Shattuck lecture--medical and societal consequences of the Human Genome Project

Precision public health in the era of genomics and big data

Precision public health (PPH) considers the interplay between genetics, lifestyle and the environment to improve disease prevention, diagnosis and treatment on a population level-thereby delivering the right interventions to the right populations at the right time. In this Review, we explore the concept of PPH as the next generation of public health. We discuss the historical context of using individual-level data in public health interventions and examine recent advancements in how data from human and pathogen genomics and social, behavioral and environmental research, as well as artificial intelligence, have transformed public health. Real-world examples of PPH are discussed, emphasizing how these approaches are becoming a mainstay in public health, as well as outstanding challenges in their development, implementation and sustainability. Data sciences, ethical, legal and social implications research, capacity building, equity research and implementation science will have a crucial role in realizing the potential for 'precision' to enhance traditional public health approaches.

CURE on yeast genes of unknown function increases students' bioinformatics proficiency and research confidence

Course-based undergraduate research experiences (CUREs) can reduce barriers to research opportunities while increasing student knowledge and confidence. However, the number of widely adopted, easily transferable CUREs is relatively small. Here, we describe a CURE aimed at determining the function of poorly characterized Saccharomyces cerevisiae genes. More than 20 years after sequencing of the yeast genome, nearly 10% of open reading frames (ORFs) still have at least one uncharacterized Gene Ontology (GO) term. We refer to these genes as "ORFans" and formed a consortium aimed at assigning functions to them. Specifically, over 70 faculty members attended summer workshops to learn the bioinformatics workflow and basic laboratory techniques described herein. Ultimately, this CURE was adapted for implementation at 34 institutions, resulting in over 1,300 students conducting course-based research on ORFans. Pre-/post-tests confirmed that students gained both (i) an understanding of gene ontology and (ii) knowledge regarding the use of bioinformatics to assign gene function. After using these data to craft their own hypotheses, then testing their predictions by constructing and phenotyping deletion strains, students self-reported significant gains in several areas, including computer modeling and exposure to a project where no one knows the outcome. Interestingly, most net gains self-reported by ORFan Gene Project participants were greater than published findings for CUREs assessed with the same survey instrument. The surprisingly strong impact of this CURE may be due to the incoming lack of experience of ORFan Project participants and/or the independent thought required to develop testable hypotheses from complex data sets.

How has the brain disease model of addiction contributed to tobacco control?

Our paper evaluates the extent to which the brain disease model of addiction (BDMA) has contributed to reducing the prevalence of tobacco smoking and  tobacco-related harm over the past 20 years. We discuss the ways in which genetic and neuroscience research on nicotine addiction have contributed to our understanding of tobacco smoking. We then examine the extent to which the BDMA has produced more effective treatments to assist smoking cessation. We also assess the degree to which the BDMA has contributed to the tobacco control policies that have produced substantial reductions in tobacco-related morbidity and mortality in the two decades since the model was first proposed by Alan Leshner. We also assess whether the BDMA has reduced the stigmatisation of people who smoke tobacco.

In Vitro Prostate Cancer Treatment via CRISPR-Cas9 Gene Editing Facilitated by Polyethyleneimine-Derived Graphene Quantum Dots

CRISPR-Cas9 is a programmable gene editing tool with a promising potential for cancer gene therapy. This therapeutic function is enabled in the present work via the non-covalent delivery of CRISPR ribonucleic protein (RNP) by cationic glucosamine/PEI-derived graphene quantum dots (PEI-GQD) that aid in overcoming physiological barriers and tracking genes of interest. PEI-GQD/RNP complex targeting the TP53 mutation overexpressed in ~50% of cancers successfully produces its double-stranded breaks in solution and in PC3 prostate cancer cells. Restoring this cancer "suicide" gene can promote cellular repair pathways and lead to cancer cell apoptosis. Its repair to the healthy form performed by simultaneous PEI-GQD delivery of CRISPR RNP and a gene repair template leads to a successful therapeutic outcome: 40% apoptotic cancer cell death, while having no effect on non-cancerous HeK293 cells. The translocation of PEI-GQD/RNP complex into PC3 cell cytoplasm is tracked via GQD intrinsic fluorescence, while EGFP-tagged RNP is detected in the cell nucleus, showing the successful detachment of the gene editing tool upon internalization. Using GQDs as non-viral delivery and imaging agents for CRISPR-Cas9 RNP sets the stage for image-guided cancer-specific gene therapy.

How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials

AIMS/HYPOTHESIS

There are two prerequisites for the precision medicine approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in the case of treatment heterogeneity, we need to detect clinical predictors to identify people who would benefit from one treatment more than from others. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. Our aim was to apply this approach to the treatment of type 2 diabetes.

