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Breast Cancer Recurrence Risk Analysis using Selected Gene Expression





A team of Berkeley Lab researchers led by Obi Griffith, under the supervision of Joe Gray and Paul Spellman, has developed a robust and low cost system for assessing the risk of breast cancer recurrence among post surgical patients with certain types of tumors. The system is based on a complex computer analysis of the expression profile of a defined set of genes and can be used in the clinical setting quickly and cost-effectively with existing diagnostic platforms.

Specifically, the technology was developed to assess relapse risk for post-surgical breast cancer patients whose tumors are found to be estrogen receptor positive (ER+), node negative (LN-), and Human Epidermal Growth Factor Receptor 2 negative (HER2-). After extensive reviews of archival gene expression data among 858 patients with tumors fitting that profile, a set of predictive gene expressions was identified. Using a statistical approach known as random forests classification, Berkeley Lab researchers were able to assign a relapse risk score for any patient based on her gene expression profile. (See figure below.)

Critical to the Berkeley Lab technology is the identification of relationships among selected genes, because it is the combination of expressed genes — rather than the state of any single gene — that determines risk. Armed with the relapse score, clinicians can assign a patient to high, intermediate, or low risk of breast cancer recurrence. In the Berkeley Lab statistical analysis of archival data, 46 percent of patients fell into the low-risk category. Post-surgical chemotherapy could potentially be avoided for such patients.

The Berkeley Lab researchers found that profiles of as few as eight genes produce sufficient data for accurate risk assignment, although a typical assay would have 17 genes. A key element of their system is the identification of “alternative” genes that can provide equally useful expression profiles should signals of any of the 8-17 selected genes be difficult to determine. As such, the Berkeley Lab technology is extraordinarily robust, and has the flexibility to be adapted to many different diagnostic platforms. In addition, the same approach used to identify the risk profiles of patients with ER+, LN-, HER2- tumors can be used to develop relapse scores for other breast cancer variants, and for other types of metastatic disease. This information on efficacy is of value not only to clinicians and patients but also to drug developers searching for subsets of patients who can benefit most from new and existing therapies.

Patients at high risk of breast cancer recurrence can benefit from post-surgical chemotherapy, but low-risk patients can be spared the side effects of these treatments. Without a reliable way to determine a patient’s risk profile, however, patients are typically given chemotherapy regardless of whether they really needed it. Finding a reliable way to determine the recurrence risk for an individual patient is therefore a high priority in cancer research. Currently available gene expression panels that evaluate relapse risk are costly and do not produce results in a timely fashion. They typically require batch processing at large clinical laboratories, potentially delaying reporting of results for weeks.



Stratifying the risk. The figure above shows the estimated likelihood of relapse at 10 years for any Random Forests Relapse Score (RFRS) value. These scores can be important indicators for doctors and patients considering post surgical chemotherapy.


DEVELOPMENT STAGE: Modeled concept.

STATUS: Patent pending. Available for licensing or collaborative research.


Combination of Anti-cancer Medications and Markers for Improved Therapeutic Response, WIB-2764

Predicting Responsiveness of Breast Cancers to Polyamine-type Chemotherapy, WIB-2423

Genetic Profile Determines Prognosis and Response to Cancer Therapy, WIB-2709


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