD, RN
Gene regulation
Regulatory networks
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 promoters. Nat Biotechnol (2019) doi:10.1038/s41587-019-0315-8
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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13 Horwitz, M. S. & Loeb, L. A. Promoters selected from random DNA sequences. Proc. Natl Acad. Sci. USA 83, 7405–7409 (1986).
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
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Carl G. de Boer
, Eeshit Dhaval Vaishnav
, Nir Friedman
& Aviv Regev
Howard Hughes Medical Institute and Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
Eeshit Dhaval Vaishnav
School of Computer Science and Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
Ronen Sadeh
& Nir Friedman
Initiative for Maximizing Student Development Program, University of New Mexico, Albuquerque, NM, USA
Esteban Luis Abeyta
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.
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.
Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Code availability
Integrated supplementary information
Risks from Dual Antiplatelet Therapy (DAPT) may be reduced by Genotyping Guidance of Cardiac Patients
Posted in CABG, Cardiac and Cardiovascular Surgical Procedures, Frontiers in Cardiology and Cardiovascular Disorders, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, PCI, Peripheral Arterial Disease & Peripheral Vascular Surgery, Pharmacotherapy of Cardiovascular Disease, Stroke on November 20, 2019| Leave a Comment »
Genotyping Cardiac Patients May Reduce Risks From DAPT
-STEMI patient study reaches noninferiority mark for adverse cardiac events
by Vicki Brower, CME Writer, MedPage Today September 7, 2019
In the investigational arm, all 1,242 patients were tested for CYP2C19 loss-of-function alleles *2 or *3. Carriers received ticagrelor or prasugrel, while noncarriers received clopidogrel, considered to be less powerful.
No genetic testing was performed in the standard treatment arm (n=1,246), in which patients largely went on to receive ticagrelor or prasugrel. Nearly all patients in both cohorts received dual antiplatelet therapy (DAPT) with aspirin.
Following primary PCI, patients went on to the P2Y12 inhibitor for at least 12 months, with drug adherence similar between the genotype-guided (84.5%) and standard groups (82.0%).
For patients with CYP2C19 loss-of-function alleles in the genotype-guided arm, 38% received ticagrelor and 1% received prasugrel. The remaining 61% of patients — the noncarriers — received clopidogrel. In the control arm, 91% were treated with ticagrelor, 2% with prasugrel, and 7% with clopidogrel, according to local protocol.
Ten Berg said that prasugrel is not typically used in the Netherlands, where eight of the centers in the trial were located, but that this might change given that the drug lowered rates of ischemic events versus ticagrelor in the head-to-head ISAR REACT 5 trial, which was also presented at ESC.
Reviewed by Robert Jasmer, MD Associate Clinical Professor of Medicine, University of California, San Francisco
New England Journal of Medicine
Source Reference: Claassens DMF, et al "A genotype-guided strategy for oral P2Y12 inhibitors in primary PCI" N Engl J Med 2019; DOI: 10.1056/NEJMoa1907096.
MedPage Today
Source Reference: Ingram I "Precision Medicine Lowers DAPT Bleeds in STEMI" 2019.
https://www.medpagetoday.org/meetingcoverage/esc/82010?vpass=1
Injectable inclisiran (siRNA) as 3rd anti-PCSK9 behind mAbs Repatha and Praluent
Posted in Atherogenic Processes & Pathology, Drug Delivery Platform Technology, Exosomes: Natural Carriers for siRNA Delivery, FDA Regulatory Affairs, Frontiers in Cardiology and Cardiovascular Disorders, Gene Therapy & Gene Editing Development, Genome Biology, Genomics Pharmacy, Origins of Cardiovascular Disease, Pharmacotherapy of Cardiovascular Disease on November 18, 2019| Leave a Comment »
Next stop, filing for approval. The Medicines Company has said it plans to submit inclisiran for FDA review by the end of 2019 and EMA review in the first quarter of 2020. If the drug's approved it'll be the third anti-PCSK9 behind mAbs Repatha and Praluent, and could try to compete on price to gain market share.