METHODS

We performed a meta-regression analysis using information from 174 placebo-controlled randomised trials with 178 placebo and 272 verum (i.e. active treatment) arms including 86,940 participants with respect to the variability of glycaemic control as assessed by HbA1c after treatment and its potential predictors.

RESULTS

The adjusted difference in log(SD) values between the verum and placebo arms was 0.037 (95% CI: 0.004, 0.069). That is, we found a small increase in the variability of HbA1c values after treatment in the verum arms. In addition, one potentially relevant predictor for explaining this increase, drug class, was observed, and GLP-1 receptor agonists yielded the largest differences in log(SD) values.

CONCLUSIONS/INTERPRETATION

The potential of the precision medicine approach in the treatment of type 2 diabetes is modest at best, at least with regard to an improvement in glycaemic control. Our finding of a larger variability after treatment with GLP-1 receptor agonists in individuals with poor glycaemic control should be replicated and/or validated with other clinical outcomes and with different study designs.

FUNDING

The research reported here received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

DATA AVAILABILITY

Two datasets (one for the log[SD] and one for the baseline-corrected log[SD]) to reproduce the analyses from this paper are available on https://zenodo.org/record/7956635 .

From target discovery to clinical drug development with human genetics

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

From information to physics to biology

Commentary to "The gene: An appraisal" by Keith Baverstock. PBMB, Volume 164, September 2021, Pages 46-62. NOTE: this short and informal commentary constructively criticizes the very interesting approach in the paper by a brief survey of the work that a few of us develop since several years. I will first recall the very pertinent critique of the Modern Synthesis and the genocentric approach presented in the paper, then suggest a methodological (and theoretical) critique of the approach by K. Baverstock and hint to alternatives paths that are compatible, but "extend" the physics for biology presented by the author. The purposes and the space allowed force a limited number of references and technical details. These may be found in the references contained in the few papers quoted below that are not the most nor the only representative contributions to the that work, but are inserted as a source of references or as synthetic presentations of our views.

Proactive functional classification of all possible missense single-nucleotide variants in KCNQ4

Clinical exome sequencing has yielded extensive disease-related missense single-nucleotide variants (SNVs) of uncertain significance, leading to diagnostic uncertainty. KCNQ4 is one of the most commonly responsible genes for autosomal dominant nonsyndromic hearing loss. According to the gnomAD cohort, approximately one in 100 people harbors missense variants in KCNQ4 (missense variants with minor allele frequency > 0.1% were excluded), but most are of unknown consequence. To prospectively characterize the function of all 4085 possible missense SNVs of human KCNQ4, we recorded the whole-cell currents using the patch-clamp technique and categorized 1068 missense SNVs as loss of function, as well as 728 loss-of-function SNVs located in the transmembrane domains. Further, to mimic the heterozygous condition in Deafness nonsyndromic autosomal dominant 2 (DFNA2) patients caused by KCNQ4 variants, we coexpressed loss-of-function variants with wild-type KCNQ4 and found 516 variants showed impaired or only partially rescued heterogeneous channel function. Overall, our functional classification is highly concordant with the auditory phenotypes in Kcnq4 mutant mice and the assessments of pathogenicity in clinical variant interpretations. Taken together, our results provide strong functional evidence to support the pathogenicity classification of newly discovered KCNQ4 missense variants in clinical genetic testing.

Playing God? Religious Perspectives on Manipulating the Genome

The Human Genome Project (HGP) is a remarkable medical science breakthrough that enables the understanding of genetics and the intervention of human health. An individual's health is influenced by physical, emotional, social, intellectual, and religious factors. Among these, religious beliefs shape our thinking on cloning, stem cells, and gene editing, affecting healthcare decisions and the motivation for seeking treatment. Is the human genome sacred? Does editing it violate the idea that we're made in God's image or allow us to "play God"? Understanding the perspectives behind the fundamental religious doctrines of Islam, Christian, Hindu, and Buddhist on gene editing/therapy in somatic and germline cells would ensure a right balance between geneticists and theologians in providing the best healthcare while catering to individual beliefs.

Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental "causal" role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a "historical" process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process - and not a static-stable condition - treatments should be tailored according to the "timing-frame" of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes - from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.

Problems and promises: How to tell the story of a Genome Wide Association Study?

The promise of treatments for common complex diseases (CCDs) is understood as an important force driving large scale genetics research over the last few decades. This paper considers the phenomenon of the Genome Wide Association Study (GWAS) via one high profile example, the Wellcome Trust Case Control Consortium (WTCCC). The WTCCC despite not fulfilling promises of new health interventions is still understood as an important step towards tackling CCDs clinically. The 'sociology of expectations' has considered many examples of failure to fulfil promises and the subsequent negative consequences including disillusionment, disappointment and disinvestment. In order to explore why some domains remain resilient in the face of apparent failure, I employ the concept of the 'problematic' found in the work of Giles Deleuze. This alternative theoretical framework challenges the idea that the failure to reach promised goals results in largely negative outcomes for a given field. I will argue that collective scientific action is motivated not only by hopes for the future but also by the drive to create solutions to the actual setbacks and successes which scientists encounter in their day-to-day work. I draw on eighteen interviews.

