Richard Owguan Chen, MD
Clinical Assistant Professor, Dermatology
Bio
Richard Chen, M.D. M.S., is Clinical Assistant Professor of Dermatology at Stanford and Chief Scientific Officer at Personalis, Inc. He attended medical school and completed residency at Stanford University, serving as Chief Resident in his final year. His interests include general dermatology, cancer genomics, precision medicine, genetics, bioinformatics and technology innovation for improved health care delivery and therapy.
Clinical Focus
- Dermatology
- Skin Cancer
Professional Education
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Medical Education: Stanford University School of Medicine (2007) CA
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Internship: Santa Clara Valley Medical Center (2008) CA
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Residency: Stanford University Dept of Dermatology (2011) CA
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Board Certification: American Board of Dermatology, Dermatology (2011)
All Publications
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Sotigalimab and/or nivolumab with chemotherapy in first-line metastatic pancreatic cancer: clinical and immunologic analyses from the randomized phase 2 PRINCE trial.
Nature medicine
2022
Abstract
Chemotherapy combined with immunotherapy has improved the treatment of certain solid tumors, but effective regimens remain elusive for pancreatic ductal adenocarcinoma (PDAC). We conducted a randomized phase 2 trial evaluating the efficacy of nivolumab (nivo; anti-PD-1) and/or sotigalimab (sotiga; CD40 agonistic antibody) with gemcitabine/nab-paclitaxel (chemotherapy) in patients with first-line metastatic PDAC ( NCT03214250 ). In 105 patients analyzed for efficacy, the primary endpoint of 1-year overall survival (OS) was met for nivo/chemo (57.7%, P=0.006 compared to historical 1-year OS of 35%, n=34) but was not met for sotiga/chemo (48.1%, P=0.062, n=36) or sotiga/nivo/chemo (41.3%, P=0.223, n=35). Secondary endpoints were progression-free survival, objective response rate, disease control rate, duration of response and safety. Treatment-related adverse event rates were similar across arms. Multi-omic circulating and tumor biomarker analyses identified distinct immune signatures associated with survival for nivo/chemo and sotiga/chemo. Survival after nivo/chemo correlated with a less suppressive tumor microenvironment and higher numbers of activated, antigen-experienced circulating T cells at baseline. Survival after sotiga/chemo correlated with greater intratumoral CD4 T cell infiltration and circulating differentiated CD4 T cells and antigen-presenting cells. A patient subset benefitting from sotiga/nivo/chemo was not identified. Collectively, these analyses suggest potential treatment-specific correlates of efficacy and may enable biomarker-selected patient populations in subsequent PDAC chemoimmunotherapy trials.
View details for DOI 10.1038/s41591-022-01829-9
View details for PubMedID 35662283
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Distinct biosignatures associate with survival after chemoimmunotherapy in a randomized, three-arm phase II study in patients with metastatic pancreatic cancer.
LIPPINCOTT WILLIAMS & WILKINS. 2022
View details for Web of Science ID 000863680301178
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A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity.
Nature communications
2022; 13 (1): 1925
Abstract
Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy.
View details for DOI 10.1038/s41467-022-29203-w
View details for PubMedID 35414054
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TUMOR-INFORMED LIQUID BIOPSY MONITORING OF EVOLVING THERAPEUTIC RESISTANCE MECHANISMS IN HEAD AND NECK SQUAMOUS CELL CARCINOMA PATIENTS RECEIVING ANTI-PD-1 THERAPY
BMJ PUBLISHING GROUP. 2021: A22
View details for DOI 10.1136/jitc-2021-SITC2021.020
View details for Web of Science ID 000774877500021
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EXTENSIVELY VALIDATED HLA LOH ALGORITHM DEMONSTRATES AN ASSOCIATION BETWEEN HLA LOH AND GENOMIC INSTABILITY
BMJ PUBLISHING GROUP. 2021: A88
View details for DOI 10.1136/jitc-2021-SITC2021.079
View details for Web of Science ID 000774877500079
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Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms.
Clinical cancer research : an official journal of the American Association for Cancer Research
2021; 27 (15): 4265-4276
Abstract
PURPOSE: While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB).EXPERIMENTAL DESIGN: Tumors from a cohort of patients with late-stage melanoma (n = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB.RESULTS: Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P = 0.016) than TMB alone (P = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P = 0.002).CONCLUSIONS: NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.
