Bio

Bio


I am a fourth-year PhD Candidate in the Biomedical Informatics Program and PhD Minor Candidate in the Department of Computer Science at Stanford University. I am co-advised by Professor Michael Snyder and Professor Russ B. Altman. I work on integrating genomics, proteomics, and histopathology studies to identify novel subtypes of cancers.

Prior to coming to Stanford, I received my MD from National Taiwan University in 2011. I worked with Professor Yu-Ju Chen in Academia Sinica and discovered serum biomarkers for colorectal cancer.

Honors & Awards


  • Student Fellow, Howard Hughes Medical Institute (HHMI) (2015-present)
  • Winston Chen Stanford Graduate Fellow, Stanford University (2012-2015)
  • Student Scholar, American Thoracic Society (ATS) (2016)
  • Travel Award, United States Human Proteome Organization (US HUPO) (2016)
  • Bio-X Travel Award, Stanford University (2015-2016)
  • Third place, Bloomberg CppCon Challenge - CodeCon III (2015)
  • Champion in Machine Learning, Teradata Code Competition, Stanford University (2014)
  • Champion in Prime Generation, IEEE / Google Algorithmic Fest Contest (2014)
  • Scholarship of Government Sponsorship for Overseas Study, Ministry of Education (Taiwan) (2012-2014)
  • Valedictorian, National Taiwan University School of Medicine (2011)
  • Best Intern Award, National Taiwan University Hospital (2011)
  • Undergraduate Research Award, National Taiwan University College of Medicine (2011)
  • Excellent Youth, China Youth Corps, Taiwan (2011)
  • Presidential Award, National Taiwan University (2005, 2008, 2009, 2010, 2011)
  • Deanĺs List, National Taiwan University (2009-2011)
  • Best Poster Award, Flexner Report Centennial International Conference (2010)
  • Research Center for Medical Excellence Award, National Taiwan University (2007)
  • Freshman Research Award, National Taiwan University College of Medicine (2005)
  • Third-place Prize, National Computer Science Contest for Senior High School Students (2003)

Education & Certifications


  • Master of Science, Stanford University, BIOM-MS (2014)
  • Doctor of Medicine, National Taiwan University, Medicine (2011)
  • Medical License, Taiwan (2011)

Stanford Advisors


Service, Volunteer and Community Work


  • Representative, Student Health Insurance Advisory Committee, Stanford University (9/1/2013 - Present)

    Location

    Stanford, CA

  • Co-Chair, Biomedical Computing at Stanford (BCATS) Symposium, Stanford University (4/1/2015 - 4/1/2016)

    Location

    Stanford, CA

  • Webmaster, Cross-disciplinary Healthcare Innovation Partnership at Stanford, Stanford University (9/1/2013 - 8/31/2015)

    Location

    Stanford, CA

  • Editor-in-chief, NTU School of Medicine Clerkship Survival Guide, National Taiwan University (September 1, 2009 - February 1, 2010)

    Location

    Taipei, Taiwan

  • President, Philosophy Thinking Club, National Taiwan University, National Taiwan University (September 1, 2005 - August 31, 2006)

    Location

    Taipei, Taiwan

  • Captain, Athletics Club, National Taiwan University College of Medicine, National Taiwan University (February 1, 2008 - August 31, 2008)

    Location

    Taipei, Taiwan

Professional

Work Experience


  • Instructor, Stanford Pre-Collegiate Studies Program, Stanford University (9/1/2014 - Present)

    Location

    Stanford, CA

  • Head Teaching Assistant and Teaching Assistant, Representations and Algorithms for Computational Molecular Biology, Stanford University (9/1/2013 - 8/31/2015)

    Location

    Stanford, CA

  • Head Teaching Assistant, Clinical Informatics Literature Review Seminar, Stanford University (9/1/2014 - Present)

    Location

    Stanford, CA

  • Student Mentor, Stanford Biosciences Graduate Programs, Stanford University (9/1/2013 - Present)