The company's been very careful not to disclose its pricing plans for inclisiran, said ORION-10 principal investigator Dr. Scott Wright, professor and cardiologist at the Mayo Clinic. But, Wright said, The Medicines Co. and other companies he advises on clinical trial design "have learned the lesson from the sponsors of the monoclonal antibodies [against PCSK9], they're not going to come in and price a drug that's out of proportion to what the market will bear."
Because the anti-PCSK9 mAbs were initially priced beyond what patients and insurers were willing to pay, "now most of the physicians that I meet have a resistance to using them just because they're fearful about the pre-approval process" with insurers, said Wright. "They're much easier to get approved and paid for today than they've ever been, but that message is not out in the medical community yet."
From: "STAT: AHA in 30 Seconds" <newsletter@statnews.com>
Reply-To: "STAT: AHA in 30 Seconds" <newsletter@statnews.com>
Date: Monday, November 18, 2019 at 2:59 PM
To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>
Subject: Interim look at Amarin data, an inclisiran update, & Philly's giant heart
Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis
Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Computational Histopathology, Deep Learning in Pathology, Genome Biology, Meta-analysis of transcriptome data on October 28, 2019| Leave a Comment »
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.
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
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.
https://www.biorxiv.org/content/10.1101/813543v1
● 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
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.
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
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
NSPR1 and DEC2 genes: Survival on 4.5 hours of Sleep per night: A mutation in the β1-adrenergic receptor gene in humans who require fewer hours of sleep than most, ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness
Posted in Circadian Rhythm and Sleep, Gene Regulation, Genome Biology, Variation in human protein-coding regions on October 22, 2019| 1 Comment »
10/2019 RESEARCH ARTICLE SLEEP
Mutant neuropeptide S receptor reduces sleep duration with preserved memory consolidation
Lijuan Xing1,*,
Guangsen Shi1,*,
Yulia Mostovoy2,
Nicholas W. Gentry1,
Zenghua Fan1,
Thomas B. McMahon1,
Pui-Yan Kwok2,3,4,
Christopher R. Jones5,
Louis J. Ptáček1,4,6,7,† and
Ying-Hui Fu1,4,6,7,†
Science Translational Medicine 16 Oct 2019:
Vol. 11, Issue 514, eaax2014
DOI: 10.1126/scitranslmed.aax2014
Sleep is a crucial physiological process for our survival and cognitive performance, yet the factors controlling human sleep regulation remain poorly understood. Here, we identified a missense mutation in a G protein–coupled neuropeptide S receptor 1 (NPSR1) that is associated with a natural short sleep phenotype in humans. Mice carrying the homologous mutation exhibited less sleep time despite increased sleep pressure. These animals were also resistant to contextual memory deficits associated with sleep deprivation. In vivo, the mutant receptors showed increased sensitivity to neuropeptide S exogenous activation. These results suggest that the NPS/NPSR1 pathway might play a critical role in regulating human sleep duration and in the link between sleep homeostasis and memory consolidation.
It is possible that drugs could be developed to target either the NSPR1 or DEC2 genes, as a treatment for insomnia or other sleep disorders. However, further understanding of exactly how these genes function would be required before this stage. Both are involved in brain function, so targeting them could lead to negative neural side effects.