View details for DOI 10.1158/1078-0432.CCR-20-4314
View details for PubMedID 34341053
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Pan-cancer survey of HLA loss of heterozygosity using a robustly validated NGS-based machine learning algorithm.
AMER ASSOC CANCER RESEARCH. 2021
View details for Web of Science ID 000680263502451
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Longitudinal exome-scale liquid biopsy monitoring of evolving therapeutic resistance mechanisms in head and neck squamous cell carcinoma patients receiving anti-PD-1 therapy.
AMER ASSOC CANCER RESEARCH. 2021
View details for Web of Science ID 000680263505282
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Precision neoantigen discovery using large-scale immunopeptidomes and composite modeling of MHC peptide presentation.
Molecular & cellular proteomics : MCP
2021: 100111
Abstract
Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anti-cancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 mono-allelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale mono-allelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 mono-allelic and 384 multi-allelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multi-allelic deconvolution and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44 fold improvement of positive predictive value compared to existing tools when evaluated on independent mono-allelic datasets and a 1.15 fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.
View details for DOI 10.1016/j.mcpro.2021.100111
View details for PubMedID 34126241
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Robust prediction of response to immunotherapy in a mixed cohort of previously treated and immunotherapy-naive melanoma patients.
LIPPINCOTT WILLIAMS & WILKINS. 2021
View details for DOI 10.1200/JCO.2021.39.15_suppl.e21548
View details for Web of Science ID 000708120305222
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Association of HLA loss of heterozygosity with allele-specific neoantigen expansion in response to immunotherapy.
LIPPINCOTT WILLIAMS & WILKINS. 2021
View details for DOI 10.1200/JCO.2021.39.15_suppl.e18030
View details for Web of Science ID 000708120303080
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Sensitive HLA loss of heterozygosity detection reveals allele-specific neoantigen expansion as resistance mechanism to anti-PD-1 therapy
AMER ASSOC CANCER RESEARCH. 2020
View details for DOI 10.1158/1538-7445.AM2020-6678
View details for Web of Science ID 000590059307315
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Exome scale liquid biopsy characterization of putative neoantigens and genomic biomarkers pre- and post anti-PD-1 therapy in squamous cell carcinoma of the head and neck.
AMER SOC CLINICAL ONCOLOGY. 2020
View details for Web of Science ID 000560368303058
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Exome scale liquid biopsy monitoring of putative neoantigens and genomic biomarkers in patients on anti-PD-1 therapy in squamous cell carcinoma of the head and neck.
AMER ASSOC CANCER RESEARCH. 2020: 94–95
View details for Web of Science ID 000518188200159
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Are minor alleles more likely to be risk alleles?
BMC MEDICAL GENOMICS
2018; 11: 3
Abstract
Genome-wide association studies (GWASs) have revealed relationships between over 57,000 genetic variants and diseases. However, unlike Mendelian diseases, complex diseases arise from the interplay of multiple genetic and environmental factors. Natural selection has led to a high tendency of risk alleles to be enriched in minor alleles in Mendelian diseases. Therefore, an allele that was previously advantageous or neutral may later become harmful, making it a risk allele.Using data in the NHGRI-EBI Catalog and the VARIMED database, we investigated whether (1) GWASs more easily detect risk alleles and (2) facilitate evolutionary insights by comparing risk allele frequencies of different diseases. We conducted computer simulations of P-values for association tests when major and minor alleles were risk alleles. We compared the expected proportion of SNVs whose risk alleles were minor alleles with the observed proportion.Our statistical results revealed that risk alleles were enriched in minor alleles, especially for variants with low minor allele frequencies (MAFs < 0.1). Our computer simulations revealed that > 50% risk alleles were minor alleles because of the larger difference in the power of GWASs to differentiate between minor and major alleles, especially with low MAFs or when the number of controls exceeds the number of cases. However, the observed ratios between minor and major alleles in low MAFs (< 0.1) were much larger than the expected ratios of GWAS's power imbalance, especially for diseases whose average risk allele frequencies were low, such as myopia, sudden cardiac arrest, and systemic lupus erythematosus.Minor alleles are more likely to be risk alleles in the published GWASs on complex diseases. One reason is that minor alleles are more easily detected as risk alleles in GWASs. Even when correcting for the GWAS's power imbalance, minor alleles are more likely to be risk alleles, especially in some diseases whose average risk allele frequencies are low. These analyses serve as a starting point for future studies on quantifying the degree of negative natural selection in various complex diseases.