    Location

    Stanford, CA

  • Medical Officer, Ministry of National Defense (August 8, 2011 - July 8, 2012)

    Location

    Taipei, Taiwan

  • Intern, National Taiwan University Hospital (May 31, 2010 - June 4, 2011)

    Location

    Taipei, Taiwan

  • Software Engineer (Intern), OmniWise International, Inc. (August 1, 2004 - September 1, 2004)

    Location

    Taipei, Taiwan

Publications

All Publications


  • Transcriptome Profiling of Patient-Specific Human iPSC-Cardiomyocytes Predicts Individual Drug Safety and Efficacy Responses InáVitro. Cell stem cell Matsa, E., Burridge, P. W., Yu, K. H., Ahrens, J. H., Termglinchan, V., Wu, H., Liu, C., Shukla, P., Sayed, N., Churko, J. M., Shao, N., Woo, N. A., Chao, A. S., Gold, J. D., Karakikes, I., Snyder, M. P., Wu, J. C. 2016

    Abstract

    Understanding individual susceptibility to drug-induced cardiotoxicity is key to improving patient safety and preventing drug attrition. Human induced pluripotent stem cells (hiPSCs) enable the study of pharmacological and toxicological responses in patient-specific cardiomyocytes (CMs) and may serve as preclinical platforms for precision medicine. Transcriptome profiling in hiPSC-CMs from seven individuals lacking known cardiovascular disease-associated mutations and in three isogenic human heart tissue and hiPSC-CM pairs showed greater inter-patientávariation than intra-patient variation, verifyingáthat reprogramming and differentiation preserve patient-specific gene expression, particularly in metabolic and stress-response genes. Transcriptome-based toxicology analysis predicted and risk-stratified patient-specific susceptibility to cardiotoxicity, and functional assays in hiPSC-CMs using tacrolimus and rosiglitazone, drugs targeting pathways predicted to produce cardiotoxicity, validated inter-patient differential responses. CRISPR/Cas9-mediated pathway correction prevented drug-induced cardiotoxicity. Our data suggest that hiPSC-CMs can be used inávitro to predict and validate patient-specific drug safety and efficacy, potentially enabling future clinical approaches to precision medicine.

    View details for DOI 10.1016/j.stem.2016.07.006

    View details for PubMedID 27545504

  • Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nature communications Yu, K. H., Zhang, C., Berry, G. J., Altman, R. B., RÚ, C., Rubin, D. L., Snyder, M. 2016; 7: 12474

    Abstract

    Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.

    View details for DOI 10.1038/ncomms12474

    View details for PubMedID 27527408

  • Omics profiling in precision oncology. Molecular & cellular proteomics : MCP Yu, K. H., Snyder, M. 2016

    Abstract

    Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology.

    View details for DOI 10.1074/mcp.O116.059253

    View details for PubMedID 27099341

  • Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures. Journal of proteome research Yu, K. H., Levine, D. A., Zhang, H., Chan, D. W., Zhang, Z., Snyder, M. 2016

    Abstract

    Ovarian cancer is the deadliest gynecologic malignancy in the United States with most patients diagnosed in the advanced stage of the disease. Platinum-based antineoplastic therapeutics is indispensable to treating advanced ovarian serous carcinoma. However, patients have heterogeneous responses to platinum drugs, and it is difficult to predict these interindividual differences before administering medication. In this study, we investigated the tumor proteomic profiles and clinical characteristics of 130 ovarian serous carcinoma patients analyzed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), predicted the platinum drug response using supervised machine learning methods, and evaluated our prediction models through leave-one-out cross-validation. Our data-driven feature selection approach indicated that tumor proteomics profiles contain information for predicting binarized platinum response (P < 0.0001). We further built a least absolute shrinkage and selection operator (LASSO)-Cox proportional hazards model that stratified patients into early relapse and late relapse groups (P = 0.00013). The top proteomic features indicative of platinum response were involved in ATP synthesis pathways and Ran GTPase binding. Overall, we demonstrated that proteomic profiles of ovarian serous carcinoma patients predicted platinum drug responses as well as provided insights into the biological processes influencing the efficacy of platinum-based therapeutics. Our analytical approach is also extensible to predicting response to other antineoplastic agents or treatment modalities for both ovarian and other cancers.

