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Biology subjects

Haque, I. S.

Publications and source records attributed to Haque, I. S..

13 recordsLinked to original sources

High-resolution genome-wide mapping of chromosome-arm-scale truncations induced by CRISPR-Cas9 editing

CRISPR-Cas9 editing is a scalable technology for mapping of biological pathways, but it has been reported to cause a variety of undesired large-scale structural changes to the genome. We performed an arrayed CRISPR-Cas9 scan of the genome in primary human cells, targeting 17,065 genes for knockout with 101,029 guides. High-dimensional phenomics reveals a "proximity bias" in which CRISPR knockouts bear unexpected phenotypic similarity to knockouts of biologically-unrelated genes on the same chromosome arm, recapitulating both canonical genome structure and structural variants. Transcriptomics connects proximity bias to chromosome-arm truncations. Analysis of published large-scale knockout and knockdown experiments confirms that this effect is general across cell types, labs, Cas9 delivery mechanisms, and assay modalities, and suggests proximity bias is caused by DNA double-strand-breaks with cell cycle control in a mediating role. Finally, we demonstrate a simple correction for large-scale CRISPR screens to mitigate this pervasive bias while preserving biological relationships.

genomics↗

RxRx3: Phenomics Map of Biology

The combination of modern genetic perturbation techniques with high content screening has enabled genome-scale cell microscopy experiments that can be leveraged to construct maps of biology. These are built by processing microscopy images to produce readouts in unified and relatable representation space to capture known biological relationships and discover new ones. To further enable the scientific community to develop methods and insights from map-scale data, here we release RxRx3, the first ever public high-content screening dataset combining genome-scale CRISPR knockouts with multiple-concentration screening of small molecules (a set of FDA approved and commercially available bioactive compounds). The dataset contains 6-channel fluorescent microscopy images and associated deep learning embeddings from over 2.2 million wells that span 17,063 CRISPR knockouts and 1,674 compounds at 8 doses each. RxRx3 is one of the largest collections of cellular screening data, and as far as we know, the largest generated consistently via a common experimental protocol within a single laboratory. Our goal in releasing RxRx3 is to demonstrate the benefits of generating consistent data, enable the development of the machine learning methods on this scale of data and to foster research, methods development, and collaboration. For more information about RxRx3 please visit RxRx.ai/rxrx3

systems biology↗

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune systems highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable "phenomics" platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-{beta}- and TNF--driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

immunology↗

Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2

To identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay, remdesivir is the only antiviral tested with strong efficacy, neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of {beta}-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with {beta}-agonists.

systems biology↗

Spatial co-fragmentation pattern of cell-free DNA recapitulates in vivo chromatin organization and identifies tissues-of-origin

Three-dimensional chromatin organization varies across cell types and is essential for gene regulation. However, current technologies are unable to assess in vivo genome-wide chromatin organization non-invasively. Here we show that distant correlations in the fragment length of cell-free DNA (cfDNA) recapitulate three-dimensional chromatin organization. The inferred organization is highly concordant with that measured by Hi-C in white blood cells from healthy donors, and is not explained by technical bias or sequence composition. Furthermore, the inferred organization reflects different genomic organization in the various cell types contributing to cfDNA, allowing identification and quantification of tissues of origin. This approach is concordant with previous methods, but with more complete representations of cfDNA. Our results, demonstrated in cfDNA from healthy individuals and cancer patients, may enable noninvasive monitoring of in vivo genome organization and accurate quantification of cell death in different clinical conditions.

genomics↗

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology↗

Challenges in Using ctDNA to Achieve Early Detection of Cancer

Early detection of cancer is a significant unmet clinical need. Improved technical ability to detect circulating tumor-derived DNA (ctDNA) in the cell-free DNA (cfDNA) component of blood plasma via next-generation sequencing and established correlations between ctDNA load and tumor burden in cancer patients have spurred excitement about the possibilities of detecting cancer early by performing ctDNA mutation detection.\n\nWe reanalyze published data on the expected ctDNA allele fraction in early-stage cancer and the population statistics of cfDNA concentration to show that under conservative technical assumptions, high-sensitivity cancer detection by ctDNA mutation detection will require either more blood volume (150-300mL) than practical for a routine screen or variant filtering that may be impossible given our knowledge of cancer evolution, and will likely remain out of economic reach for routine population screening without multiple-order-of-magnitude decreases in sequencing cost. Instead, new approaches that integrate ctDNA mutations with multiple other blood-based analytes (such as exosomes, circulating tumor cells, ctDNA epigenetics, metabolites) as well as integration of these signals over time for each individual may be needed.

cancer biology↗

Development and validation of an expanded carrier screen that optimizes sensitivity via full-exon sequencing and panel-wide copy-number-variant identification

