All of us who’ve had breast cancer live with the fear of a recurrence. That is why I was so pleased to meet and interview Dr. Ryan Van Laar, founder of ChipDX (www.ChipDX.com) a company that developed a Breast Cancer Module that can assess a patient’s risk of experiencing a recurrence by analyzing a sample of her/his biopsy. This information can help determine the need for and effectiveness of treatments such as chemotherapy and hormone therapy.
In our interview, Dr. Van Laar, described his work, “ChipDX is developing a web-based analysis system that it hopes will one day allow doctors to analyze a breast cancer patients’ genome and use this data to make personalized predictions of future events, such as resistance to a specific treatment or development of distant metastases within 5-10 years. The online system is based around the universally-available Affymetrix GeneChip platform, which can be used to create a molecular ‘fingerprint’ of a tumor. This information can be uploaded to www.ChipDX.com, currently available for non-clinical testing and evaluation, and analyzed using a variety of quality-control and diagnostic modules, designed to address specific unmet medical needs.
The ChipDX Breast Cancer Module contains a novel 200-gene signature, that, when overlaid onto the genomic profile of an individual patient, generates a prediction of high or low risk for disease recurrence. By retrospectively analyzing the genomic profiles of over 1000 patients who were followed for up to 10 years following their diagnosis, ChipDX have shown that individuals classified as ‘low risk’ have >90% chance of remaining disease free. Patients classified as high risk have a ~30-40% chance of recurrence. This highly personalized diagnostic prediction may be used by doctors to plan a more intensive treatment regimen for some individuals, with the long term goal of preventing breast cancer recurrences.
As an example of how this new technology may one-day assist doctors in stratifying patients into high or low risk groups, with greater accuracy achieved with currently used methods, the following two cases are presented.
- Patient A: diagnosed with a 15mm estrogen-receptor positive tumor at age 60. The tumor was Grade II (moderately differentiated) and had spread to the lymph nodes.
- Patient B: diagnosed with a 20mm estrogen-receptor positive tumor at age 59. Tumor was also grade 2 however it had not spread to the lymph nodes.
Based on the lymph-node invasion observed in Patient A, the clinical picture would suggest this individual may be at higher risk of relapse than Patient B, as the tumor grade, ER status and patient age’s were similar.
Despite this, the 200-gene signature developed by ChipDX classified Patient A as ‘Low Risk’ of recurrence and Patient B as ‘High Risk’, predictions which turned out to predict the true outcome of these patients.
Patient A remained free of breast cancer for at least 16 years, while Patient B developed distant metastases only 3 years after the initial diagnosis. Neither patient was treated with chemotherapy following their diagnosis; however the ChipDX analysis and subsequent outcome observed suggest that Patient B may have benefited from this or another form of adjuvant therapy.
|Patient||Age at diagnosis||Tumor grade||ER status||Lymph node involvement||Tumor size||ChipDX 200 gene prediction||Outcome|
|A||60 years||2||Pos.||Yes||15mm||Low Risk of recurrence||Patient free of distant metastases for 16 years|
|B||59 years||2||Pos.||No||20mm||High Risk of recurrence||Patient diagnosed with distant metastases after 3 years.|
A manuscript describing the development of the 200-gene signature and retrospective application to genomic data from over 1,000 breast cancer patients is currently in the review process with a leading medical journal and ChipDX is exploring ways to bring this and other multi-gene diagnostic tests to the market as soon as possible.”
Recently, Dr. Van Ryan’s paper on, “Design and Multi-series Validation of a Web-based Gene Expression Assay for Predicting Breast Cancer Recurrence and Patient Survival“ was accepted for publication in The Journal of Molecular Diagnostics and can be read on their website at (http://jmd.amjpathol.org.)