Volume 103, Issue 6, 25 September 2019, Pages 1044-1055.e7
A Rare Mutation of β1-Adrenergic Receptor Affects Sleep/Wake Behaviors
Guangsen Shi
Lijuan Xing
Andrew Krystal
Louis J. Ptáček
Ying-Hui Fu 7
Show all authors
Published:August 28, 2019DOI:https://doi.org/10.1016/j.neuron.2019.07.026
https://www.cell.com/neuron/fulltext/S0896-6273(19)30652-X
PlumX Metrics
A mutation in ADRB1 leads to natural short sleep trait in humans
Mice engineered with same mutation have similar short sleep behavior as humans
Activity of dorsal pons ADRB1 + neurons associates with REM sleep and wakefulness
Mutation increases the population activity of dorsal pons ADRB1 + neurons
Sleep is crucial for our survival, and many diseases are linked to long-term poor sleep quality. Before we can use sleep to enhance our health and performance and alleviate diseases associated with poor sleep, a greater understanding of sleep regulation is necessary. We have identified a mutation in the β 1-adrenergic receptor gene in humans who require fewer hours of sleep than most. In vitro, this mutation leads to decreased protein stability and dampened signaling in response to agonist treatment. In vivo, the mice carrying the same mutation demonstrated short sleep behavior. We found that this receptor is highly expressed in the dorsal pons and that these ADRB1 + neurons are active during rapid eye movement (REM) sleep and wakefulness. Activating these neurons can lead to wakefulness, and the activity of these neurons is affected by the mutation. These results highlight the important role of β 1-adrenergic receptors in sleep/wake regulation.
familial natural short sleep
ADRB1
sleep duration
dorsal pons
Second Gene Mutation that Lets People Survive on Less Sleep
Other related articles on Circadian Rhythm and Sleep published in this Open Access Online Scientific Journal include the following:
2017 Nobel Prize in Physiology or Medicine jointly to Jeffrey C. Hall (ex-Brandeis, University of Maine), Michael Rosbash (Brandeis University) and Michael W. Young (Rockefeller University in New York) for their discoveries of molecular mechanisms controlling the circadian rhythm
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2017/10/02/2017-nobel-prize-in-physiology-or-medicine-jointly-to-jeffrey-c-hall-michael-rosbash-and-michael-w-young-for-their-discoveries-of-molecular-mechanisms-controlling-the-circadian-rhythm/
Patient-Reported Outcomes Study, Presented at SLEEP 2018, Provides Confirmatory Real-World Evidence of the Previously Presented 7-hour Action of REMfresh®, the First Continuous Release and Absorption Melatonin™
Reporter: Gail S. Thornton, PhD(c)
https://pharmaceuticalintelligence.com/2018/06/10/patient-reported-outcomes-study-presented-at-sleep-2018-provides-confirmatory-real-world-evidence-of-the-previously-presented-7-hour-action-of-remfresh-the-first-continuous-release-and-absorp/
Clinically Studied, Continuous Release and Absorption Melatonin, REMfresh, Designed to Give Patients Up to 7 Hours of Sleep Support
Reporter: Gail S. Thornton, M.A.
https://pharmaceuticalintelligence.com/2019/06/19/clinically-studied-continuous-release-and-absorption-melatonin-remfresh-designed-to-give-patients-up-to-7-hours-of-sleep-support/
2017 award recipients including Thomas S. Kilduff, PhD, Director, Center for Neuroscience at SRI International in Menlo Park, California
https://pharmaceuticalintelligence.com/2017/04/28/sleep-research-society-announces-2017-award-recipients-including-thomas-s-kilduff-phd-director-center-for-neuroscience-at-sri-international-in-menlo-park-california/
Sleep and Memory
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/03/26/sleep-and-memory/
Sleep Science
https://pharmaceuticalintelligence.com/2016/03/16/sleep-science/
Genetic Link to Sleep and Mood Disorders
https://pharmaceuticalintelligence.com/2016/02/27/genetic-link-to-sleep-and-mood-disorders/
Sleep Apnea Insular Glutamate and GABA Levels
https://pharmaceuticalintelligence.com/2016/02/12/sleep-apnea-insular-glutamate-and-gaba-levels/
