Sex contextualism in laboratory research: Enhancing rigor and precision in the study of sex-related variables.

Understanding sex-related variation in health and illness requires rigorous and precise approaches to revealing underlying mechanisms. A first step is to recognize that sex is not in and of itself a causal mechanism; rather, it is a classification system comprising a set of categories, usually assigned according to a range of varying traits. Moving beyond sex as a system of classification to working with concrete and measurable sex-related variables is necessary for precision. Whether and how these sex-related variables matter-and what patterns of difference they contribute to-will vary in context-specific ways. Second, when researchers incorporate these sex-related variables into research designs, rigorous analytical methods are needed to allow strongly supported conclusions. Third, the interpretation and reporting of sex-related variation require care to ensure that basic and preclinical research advance health equity for all.

Ancestry matters: Building inclusivity into preclinical study design.

Human cell lines (CLs) are key assets for biomedicine but lack ancestral diversity. Here, we explore why genetic diversity among cell-based models is essential for making preclinical research more equitable and widely translatable. We lay out practical actions that can be taken to improve inclusivity in study design.

The next horizon in precision oncology: Proteogenomics to inform cancer diagnosis and treatment.

When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.

Integrative Proteomic Characterization of Human Lung Adenocarcinoma.

Genomic studies of lung adenocarcinoma (LUAD) have advanced our understanding of the disease's biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD remain poorly understood. We carried out a comprehensive proteomics analysis of 103 cases of LUAD in Chinese patients. Integrative analysis of proteome, phosphoproteome, transcriptome, and whole-exome sequencing data revealed cancer-associated characteristics, such as tumor-associated protein variants, distinct proteomics features, and clinical outcomes in patients at an early stage or with EGFR and TP53 mutations. Proteome-based stratification of LUAD revealed three subtypes (S-I, S-II, and S-III) related to different clinical and molecular features. Further, we nominated potential drug targets and validated the plasma protein level of HSP 90ß as a potential prognostic biomarker for LUAD in an independent cohort. Our integrative proteomics analysis enables a more comprehensive understanding of the molecular landscape of LUAD and offers an opportunity for more precise diagnosis and treatment.

Large-Scale Whole-Genome Sequencing of Three Diverse Asian Populations in Singapore.

Underrepresentation of Asian genomes has hindered population and medical genetics research on Asians, leading to population disparities in precision medicine. By whole-genome sequencing of 4,810 Singapore Chinese, Malays, and Indians, we found 98.3 million SNPs and small insertions or deletions, over half of which are novel. Population structure analysis demonstrated great representation of Asian genetic diversity by three ethnicities in Singapore and revealed a Malay-related novel ancestry component. Furthermore, demographic inference suggested that Malays split from Chinese ∼24,800 years ago and experienced significant admixture with East Asians ∼1,700 years ago, coinciding with the Austronesian expansion. Additionally, we identified 20 candidate loci for natural selection, 14 of which harbored robust associations with complex traits and diseases. Finally, we show that our data can substantially improve genotype imputation in diverse Asian and Oceanian populations. These results highlight the value of our data as a resource to empower human genetics discovery across broad geographic regions.

A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity.

Breast cancer (BC) comprises multiple distinct subtypes that differ genetically, pathologically, and clinically. Here, we describe a robust protocol for long-term culturing of human mammary epithelial organoids. Using this protocol, >100 primary and metastatic BC organoid lines were generated, broadly recapitulating the diversity of the disease. BC organoid morphologies typically matched the histopathology, hormone receptor status, and HER2 status of the original tumor. DNA copy number variations as well as sequence changes were consistent within tumor-organoid pairs and largely retained even after extended passaging. BC organoids furthermore populated all major gene-expression-based classification groups and allowed in vitro drug screens that were consistent with in vivo xeno-transplantations and patient response. This study describes a representative collection of well-characterized BC organoids available for cancer research and drug development, as well as a strategy to assess in vitro drug response in a personalized fashion.

Decoding the Genomics of Abdominal Aortic Aneurysm.

A key aspect of genomic medicine is to make individualized clinical decisions from personal genomes. We developed a machine-learning framework to integrate personal genomes and electronic health record (EHR) data and used this framework to study abdominal aortic aneurysm (AAA), a prevalent irreversible cardiovascular disease with unclear etiology. Performing whole-genome sequencing on AAA patients and controls, we demonstrated its predictive precision solely from personal genomes. By modeling personal genomes with EHRs, this framework quantitatively assessed the effectiveness of adjusting personal lifestyles given personal genome baselines, demonstrating its utility as a personal health management tool. We showed that this new framework agnostically identified genetic components involved in AAA, which were subsequently validated in human aortic tissues and in murine models. Our study presents a new framework for disease genome analysis, which can be used for both health management and understanding the biological architecture of complex diseases. VIDEO ABSTRACT.