    View details for DOI 10.1021/acs.jproteome.5b01129

    View details for PubMedID 27312948

  • Exome Sequencing of Neonatal Blood Spots and the Identification of Genes Implicated in Bronchopulmonary Dysplasia AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE Li, J., Yu, K., Oehlert, J., Jeliffe-Pawlowski, L. L., Gould, J. B., Stevenson, D. K., Snyder, M., Shaw, G. M., O'Brodovich, H. M. 2015; 192 (5): 589-596

    Abstract

    Bronchopulmonary dysplasia (BPD), a prevalent severe lung disease of premature infants, has a strong genetic component. Large-scale genome-wide association studies for common variants have not revealed its genetic basis.Given the historical high mortality rate of extremely preterm infants who now survive and develop BPD, we hypothesized that risk loci underlying this disease are under severe purifying selection during evolution; thus, rare variants likely explain greater risk of the disease.We performed exome sequencing on 50 BPD-affected and unaffected twin pairs using DNA isolated from neonatal blood spots and identified genes affected by extremely rare nonsynonymous mutations. Functional genomic approaches were then used to systematically compare these affected genes.We identified 258 genes with rare nonsynonymous mutations in patients with BPD. These genes were highly enriched for processes involved in pulmonary structure and function including collagen fibril organization, morphogenesis of embryonic epithelium, and regulation of Wnt signaling pathway; displayed significantly elevated expression in fetal and adult lungs; and were substantially up-regulated in a murine model of BPD. Analyses of mouse mutants revealed their phenotypic enrichment for embryonic development and the cyanosis phenotype, a clinical manifestation of BPD.Our study supports the role of rare variants in BPD, in contrast with the role of common variants targeted by genome-wide association studies. Overall, our study is the first to sequence BPD exomes from newborn blood spot samples and identify with high confidence genes implicated in BPD, thereby providing important insights into its biology and molecular etiology.

    View details for DOI 10.1164/rccm.201501-0168OC

    View details for Web of Science ID 000361344500012

    View details for PubMedID 26030808

  • Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell Zhang, H., Liu, T., Zhang, Z., Payne, S. H., Zhang, B., McDermott, J. E., Zhou, J. Y., Petyuk, V. A., Chen, L., Ray, D., Sun, S., Yang, F., Chen, L., Wang, J., Shah, P., Cha, S. W., Aiyetan, P., Woo, S., Tian, Y., Gritsenko, M. A., Clauss, T. R., Choi, C., Monroe, M. E., Thomas, S., Nie, S., Wu, C., Moore, R. J., Yu, K. H., Tabb, D. L., Feny÷, D., Bafna, V., Wang, Y., Rodriguez, H., Boja, E. S., Hiltke, T., Rivers, R. C., Sokoll, L., Zhu, H., Shih, I. M., Cope, L., Pandey, A., Zhang, B., Snyder, M. P., Levine, D. A., Smith, R. D., Chan, D. W., Rodland, K. D. 2016

    Abstract

    To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC.

    View details for DOI 10.1016/j.cell.2016.05.069

    View details for PubMedID 27372738

  • Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics. Journal of the American Medical Informatics Association : JAMIA Roberts, K., Boland, M. R., Pruinelli, L., Dcruz, J., Berry, A., Georgsson, M., Hazen, R., Sarmiento, R. F., Backonja, U., Yu, K. H., Jiang, Y., Brennan, P. F. 2016

    Abstract

    The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive.

