Top

Cancer Genomics Laboratory

RESEARCH PLATFORM FOR

THE MOON SHOTS PROGRAM

We're accelerating Moon Shot initiatives

Cancer is a disease of the genome. Every person’s cancer is different and every tumor has its own unique abnormalities at the genetic level. Some of these abnormalities are clinically significant and, if identified, can be exploited to improve cancer detection, management and treatment. The Cancer Genomics Laboratory (CGL) platform uses genome sequencing, or next-generation sequencing, to uncover the genetic changes unique to a patient’s particular tumor by analyzing the genetic sequence of tumor tissue and comparing it to the same individual’s normal (non-cancer) sequence. The difference between tumor and normal sequences indicate where potential genetic abnormalities that may make cancers vulnerable to a particular drug or therapy. This information may also help us understand how different patients respond to treatment.

The CGL platform of MD Anderson’s Moon Shots Program^® performs next-generation sequencing for all of the Moon Shot^® teams, providing these researchers with genomic data that is crucial for addressing knowledge gaps in several cancer types.

The resulting high-quality sequencing data then flows into a database called the Translational Research Accelerator (TRA) which is also a Moon Shot platform, allowing all MD Anderson researchers to access the stored data. Since many cancer types share genetic abnormalities, this effort allows cancer experts across all disciplines and disease sites to use the data to address knowledge gaps that exist in their disease specialty. The CGL platform serves as an essential part of this process, ensuring that data from multiple Moon Shot cancer types are consistent and comparable.

Cancer Genomics Laboratory Leaders

Andrew Futreal, Ph.D.

Chair, Genomic Medicine
Kenna Shaw, Ph.D.

Executive Director

How we're advancing cancer genomics

Over the last year more than 3000 samples have been sequenced across eleven Moon Shot disease sites. Nearly 50% of our sequencing effort was directed towards leukemia samples for the Moon Shot Adaptive Patient-Oriented Longitudinal Learning and Optimization (APOLLO) platform. The APOLLO platform is analyzing data collected from the same patient over a long period of time (longitudinal data) to better understand why disease responds to or resists certain treatments.

Research progress

Traditionally, invasive and expensive biopsies have been required to collect the amount of tumor sample needed to perform next-generation sequencing. Technology developments have enabled us to implement “low input” protocols, which allow for reliable sequencing of low quantity and quality genetic samples. In addition, we’ve developed targeted gene panels that rely on cell free DNA, which can be found circulating throughout the bloodstream. These panels direct our sequencing efforts on the specific genes or gene regions that have a known or suspected association of the cancer of interest. With these new technologies and tools in place, in some cases a simple blood draw may be all that is required to interrogate the genome of a patient’s tumor.

ABOUT GENOMICS

How 'omics improves cancer treatment

Genomics examines all of your genes – your genome – and how they interact. This data-heavy way of looking at cancer can help generate research questions, validate clinical trials and lead to more research studies.

Learn how 'omics impacts cancer research >

MOON SHOT COLLABORATION

The impact of HPV on cancer

Recent efforts have included generating data for researchers studying cancers caused by infection with human papillomavirus (HPV). CGL's genetic sequencing work helped the HPV-Related Cancers Moon Shot^® team understand how the virus inserts itself into a person’s DNA, and how that impacts the development of cervical and head and neck cancers. A follow-up study is examining HPV-positive and HPV-negative cell lines to further understand cancer development and patient responsiveness to available therapies. The tumor data is also being used to identify potential biomarkers that can be used for personalized cancer therapies.