Fat, Sleep and the Gut
https://pharmaceuticalintelligence.com/2016/02/06/fat-sleep-and-the-gut/
23andMe Genome-Wide Association Study on Human propensity to Get up early or Sleep in the Morning
https://pharmaceuticalintelligence.com/2016/02/02/23andme-genome-wide-association-study-on-human-propensity-to-get-up-early-or-sleep-in-the-morning/
Sleep Quality, Amyloid and Cognitive Decline
https://pharmaceuticalintelligence.com/2015/10/31/sleep-quality-amyloid-and-cognitive-decline/
Study Shows Learning Is Best Enhanced During Sleep – Jewish Business News
https://pharmaceuticalintelligence.com/2014/09/02/study-shows-learning-is-best-enhanced-during-sleep-jewish-business-news/
Beta-Blockers Cause Lack Of Restful Sleep – Life Extension
https://pharmaceuticalintelligence.com/2014/08/04/beta-blockers-cause-lack-of-restful-sleep-life-extension/
Topical Antispasmodics conducive for Uninterrupted Sleep – A Potential Cardiovascular Chrono-therapeutics
https://pharmaceuticalintelligence.com/2014/02/13/topical-antispasmodics-conducive-for-uninterrupted-sleep-a-potential-cardiovascular-chrono-therapeutics/
Prolonged Wakefulness: Lack of Sufficient Duration of Sleep as a Risk Factor for Cardiovascular Diseases – Indications for Cardiovascular Chrono-therapeutics
https://pharmaceuticalintelligence.com/2014/02/02/prolonged-wakefulness-lack-of-sufficient-duration-of-sleep-as-a-risk-factor-for-cardiovascular-diseases-indications-for-cardiovascular-chrono-therapeutics/
Sleep Apnea and Non-invasive positive Pressure Breathing
https://pharmaceuticalintelligence.com/2013/06/11/sleep-apnea-and-non-invasive-positive-pressure-breathing/
How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancer?
Author: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2013/03/20/how-might-sleep-apnea-lead-to-serious-health-concerns-like-cardiac-and-cancers/
2019 Warren Alpert Foundation Award goes to Four Scientists for Seminal Discoveries in OptoGenetics – Illuminating the Human Brain
https://pharmaceuticalintelligence.com/2019/07/18/2019-warren-alpert-foundation-award-goes-to-four-scientists-for-seminal-discoveries-in-optogenetics-illuminating-the-human-brain/
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2012pharmaceutical
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text:
By Tim Grierson, Senior US Critic2014-09-06T12:25:00+01:00
Dir/scr: Dan Gilroy. US. 2014. 117mins
In Nightcrawler, Jake Gyllenhaal gives a fully invested, gutsy performance as a morally slippery Angeleno who stumbles into the shadowy world of nighttime freelance news videographers. Unfortunately, filmmaker Dan Gilroy's tonally bold directorial debut can't quite pull together its differing ambitions, resulting in a story that's not entirely satisfying as a dark character study, amoral thriller or curt commentary on the sensationalism of local TV news.
Gilroy and cinematographer Robert Elswit have given us a lurid portrait of nighttime L.A. that hums with raw dread, but Nightcrawler can't execute its story as effectively as its pungent mood.
Screening at the Toronto Film Festival before hitting US theatres on October 31, this Open Road offering will benefit from Gyllenhaal's modest box office appeal and its thriller trappings. But because the likeable star plays a quietly sinister, mentally unstable man, Nightcrawler may suffer some in terms of word of mouth. Still, praise for Gyllenhaal's controlled turn may make this movie an attractive counterprogramming option for those who will want to ditch the late-October glut of horror films.
Set in Los Angeles, Nightcrawler stars Gyllenhaal as Lou Bloom, who is stealing raw materials from a construction site and overwhelming a security guard when the movie begins, an ominous warning about the sort of individual we're dealing with. A drifter without much direction but armed with a fierce determination to make something of himself, Lou is looking for a job when he happens upon a fiery car accident on the freeway.