The need to incorporate human variation and evolutionary theory in forensic anthropology: A call for reform

In 1992, Norm Sauer called for a language shift in which practitioners would move away from the socially loaded term "race" and replace it with the less provocative term "ancestry." While many heeded the call and moved towards ancestry in their research and reports, the actual approach to research and analysis did not change. In response to this change, there was a large growth in ancestry estimation method development in the early decade of the 2000s. However, the practice of ancestry estimation did not adequately incorporate evolutionary theory in interpretation or trait selection and continued with little critical reflection. In the past decade, there has been an increase in ancestry validation methods with little critique of the "race" concept or discussion of modern human variation or reference samples. To advance, forensic anthropologists need to reckon with the practice of ancestry estimation as it is currently practiced. We are calling for another reform in the axiom focusing on evolutionary theory, population history, trait selection, and population-level reference samples. The practice needs to abandon the terms ancestry and race completely and recalibrate to an analysis of population affinity. Population affinity is a statistical approach based on the underlying population structure that would allow the understanding of how microevolutionary forces act in concert with historical events (e.g., colonization, the Transatlantic Slave Trade, etc.) to shape modern human variation. This is not to be confused with geographic ancestry that all too often can be perceived as interchangeable with social race and as an affirmation of the biological concept of race. It is time to critically evaluate the social and scientific implications of the current practice of ancestry estimation, and re-frame our approach to studying and analyzing modern human variation through a population structure approach.

Personalized medicine drugs and the burden of disease in Germany

Objectives

This study aimed to assess the burden attributable to diseases with subtypes that are indications for the personalized medicine (PM) drugs approved in Germany.

Methods

A secondary analysis of a PM drug database and Global Burden of Disease (GBD) Study 2019 data was conducted. Indications of the PM drugs approved in Germany for biomarker-targeted therapy were matched with disease burden causes to quantify the portion of the disease burden attributable to causes that, in some instances, may be treated with PM drugs.

Results

Between 1995 and 2020, the number of PM drugs approved in Germany rose from 0 to 83. Accordingly, the portion of the disease burden due to causes of disease with subtypes that are PM drug indications has risen. Indications for use of the 83 PM drugs approved in Germany by the end of 2020 related to 39 of 369 GBD causes, to which 7825 disability-adjusted life years (DALYs) or 24.3% of the total burden of 32 162 DALYs per 100 000 population in Germany were attributed. Twenty years earlier, in 2000, 5 PM drugs related to 2 GBD causes, to which 978 DALYs (3.1%) of a total burden of 31 878 DALYs per 100 000 population were attributed. Considering the median frequency of biomarkers that can change pharmacological treatment resulted in estimating that not more than 3.0% (interquartile range: 1.1–7.3) of the current German disease burden is affected by personalized pharmacotherapy.

Conclusions

Mapping PM drug indications to disease burden causes allowed to quantify the disease burden within and outside the domain of personalized pharmacotherapy in Germany.

Utility and Diversity: Challenges for Genomic Medicine

Genomic information is poised to play an increasing role in clinical care, extending beyond highly penetrant genetic conditions to less penetrant genotypes and common disorders. But with this shift, the question of clinical utility becomes a major challenge. A collaborative effort is necessary to determine the information needed to evaluate different uses of genomic information and then acquire that information. Another challenge must also be addressed if that process is to provide equitable benefits: the lack of diversity of genomic data. Current genomic knowledge comes primarily from populations of European descent, which poses the risk that most of the human population will be shortchanged when health benefits of genomics emerge. These two challenges have defined my career as a geneticist and have taught me that solutions must start with dialogue across disciplinary and social divides.

From axiomatic systems to the Dogmatic gene and beyond

The positivistic views that dominated the early debate on the foundations of mathematics, at the beginning of the 20th century, survived the "negative results" that have shown the limits of the axiomatic approach since the 1930s. Rigour, abstraction and symbolism have been confused with formalism, based on finite strings of signs, pre-given axioms, and potentially mechanisable rewriting rules. This contributed to major clarifications in the mathematical praxes but obscured the limits of formalisms due to the exclusion of the historical creation of sense proper to any science. We expand on this sometimes fruitful confusion with some case studies. We then hint to the historical creation of sense as a component of an epistemology of mathematics. We continue with an analogy with genocentric approaches in biology, as similar positivistic views resurfaced there fifty years later. Finite sequences of letters in the DNA would completely determine ontogenesis and phylogenesis, according to the Central Dogma of molecular biology. Limits and "negative evidence" have been disregarded while searching for the "gene for" everything. Alternative perspectives require a reconstruction of the sense of history as locus for the constitution of any object of biological knowledge. In particular, the historicity of biological evolution will be understood in terms of changing phase spaces and of the role of rare events in all phylogenetic trajectories. The analysis of the evolutionary production of variability, adaptivity and ecosystemic diversity is a key component of the project we hint to, as part of a renewed relation to the biological environment.