View details for PubMedID 29351777
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Early somatic mosaicism is a rare cause of long-QT syndrome
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2016; 113 (41): 11555-11560
Abstract
Somatic mosaicism, the occurrence and propagation of genetic variation in cell lineages after fertilization, is increasingly recognized to play a causal role in a variety of human diseases. We investigated the case of life-threatening arrhythmia in a 10-day-old infant with long QT syndrome (LQTS). Rapid genome sequencing suggested a variant in the sodium channel NaV1.5 encoded by SCN5A, NM_000335:c.5284G > T predicting p.(V1762L), but read depth was insufficient to be diagnostic. Exome sequencing of the trio confirmed read ratios inconsistent with Mendelian inheritance only in the proband. Genotyping of single circulating leukocytes demonstrated the mutation in the genomes of 8% of patient cells, and RNA sequencing of cardiac tissue from the infant confirmed the expression of the mutant allele at mosaic ratios. Heterologous expression of the mutant channel revealed significantly delayed sodium current with a dominant negative effect. To investigate the mechanism by which mosaicism might cause arrhythmia, we built a finite element simulation model incorporating Purkinje fiber activation. This model confirmed the pathogenic consequences of cardiac cellular mosaicism and, under the presenting conditions of this case, recapitulated 2:1 AV block and arrhythmia. To investigate the extent to which mosaicism might explain undiagnosed arrhythmia, we studied 7,500 affected probands undergoing commercial gene-panel testing. Four individuals with pathogenic variants arising from early somatic mutation events were found. Here we establish cardiac mosaicism as a causal mechanism for LQTS and present methods by which the general phenomenon, likely to be relevant for all genetic diseases, can be detected through single-cell analysis and next-generation sequencing.
View details for DOI 10.1073/pnas.1607187113
View details for PubMedID 27681629
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Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants
PLOS COMPUTATIONAL BIOLOGY
2016; 12 (4)
Abstract
Patterns of disease co-occurrence that deviate from statistical independence may represent important constraints on biological mechanism, which sometimes can be explained by shared genetics. In this work we study the relationship between disease co-occurrence and commonly shared genetic architecture of disease. Records of pairs of diseases were combined from two different electronic medical systems (Columbia, Stanford), and compared to a large database of published disease-associated genetic variants (VARIMED); data on 35 disorders were available across all three sources, which include medical records for over 1.2 million patients and variants from over 17,000 publications. Based on the sources in which they appeared, disease pairs were categorized as having predominant clinical, genetic, or both kinds of manifestations. Confounding effects of age on disease incidence were controlled for by only comparing diseases when they fall in the same cluster of similarly shaped incidence patterns. We find that disease pairs that are overrepresented in both electronic medical record systems and in VARIMED come from two main disease classes, autoimmune and neuropsychiatric. We furthermore identify specific genes that are shared within these disease groups.
View details for DOI 10.1371/journal.pcbi.1004885
View details for Web of Science ID 000376584400019
View details for PubMedID 27115429
View details for PubMedCentralID PMC4846031
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Genetic analysis in a patient with nine primary malignant neoplasms: A rare case of Li-Fraumeni syndrome
ONCOLOGY REPORTS
2016; 35 (3): 1519-1528
Abstract
To identify rare mutations and retrospectively estimate the cancer risk of a 45-year old female patient diagnosed with Li-Fraumeni syndrome (LFS), who developed nine primary malignant neoplasms in a period of 38 years, we conducted next-generation sequencing in this patient. Whole-genome and whole-exome sequencing were performed in DNA of whole blood obtained a year prior to the diagnosis of acute myeloid leukemia (AML) and at the time of diagnosis of AML, respectively. We analyzed rare mutations in cancer susceptibility genes using a candidate strategy and estimated cancer risk using the Risk-O-Gram algorithm. We found rare mutations in cancer susceptibility genes associated with an increased hereditary cancer risk in the patient. Notably, the number of mutated genes in p53 signaling pathway was significantly higher than expected (p=0.02). However, the phenotype of multiple malignant neoplasms of the studied patient was unlikely to be caused by accumulation of common cancer risk alleles. In conclusion, we established the mutation profile in a rare case of Li-Fraumeni syndrome, illustrating that the rare mutations rather than the cumulative of common risk alleles leading to an increased cancer risk in the patient.