Defining a Cancer Dependency Map.

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Implementing Genome-Driven Oncology.

Early successes in identifying and targeting individual oncogenic drivers, together with the increasing feasibility of sequencing tumor genomes, have brought forth the promise of genome-driven oncology care. As we expand the breadth and depth of genomic analyses, the biological and clinical complexity of its implementation will be unparalleled. Challenges include target credentialing and validation, implementing drug combinations, clinical trial designs, targeting tumor heterogeneity, and deploying technologies beyond DNA sequencing, among others. We review how contemporary approaches are tackling these challenges and will ultimately serve as an engine for biological discovery and increase our insight into cancer and its treatment.

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms.

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. VIDEO ABSTRACT.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics.

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Acquiring tissue for advanced lung cancer diagnosis and comprehensive biomarker testing: A National Lung Cancer Roundtable best-practice guide.

Advances in biomarker-driven therapies for patients with nonsmall cell lung cancer (NSCLC) both provide opportunities to improve the treatment (and thus outcomes) for patients and pose new challenges for equitable care delivery. Over the last decade, the continuing development of new biomarker-driven therapies and evolving indications for their use have intensified the importance of interdisciplinary communication and coordination for patients with or suspected to have lung cancer. Multidisciplinary teams are challenged with completing comprehensive and timely biomarker testing and navigating the constantly evolving evidence base for a complex and time-sensitive disease. This guide provides context for the current state of comprehensive biomarker testing for NSCLC, reviews how biomarker testing integrates within the diagnostic continuum for patients, and illustrates best practices and common pitfalls that influence the success and timeliness of biomarker testing using a series of case scenarios.

MED12 and BRD4 cooperate to sustain cancer growth upon loss of mediator kinase.

Mediator kinases (CDK8/19) are transcriptional regulators broadly implicated in cancer. Despite their central role in fine-tuning gene-expression programs, we find complete loss of CDK8/19 is tolerated in colorectal cancer (CRC) cells. Using orthogonal functional genomic and pharmacological screens, we identify BET protein inhibition as a distinct vulnerability in CDK8/19-depleted cells. Combined CDK8/19 and BET inhibition led to synergistic growth retardation in human and mouse models of CRC. Strikingly, depletion of CDK8/19 in these cells led to global repression of RNA polymerase II (Pol II) promoter occupancy and transcription. Concurrently, loss of Mediator kinase led to a profound increase in MED12 and BRD4 co-occupancy at enhancer elements and increased dependence on BET proteins for the transcriptional output of cell-essential genes. In total, this work demonstrates a synthetic lethal interaction between Mediator kinase and BET proteins and exposes a therapeutic vulnerability that can be targeted using combination therapies.

Complex Autoinflammatory Syndrome Unveils Fundamental Principles of JAK1 Kinase Transcriptional and Biochemical Function.

Autoinflammatory disease can result from monogenic errors of immunity. We describe a patient with early-onset multi-organ immune dysregulation resulting from a mosaic, gain-of-function mutation (S703I) in JAK1, encoding a kinase essential for signaling downstream of >25 cytokines. By custom single-cell RNA sequencing, we examine mosaicism with single-cell resolution. We find that JAK1 transcription was predominantly restricted to a single allele across different cells, introducing the concept of a mutational "transcriptotype" that differs from the genotype. Functionally, the mutation increases JAK1 activity and transactivates partnering JAKs, independent of its catalytic domain. S703I JAK1 is not only hypermorphic for cytokine signaling but also neomorphic, as it enables signaling cascades not canonically mediated by JAK1. Given these results, the patient was treated with tofacitinib, a JAK inhibitor, leading to the rapid resolution of clinical disease. These findings offer a platform for personalized medicine with the concurrent discovery of fundamental biological principles.

Clinical management of metastatic colorectal cancer in the era of precision medicine.