Diffuse Glioma Heterogeneity and Its Therapeutic Implications

Diffuse gliomas represent a heterogeneous group of universally lethal brain tumors characterized by minimally effective genotype-targeted therapies. Recent advances have revealed that a remarkable level of genetic, epigenetic, and environmental heterogeneity exists within each individual glioma. Together, these interconnected layers of intratumoral heterogeneity result in extreme phenotypic heterogeneity at the cellular level, providing for multiple mechanisms of therapeutic resistance and forming a highly adaptable and resilient disease. In this review, we discuss how glioma intratumoral heterogeneity and malignant cellular state plasticity drive resistance to existing therapies and look to a future in which these challenges may be overcome. SIGNIFICANCE: Glioma intratumoral heterogeneity and malignant cell state plasticity represent formidable hurdles to the development of novel targeted therapies. However, the convergence of genotypically diverse glioma cells into a limited set of epigenetically encoded transcriptional cell states may present an opportunity for a novel therapeutic strategy we call "State Selective Lethality." In this approach, cellular states (as opposed to genetic perturbations/mutations) are the subject of therapeutic targeting, and plasticity-mediated resistance is minimized through the design of cell state "trapping agents."

Extracellular Vesicles Contain Putative Cancer Biomarkers

Emerging evidences have implicated extracellular vesicles (EVs), nanoparticles secreted by cells, in regulating cancer progression. Several seminal studies on EVs have added an additional layer to the previously unanswered questions in understanding the complexity of diseases such as cancer. It has been observed that EV content is highly heterogenous and it likely reflects the dynamic state of the parent cell. Hence, these nano-sized vesicles have been proposed as reservoirs of cancer biomarkers for diagnostic and prognostic purposes. Due to their presence in almost all biological fluids, ability to display membrane, and sometimes cytosolic, cargo of its host cell and increase in their number during disease states has supported the potential utility of EVs as an alternative to current methods of cancer diagnosis. The following chapter will discuss the use of cancer cell-derived EVs as a resource of tumor specific biomarkers for the early diagnosis of disease. In addition, EVs could also be used in personalised medicine as a resource of predictive biomarkers to understand a patient's response to therapy. Overall, EVs could be exploited as a source of cancer biomarkers and could aid in treatment and stratification options to improve patient survival and quality of life.

ASH2L drives proliferation and sensitivity to bleomycin and other genotoxins in Hodgkin's lymphoma and testicular cancer cells

It is of clinical importance to identify biomarkers predicting the efficacy of DNA damaging drugs (genotoxins) so that nonresponders are not unduly exposed to the deleterious effects of otherwise inefficient drugs. Here, we initially focused on the bleomycin genotoxin because of the limited information about the genes implicated in the sensitivity or resistance to this compound. Using a whole-genome CRISPR/Cas9 gene knockout approach, we identified ASH2L, a core component of the H3K4 methyl transferase complex, as a protein required for bleomycin sensitivity in L1236 Hodgkin lymphoma. Knocking down ASH2L in these cells and in the NT2D1 testicular cancer cell line rendered them resistant to bleomycin, etoposide, and cisplatin but did not affect their sensitivity toward ATM or ATR inhibitors. ASH2L knockdown decreased cell proliferation and facilitated DNA repair via homologous recombination and nonhomologous end-joining mechanisms. Data from the Tumor Cancer Genome Atlas indicate that patients with testicular cancer carrying alterations in the ASH2L gene are more likely to relapse than patients with unaltered ASH2L genes. The cell models we have used are derived from cancers currently treated either partially (Hodgkin’s lymphoma), or entirely (testicular cancer) with genotoxins. For such cancers, ASH2L levels could be used as a biomarker to predict the response to genotoxins. In situations where tumors are expressing low levels of ASH2L, which may allow them to resist genotoxic treatment, the use of ATR or ATM inhibitors may be more efficacious as our data indicate that ASH2L knockdown does not affect sensitivity to these inhibitors.

Novel Variants in LRRK2 and GBA Identified in Latino Parkinson Disease Cohort Enriched for Caribbean Origin

Background: The Latino population is greatly understudied in biomedical research, including genetics. Very little information is available on presence of known variants originally identified in non-Hispanic white patients or novel variants in the Latino population. The Latino population is admixed, with contributions of European, African, and Amerindian ancestries. Therefore, the ancestry surrounding a gene (local ancestry, LA) can be any of the three contributing ancestries and thus can determine the presence or risk effect of variants detected.