View details for DOI 10.3892/or.2015.4501
View details for Web of Science ID 000368228900034
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Genetic analysis in a patient with nine primary malignant neoplasms: a rare case of Li-Fraumeni syndrome.
Oncology reports
2016; 35 (3): 1519-28
Abstract
To identify rare mutations and retrospectively estimate the cancer risk of a 45-year old female patient diagnosed with Li-Fraumeni syndrome (LFS), who developed nine primary malignant neoplasms in a period of 38 years, we conducted next-generation sequencing in this patient. Whole-genome and whole-exome sequencing were performed in DNA of whole blood obtained a year prior to the diagnosis of acute myeloid leukemia (AML) and at the time of diagnosis of AML, respectively. We analyzed rare mutations in cancer susceptibility genes using a candidate strategy and estimated cancer risk using the Risk-O-Gram algorithm. We found rare mutations in cancer susceptibility genes associated with an increased hereditary cancer risk in the patient. Notably, the number of mutated genes in p53 signaling pathway was significantly higher than expected (p=0.02). However, the phenotype of multiple malignant neoplasms of the studied patient was unlikely to be caused by accumulation of common cancer risk alleles. In conclusion, we established the mutation profile in a rare case of Li-Fraumeni syndrome, illustrating that the rare mutations rather than the cumulative of common risk alleles leading to an increased cancer risk in the patient.
View details for DOI 10.3892/or.2015.4501
View details for PubMedID 26707089
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Disease Variant Landscape of a Large Multiethnic Population of Moyamoya Patients by Exome Sequencing
G3-GENES GENOMES GENETICS
2016; 6 (1): 41-49
Abstract
Moyamoya disease (MMD) is a rare disorder characterized by cerebrovascular occlusion and development of hemorrhage-prone collateral vessels. Approximately 10-12% of cases are familial, with a presumed low penetrance autosomal dominant pattern of inheritance. Diagnosis commonly occurs only after clinical presentation. The recent identification of the RNF213 founder mutation (p.R4810K) in the Asian population has made a significant contribution, but the etiology of this disease remains unclear. To further develop the variant landscape of MMD, we performed high-depth whole exome sequencing of 125 unrelated, predominantly nonfamilial, ethnically diverse MMD patients in parallel with 125 internally sequenced, matched controls using the same exome and analysis platform. Three subpopulations were established: Asian, Caucasian, and non-RNF213 founder mutation cases. We provided additional support for the previously observed RNF213 founder mutation (p.R4810K) in Asian cases (P = 6.01×10(-5)) that was enriched among East Asians compared to Southeast Asian and Pacific Islander cases (P = 9.52×10(-4)) and was absent in all Caucasian cases. The most enriched variant in Caucasian (P = 7.93×10(-4)) and non-RNF213 founder mutation (P = 1.51×10(-3)) cases was ZXDC (p.P562L), a gene involved in MHC Class II activation. Collapsing variant methodology ranked OBSCN, a gene involved in myofibrillogenesis, as most enriched in Caucasian (P = 1.07×10(-4)) and non-RNF213 founder mutation cases (P = 5.31×10(-5)). These findings further support the East Asian origins of the RNF213 (p.R4810K) variant and more fully describe the genetic landscape of multiethnic MMD, revealing novel, alternative candidate variants and genes that may be important in MMD etiology and diagnosis.
View details for DOI 10.1534/g3.115.020321
View details for Web of Science ID 000367725000004
View details for PubMedCentralID PMC4704723
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Achieving high-sensitivity for clinical applications using augmented exome sequencing
GENOME MEDICINE
2015; 7
Abstract
Whole exome sequencing is increasingly used for the clinical evaluation of genetic disease, yet the variation of coverage and sensitivity over medically relevant parts of the genome remains poorly understood. Several sequencing-based assays continue to provide coverage that is inadequate for clinical assessment.Using sequence data obtained from the NA12878 reference sample and pre-defined lists of medically-relevant protein-coding and noncoding sequences, we compared the breadth and depth of coverage obtained among four commercial exome capture platforms and whole genome sequencing. In addition, we evaluated the performance of an augmented exome strategy, ACE, that extends coverage in medically relevant regions and enhances coverage in areas that are challenging to sequence. Leveraging reference call-sets, we also examined the effects of improved coverage on variant detection sensitivity.We observed coverage shortfalls with each of the conventional exome-capture and whole-genome platforms across several medically interpretable genes. These gaps included areas of the genome required for reporting recently established secondary findings (ACMG) and known disease-associated loci. The augmented exome strategy recovered many of these gaps, resulting in improved coverage in these areas. At clinically-relevant coverage levels (100 % bases covered at ≥20×), ACE improved coverage among genes in the medically interpretable genome (>90 % covered relative to 10-78 % with other platforms), the set of ACMG secondary finding genes (91 % covered relative to 4-75 % with other platforms) and a subset of variants known to be associated with human disease (99 % covered relative to 52-95 % with other platforms). Improved coverage translated into improvements in sensitivity, with ACE variant detection sensitivities (>97.5 % SNVs, >92.5 % InDels) exceeding that observed with conventional whole-exome and whole-genome platforms.Clinicians should consider analytical performance when making clinical assessments, given that even a few missed variants can lead to reporting false negative results. An augmented exome strategy provides a level of coverage not achievable with other platforms, thus addressing concerns regarding the lack of sensitivity in clinically important regions. In clinical applications where comprehensive coverage of medically interpretable areas of the genome requires higher localized sequencing depth, an augmented exome approach offers both cost and performance advantages over other sequencing-based tests.
