Computational Biologist - Getz Lab

Description & Requirements

Job Description Overview : Are you an individual with strong quantitative skills looking to apply your talents to help develop cutting-edge cancer therapeutics? If so, you may be an excellent candidate for this opening in the Getz Lab at the Broad Institute and MGH. The Getz lab is a world-leading laboratory for cancer genome analysis, and has developed many of the landmark statistical methods widely used by the field for (i) calling somatic mutations, (ii) identifying genomic alterations that drive cancer, (iii) mathematically modeling tumor evolution, (iv) identifying the genetic causes of therapeutic resistance, and (iv) many other contributions. Our lab has close collaborations with dozens of leading clinicians and experimental researchers in Boston-area hospitals and elsewhere, allowing us to generate new genomic data and from patients samples from a wide variety of disease settings and therapeutic trials. While the comprehensive analysis of cancer genomes is ongoing, major barriers still exist in converting this information to patient benefit and achieving the goal of personalized medicine.

Our work stands at the forefront of cancer genome science, and our research is regularly published in top-tier journals (see our work on Google Scholar and PubMed). We are dedicated to innovating and pushing the limits of what we know and what can be known in understanding the complexities of human cancer.

Getz Lab website : getzlab.org

Environment / Lab Culture : Our lab is comprised of an interdisciplinary group of scientists, engineers, and clinicians who work together in a mutually supportive and respectful environment. Ideas are freely shared, and contributions are highly valued.

Moreover, Dr. Getz places a high priority on career development, and his lab, as well as the environments at the Broad Institute and Massachusetts General Hospital, provide frequent and varied educational and skill-building opportunities.

The lab is engaged in the larger Boston-area ecosystem and the cancer research community worldwide, and provides a vibrant research environment for your contributions to be disseminated and recognized in the field. Integrating our group's computational expertise with our collaborators' in vitro cell line-based assay capabilities enables us to address key questions at a deeper and more impactful level. Indeed, we constantly use and develop new tools and technologies to help unlock new findings.

Project Background : Recently, massive progress in patient outcomes has been made utilizing classes of therapeutics that target specific proteins on the cell surface. For example, chimeric antigen receptor (CAR)-T therapies, which engineer a patient's own immune cell towards the molecule CD19 on lymphomas and leukemias, have proven to provide decade-long durable cures in patients who were unresponsive to standard of care therapy. Antibody-drug conjugates, which follow a "guided missile" approach that links a highly toxic chemotherapeutic payload to an antibody that directs it to a molecule on the tumor cell surface, have shown great success across multiple cancer types such as metastatic breast cancer. However, in general, identification of suitable targets for both these approaches is non-trivial, as most proteins are also expressed on various critical non-cancerous cells across the body. The goal of this collection of projects is to leverage both large-scale public and newly generated genomics datasets to identify novel targets for emerging variants for these surface-targeting approaches.

Who we are searching for : We are currently seeking a highly motivated and talented individual with a solid computational background to join this team tasked with identification of novel cancer surface targets. The successful candidate will possess strong computational and statistical skills, ideally with experience in analyzing DNA / RNA sequencing and proteomics data. This expertise will be essential for identifying candidate genes as suitable targets for emerging surface-targeting approaches. Our team works in close collaboration with industry and academic partners, with the goal of developing novel therapeutics for different cancer types. Our hope is that our discoveries will directly impact the outcomes of cancer patients.

As a member of our team and this project, you will collaborate with other academic and industry scientists, engineers, and clinicians in a collegial work environment with an emphasis on intellectual rigor. Indeed, our collective brainpower and creativity-our best asset-creates an excellent environment for deep innovation, out-of-the-box thinking, and creative problem solving. This position may be ideal for applicants looking to further their expertise in state-of-the-art cancer genomics, or for those looking to apply and acquire knowledge across different disciplines in a stimulating and supportive environment. We encourage you to bring your energy, expertise, and talents to this world-class dynamic team!

Role Expectations

• Demonstrate initiative in completing projects independently, effectively utilizing available resources and seeking guidance when appropriate.
• Design and execute data analysis strategies to support research projects involving multiple data types.
• Ability to explore and develop tools for analyzing novel data types.
• Conduct QC analysis of data, identify technical bottlenecks or challenges, and implement solutions to overcome these barriers.
• Build and run pipelines for processing short- and long-read RNA-seq, proteomic, and imaging data.
• Develop methods to rank candidate targets based on suitability for therapeutic targeting.
• Effectively communicate research findings and ideas to diverse audiences within the Getz lab and external clinical collaborators, both verbally and in writing.
• Ability to critically evaluate new scientific findings within the context of existing dogma, and effectively summarize ideas to make significant contributions to scientific publications, presentations, grants, and patents.
• Opportunity to teach and mentor junior team members.

Qualifications

Keywords : Cancer, Personalized Medicine, Genomics, Machine Learning, Computational biology, Statistics, Cancer resistance, Biomarker discovery, Computational modeling, Tumor evolution, Predictive models, Single-Cell Sequencing, RNA sequencing, Long-read sequencing, Proteomics, CAR-T, Antibody-drug Conjugates

Twitter : @getz_lab

Hashtags : #interdisciplinary #computationalbiologist #datascience #collaborate #cancergenomics #computationalmodels #Broadinstitute #AI #MGHCancerCenter