While the cops try to rescue the driver, Lou notices Joe (Bill Paxton), a man with a video camera who swoops in to record the incident. Joe explains that he's a "nightcrawler," a freelance videographer who films horrible car crashes and bloody murder scenes, selling the footage to local news stations that can't get enough grisly material. Intrigued, Lou decides to become a nightcrawler as well, hiring a well-meaning, oblivious young man named Rick (Riz Ahmed) to be his assistant as they drive around L.A. with their police scanner looking for potentially horrifying footage.
Gilroy, best known as a co-writer on The Fall and The Bourne Legacy, has built his movie around Lou's puzzling, intriguing strangeness. Decked out with big bushy eyebrows, Gyllenhaal speaks in an unsettlingly low tone, trying to sell himself as a confident, put-together man who nonetheless lets his façade drop occasionally so that we see the true unease within him. With echoes of Robert De Niro's performances as unhinged people on the margins in Taxi Driver and The King Of Comedy, Gyllenhaal digs deep into Lou's misanthropy and need to belong — qualities that, ironically, make him an ace nightcrawler because he's willing to get shots that no one else will, even if it pushes up against the boundaries of taste or ethics.
The problem with Nightcrawler is that Gilroy has created a vivid world of lowlifes and losers without always making that world convincing. Most notably is his depiction of the local news station that soon becomes Lou's bread and butter. Rene Russo plays an unscrupulous news editor who embraces the "if it bleeds, it leads" ethos of bottom-feeding sensationalist journalism. Russo gives the woman enjoyable layers of sliminess, but Nightcrawler consistently seems overly pleased with its ho-hum revelations about TV journalism that aren't particularly shocking. This is even more of an issue when it becomes clear relatively early on that Lou's rise up the nightcrawler ranks is meant to be a scathing, deadpan indictment of an industry so soulless and corrupt that it embraces a psychopath like him.
The film works best in its exchanges between Lou and Rick where we see the depths of self-delusion eating into Lou's personality. Intimidating the younger, impressionable Rick, Lou rules the roost in their nightly drives around L.A., and as his actions become increasingly ethically questionable, Nightcrawler creates a claustrophobic unreality between them. The two actors have a chilly rapport that's apt for their relationship as the audience (along with Rick) slowly begins to understand the depths of Lou's depravity.
About two-thirds of the way through Nightcrawler, Gilroy throws a major twist into the narrative, shifting the film away from character study into the realm of thriller. The filmmaker does a fine job increasing the tension, leading to a few expert action and suspense sequences, but it doesn't fit smoothly into what comes before — and it segues awkwardly into a finale that proves too predictable in terms of Lou's ultimate arc. Gilroy and cinematographer Robert Elswit have given us a lurid portrait of nighttime L.A. that hums with raw dread, but Nightcrawler can't execute its story as effectively as its pungent mood.
Production company: Bold Films
International sales: Sierra/Affinity, www.sierra-affinity.com
Producers: Michel Litvak, Jake Gyllenhaal, David Lancaster, Jennifer Fox, Tony Gilroy
Executive producers: Gary Michael Walters, Betsy Danbury
Cinematography: Robert Elswit
Production designer: Kevin Kavanaugh
Editor: John Gilroy
Music: James Newton Howard
Main cast: Jake Gyllenhaal, Rene Russo, Riz Ahmed, Bill Paxton
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Garrett Bradley named best director for Time .

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Have you done your taxes yet? Your W-2s or 1099s have started to trickle in with the mail over the past few weeks. You might also be in the process of gathering the right receipts and pertinent statements.
Before you sign an IRS tax return and send it off, though, make sure you know about these new or improved tax breaks for this year's tax season. By taking advantage of these tax breaks, you can reduce your total tax debt.
Take heart if you missed the 60-day time limit for transferring distributed funds from your IRA or workplace retirement fund to another qualifying fund. Thanks to a new rule, you might qualify for a waiver that will prevent you from having to pay early distribution taxes.