View details for DOI 10.1186/s13073-015-0197-4
View details for Web of Science ID 000359428300001
View details for PubMedID 26269718
View details for PubMedCentralID PMC4534066
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Achieving high-sensitivity for clinical applications using augmented exome sequencing.
Genome medicine
2015; 7 (1): 71-?
Abstract
Whole exome sequencing is increasingly used for the clinical evaluation of genetic disease, yet the variation of coverage and sensitivity over medically relevant parts of the genome remains poorly understood. Several sequencing-based assays continue to provide coverage that is inadequate for clinical assessment.Using sequence data obtained from the NA12878 reference sample and pre-defined lists of medically-relevant protein-coding and noncoding sequences, we compared the breadth and depth of coverage obtained among four commercial exome capture platforms and whole genome sequencing. In addition, we evaluated the performance of an augmented exome strategy, ACE, that extends coverage in medically relevant regions and enhances coverage in areas that are challenging to sequence. Leveraging reference call-sets, we also examined the effects of improved coverage on variant detection sensitivity.We observed coverage shortfalls with each of the conventional exome-capture and whole-genome platforms across several medically interpretable genes. These gaps included areas of the genome required for reporting recently established secondary findings (ACMG) and known disease-associated loci. The augmented exome strategy recovered many of these gaps, resulting in improved coverage in these areas. At clinically-relevant coverage levels (100 % bases covered at ≥20×), ACE improved coverage among genes in the medically interpretable genome (>90 % covered relative to 10-78 % with other platforms), the set of ACMG secondary finding genes (91 % covered relative to 4-75 % with other platforms) and a subset of variants known to be associated with human disease (99 % covered relative to 52-95 % with other platforms). Improved coverage translated into improvements in sensitivity, with ACE variant detection sensitivities (>97.5 % SNVs, >92.5 % InDels) exceeding that observed with conventional whole-exome and whole-genome platforms.Clinicians should consider analytical performance when making clinical assessments, given that even a few missed variants can lead to reporting false negative results. An augmented exome strategy provides a level of coverage not achievable with other platforms, thus addressing concerns regarding the lack of sensitivity in clinically important regions. In clinical applications where comprehensive coverage of medically interpretable areas of the genome requires higher localized sequencing depth, an augmented exome approach offers both cost and performance advantages over other sequencing-based tests.
View details for DOI 10.1186/s13073-015-0197-4
View details for PubMedID 26269718
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Disease Variant Landscape of a Large Multiethnic Population of Moyamoya Patients by Exome Sequencing.
G3 (Bethesda, Md.)