Colorectal cancer (CRC) represents approximately 10% of all cancers and is the second most common cause of cancer deaths. Initial clinical presentation as metastatic CRC (mCRC) occurs in approximately 20% of patients. Moreover, up to 50% of patients with localized disease eventually develop metastases. Appropriate clinical management of these patients is still a challenging medical issue. Major efforts have been made to unveil the molecular landscape of mCRC. This has resulted in the identification of several druggable tumor molecular targets with the aim of developing personalized treatments for each patient. This review summarizes the improvements in the clinical management of patients with mCRC in the emerging era of precision medicine. In fact, molecular stratification, on which the current treatment algorithm for mCRC is based, although it does not completely represent the complexity of this disease, has been the first significant step toward clinically informative genetic profiling for implementing more effective therapeutic approaches. This has resulted in a clinically relevant increase in mCRC disease control and patient survival. The next steps in the clinical management of mCRC will be to integrate the comprehensive knowledge of tumor gene alterations, of tumor and microenvironment gene and protein expression profiling, of host immune competence as well as the application of the resulting dynamic changes to a precision medicine-based continuum of care for each patient. This approach could result in the identification of individual prognostic and predictive parameters, which could help the clinician in choosing the most appropriate therapeutic program(s) throughout the entire disease journey for each patient with mCRC. CA Cancer J Clin. 2022;72:000-000.

Blueprint for cancer research: Critical gaps and opportunities.

We are experiencing a revolution in cancer. Advances in screening, targeted and immune therapies, big data, computational methodologies, and significant new knowledge of cancer biology are transforming the ways in which we prevent, detect, diagnose, treat, and survive cancer. These advances are enabling durable progress in the goal to achieve personalized cancer care. Despite these gains, more work is needed to develop better tools and strategies to limit cancer as a major health concern. One persistent gap is the inconsistent coordination among researchers and caregivers to implement evidence-based programs that rely on a fuller understanding of the molecular, cellular, and systems biology mechanisms underpinning different types of cancer. Here, the authors integrate conversations with over 90 leading cancer experts to highlight current challenges, encourage a robust and diverse national research portfolio, and capture timely opportunities to advance evidence-based approaches for all patients with cancer and for all communities.

Are We Meeting the Promise of Endotypes and Precision Medicine in Asthma?

While the term asthma has long been known to describe heterogeneous groupings of patients, only recently have data evolved which enable a molecular understanding of the clinical differences. The evolution of transcriptomics (and other 'omics platforms) and improved statistical analyses in combination with large clinical cohorts opened the door for molecular characterization of pathobiologic processes associated with a range of asthma patients. When linked with data from animal models and clinical trials of targeted biologic therapies, emerging distinctions arose between patients with and without elevations in type 2 immune and inflammatory pathways, leading to the confirmation of a broad categorization of type 2-Hi asthma. Differences in the ratios, sources, and location of type 2 cytokines and their relation to additional immune pathway activation appear to distinguish several different (sub)molecular phenotypes, and perhaps endotypes of type 2-Hi asthma, which respond differently to broad and targeted anti-inflammatory therapies. Asthma in the absence of type 2 inflammation is much less well defined, without clear biomarkers, but is generally linked with poor responses to corticosteroids. Integration of "big data" from large cohorts, over time, using machine learning approaches, combined with validation and iterative learning in animal (and human) model systems is needed to identify the biomarkers and tightly defined molecular phenotypes/endotypes required to fulfill the promise of precision medicine.

Circadian Regulation of Drug Responses: Toward Sex-Specific and Personalized Chronotherapy.

Today's challenge for precision medicine involves the integration of the impact of molecular clocks on drug pharmacokinetics, toxicity, and efficacy toward personalized chronotherapy. Meaningful improvements of tolerability and/or efficacy of medications through proper administration timing have been confirmed over the past decade for immunotherapy and chemotherapy against cancer, as well as for commonly used pharmacological agents in cardiovascular, metabolic, inflammatory, and neurological conditions. Experimental and human studies have recently revealed sexually dimorphic circadian drug responses. Dedicated randomized clinical trials should now aim to issue personalized circadian timing recommendations for daily medical practice, integrating innovative technologies for remote longitudinal monitoring of circadian metrics, statistical prediction of molecular clock function from single-timepoint biopsies, and multiscale biorhythmic mathematical modelling. Importantly, chronofit patients with a robust circadian function, who would benefit most from personalized chronotherapy, need to be identified. Conversely, nonchronofit patients could benefit from the emerging pharmacological class of chronobiotics targeting the circadian clock.

Health Digital Twins in Life Science and Health Care Innovation.

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine.

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.