Methods: We sequenced the major exons and exons of reported Latino-specific variants in GBA and LRRK2 and performed genome-wide genotyping for LA assessments in 79 Latino Parkinson disease (PD) patients, of which ~80% identified as Caribbean Latino.

Results: We observed five carriers of LRRK2 p.G2019S, one GBA p.T408M, and three GBA p.N409S on European as well as three GBA p.L13R on African LA backgrounds. Previous Latino variant GBA p.K237E was not observed in this dataset. A novel highly conserved and predicted damaging variant LRRK2 p.D734N was identified in two unrelated individuals with African LA. Additionally, we identified rare, functional variants LRRK2 p.P1480L and GBA p.S310G in one individual each heterozygous for European/Amerindian LA.

Discussion: Additional functional analysis will be needed to determine the pathogenicity of the novel variants in PD. However, the identification of novel disease variants in the Latino cohort potentially contributing to PD supports to importance of inclusion of Latinos in genetics research to provide insight in PD genetics in Latinos specifically as well as other populations with the same ancestral contributions.

The mediating effects of public genomic knowledge in precision medicine implementation: A structural equation model approach

Precision medicine emphasizes predictive, preventive and personalized treatment on the basis of information gleaned from personal genetic and environmental data. Its implementation at health systems level is regarded as multifactorial, involving variables associated with omics technologies, public genomic awareness and adoption tendencies for new medical technologies. However, interrelationships of the various factors and their synergy has not been sufficiently quantified. Based on a survey of 270 participants involved in the use of molecular tests (omics-based biomarkers, OBMs), this study examined how characteristics of omics biomarkers influence precision medicine implementation outcomes (ImO) through an intermediary factor, public genomic awareness (represented by User Response, UsR). A structural equation modelling (SEM) approach was applied to develop and test a 3 latent variable mediation model; each latent variable being measured by a set of indicators ranging between three and six. Mediation analysis results confirmed a partial mediation effect (an indirect effect represented as the product of paths 'a' and 'b' (a*b)) of 0.36 at 90% confidence level, CI = [0.03, 9.94]. Results from the individual mediation paths 'a' and 'b' however, showed that these effects were negative(a = -0.38, b = -0.94). Path 'a' represents the effect of characteristics of OBMs on the mediator, UsR; 'b' represents the effect of the mediator, UsR on implementation outcomes, ImO, holding OBMs constant. The results have both theoretical and practice implications for biomedical genomics research and clinical genomics, respectively. For instance, the results imply better ways have to be devised to more effectively engage the public in addressing extended family support for extended family cascade screening, especially for monogenic hereditary conditions like BRCA-related breast cancer and colorectal cancer in Lynch syndrome families. At basic biomedical research level, results suggest an integrated biomarker development pipeline, with early consideration of factors that may influence biomarker uptake. The results are also relevant at health systems level in indicating which factors should be addressed for successful.

Genomics and Transcriptomics: The Powerful Technologies in Precision Medicine

In a clinical trial, people with the same disease can show different responses after treatment with the same drug and exactly under the same conditions. Some of them may improve, some may not show any response, and occasionally side effects may be observed. In other words, people with the same disease process under the same therapeutic conditions may have different responses. Today, some diseases are resistant to conventional (standard) treatment procedures. Why do people with the same disease show different responses to the treatment with the same drug? This is primarily due to differences in molecular pathways (especially genetic variations) associated with the disease. On the other hand, designing and delivery of a new drug is a time-consuming and costly process, so any mistake in any stage of this process can have irreparable consequences for pharmaceutical companies and consumer patients. Therefore, we can achieve more accurate and reliable treatments by acquiring precise insight into different aspects of precision medicine including genomics and transcriptomics. The aim of this paper is to address the role of genomics and transcriptomics in precision medicine.

Cellular State Transformations Using Deep Learning for Precision Medicine Applications

We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples. We demonstrate that the application of TSPG to expression data obtained from a biopsy sample of a patient's kidney cancer can identify patient-specific differentially expressed genes between their individual tumor sample and a target class of healthy kidney gene expression. By utilizing TSPG in a precision medicine application in which the patient sample is not replicated (i.e., n=1), we present a novel technique of determining significant transcriptional aberrations that can be used to help identify potential targeted therapies.

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We present the Transcriptome State Perturbation Generator (TSPG) application

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We apply TSPG to The Cancer Genome Atlas data to perturb gene expression states

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TSPG was used to learn patient-specific (n = 1) gene expression tumor alterations

Deep learning has shown tremendous success in image and natural language processing; however, attempts to apply the tools of machine learning to better understanding biological systems are still in the stage of early adoption. We propose a novel deep-learning tool that can be used to process samples of RNA-sequencing data. By applying the Transcriptome State Perturbation Generator to human samples, we show that deep learning derives insight into the gene expression shifts required for transition between two biological conditions (e.g., normal versus tumor). RNA-sequencing data derived from a single patient's tumor were analyzed using this tool to determine gene expression aberrations specific to that patient's tumor. As medicine shifts from cohort-based population studies to individual-based precision treatments, our example demonstrates that deep learning is a powerful ally in the quest to understand how complex biological systems have shifted for a single patient.

