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Predicting Risk of Significant Side Effects With OnPART

TON - March 2013, Vol 6, No 2 published on March 26, 2013 in Supportive Care
Alice Goodman

OnPART can predict 6 common side effects of dose-dense doxorubicin plus cyclophosphamide plus paclitaxel chemotherapy in patients with breast cancer with a high degree of accuracy, according to the results of a study presented at the CTRC-AACR San Antonio Breast Cancer Symposium. The 78 patients with breast cancer were part of a larger study of 384 patients that also included patients with colorectal, ovarian, and lung 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 regimens for colorectal cancer, and in patients receiving 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. OnPART is a personalized medicine product from Inform Genomics, Inc.

“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 of the West Clinic in Memphis, Tennessee. “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 Genomics 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 the 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 doxorubicin plus cyclophosphamide plus paclitaxel chemotherapy. Subjects provided a saliva sample using a DNA Genotek collection tube. SNP expression was determined using microarray technology. Patients received support 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 reported in 25% of patients who reported side effects, despite the fact that the patients received recommended antiemetic supportive care prior to treatment. SNP-based Bayesian networks were highly predictive for each side effect of interest. The accuracy 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.

Reference
Schwartzberg LS, Sonis ST, Walker MS, et al. Single nucleotide polymorphism (SNP) Bayesian networks (BNs) predict risk of chemotherapy-induced side effects in patients with breast cancer receiving dose dense (DD) doxorubicin/cyclophosphamide plus paclitaxel (AC+T). Presented at: 2012 CTRC-AACR San Antonio Breast Cancer Symposium; December 4-8, 2012; San Antonio, TX. Poster P1-15-12.

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Last modified: September 9, 2019