To make things easy on the taxpayer, the IRS website has a sample letter that you can fill in and print to explain which of the 11 reasons applies to you. Look for Revenue Procedure 2016-47.
An ABLE account—achieving a better life experience—offers tax-advantaged opportunities for disabled people and their families. It helps them save for and pay for expenses related to the disability. Although the legislation was passed in 2014, the specialized accounts only became available on a general basis in 2016.
These accounts offer both state and federal tax advantages. In addition, the first $100,000 in an ABLE account does not count as income or assets when disabled individuals try to qualify for public assistance programs.
Although only nine states currently have ABLE programs, you can open an account—or contribute to one—in a state other than your own.
The positive effect of getting a cost-of-living increase or raise at your job can be a mixed blessing if it lifts you into a higher tax bracket. Fortunately, the IRS raised the tax thresholds for 2016, meaning you're less likely to have to pay a greater percentage of your wages toward taxes if you earned more last year than you did in 2015.
Married couples filing jointly slid up from a 15 percent tax bracket to 25 percent once they earned more than $74,900 in household income in 2015. In 2016, such filers had to earn more than $75,300 before moving into the 25 percent bracket.
Those filing as head of household in 2016 will enjoy a $50 increase in their standard deduction, from $9,250 in 2015 to $9,300 in 2016.
The standard deduction didn't increase for other filers. Singles still receive a $6,300 standard deduction and married couples filing jointly receive a $12,600 standard deduction.
You can get a larger standard deduction if you are blind, age 65 or older, or both. Depending on your circumstances, the increase can be as much as $1,550.
Enter your standard deduction on line 40 of Form 1040. Instructions in the left-hand column of your tax return help you figure out how much you can claim.
Line 42 of Form 1040 lets you claim personal exemptions, which are amounts you can deduct from your adjusted gross income for yourself and your dependents. Such exemptions are in addition to your itemized deductions or standard deduction.
In 2016, the per-person exemption rose $50 to $4,050 per person. Note that a person can only be claimed as an exemption on one tax return. So, let any of your working dependents know that they cannot take themselves as an exemption even if they earn enough money to file their own tax return.
The exemption phases out once you reach certain income levels. Your exemptions decrease by 2 percent for each $2,500 above these income levels.
If you have low or moderate income, you might qualify for the federal earned income credit. The income-based credit is for single, head of household, married filing jointly or widowed taxpayers with or without children.
The maximum credit for 2016 is $6,269 for filers with three or more qualifying children. The amount reflects a $27 increase from 2015's figure of $6,242.
Low-income people without children receive the lowest credit. Single, head of household or widowed taxpayers receive a maximum credit of $506 if they make less than $14,880. Those who are married filing jointly receive the same amount provided their income is less than $20,430. The credit amount reflects a $3 increase from the 2015 credit of $503. Income limits have increased $60 for singles and $100 for those in the married filing jointly category.
Take advantage of the earned income credit by filling out Schedule EIC and attaching it to your tax return if you have a qualifying child and meet the income requirement. Then, enter the amount of your credit on line 66a of Form 1040. If you don't have a qualifying child but meet the income requirements, simply enter your credit amount on line 66a.
You have been physically out of the U.S. for at least 330 full days during any 12-month period.
You have been "a bona fide resident of a foreign country or countries for an uninterrupted period that includes an entire tax year," according to the IRS.
You have been a U.S. resident alien who is a "citizen or national of a country with which the United States has an income tax treaty in effect," according to the IRS. In addition, you must meet the second requirement above.
If you qualify for the exclusion, you can exclude up to $101,300 of income earned in a foreign country, $500 more income than 2015's $100,800 tax exclusion.
To claim the exclusion, you must file a U.S. income tax return even if the money you made was less than the $101,300 exclusion. Fill out Form 2555 or 2555-EZ.


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