PurposeBy identifying pathogenic variants across hundreds of genes, expanded carrier screening (ECS) enables prospective parents to assess risk of transmitting an autosomal recessive or X-linked condition. Detection of at-risk couples depends on the number of conditions tested, the diseases respective prevalences, and the screens sensitivity for identifying disease-causing variants. Here we present an analytical validation of a 235-gene sequencing-based ECS with full coverage across coding regions, targeted assessment of pathogenic noncoding variants, panel-wide copy-number-variant (CNV) calling, and customized assays for technically challenging genes.\n\nMethodsNext-generation sequencing, a customized bioinformatics pipeline, and expert manual call review were used to identify single-nucleotide variants, short insertions and deletions, and CNVs for all genes except FMR1 and those whose low disease incidence or high technical complexity precludes novel variant identification or interpretation. Variant calls were compared to reference and orthogonal data.\n\nResultsValidation of our ECS data demonstrated >99% analytical sensitivity and >99% specificity. A preliminary assessment of 15,177 patient samples reveals the substantial impact on fetal disease-risk detection attributable to novel CNV calling (13.9% of risk) and technically challenging conditions (15.5% of risk), such as congenital adrenal hyperplasia.\n\nConclusionValidated, high-fidelity identification of different variant types--especially in diseases with complicated molecular genetics--maximizes at-risk couple detection.

genetics↗

Noninvasive Prenatal Screening at Low Fetal Fraction: Comparing Whole- Genome Sequencing and Single-Nucleotide Polymorphism Methods

ObjectivePerformance of noninvasive prenatal screening (NIPS) methodologies when applied to low fetal fraction samples is not well established. The single-nucleotide polymorphism (SNP) method fails samples below a predetermined fetal fraction threshold, whereas some laboratories employing the whole-genome sequencing (WGS) method report aneuploidy calls for all samples. Here, the performance of the two methods was compared to determine which approach actually detects more fetal aneuploidies.\n\nMethodsComputational models were parameterized with up-to-date published data and used to compare the performance of the two methods at calling common fetal trisomies (T21, T18, T13) at low fetal fractions. Furthermore, clinical experience data were reviewed to determine aneuploidy detection rates based on compliance with recent invasive screening recommendations.\n\nResultsThe SNP methods performance is dependent on the origin of the trisomy, and is lowest for the most common trisomies (maternal M1 nondisjunction). Consequently, the SNP method cannot maintain acceptable performance at fetal fractions below ~3%. In contrast, the WGS method maintains high specificity independent of fetal fraction and has >80% sensitivity for trisomies in low fetal fraction samples.\n\nConclusionThe WGS method will detect more aneuploidies below the fetal fraction threshold at which many labs issue a no-call result, avoiding unnecessary invasive procedures.

genomics↗

The population genetics of human disease: the case of recessive, lethal mutations

Do the frequencies of disease mutations in human populations reflect a simple balance between mutation and purifying selection? What other factors shape the prevalence of disease mutations? To begin to answer these questions, we focused on one of the simplest cases: recessive mutations that alone cause lethal diseases or complete sterility. To this end, we generated a hand-curated set of 417 Mendelian mutations in 32 genes, reported to cause a recessive, lethal Mendelian disease. We then considered analytic models of mutation-selection balance in infinite and finite populations of constant sizes and simulations of purifying selection in a more realistic demographic setting, and tested how well these models fit allele frequencies estimated from 33,370 individuals of European ancestry. In doing so, we distinguished between CpG transitions, which occur at a substantially elevated rate, and three other mutation types. The observed frequency for CpG transitions is slightly higher than expectation but close, whereas the frequencies observed for the three other mutation types are an order of magnitude higher than expected. This discrepancy is even larger when subtle fitness effects in heterozygotes or lethal compound heterozygotes are taken into account. In principle, higher than expected frequencies of disease mutations could be due to widespread errors in reporting causal variants, compensation by other mutations, or balancing selection. It is unclear why these factors would have a greater impact on variants with lower mutation rates, however. We argue instead that the unexpectedly high frequency of disease mutations and the relationship to the mutation rate likely reflect an ascertainment bias: of all the mutations that cause recessive lethal diseases, those that by chance have reached higher frequencies are more likely to have been identified and thus to have been included in this study. Beyond the specific application, this study highlights the parameters likely to be important in shaping the frequencies of Mendelian disease alleles.\n\nAuthor SummaryWhat determines the frequencies of disease mutations in human populations? To begin to answer this question, we focus on one of the simplest cases: mutations that cause completely recessive, lethal Mendelian diseases. We first review theory about what to expect from mutation and selection in a population of finite size and further generate predictions based on simulations using a realistic demographic scenario of human evolution. For a highly mutable type of mutations, such as transitions at CpG sites, we find that the predictions are close to the observed frequencies of recessive lethal disease mutations. For less mutable types, however, predictions substantially under-estimate the observed frequency. We discuss possible explanations for the discrepancy and point to a complication that, to our knowledge, is not widely appreciated: that there exists ascertainment bias in disease mutation discovery. Specifically, we suggest that alleles that have been identified to date are likely the ones that by chance have reached higher frequencies and are thus more likely to have been mapped. More generally, our study highlights the factors that influence the frequencies of Mendelian disease alleles.