2015; 6 (1): 41-49
Abstract
Moyamoya disease (MMD) is a rare disorder characterized by cerebrovascular occlusion and development of hemorrhage-prone collateral vessels. Approximately 10-12% of cases are familial, with a presumed low penetrance autosomal dominant pattern of inheritance. Diagnosis commonly occurs only after clinical presentation. The recent identification of the RNF213 founder mutation (p.R4810K) in the Asian population has made a significant contribution, but the etiology of this disease remains unclear. To further develop the variant landscape of MMD, we performed high-depth whole exome sequencing of 125 unrelated, predominantly nonfamilial, ethnically diverse MMD patients in parallel with 125 internally sequenced, matched controls using the same exome and analysis platform. Three subpopulations were established: Asian, Caucasian, and non-RNF213 founder mutation cases. We provided additional support for the previously observed RNF213 founder mutation (p.R4810K) in Asian cases (P = 6.01×10(-5)) that was enriched among East Asians compared to Southeast Asian and Pacific Islander cases (P = 9.52×10(-4)) and was absent in all Caucasian cases. The most enriched variant in Caucasian (P = 7.93×10(-4)) and non-RNF213 founder mutation (P = 1.51×10(-3)) cases was ZXDC (p.P562L), a gene involved in MHC Class II activation. Collapsing variant methodology ranked OBSCN, a gene involved in myofibrillogenesis, as most enriched in Caucasian (P = 1.07×10(-4)) and non-RNF213 founder mutation cases (P = 5.31×10(-5)). These findings further support the East Asian origins of the RNF213 (p.R4810K) variant and more fully describe the genetic landscape of multiethnic MMD, revealing novel, alternative candidate variants and genes that may be important in MMD etiology and diagnosis.
View details for DOI 10.1534/g3.115.020321
View details for PubMedID 26530418
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Transcriptome sequencing in Sezary syndrome identifies Sezary cell and mycosis fungoides-associated lncRNAs and novel transcripts
BLOOD
2012; 120 (16): 3288-3297
Abstract
Sézary syndrome (SS) is an aggressive cutaneous T-cell lymphoma (CTCL) of unknown etiology in which malignant cells circulate in the peripheral blood. To identify viral elements, gene fusions, and gene expression patterns associated with this lymphoma, flow cytometry was used to obtain matched pure populations of malignant Sézary cells (SCs) versus nonmalignant CD4(+) T cells from 3 patients for whole transcriptome, paired-end sequencing with an average depth of 112 million reads per sample. Pathway analysis of differentially expressed genes identified mis-regulation of PI3K/Akt, TGFβ, and NF-κB pathways as well as T-cell receptor signaling. Bioinformatic analysis did not detect either nonhuman transcripts to support a viral etiology of SS or recurrently expressed gene fusions, but it did identify 21 SC-associated annotated long noncoding RNAs (lncRNAs). Transcriptome assembly by multiple algorithms identified 13 differentially expressed unannotated transcripts termed Sézary cell-associated transcripts (SeCATs) that include 12 predicted lncRNAs and a novel transcript with coding potential. High-throughput sequencing targeting the 3' end of polyadenylated transcripts in archived tumors from 24 additional patients with tumor-stage CTCL confirmed the differential expression of SC-associated lncRNAs and SeCATs in CTCL. Our findings characterize the SS transcriptome and support recent reports that implicate lncRNA dysregulation in human malignancies.
View details for DOI 10.1182/blood-2012-04-423061
View details for PubMedID 22936659
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The direct cellular target of topically applied pimecrolimus may not be infiltrating lymphocytes
BRITISH JOURNAL OF DERMATOLOGY
2011; 164 (5): 996-1003
Abstract
Topically applied calcineurin inhibitors have been shown to be effective in the treatment of atopic dermatitis. When systemically administered, these agents cause immunosuppression via inhibition of calcineurin in lymphocytes. As topical agents, the mechanism of action is poorly defined.To test the hypothesis that skin-infiltrating lymphocytes are directly targeted when calcineurin inhibitors are applied to the skin.Ten patients with atopic dermatitis were treated with 1% pimecrolimus cream twice daily to target lesions. Skin biopsies were performed before and 48 h after beginning therapy. We assessed the cellular localization of NFAT1 and NFAT2 as a surrogate measure of intracellular calcineurin activity (e.g. increasing cytoplasmic localization with increasing calcineurin inhibition).All patients showed a clinical response, at both 48 h and 2 weeks. As previously described, NFAT2 localized to the follicular keratinocytes, and its activation was partially inhibited by topical pimecrolimus. NFAT1 was found to be expressed by follicular and interfollicular keratinocytes, and its mostly nuclear localization was not affected by topical pimecrolimus therapy. Both NFAT1 and NFAT2 were found in the infiltrating lymphocytes. However, using both manual counting as well as an automated method to assess nuclear intensity of NFAT staining, we found that the proportion of infiltrating leucocytes with nuclear ('activated') NFAT did not change following therapy with pimecrolimus.Our results suggest that topical pimecrolimus does not act primarily by inhibiting the calcineurin/NFAT axis in lymphocytes but may instead act by other mechanisms, possibly by decreasing NFAT2 activity in follicular keratinocytes.