The Clinical Immunogenomics Research Consortium Australasia (CIRCA): a Distributed Network Model for Genomic Healthcare Delivery

The Clinical Immunogenomics Research Consortium Australasia (CIRCA) crowdsources expertise in medicine, genomics, data science, and fundamental biology to diagnose and treat patients with rare inborn errors of immunity. This distributed network model operates free of geographic borders and allows rapid progression through the full research/translation/clinical management pipeline, from initial gene variant discovery, through functional validation, and on to precision mechanism-based treatment of patients throughout Australia and New Zealand. The model is scalable and applicable to other rare diseases where clinical experience and scientific know-how are limited, and enables efficient delivery of genomics for all.

Des tests génétiques pour prédire des maladies communes

Introduit au lendemain de l’identification des « thrombophilies non rares » (TNR), au milieu des années 1990 afin de prédire et de prévenir la maladie thromboembolique veineuse (MTEV), le bilan génétique pour ces thrombophilies est un exemple assez rare de test génétique de susceptibilité pour une maladie complexe, à avoir franchi le pas d’un véritable usage de routine en clinique. Bien que ce test soit le plus répandu des tests de génétique post-natale en France, son usage (À qui proposer le test ? Que faire des résultats?) fait encore l’objet de débats. Cet article analyse la trajectoire de régulation clinique de ce test et illustre l’importance du contexte spécifique d’usage pour comprendre sa diffusion. Cette analyse vise à nourrir une réflexion plus générale sur les enjeux que pose l’intégration clinique des tests génétiques pour les maladies communes, en considérant notamment les modalités de définition de l’utilité clinique d’un test (statistiqueversusbiologique), des sujets du test (le cas indexversusses apparentés), et des critères en sous-tendant l’accès (modalités des calculs médico-économiques).

MaRe: Processing Big Data with application containers on Apache Spark

BACKGROUND

Life science is increasingly driven by Big Data analytics, and the MapReduce programming model has been proven successful for data-intensive analyses. However, current MapReduce frameworks offer poor support for reusing existing processing tools in bioinformatics pipelines. Furthermore, these frameworks do not have native support for application containers, which are becoming popular in scientific data processing.

RESULTS

Here we present MaRe, an open source programming library that introduces support for Docker containers in Apache Spark. Apache Spark and Docker are the MapReduce framework and container engine that have collected the largest open source community; thus, MaRe provides interoperability with the cutting-edge software ecosystem. We demonstrate MaRe on 2 data-intensive applications in life science, showing ease of use and scalability.

CONCLUSIONS

MaRe enables scalable data-intensive processing in life science with Apache Spark and application containers. When compared with current best practices, which involve the use of workflow systems, MaRe has the advantage of providing data locality, ingestion from heterogeneous storage systems, and interactive processing. MaRe is generally applicable and available as open source software.

Novel phylogenetic methods are needed for understanding gene function in the era of mega-scale genome sequencing

Ongoing large-scale genome sequencing projects are forecasting a data deluge that will almost certainly overwhelm current analytical capabilities of evolutionary genomics. In contrast to population genomics, there are no standardized methods in evolutionary genomics for extracting evolutionary and functional (e.g. gene-trait association) signal from genomic data. Here, we examine how current practices of multi-species comparative genomics perform in this aspect and point out that many genomic datasets are under-utilized due to the lack of powerful methodologies. As a result, many current analyses emphasize gene families for which some functional data is already available, resulting in a growing gap between functionally well-characterized genes/organisms and the universe of unknowns. This leaves unknown genes on the 'dark side' of genomes, a problem that will not be mitigated by sequencing more and more genomes, unless we develop tools to infer functional hypotheses for unknown genes in a systematic manner. We provide an inventory of recently developed methods capable of predicting gene-gene and gene-trait associations based on comparative data, then argue that realizing the full potential of whole genome datasets requires the integration of phylogenetic comparative methods into genomics, a rich but underutilized toolbox for looking into the past.

Radiomics: from qualitative to quantitative imaging

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.

Relationships of health information orientation and cancer history on preferences for consent and control over biospecimens in a biobank: A race-stratified analysis

In this study, we investigated how patients' self-reported health information efficacy, relationship with health providers, and cancer history are associated with their preferences for informed consent and need for control over biobank biospecimens. We recruited 358 women aged 40 and older (56% African American; 44% European American) and analyzed the data using multivariable regression models. Results show that African American participants' health information efficacy was significantly and negatively associated with their need for control over biospecimens and preference for a study-specific model. European American participants' dependency on doctors was a significant and negative predictor of their preference for a study-specific model. Several significant interaction effects, which varied across races, were found with regard to health information efficacy, personal cancer history, need for control, and preference for a study-specific model. The study findings suggest it is important to consider health information efficacy, relationship with providers, and need for control when developing large diverse biobanks.

