February 2013, Vol 2, No 1
Predicting Risk of Significant Side Effects Made Possible by OnPARTUncategorized
OnPART can predict 6 common side effects of dose-dense doxorubicin plus cyclophosphamide and paclitaxel chemotherapy (ACT) with a high degree of accuracy in patients with breast cancer, according to a study presented at a poster session at the CTRC-AACR San Antonio Breast Cancer Symposium. The 78 breast cancer patients were part of a larger study with a total of 384 patients, including patients with colorectal and ovarian cancer. In the larger study, OnPART was able to predict the same 6 side effects with greater than 92% accuracy in patients receiving dose-dense 5-fluorouracil and oxaliplatin for colorectal cancer and carboplatin plus paclitaxel-based regimens for lung and ovarian cancer.
OnPART utilizes Bayesian networks to identify single nucleotide polymorphisms (SNPs) from the DNA of patients’ saliva samples that identify whether the patients are at risk for the following 6 common side effects of dose-dense chemotherapy: oral mucositis, nausea and vomiting, diarrhea, fatigue, cognitive dysfunction, and peripheral neuropathy.
“We can now identify patients at risk for 6 common side effects before they ever receive chemotherapy,” said lead author Lee Schwartzberg, MD, senior partner and medical director, The West Clinic in Memphis, TN. “This allows us to customize our chemotherapy regimens and side effect control interventions in a
patient-centered care paradigm. These side effects can impair function, create inefficiencies in medical practice, and are costly to patients and payers. We look forward to working with Inform Genetics to help bring this novel product to the market as quickly as possible.”
The rationale for this study was based on the hypothesis that the genetic impact on risk of side effects depends on “teams” of genes working synergistically, Schwartzberg explained.
The study focused on 78 patients with breast cancer treated with at least 3 cycles of dose-dense ACT. Using a DNA Genotek collection tube, subjects provided a saliva sample from which DNA was extracted. SNP expression was determined using microarray technology. Patients received supportive care with each chemotherapy cycle. The Patient Care Monitor, a validated patient-reported symptom assessment tool, was used to measure the frequency and severity of the 6 side effects. Those rated ?4 were considered to be significant. Bayesian methodological programming developed predictive SNP networks for each of the 6 side effects.
Based on the literature, the incidence of moderate-to-severe toxicity for all 6 side effects was higher than expected. Significant fatigue, oral mucositis, and nausea and vomiting were found in 25% of all patients who reported side effects, despite receiving recommended antiemetic supportive care prior to treatment. SNP-based Bayesian networks were highly predictive for each side effect of interest. Accuracy of prediction ranged from 92% for predicting nausea and vomiting to 100% for predicting cognitive dysfunction and peripheral neuropathy.
The study showed that despite current supportive care options, the risk of side effects from common chemotherapy is significant. Previously, no method existed to predict which patients are at risk for these side effects from widely used chemotherapy regimens. The ability to identify prospectively the risk of side effects can lead to modifications in treatment regimens and aggressiveness of care.
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