Lung Cancer System Genetics

Lung Cancer Systems Genetics, An Approach to Individualized Lung Cancer Diagnosis and Therapy, is the ambitous project of  Allan Balmain, Ph.D., one of world's leading molecular geneticists and David Jablons, MD, an accomplished  physician-scientist with one of the world's largest lung cancer tissue banks. 

The research is supported by a generous grant from The  Bonnie J. Addario Lung Cancer Foundation, the nation's largest philanthropy devoted exclusively to eradicating lung cancer, through research, early detection, education, prevention, and treatment.  

Together with Minh To, Ph.D.,  a post-doctoral fellow in the Balmain Lab, the team's goal is to radically jumpstart existing research which, to date, has failed to alter the abysmal 15% five year survival of for lung cancer. What distinguishes this project from earlier research is its scope, high quality tissue samples, quantitative methodology and multi-platform analysis.

This project includes work in five key areas: 

1. Discovery of Lung Cancer Susceptibility Genes

The focus here is the analysis of inherited or germline DNA, DNA present in every cell in the body at birth. The project's goal is to identify small variations in the germline, known as "single nucleotide polymorphisms" or SNPs, by analyzing the normal tissue of lung cancer patients . When overlaid on clinical data, insights can be gained as to which individuals are at elevated risk for developing the disease. Such discoveries have already been made in breast cancer (BRCA genes 1 and 2), and in colon cancer (abnormal Rb gene in HNPCC), but not in lung cancer. This work has clear relevance to early detection research because it can help define the population of individuals who should be screened.

2. Characterizing Mutations in Lung Cancer Tumors

As a person ages, the accumulation of "somatic" mutations in cells can reach a tipping point and result in the development of lung cancer. In genetics, "somatic" refers to cells or tissue in the body that reside outside the germline, cells that are constantly being regenerated. Somatic mutations can result from environmental causes, i.e. smoking, air pollution, radon, or can occur sporadically as when the cell malfunctions during gene replication. Known mutations and the pathways within which they reside are logical therapeutic targets for discovery. When paired with clinical outcomes data, mutations can serve as diagnostic indicators, predictors of survival, and biomarkers for tumor aggressiveness. Mutations can also predict response to therapy, i.e. EGFR mutation predicts response to Tarceva.

3. Exploring Variation in Gene Copy Number (CNVs)

Extra copies of identical genes and/or missing stretches of DNA in lung cancer tumors may be important  even with no mutation involved. CNVs (copy number variation), like mutations, appear to be useful as prognostic biomarkers and predictors of response to therapy. In non-small cell lung cancer (NSCLC), for example, some scientists believe a patient's EGFR copy number is more clinically significant than the EGFR mutation.

4. Gene Expression Analysis

This aspect of the research focuses on gene expression  - the output of gene products within the cancer cell. A common measure of gene expression is the level of messenger RNA (mRNA) being produced. mRNA is a molecule that carries the blueprint for production of cellular proteins. The amount of RNA present suggests what genes are active within the cell. The unique pattern of gene activity serves as a "genetic signature" that can be correlated with clinical outcomes to drive treatment decisions.

5. Gene Sequence Analysis

Gene or DNA sequencing is the process of defining the contours of a stretch of DNA as a distinct subunit, i.e. a gene, and then mapping its function. The work above will generate clues as to which regions in the cell might contain genes important in lung cancer. The investigators will sequence genes already known to be active in the disease as well as any others discovered during the research.

To date, there have been no attempts to link both germline and somatic approaches to lung cancer in a concerted systems approach. Other labs have concentrated on one or two of these platforms at most. This project is unique because it leverages all platforms, combining analysis of germline SNPs with knowledge of somatic mutations, gene copy number abnormalities, and gene expression changes in patients' tumors, all with the goal of developing predictive molecular biomarkers. By using multiple datasets derived from a patient's lung tumor and normal tissue, a more accurate readout of the tumor becomes possible.
The Jablons Lab has accumulated an invaluable set of tissue specimens comprised of both normal and tumor tissue from nearly a thousand lung cancer patients. UCSF's Thoracic Oncology Tissue Bank is one of the largest in the U.S., consisting of "high quality" frozen section specimens. Most other labs have preserved lung tissue in Paraffin,  far from ideal for this type of research.

In this project, the tumor of each patient will be characterized at the molecular level to pinpoint the diagnosis, and consequently the best treatment. The processing of these tumors utilize state-of-the-art technology from Affymetrix and Illumina, two leaders in the analysis of complex genetic information for biomedical purposes. Those technology platforms include:

Objectives and Methods of Lung Cancer Systems Genetics Project

The resulting data will be mined using advanced computational methods and mathematical algorithms pioneered at UCSF. These datasets will provide an unprecedented opportunity to develop new diagnostics, biomarkers for risk assessment and prognosis, and novel combinations of therapeutic targets. The ultimate objective is to establish the wiring diagram of the lung cancer cell - the network of genetic variants and their expression patterns that influence individual lung cancer susceptibility, risk of progression, and response to therapy. 

Cancer treatment is rapidly proceeding towards the era of personalized medicine where treatment is based on the distinctive molecular characteristics of a patient's tumor. The data from this project will be used to construct networks that will include gene expression profiles of lung tumors. These networks will capture the complexity of somatic events intrinsic to the tumor layered over the genetic background that is inherent to the individual. This knowledge will help to more accurately predict disease outcome so that patients at high risk of relapse will receive the most aggressive treatment. It will also allow patients to receive novel combinations of therapies that will afford maximum treatment.