Use of prognostic and predictive factors
James Fleck, MD, PhD: Anticancerweb 05 (06), 2021
Adjuvant treatment of patients with invasive breast cancer is aimed at a presumed residual microscopic disease, which is directly related to tumor aggressiveness. Considering that clinical scanning methods cannot routinely identify micrometastases, adjuvant treatment has a probabilistic reasoning and the physician recommendation must rely on prognostic and predictive factors. Effective adjuvant chemotherapy is usually associated with an approximately 30% reduction in the risk of breast cancer recurrence. However, in some estrogen receptor-positive (ER+) and node-negative (No) invasive breast cancer patients, the absolute adjuvant chemotherapy gain may be less than 5%. Internationally recognized clinical trials have been used to describe clinical and pathological parameters useful in constructing prognostic and predictive indices for invasive breast cancer, especially in the ER + / No subgroup.
Clinical prognostic factors are based on tumor size (T) and histological grade (G). By definition, there are two main categories: low clinical risk (G1 + T ≤ 3 cm, G2 + T ≤ 2 cm and G3 + T ≤ 1 cm) and high clinical risk (G1 + T > 3 cm, G2 + T > 2 cm and G3 + T> 1 cm). Unfortunately, decision-making based solely on clinical risk is not enough. Breast cancer is a very heterogeneous disease expressed by a wide range of tumor-related genes. The Oncotype-21 DX Breast Recurrence Score (BRS) is a quantitative assay designed to guide adjuvant treatment decisions in patients with ER-positive, HER2-negative, No/N1 breast cancer. Currently, in medical practice, BRS has been associated with the clinical risk categories previously described, supporting the development of therapeutic algorithms. The Oncotype-21 DX Breast Recurrence Score (BRS) ranges from 0 to 100 indicating a progressive increase in tumor aggressiveness, risk of distant recurrence and need for adjuvant chemotherapy. High risk was defined as BRS ≥ 31 based on the NSABP and SWOG 8814 trial cohorts and BRS ≥ 26 in the TAYLOR x and WSG Plan B trial cohorts.
ER+ = Estrogen Receptor positive, Tam = Tamoxifen, IBCP = Invasive Breast Cancer Patients, BRS = Breast Recurrence Score, RDR = Risk of Distant Recurrence, DRFI = Distant Relapse-Free Interval, OS = Overall Survival, CT = Chemotherapy, Post-M = Post-menopausal, N+ = Lymph node positive, No = Lymph node negative, pN1mi = Sentinel Lymph node metastases > 0.2 mm and ≤ 2 mm, LR = Low-risk, IR = Intermediate Risk, HR = High Risk, BCSM = Breast Cancer Specific Mortality, DR = Distant Recurrence, HT = Hormone Therapy, DFS = Disease-Free Survival
Half of the invasive breast cancer diagnosed in USA are ER + / No. A low risk BRS (0 - 10), is associated to a 2% distant recurrence rate, which makes it very unlikely to be affected by adjuvant chemotherapy. TAYLOR x trial had also shown that ER+ / No breast cancer patients classified as intermediate-risk BRS (11 – 25) do not benefit from adjuvant chemotherapy, except for women 50-years of age or younger. Intermediate-risk younger patients (≤ 50) expressing high clinical risk are estimated to have a 9-year distant recurrence rate higher than 10%. Exploratory analysis revealed a significant interaction between adjuvant chemotherapy, age (≤ 50) / menopausal status and BRS, indicating an adjuvant chemotherapy benefit in younger (≤ 50) breast cancer patients with BRS in the range 11 – 25. The reduction in distant recurrence rate may be attributed to both cytotoxic and antiestrogenic effect of adjuvant chemotherapy. Recently, a metanalysis including more than 10,000 women with ER+, HER2-, No invasive breast cancer using data from NSABP B-14, NSABP B-20, TAILOR x and CLALIT Registry cohorts allowed the development a new tool that integrates clinical pathological factors (tumor grade + tumor size + age) with Oncotype-21 DX genomic risk. The new tool, called RSClin, is predictive of adjuvant chemotherapy benefit and provide more individualized information than clinical-pathological or genomic data alone. In the near future, the progressive use of artificial intelligence and technological improvement of deep learning will help in a more precise definition of prognostic and predictive factors (clinical + genomic), leading to a fully customized treatment for invasive breast cancer and very close to the ideal therapeutic index.
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9. Photo by ThisisEngineering RAEng on Unsplash (modified)