Playing the genome card

In the 1990s, prominent biologists and journalists predicted that by 2020 each of us would carry a genome card, which would allow physicians to access our entire genome sequence and routinely use this information to diagnose and treat common and debilitating conditions. This is not yet the case. Why not? Common and debilitating diseases are rarely caused by single-gene mutations, and this was recognized before these genome card predictions had been made. Debilitating conditions, including common psychiatric disorders, are typically caused either by rare mutations or by complex interactions of many genes, each having a small effect, and epigenetic, environmental, and microbial factors. In such cases, having a complete genome sequence may have limited utility in diagnosis and treatment. Genome sequencing technologies have transformed biological research in many ways, but had a much smaller effect than expected on treatments of common diseases. Thus, early proponents of genome sequencing effectively "mis-promised" its benefits. One reason may be that there are incentives for both biologists and journalists to tell simple stories, including the idea of relatively simple genetic causation of common, debilitating diseases. These incentives may have led to misleading predictions, which to some extent continue today. Although the Human Genome Project has facilitated biological research generally, the mis-promising of medical benefits, at least for treating common and debilitating disorders, could undermine support for scientific research over the long term.

Two Threats to Precision Medicine Equity

In January 2015, President Barack Obama unveiled the "Precision Medicine Initiative," a nationwide research effort to help bring an effective, preventive, and therapeutic approach to medicine. The purpose of the initiative is to bring a precise understanding of the genetic and environmental determinants of disease into clinical settings across the United States.¹ The announcement was coupled with $216 million provided in the President's proposed budget for a million-person national research cohort including public and private partnerships with academic medical centers, researchers, foundations, privacy experts, medical ethicists, and medical product innovators. The Initiative promises to expand the use of precision medicine in cancer research and modernize regulatory approval processes for genome sequencing technologies. In response, Congress passed the 21st Century Cures Act in December 2016, authorizing a total of $1.5 billion over 10 years for the program.² Although the Precision Medicine Initiative heralds great promise for the future of disease treatment and eradication, its implementation and development must be carefully guided to ensure that the millions of federal dollars expended will be spent equitably. This commentary discusses two key threats to the Precision Medicine Initiative's ability to proceed in a manner consistent with the United States Constitutional requirement that the federal government shall not "deny to any person . . . the equal protection of the laws."³ In short, this commentary sounds two cautionary notes, in order to advance precision medicine equity. First, achieving precision medicine equity will require scientists and clinicians to fulfill their intellectual, moral, and indeed legal duty to work against abusive uses of precision medicine science to advance distorted views of racial group variation. Precision medicine scientists must decisively denounce and distinguish this Initiative from the pseudo-science of eugenics - the immoral and deadly pseudo-science that gave racist and nationalist ideologies what Troy Duster called a "halo of legitimacy" during the first half of the 20th century.⁴ Second, to combat the social threat to precision medicine, scientists must incorporate a comprehensive, ecological understanding of the fundamental social and environmental determinants of health outcomes in all research. Only then will the Precision Medicine Initiative live up to its potential to improve and indeed transform health care delivery for all patients, regardless of race, color, or national origin.

Creating a common language: defining individualized, personalized and precision prevention in public health

Background

Because of the limited success of population-based prevention methods and due to developments in genomic screening, public health professionals and health policy makers are increasingly interested in more individualized prevention strategies. However, the terminology applied in this field is still ambiguous and thus has the potential to create misunderstandings.

Methods

A narrative literature review was conducted to identify how individualized, personalized and precision prevention are used in research papers and documents. Based on the findings a set of definitions were created that distinguish between these activities in a meaningful way.

Results

Definitions were found only for precision prevention, not for individualized or personalized prevention. The definitions of individualized, personalized and precision medicine were therefore used to create the definitions for their prevention counterparts. By these definitions, individualized prevention consists of all types of prevention that are individual-based; personalized prevention also consists of at least one form of -omic screening; and precision prevention further includes psychological, behavioral and socioeconomic data for each patient.

Conclusions

By defining these three key terms for different types of individual-based prevention both researchers and health policy makers can differentiate and use them in their proper context.

Value-based genomic screening: exploring genomic screening for chronic diseases using triple value principles

BACKGROUND

Genomic screening has unique challenges which makes it difficult to easily implement on a wide scale. If the costs, benefits and tradeoffs of investing in genomic screening are not evaluated properly, there is a risk of wasting finite healthcare resources and also causing avoidable harm.

