Located in Boston and the surrounding communities, Dana-Farber Cancer Institute brings together world renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, we provide compassionate and comprehensive care to patients of all ages; we conduct research that advances treatment; we educate tomorrow's physician/researchers; we reach out to underserved members of our community; and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.
The Carter lab at The Dana-Farber Cancer Institute is seeking a talented and highly motivated post-doctoral fellow in population genetics to leverage our unique resource of painstakingly collected human cancer-tissue samples in order to elucidate mechanisms of cancer drug-resistance and metastasis. The Carter lab has pioneered the application of whole exome sequencing to characterize somatic genetic alterations in archival samples and to infer phylogenetic relationships between cancer subclones. We partner closely with the Brastianos lab at MGH to study genomic alterations in human brain metastasis patients, experimentally validate hypotheses in animal models, and translate findings to clinical trials. We recently received a 5-year NIH R01 grant to perform whole-exome sequencing on hundreds of brain metastases from lung adenocarcinoma. We anticipate that this dataset will nominate dozens of somatic metastasis drivers, which we will follow-up on with functional studies.
We are seeking a skilled quantitative geneticist to analyze data from brain metastases and unpaired primary tumors of the same histology, in a novel somatic case-control mutational burden analysis. The aim of this analysis is to optimally compute the evidence for increased positive-selection in the brain metastasis (case) vs. primary lung cancer (control) cohorts.
The fellow will be responsible for the development and optimization of novel statistical tools enabling calibrated genome-wide discovery of differential positive selection, while controlling for multiple confounding sources of variation, both at the sample level (e.g. exposure to mutational processes, germline and somatic mutation status), and at the genome level (e.g. early vs. late-replicating DNA, open vs. closed chromatin). Because accounting for mutational heterogeneity is so central to sensitive and specific detection of positive selection in cancer genomes, the fellow is expected to acquire expertise in DNA damage and repair, as well as processes that influence regional variation in somatic mutation rates observed in cancer genomes.
The fellow will join a strong and diverse team of computational biologists, clinical oncologists, technologists, experimental scientists, and software engineers and benefit from immersion in the scientific community at Harvard, Dana-Farber, MGH, and the Broad Institute. Successful candidates are expected to excel at critical thinking, be quick learners for new analytical approaches, and capable of applying or developing novel computational methods for solving complex problems. Candidates must have a doctoral degree, an excellent publication record, and great communication skills. The ideal candidate has a strong quantitative background and a demonstrated ability to tackle large, complex programming projects.
This position is suited to a person who is excited by the prospect of applying their quantitative genetics skills to uncover key drivers of deadly metastatic cancer.
• Develop algorithms for detecting positive selection in metastasis that advance the state-of-the-art
• Implement and maintain robust analysis pipelines for detecting positive selection
• Work with lab members to interpret the biological significance of findings, plan validation experiments,
and design follow-up studies
• Publish new methods and results in academic journals and conferences
• Present findings to internal and external multidisciplinary audiences in a clear and cohesive manner
• Critically evaluate computational biology tools
• Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field, including evaluating newly developed methods
• Support analysis by biologist/clinician colleagues without computational training
• Regularly attend and present at lab and project team meetings to ensure continuous communication around methods and tools developed
• A PhD in a quantitative field with a heavy programming component as well as a strong interest in population genetics as demonstrated by a track record of peer-reviewed publications
• Experience with analysis of high-throughput DNA sequencing data is desirable
• Strong demonstrated skill in statistical algorithm development
• Independent, highly motivated, highly collaborative and works well with others
• Excellent communication, organization, and time management skills
Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.