Helio Costa, PhD, is a geneticist with expertise in genomics, molecular biology, molecular oncology, and bioinformatics. He is currently an Instructor within the Departments of Pathology and Biomedical Data Science at Stanford Medical School. Dr. Costa's research utilizes next-generation sequencing to develop new clinical genome and transcriptome profiling methods with the end goal of translating these tools to clinical diagnostic tests for implementation at Stanford Health Care. His research group is also developing data science and machine learning methods to model and predict clinical outcomes and aid in clinical decision support. He is a co-Investigator on the NIH-funded Clinical Genome Resource (ClinGen) Consortium, and leads the engineering and biocuration teams building the Gene and Variant Curation Interfaces which are used by health care providers, researchers and the clinical genetics community to interpret the clinical significance of genetic alterations identified in patients during routine DNA sequencing. He is the founding director of the Stanford Clinical Data Science Fellowship where post-doctoral fellows engage in interdisciplinary clinical research and embed in health care workflows learning, building and deploying real-world health data solutions in the Stanford Health Care system. Additionally, he is an Attending Geneticist, and Assistant Lab Director of the Molecular Genetic Pathology Laboratory for Stanford Health Care. Dr. Costa received his BS in Genetics from University of California, Davis, his PhD in Genetics from Stanford University School of Medicine, and his ABMGG Clinical Molecular Genetics and Genomics fellowship training from Stanford University School of Medicine.
- Molecular Oncology
- Molecular Pathology
- Medical Genetics
- Technologist in Molecular Biology
Founding Director, Stanford Clinical Data Science Fellowship (2018 - Present)
Assistant Lab Director, Molecular Genetic Pathology Laboratory (2017 - Present)
Board Certification: Technologist in Molecular Biology, American Society for Clinical Pathology (2018)
Fellowship:Stanford University Molecular Genetic Pathology FellowshipCA
PhD Training:Stanford University School of Medicine Registrar (2015) CA
Fellowship, Stanford University School of Medicine, ABMGG Clinical Molecular Genetics and Genomics (2017)
Doctor of Philosophy, Stanford University School of Medicine, Genetics (2015)
Bachelor of Science, University of California, Davis, Genetics (2010)
Promoting appropriate genetic testing: the impact of a combined test review and consultative service.
Genetics in medicine
Genetic test misorders can adversely affect patient care. However, little is known about the types of misorders and the overall impact of a utilization management (UM) program on curbing misorders. This study aimed to identify different types of misorders and analyze the impact of a combined test review and consultative service on reducing misorders over time.Selected genetic tests were systematically reviewed between January and December 2015 at Stanford Health Care. Misorders were categorized into five types: clerical errors, redundant testing, better alternatives, controversial, and uncategorized. Moreover, consultations were offered to help clinicians with test selection.Of the 629 molecular test orders reviewed, 13% were classified as misorders, and 7% were modified or canceled. Controversial misorders constitute the most common type (42%); however, unlike the other misorder types, they were negligibly affected by test review. Simultaneously, 71 consults were received. With the introduction of the UM program, genetic test misorders went from 22% at baseline to 3% at the end of the year.Our results show that the combined approach of test review and consultative service effectively reduced misorders over time and suggest that a UM program focused on eliminating misorders can positively influence health-care providers' behaviors.Genet Med advance online publication 26 January 2017Genetics in Medicine (2017); doi:10.1038/gim.2016.219.
View details for DOI 10.1038/gim.2016.219
View details for PubMedID 28125079
Identification of a Novel Somatic Mutation Leading to Allele Dropout for EGFR L858R Genotyping in Non-Small Cell Lung Cancer
Molecular Diagnosis & Therapy
View details for DOI 10.1007/s40291-017-0275-y
Discovery and functional characterization of a neomorphic PTEN mutation
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2015; 112 (45): 13976-13981
Although a variety of genetic alterations have been found across cancer types, the identification and functional characterization of candidate driver genetic lesions in an individual patient and their translation into clinically actionable strategies remain major hurdles. Here, we use whole genome sequencing of a prostate cancer tumor, computational analyses, and experimental validation to identify and predict novel oncogenic activity arising from a point mutation in the phosphatase and tensin homolog (PTEN) tumor suppressor protein. We demonstrate that this mutation (p.A126G) produces an enzymatic gain-of-function in PTEN, shifting its function from a phosphoinositide (PI) 3-phosphatase to a phosphoinositide (PI) 5-phosphatase. Using cellular assays, we demonstrate that this gain-of-function activity shifts cellular phosphoinositide levels, hyperactivates the PI3K/Akt cell proliferation pathway, and exhibits increased cell migration beyond canonical PTEN loss-of-function mutants. These findings suggest that mutationally modified PTEN can actively contribute to well-defined hallmarks of cancer. Lastly, we demonstrate that these effects can be substantially mitigated through chemical PI3K inhibitors. These results demonstrate a new dysfunction paradigm for PTEN cancer biology and suggest a potential framework for the translation of genomic data into actionable clinical strategies for targeted patient therapy.
View details for DOI 10.1073/pnas.1422504112
View details for PubMedID 26504226
Transcriptome sequencing from diverse human populations reveals differentiated regulatory architecture.
2014; 10 (8)
Large-scale sequencing efforts have documented extensive genetic variation within the human genome. However, our understanding of the origins, global distribution, and functional consequences of this variation is far from complete. While regulatory variation influencing gene expression has been studied within a handful of populations, the breadth of transcriptome differences across diverse human populations has not been systematically analyzed. To better understand the spectrum of gene expression variation, alternative splicing, and the population genetics of regulatory variation in humans, we have sequenced the genomes, exomes, and transcriptomes of EBV transformed lymphoblastoid cell lines derived from 45 individuals in the Human Genome Diversity Panel (HGDP). The populations sampled span the geographic breadth of human migration history and include Namibian San, Mbuti Pygmies of the Democratic Republic of Congo, Algerian Mozabites, Pathan of Pakistan, Cambodians of East Asia, Yakut of Siberia, and Mayans of Mexico. We discover that approximately 25.0% of the variation in gene expression found amongst individuals can be attributed to population differences. However, we find few genes that are systematically differentially expressed among populations. Of this population-specific variation, 75.5% is due to expression rather than splicing variability, and we find few genes with strong evidence for differential splicing across populations. Allelic expression analyses indicate that previously mapped common regulatory variants identified in eight populations from the International Haplotype Map Phase 3 project have similar effects in our seven sampled HGDP populations, suggesting that the cellular effects of common variants are shared across diverse populations. Together, these results provide a resource for studies analyzing functional differences across populations by estimating the degree of shared gene expression, alternative splicing, and regulatory genetics across populations from the broadest points of human migration history yet sampled.
View details for DOI 10.1371/journal.pgen.1004549
View details for PubMedID 25121757