View details for DOI 10.1111/j.1365-2133.2010.10190.x
View details for Web of Science ID 000289898200012
View details for PubMedID 21166661
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Automated diagnosis of data-model conflicts using metadata
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
1999; 6 (5): 374-392
Abstract
The authors describe a methodology for helping computational biologists diagnose discrepancies they encounter between experimental data and the predictions of scientific models. The authors call these discrepancies data-model conflicts. They have built a prototype system to help scientists resolve these conflicts in a more systematic, evidence-based manner. In computational biology, data-model conflicts are the result of complex computations in which data and models are transformed and evaluated. Increasingly, the data, models, and tools employed in these computations come from diverse and distributed resources, contributing to a widening gap between the scientist and the original context in which these resources were produced. This contextual rift can contribute to the misuse of scientific data or tools and amplifies the problem of diagnosing data-model conflicts. The authors' hypothesis is that systematic collection of metadata about a computational process can help bridge the contextual rift and provide information for supporting automated diagnosis of these conflicts. The methodology involves three major steps. First, the authors decompose the data-model evaluation process into abstract functional components. Next, they use this process decomposition to enumerate the possible causes of the data-model conflict and direct the acquisition of diagnostically relevant metadata. Finally, they use evidence statically and dynamically generated from the metadata collected to identify the most likely causes of the given conflict. They describe how these methods are implemented in a knowledge-based system called GRENDEL and show how GRENDEL can be used to help diagnose conflicts between experimental data and computationally built structural models of the 30S ribosomal subunit.
View details for Web of Science ID 000082447300006
View details for PubMedID 10495098
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RIBOWEB: Linking structural computations to a knowledge base of published experimental data
5th International Conference on Intelligent Systems for Molecular Biology (ISMB-97)
AMER ASSOC ARTIFICIAL INTELLIGENCE. 1997: 84–87
Abstract
The world wide web (WWW) has become critical for storing and disseminating biological data. It offers an additional opportunity, however, to support distributed computation and sharing of results. Currently, computational analysis tools are often separated from the data in a manner that makes iterative hypothesis testing cumbersome. We hypothesize that the cycle of scientific reasoning (using data to build models, and evaluating models in light of data) can be facilitated with resources that link computations with semantic models of the data. Riboweb is an on-line knowledge-based resource that supports the creation of three-dimensional models of the 30S ribosomal subunit. It has three components: (I) a knowledge base containing representations of the essential physical components and published structural data, (II) computational modules that use the knowledge base to build or analyze structural models, and (III) a web-based user interface that supports multiple users, sessions and computations. We have built a prototype of Riboweb, and have used it to refine a rough model of the central domain of the 30S subunit from E. coli. procedure. Our results suggest that sophisticated and integrated computational capabilities can be delivered to biologists using this simple three-component architecture.
View details for Web of Science ID 000072320000011
View details for PubMedID 9322019
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RNA secondary structure as a reusable interface to biological information resources
GENE-COMBIS
1997; 190: GC59-GC70
Abstract
The dissemination of biological information has become critically dependent on the Internet and World Wide Web (WWW), which enable distributed access to information in a platform independent manner. The mode of interaction between biologists and on-line information resources, however, has been mostly limited to simple interface technologies such has hypertext links, tables and forms. The introduction of platform-independent runtime environments facilitates the development of more sophisticated WWW-based user interfaces. Until recently, most such interfaces have been tightly coupled to the underlying computation engines, and not separated as reusable components. We believe that many subdisciplines of biology have intuitive and familiar graphical representations of knowledge that can serve as multipurpose user interface elements. We call such graphical idioms "domain graphics". In order to illustrate the power of such graphics, we have built a reusable interface based on the standard two dimensional (2D) layout of RNA secondary structure. The interface can be used to represent any pre-computed layout of RNA, and takes as a parameters the sets of actions to be performed as a user interacts with the interface. It can provide to any associated application program information about the base, helix, or subsequence selected by the user. We show the versatility of this interface by using it as a special purpose interface to BLAST, Medline and the RNA MFOLD search/compute engines. These demonstrations are available at: http://www-smi.stanford.edu/projects/helix/pubs/ gene-combis-96/
View details for PubMedID 9197551
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Standardized representations of the literature: Combining diverse sources of ribosomal data