genetics↗

Development and validation of a 36-gene sequencing assay for hereditary cancer risk assessment

The past two decades have brought many important advances in our understanding of the hereditary susceptibility to cancer. Numerous studies have provided convincing evidence that identification of germline mutations associated with hereditary cancer syndromes can lead to reductions in morbidity and mortality through targeted risk management options. Additionally, advances in gene sequencing technology now permit the development of multigene hereditary cancer testing panels. Here, we describe the 2016 revision of the Counsyl Inherited Cancer Screen for detecting single-nucleotide variants (SNVs), short insertions and deletions (indels), and copy number variants (CNVs) in 36 genes associated with an elevated risk for breast, ovarian, colorectal, gastric, endometrial, pancreatic, thyroid, prostate, melanoma, and neuroendocrine cancers. To determine test accuracy and reproducibility, we performed a rigorous analytical validation across 341 samples, including 118 cell lines and 223 patient samples. The screen achieved 100% test sensitivity across different mutation types, with high specificity and 100% concordance with conventional Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA). We also demonstrated the screens high intra-run and inter-run reproducibility and robust performance on blood and saliva specimens. Furthermore, we showed that pathogenic Alu element insertions can be accurately detected by our test. Overall, the validation in our clinical laboratory demonstrated the analytical performance required for collecting and reporting genetic information related to risk of developing hereditary cancers.

genetics↗

Smith-Lemli-Opitz syndrome carrier frequency and estimates of in utero mortality rates

Objective: To tabulate individual allele frequencies and total carrier frequency for Smith-Lemli-Opitz syndrome (SLOS) and compare expected versus observed birth incidences.\n\nDesign: Retrospective analysis of patients from the general population who have undergone carrier screening for SLOS.\n\nSetting: Individuals were offered and elected carrier screening in their respective physicians offices.\n\nPatients: 262,399 individuals with no indication of family or personal history of Smith-Lemli-Opitz syndrome, primarily US-based, screened for Smith-Lemli-Opitz syndrome mutations as part of an expanded carrier screening panel.\n\nIntervention(s): Data on mutations in the DHCR7 gene causing SLOS were analyzed to estimate carrier frequencies in multiple ethnic groups. SLOS birth incidences obtained from existing literature are then compared to our data to estimate the effect of SLOS on fetal survival.\n\nMain Outcome Measure(s): Individual and cumulative allele frequencies stratified by self-reported patient ethnicity.\n\nResults: SLOS carrier frequency is highest in individuals of Ashkenazi Jewish ancestry (1 in 43) and Northern Europeans (1 in 54). Comparing predicted birth incidence to that observed in published literature suggests that approximately 42% to 88% of affected conceptuses experience fetal demise.\n\nConclusion: SLOS is relatively frequent in certain populations and, due to its impact on fetal survival, merits preconception screening.

genetics↗

Systematic Design and Comparison of Expanded Carrier Screening Panels

Purpose: The recent growth in pan-ethnic expanded carrier screening (ECS) has raised questions about how such panels might be designed and evaluated systematically. Design principles for ECS panels might improve clinical detection of at-risk couples and facilitate objective discussions of panel choice.\n\nMethods: Guided by medical-society statements, we propose a method for the design of ECS panels that aims to maximize the aggregate and per-disease sensitivity and specificity across a range of Mendelian disorders considered serious by a systematic classification scheme. We evaluated this method retrospectively using results from 474,644 de-identified carrier screens. We then constructed several idealized panels to highlight strengths and limitations of different ECS methodologies.\n\nResults: Based on modeled fetal risks for \"severe\" and \"profound\" diseases, a commercially available ECS panel (Counsyl) is expected to detect 183 affected conceptuses per 100,000 US births. A screens sensitivity is greatly impacted by two factors: (1) the methodology used (e.g., full-exon sequencing finds up to 46 more affected fetuses per 100,000 than targeted genotyping with an optimal 50 variant panel), and (2) the detection rate of the screen for diseases with high prevalence and complex molecular genetics (e.g., fragile X syndrome, spinal muscular atrophy, 21-hydroxylase deficiency, and alpha-thalassemia account for 54 affected fetuses per 100,000).\n\nConclusion: The described approaches allow principled, quantitative evaluation of which diseases and methodologies are appropriate for pan-ethnic expanded carrier screening.

genetics↗