Different types of cancers vary in response to treatment. Consequently, not all patients will benefit from the same treatment. In older therapy approaches, using standard chemotherapy, treatment plans were attempted without customization and were tried in sequence until the patient responded. This approach may have attempted one or several therapies before identifying the best treatment. Not only would these repeated rounds of treatment expose the patient to ineffective treatment and severe side effects, but the cost often became very burdensome for both the patient and the healthcare system.
With the predictive biomarkers available today, selecting a more specific therapy with the highest likelihood of effectively treating each patient is possible. This directed approach eliminates much of the "trial and error" with its associated and unnecessary side effects and costs.
But how genuinely predictive are these diagnostic results? Research has shown that patients with breast cancers containing as few as 1–10% ER-positive staining cells will show improvement with estrogen-directed hormone therapy, such as tamoxifen. Thus, it is essential that predictive testing be highly accurate in identifying those patients who will most benefit from these specific treatments.