    View details for DOI 10.1093/jamia/ocw103

    View details for PubMedID 27497798

  • The genetic predisposition to bronchopulmonary dysplasia. Current opinion in pediatrics Yu, K. H., Li, J., Snyder, M., Shaw, G. M., O'Brodovich, H. M. 2016; 28 (3): 318-23

    Abstract

    Bronchopulmonary dysplasia (BPD) is a prevalent chronic lung disease in premature infants. Twin studies have shown strong heritability underlying this disease; however, the genetic architecture of BPD remains unclear.A number of studies employed different approaches to characterize the genetic aberrations associated with BPD, including candidate gene studies, genome-wide association studies, exome sequencing, integrative omics analysis, and pathway analysis. Candidate gene studies identified a number of genes potentially involved with the development of BPD, but the etiological contribution from each gene is not substantial. Copy number variation studies and three independent genome-wide association studies did not identify genetic variations significantly and consistently associated with BPD. A recent exome-sequencing study pointed to rare variants implicated in the disease. In this review, we summarize these studies' methodology and findings, and suggest future research directions to better understand the genetic underpinnings of this potentially life-long lung disease.Genetic factors play a significant role in the development of BPD. Recent studies suggested that rare variants in genes participating in lung development pathways could contribute to BPD susceptibility.

    View details for DOI 10.1097/MOP.0000000000000344

    View details for PubMedID 26963946

  • A Tale of Two Cities: Understanding the Differences in Medical Professionalism Between Two Chinese Cultural Contexts ACADEMIC MEDICINE Ho, M., Yu, K., Pan, H., Norris, J. L., Liang, Y., Li, J., Hirsh, D. 2014; 89 (6): 944-950
  • Prioritization of cancer marker candidates based on the immunohistochemistry staining images deposited in the human protein atlas. PloS one Chiang, S., Han, C., Yu, K., Chen, Y., Wu, K. 2013; 8 (11)

    Abstract

    Cancer marker discovery is an emerging topic in high-throughput quantitative proteomics. However, the omics technology usually generates a long list of marker candidates that requires a labor-intensive filtering process in order to screen for potentially useful markers. Specifically, various parameters, such as the level of overexpression of the marker in the cancer type of interest, which is related to sensitivity, and the specificity of the marker among cancer groups, are the most critical considerations. Protein expression profiling on the basis of immunohistochemistry (IHC) staining images is a technique commonly used during such filtering procedures. To systematically investigate the protein expression in different cancer versus normal tissues and cell types, the Human Protein Atlas is a most comprehensive resource because it includes millions of high-resolution IHC images with expert-curated annotations. To facilitate the filtering of potential biomarker candidates from large-scale omics datasets, in this study we have proposed a scoring approach for quantifying IHC annotation of paired cancerous/normal tissues and cancerous/normal cell types. We have comprehensively calculated the scores of all the 17219 tested antibodies deposited in the Human Protein Atlas based on their accumulated IHC images and obtained 457110 scores covering 20 different types of cancers. Statistical tests demonstrate the ability of the proposed scoring approach to prioritize cancer-specific proteins. Top 100 potential marker candidates were prioritized for the 20 cancer types with statistical significance. In addition, a model study was carried out of 1482 membrane proteins identified from a quantitative comparison of paired cancerous and adjacent normal tissues from patients with colorectal cancer (CRC). The proposed scoring approach demonstrated successful prioritization and identified four CRC markers, including two of the most widely used, namely CEACAM5 and CEACAM6. These results demonstrate the potential of this scoring approach in terms of cancer marker discovery and development. All the calculated scores are available at http://bal.ym.edu.tw/hpa/.

