Genomics May Predict Cancer Treatment Side Effects

While much attention is focused on using genomic markers to target the effectiveness of treatments, simultaneous research is exploring use of genomic markers to identify patients at higher risk of therapy-induced side effects.

Two recent studies highlight the use of genomic markers to identify anthracycline-induced congestive heart failure (CHF) and guide radiotherapy dose to decrease complication risk.

Chemotherapy-Induced Heart Failure
A single nucleotide polymorphism (SNP) identifies patients at high risk for chemotherapy-induced CHF, according to a study published in the January issue of Clinical Cancer Research.

Anthracyclines are a widely used class of chemotherapy known to have "doselimiting side effects," including CHF. Previous research showed that patients that received an anthracycline were five times more likely to develop cardiac symptoms. While the total rate of CHF in patients receiving anthracyclines is low (estimated at approximately 2 percent), the ability to predict which patients might be at increased risk prior to exposure, would be valuable information to optimally counsel patients, clinicians say.

"Many patients still benefit from anthracyclines based on risk or underlying biology of the tumor, [but] the severity of the side effect necessitates a means to identify high-risk patients," write the authors led by Bryan Schneider, M.D., from Indiana University in Indianapolis.

The researchers conducted a genome-wide association study for biomarker discovery and validated findings in two additional cohorts. The discovery cohort included participants in a randomized phase III adjuvant breast cancer trial, while validation occurred in two independent phase III adjuvant breast cancer trials. In total, the three trials enrolled more than 12,500 patients.

The international group of researchers discovered that rs28714259 is associated with nearly a two-time increased risk of anthracycline-induced CHF.

Personalizing Radiotherapy Dose
Current one-size-fits-all radiotherapy dosing protocols can be optimized for effectiveness and toxicity using tumor-specific genomic data, according to a study published in the February issue of The Lancet Oncology.

Unlike progress in targeted chemotherapy, radiotherapy, the most commonly used oncological therapeutic agent, has not yet entered the realm of personalized medicine and its dosing is not adjusted based on tumor biology. Current clinical practice uses uniform protocols, despite the possibility that not every patient will derive similar benefit from radiotherapy.

The Moffitt Cancer Center researchers previously developed a geneexpression based radiosensitivity index (RSI) that predicts tumor sensitivity to radiation therapy based on the expression of 10 specific genes. In the present study, the researchers used the RSI to develop a genomics model called the genomic-adjusted radiation dose (GARD).

Initially, the researchers analyzed tumors from 8,271 adult patients enrolled in the Total Cancer Care (TCC) protocol using an Affymetrix assay. GARD was calculated for primary tumors from 20 disease sites. GARD was independently evaluated to test associations with clinical outcome in five separate clinical cohorts.

The researchers found a wide range of GARD values across the TCC cohort (range, 1.66 to 172.4) and within tumor type groups. Higher GARD values predicted a higher therapeutic effect from radiotherapy, the authors say, and independently predicted radiotherapy-specific outcome.

"We emphasize that GARD is not a predictive assay or biomarker for clinical outcome, but rather a model to adapt the prescribed radiation dose to match individual tumour radiosensitivity," write the authors led by Jacob Scott, M.D., from the Moffitt Cancer Center in Tampa, Fla. "GARD could provide a scientific framework to adjust radiotherapy doses that have already shown to be safe, both in terms of increasing tumor control (increasing dose to more resistant tumors) and decreasing complication risks (lowering the dose to more sensitive tumors).

Takeaway: Genomic information may provide valuable information to guide treatment decisions and personalize therapy by identifying which patients might be at higher risk for this serious treatment-related toxicity.