MAIN TEXT

If healthcare professionals - including policy makers, payers and providers - wish to incorporate genomic screening into healthcare while minimizing waste, maximizing benefits, and considering results that matter to patients, using the principles of triple value (allocative, technical, and personal value) could help them to evaluate tough decisions and tradeoffs. Allocative value focuses on the optimal distribution of limited healthcare resources to maximize the health benefits to the entire population while also accounting for all the costs of care delivery. Technical value ensures that for any given condition, the right intervention is chosen and delivered in the right way. Various methods (e.g. ACCE, HTA, and Wilson and Jungner screening criteria) exist that can help identify appropriate genomic applications. Personal value incorporates preference based informed decision making to ensure that patients are informed about the benefits and harms of the choices available to them and to ensure they make choices based on their values and preferences.

CONCLUSIONS

Using triple value principles can help healthcare professionals make reasoned and tough judgements about benefits and tradeoffs when they are exploring the role genomic screening for chronic diseases could play in improving the health of their patients and populations.

Polygenic scores: Are they a public health hazard?

I argue here that polygenic scores are a public health hazard because the underlying methodology, genome wide association, from which they are derived, incorrectly assumes that the information encoded in the genomic DNA sequence is causal in terms of the cellular phenotype. This is not so when the cell is viewed from the perspective of a) fundamental physics, b) the protein chemistry that characterises the cellular cytoplasm and c) the fundamental requirement for evolution to yield unlimited species diversity.

Motivations for Participation in Parkinson Disease Genetic Research Among Hispanics versus Non-Hispanics

Involvement of participants from different racial and ethnic groups in genomic research is vital to reducing health disparities in the precision medicine era. Racial and ethnically diverse populations are underrepresented in current genomic research, creating bias in result interpretation. Limited information is available to support motivations or barriers of these groups to participate in genomic research for late-onset, neurodegenerative disorders. To evaluate willingness for research participation, we compared motivations for participation in genetic studies among 113 Parkinson disease (PD) patients and 49 caregivers visiting the Movement Disorders clinic at the University of Miami. Hispanics and non-Hispanics were equally motivated to participate in genetic research for PD. However, Hispanic patients were less likely to be influenced by the promise of scientific advancements (N = 0.01). This lack of scientific interest, but not other motivations, was found to be likely confounded by lower levels of obtained education (N = 0.001). Overall, these results suggest that underrepresentation of Hispanics in genetic research may be partly due to reduced invitations to these studies.

What Psychotherapists Should Understand About DNA Methylation

This column reviews the basics of DNA methylation and how this molecular mechanism is related to the experience of trauma, the causation of mental disorders, and recovery from both trauma and mental disorders. Emerging molecular genetic findings about the role of DNA methylation in mental disorders fit well with our understanding of what is valuable about psychotherapy. DNA methylation offers a heuristically useful metaphor to broaden our understanding of how psychotherapy works.

In the Beginning was the Genome: Genomics and the Bi-textuality of Human Existence

This paper addresses the cultural impact of genomics and the Human Genome Project (HGP) on human self-understanding. Notably, it addresses the claim made by Francis Collins (director of the HGP) that the genome is the language of God and the claim made by Max Delbrück (founding father of molecular life sciences research) that Aristotle must be credited with having predicted DNA as the soul that organises bio-matter. From a continental philosophical perspective I will argue that human existence results from a dialectical interaction between two types of texts: the language of molecular biology and the language of civilisation; the language of the genome and the language of our socio-cultural, symbolic ambiance. Whereas the former ultimately builds on the alphabets of genes and nucleotides, the latter is informed by primordial texts such as the Bible and the Quran. In applied bioethics deliberations on genomics, science is easily framed as liberating and progressive, religious world-views as conservative and restrictive (Zwart 1993). This paper focusses on the broader cultural ambiance of the debate to discern how the bi-textuality of human existence is currently undergoing a transition, as not only the physiological, but also the normative dimension is being reframed in biomolecular and terabyte terms.

A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.

Prevention of obesity and diabetes in pregnancy: is it an impossible dream?

The obesity and diabetes epidemic is an unintended consequence of economic, social, and technological changes. In nonpregnancy, people identified as high risk to develop type 2 diabetes may delay progression by 30-70% with lifestyle interventions and pharmacological agents. In pregnancy, lifestyle interventions have been the primary focus to prevent fetal short- and long-term complications that may evolve into substantial weight gain and gestational diabetes mellitus. The dilemma for obstetricians is whether diabetes and obesity can be prevented and not simply treated after the fact. Interventions after women become pregnant may be too late to see the kinds of meaningful improvements in child and maternal health because there is a short interval from gestational diabetes mellitus diagnosis to delivery. Therefore, future efforts need to incorporate quality research, lifestyle interventions that designate time of initiation and duration during pregnancy, the preventative intervention of a prepregnant "fourth trimester," coupled with the concept of precision medicine so that there is the potential to make the impossible dream a reality.