    View details for DOI 10.1371/journal.pone.0081079

    View details for PubMedID 24303032

  • Does One Size Fit All? Building a Framework for Medical Professionalism ACADEMIC MEDICINE Ho, M., Yu, K., Hirsh, D., Huang, T., Yang, P. 2011; 86 (11): 1407-1414

    Abstract

    Medical professionalism has gained global attention over the past decade, but there is a paucity of literature on the universal applicability of the dominant professionalism framework developed in the West. This study proposes an institutional approach to build a framework for medical professionalism that incorporates historical and sociocultural contexts.From 2008 to 2009, the authors adopted nominal group technique (NGT) to determine professional competencies valued by 91 critical stakeholders of medical education (divided into 12 discipline-specific groups) at their institution and in their native society, Taiwan. An expert committee subsequently constructed a framework for professionalism which accounted for a literature review and their understanding of the institution's values and historical roots. To confirm that the framework encompassed the attributes nominated by NGT participants, the authors analyzed transcripts of NGT exercises to refine the final document.Each of 12 NGT groups raised 5 to 23 core competencies and determined the most important five competencies by summing participants' ratings of each item. The expert panel reached consensus on a framework that included eight competencies. The framework differs from the Western framework in the centrality of self-integrity, harmonizing personal and professional roles. Text analysis of the NGT transcripts demonstrated that the framework successfully incorporated top-ranked NGT results.This study challenges the universal applicability of the Western framework of medical professionalism and proposes a process to build a professionalism framework that reflects the cultural heritage and the values of local stakeholders.

    View details for DOI 10.1097/ACM.0b013e31823059d1

    View details for Web of Science ID 000296624900049

    View details for PubMedID 21971298

  • An Informatics-assisted Label-free Approach for Personalized Tissue Membrane Proteomics: Case Study on Colorectal Cancer MOLECULAR & CELLULAR PROTEOMICS Han, C., Chen, J., Chan, E., Wu, C., Yu, K., Chen, K., Tsou, C., Tsai, C., Chien, C., Kuo, Y., Lin, P., Yu, J., Hsueh, C., Chen, M., Chan, C., Chang, Y., Chen, Y. 2011; 10 (4)

    Abstract

    We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and reproducible (34% relative S.D.) quantification of three independently purified membrane fractions from the same human colorectal cancer (CRC) tissue. Using CRC as a model, we constructed the personalized membrane protein atlas of paired tumor and adjacent normal tissues from 28 patients with different stages of CRC. Without fractionation, this strategy confidently quantified 856 proteins (?2 unique peptides) across different patients, including the first and robust detection (Mascot score: 22,074) of the well-documented CRC marker, carcinoembryonic antigen 5 by a discovery-type proteomics approach. Further validation of a panel of proteins, annexin A4, neutrophils defensin A1, and claudin 3, confirmed differential expression levels and high occurrences (48-70%) in 60 CRC patients. The most significant discovery is the overexpression of stomatin-like 2 (STOML2) for early diagnostic and prognostic potential. Increased expression of STOML2 was associated with decreased CRC-related survival; the mean survival period was 34.77 ▒ 2.03 months in patients with high STOML2 expression, whereas 53.67 ▒ 3.46 months was obtained for patients with low STOML2 expression. Further analysis by ELISA verified that plasma concentrations of STOML2 in early-stage CRC patients were elevated as compared with those of healthy individuals (p < 0.001), suggesting that STOML2 may be a noninvasive serological biomarker for early CRC diagnosis. The overall sensitivity of STOML2 for CRC detection was 71%, which increased to 87% when combined with CEA measurements. This study demonstrated a sensitive, label-free strategy for differential analysis of tissue membrane proteome, which may provide a roadmap for the subsequent identification of molecular target candidates of multiple cancer types.

    View details for DOI 10.1074/mcp.M110.003087

    View details for Web of Science ID 000289067300004

    View details for PubMedID 21209152

  • Inherited human disease Science Study Monthly Yu KH 2011; 50 (12): 8-14
  • Medical Professionalism Revisited: Application of the Nominal Group Technique Journal of Medical Education Yu KH, Huang TS, Yang PC, Ho MJ 2010; 14: 15-22

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