5th International Conference on Intelligent Systems for Molecular Biology (ISMB-97)
AMER ASSOC ARTIFICIAL INTELLIGENCE. 1997: 15–24
Abstract
We are building a knowledge base (KB) of published structural data on the 30s ribosomal subunit in prokaryotes. Our KB is distinguished by a standardized representation of biological experiments and their results, in a reusable format. It can be accessed by computer programs that exploit the rich interconnections within the data. The KB is designed to support the construction of 3D models of the 30S subunit, as well as the analysis and extension of relevant functional and phylogenetic information. Most published information about the structure of the ubiquitous ribosome focuses on E. coli as a model system. At the same time, thousands of RNA sequences for the ribosome have been gathered and cataloged. The volume and complexity of these data can complicate attempts to separate structural data peculiar to E. coli from data of universal relevance. We have written an application that dynamically queries the KB and the Ribosome Database Project, a repository of ribosomal RNA sequences from other organisms, in order to assess the relevance of structural data to particular organisms. The application uses the RDP alignment to determine whether a set of data refer primarily to conserved, mismatched, or gapped positions. For a set of 16 representative articles evaluated over 211 sequences, 73% of observations have unambiguous translations from E. coli to the other organisms, 21% have somewhat ambiguous translations, and 6% have no translations. There is a wide variation in these numbers over different articles and organisms, confirming that some articles report structural information specific to E. coli while others report information that is quite general.
View details for Web of Science ID 000072320000002
View details for PubMedID 9322010
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Computational methods for defining the allowed conformational space of 16S rRNA based on chemical footprinting data
RNA-A PUBLICATION OF THE RNA SOCIETY
1996; 2 (9): 851-866
Abstract
Structural models for 16S ribosomal RNA have been proposed based on combinations of crosslinking, chemical protection, shape, and phylogenetic evidence. These models have been based for the most part on independent data sets and different sets of modeling assumptions. In order to evaluate such models meaningfully, methods are required to explicitly model the spatial certainty with which individual structural components are positioned by specific data sets. In this report, we use a constraint satisfaction algorithm to explicitly assess the location of the secondary structural elements of the 16S RNA, as well as the certainty with which these elements can be positioned. The algorithm initially assumes that these helical elements can occupy any position and orientation and then systematically eliminates those positions and orientations that do not satisfy formally parameterized interpretations of structural constraints. Using a conservative interpretation of the hydroxyl radical footprinting data, the positions of the ribosomal proteins as defined by neutron diffraction studies, and the secondary structure of 16S rRNA, the location of the RNA secondary structural elements can be defined with an average precision of 25 A (ranging from 12.8 to 56.3 A). The uncertainty in individual helix positions is both heterogeneous and dependent upon the number of constraints imposed on the helix. The topology of the resulting model is consistent with previous models based on independent approaches. The result of our computation is a conservative upper bound on the possible positions of the RNA secondary structural elements allowed by this data set, and provides a suitable starting point for refinement with other sources of data or different sets of modeling assumptions.
View details for Web of Science ID A1996VH69500001
View details for PubMedID 8809013
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Constraining volume by matching the moments of a distance distribution
COMPUTER APPLICATIONS IN THE BIOSCIENCES
1996; 12 (4): 319-326
Abstract
The problem of computing a molecular structure from a set of distances arises in the interpretation of NMR data as well as other experimental methods that yield distance information. Techniques for computing structures must find conformations consistent with the distance data. There are often other constraints on the structure that must be satisfied as well. One of the most problematic constraints is the constraint on the total volume occupied by the atoms. In this paper, we use the first two moments (mean and variance) of an estimated distance distribution to constrain the volume of a computed structure. We show that a probabilistic algorithm for matching the first two moments of the estimated distance distribution significantly improves the quality of the solution, especially when the distance information alone is not sufficient to define the structure precisely. We also show that our method is not sensitive to small errors in the estimates of mean and variance of the distance distribution. Finally, we demonstrate the use of this constraint in computing a low-resolution structure of the 30S prokaryotic ribosomal subunit. Quantitative analysis of our results allows us to assess the information content contained in constraints on volume, and to show that in some cases addition of a volume constraint adds information roughly equivalent to doubling the number of input distances. Our results also demonstrate the flexibility of probabilistic representations of structural constraints, and the importance of including volume information to constrain structural computations-especially in the case of sparse data.
View details for Web of Science ID A1996VM02500008
View details for PubMedID 8902359