Relapsed ALL is associated with poor outcomes and understanding the genetic underpinnings of relapse can lead to novel therapeutic strategies. The Ma lab has pioneered the genomics studies of relapsed ALL with discoveries including the rise and fall patterns of subclones during evolution, genes with enriched mutations in relapsed tumors, and novel methods for cancer clonal tracking and early detection. We also coined the concept of cancer-specific genetic vulnerabilities embedded in somatic mutations responsible for subtype-defining alterations including fusions. A strong postdoc candidate will fill this position by developing novel computational methods to further our understanding of tumorigenesis via analysis of somatic mutations in patient cohort and in cell models. Promising novel biomarkers will be tested experimentally using cell models. The developed computational pipelines will also be applicable for other cancer types and will be integrated into the comprehensive infrastructure built by the Ma lab and the Computational Biology department. The analytical findings from patient cohorts and cell models will be disseminated through scientific publications and conference presentations with our expert physician-scientist collaborators. The Ma laboratory has four active research areas, all aimed at furthering our understanding on the genetic underpinnings of childhood cancers: 1). Cancer genomics, etiology, and editing-based therapeutics. Through close collaboration with physician-scientists, we study the somatic and germline alterations across large cohort of patient tumors to define cancer drivers. By investigating the etiology of these drivers, we hope to develop novel vulnerabilities for genome-editing based therapeutics. 2). Mathematical modeling of intra-tumor heterogeneity. The challenge of cancer management is resistance to treatment, which has been shown to stem in intra-tumor heterogeneity. Therefore, our lab is particularly interested in understanding intra-tumor heterogeneity and its relationship to treatment responses. Discoveries from such studies can lead to novel therapeutic strategies to improve cure. 3). Cancer early detection and monitoring. Early-stage cancers are known to have a much higher cure rate than late-stage cancers. With extensive knowledges in the genetics of “terminal” stages of childhood cancers, we hope to develop sensitive assays to accurately detect and forecast cancers before overt symptoms to enable early intervention. We use relapsed leukemia as a model system for this direction. 4).Cell line and PDX models. To validate and test the discoveries made from patient specimens, we are currently characterizing the genetics of large collection of cell models, which can also serve as an invaluable resource for the research community. Closely working with the supervisor, the postdoctoral fellow will propose, conduct, and present/publish research projects on the development and utilization of novel methods for investigating the genomic underpinnings of childhood cancers using patient cohort and cell line models, with following main components: 1) cohort management, to track and manage the progress of analyses; 2) executing and monitoring of established pipelines to ensure timely completion and reporting; 3) trouble-shooting the pipeline or data when problems occur; 4) develop novel procedures for new analysis; 5) review of output data with the supervisor to ensure precise interpretation of findings and decisions on follow up experiments; 6) compilation of research findings to scientific presentations and publications. The postdoctoral fellow will have extensive interactions with senior scientists in the team to learn established procedures and best practices to enhance computational and project management skills. The postdoctoral fellow will have access to other resources to advance their professional development, including those for grant writing, scientific communications, and research methodologies, and access to exceptional core facilities and biostatistical/data science support. Required: Candidates should have a track record of productivity, for example, leading author papers or critical contributions to large projects; applicants from a quantitative field such as computational biology, genetics/genomics, statistics/mathematics, physics or computer science are encouraged to apply; applicants are expected to have a strong knowledge in Molecular Biology, Genetics and Statistics. Excellent communication and critical thinking skills are required. Programming skills: Python or Perl or C/C++ for data processing R (for data processing, exploration, and figure generation) Linux shell scripting Office software: Excel-based data analysis Adobe Illustrator-based figure beautification Word/EndNote- or LaTex-based manuscript drafting PowerPoint Preferred: Strong experience in next generation sequencing data analysis, including whole genome, exome, transcription, or targeted capture sequencing data analysis. Applicants with detail-oriented mindset are preferred. Previous experience in managing and analyzing big and complex datasets is preferred. Interested applicants should send a CV, a cover letter describing their research interests and accomplishments, and the names and addresses of 3 references to: Xiaotu.Ma@stjude.org Contact Information Xiaotu Ma, PhD Department of Computational Biology St. Jude Children’s Research Hospital 262 Danny Thomas Place Memphis, TN 38105 Xiaotu.Ma@stjude.org https://www.stjude.org/research/labs/ma-lab.html Diversity, Equity and Inclusion St. Jude Children’s Research Hospital has a diverse, global patient population and workforce, built on the principles of diversity, equity and inclusion. Our founder Danny Thomas envisioned a hospital that would treat children of the world—regardless of race, religion or a family’s ability to pay. Learn more about our history and commitment. Today, we continue the mission to advance cures and means of prevention for pediatric catastrophic diseases through research and treatment. 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