Michael R. Crager

Background: In NSABP B-14, the quantitative levels of ESR1 mRNA, assessed using the standardized 21-gene assay and qRT-PCR platform predicted tamoxifen benefit (interaction p-value 9.1. Recurrence-free interval was a secondary endpoint. Results: The results of the assay were available for 587 patients. The median ER score was 10.2 (IQR 9.3-11.0). There were 131 patients (23.2% weighted) with ER ≤9.1 and 456 (76.8% weighted) with ER >9.1. No significant interaction of the effect of extended letrozole treatment was found for either the ER single gene score (interaction hazard ratio letrozole vs. placebo with an IQR change in ER score 1.10, 95% CI 0.66 - 1.82, p=.72) or the categories ER ≤9.1 (treatment HR=0.40, 95% CI 0.15-1.06) or ER >9.1 (treatment HR=0.70, 95% CI 0.43-1.12) (interaction p=.32). There was also no apparent prognostic effect of the ER single gene score for distant recurrence with placebo treatment after 5 years of endocrine therapy (p=.12). Results were similar in analyses of any recurrence, analyses adjusting for the proliferation axis from the 21-gene assay, and subgroup analyses by nodal and HER2-status. Conclusions: The B-42 study provided no evidence that ESR1 mRNA as measured by the ER single gene score can inform decisions regarding extended letrozole therapy after 5 years of adjuvant endocrine therapy. Confidence intervals were relatively wide but rule out a strong predictive effect of the ER single gene score in the expected direction. Support: U10CA180868, -180822, U24CA196067; Novartis; Exact Sciences Citation Format: Eleftherios Mamounas, Hanna Bandos, Priya Rastogi, Michael R Crager, Carolyn Mies, Peter C Lucas, Charles E Geyer, Jr, Louis Fehrenbacher, Mark L Graham, Stephen KL Chia, Adam M Brufsky, Janice M Walshe, Gamini S Soori, Shaker R Dakhil, Soonmyung Paik, Sandra M Swain, Frederick L Baehner, Steven Shak, Norman Wolmark. Assessment of estrogen receptor (ESR1) mRNA expression for prediction of extended aromatase inhibitor benefit in HR-positive breast cancer using NRG Oncology/NSABP B-42 [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD15-05.

PURPOSE: To assess the association between the Oncotype DX Genomic Prostate Score (GPS) result and long-term oncological outcomes following radical prostatectomy (RP). METHODS: We evaluated the association of the GPS result assayed from the index lesion from RP tissue with the risk of distant metastases (DM) and prostate cancer–specific mortality (PCSM) over the 20 years following RP in a stratified cohort sample of 428 patients from 2,641 treated between 1987 and 2004. Cox regression of cause-specific hazards was used to estimate the absolute risk of both end points, with death from other causes treated as a competing risk. A correction for regression to the mean (RM) was applied since the GPS test was developed using this cohort. Exploratory analysis using presurgical parameters and the GPS test as prognostic variables was performed to assess the additional value of the GPS test on 20-year risk of DM and PCSM. Model discrimination was measured using the area under the receiver operating characteristic curve. RESULTS: The GPS test appears to be independently associated with both 20-year risk of DM and PCSM with a low false discovery rate. Per 20-unit increase in GPS, multivariable analysis with RM correction estimated hazard ratios of 2.24 (95% CI, 1.49 to 3.53) and 2.30 (95% CI, 1.45 to 4.36) for DM and PCSM, respectively. Accuracy of models including clinical risk factors alone appeared to improve when including the GPS test in assessing risk of both end points. CONCLUSION: The results suggest that the GPS test provides information on the risk for the meaningful long-term outcomes of DM and PCSM.

PURPOSE: The 21-gene recurrence score (RS) is prognostic for distant recurrence (DR) and predictive for chemotherapy benefit in early breast cancer, whereas clinical-pathological factors are only prognostic. Integration of genomic and clinical features offers the potential to guide adjuvant chemotherapy use with greater precision.METHODS: We developed a new tool (RSClin) that integrates RS with tumor grade, tumor size, and age using a patient-specific meta-analysis including 10,004 women with hormone receptor–positive, human epidermal growth factor receptor 2–negative, and node-negative breast cancer who received endocrine therapy alone in the B-14 (n = 577) and TAILORx (n = 4,854) trials or plus chemotherapy in TAILORx (n = 4,573). Cox models for RSClin were compared with RS alone and clinical-pathological features alone using likelihood ratio tests. RSClin estimates of DR used a baseline risk with TAILORx event rates to reflect current medical practice. A patient-specific estimator of absolute chemotherapy benefit was computed using individualized relative chemotherapy effect from the randomized TAILORx and B-20 trials. External validation of risk estimation was performed by comparing RSClin estimated risk and observed risk in 1,098 women in the Clalit registry.RESULTS: RSClin provides more prognostic information (likelihood ratio χ²) for DR than RS or clinical-pathological factors alone (both P < .001, likelihood ratio test). In external validation, the RSClin risk estimate was prognostic for DR risk in the Clalit registry ( P < .001) and the estimated risk closely approximated the observed 10-year risk (Lin concordance 0.962). The absolute chemotherapy benefit estimate ranges from 0% to 15% as the RS ranges from 11 to 50 using RSClin in a 55-year-old woman with a 1.5-cm intermediate-grade tumor.CONCLUSION: The RSClin tool integrates clinical-pathological and genomic risk to guide adjuvant chemotherapy in node-negative breast cancer and provides more individualized information than clinical-pathological or genomic data alone.

The absolute standardized hazard ratio (ASHR) is a scale-invariant scalar measure of the strength of association of a vector of covariates with the risk of an event. It is derived from proportional hazards regression. The ASHR is useful for making comparisons among different sets of covariates. Extensions of the ASHR concept and practical considerations regarding its computation are discussed. These include a new method to conduct preliminary checks for collinearity among covariates, a partial ASHR to evaluate the association with event risk of some of the covariates conditioning on others, and the ASHR for interactions. To put the ASHR in context, its relationship to measures of explained variation and other measures of separation of risk is discussed. A new measure of the contribution of each covariate to the risk score variance is proposed. This measure, which is derived from the ASHR calculations, is interpretable as variable importance within the context of the multivariable model.

The NSABP B-20 prospective-retrospective study of the 21-gene Oncotype DX Breast Cancer Recurrence Score® test predicted benefit from addition of chemotherapy to tamoxifen in node-negative, estrogen-receptor positive breast cancer when recurrence score (RS) was ≥31. HER2 is a component of the RS algorithm with a positive coefficient and contributes to higher RS values. Accrual to B-20 occurred prior to routine testing for HER2, so questions have arisen regarding assay performance if HER2-positive patients were identified and excluded. We report an exploratory reanalysis of the B-20, 21-gene study following exclusion of such patients. Patients were considered HER2 positive if quantitative RT-PCR for HER2 was ≥11.5 units, and excluded from re-analyses performed using the original cutoffs: P = 0.06), but substantial benefit from chemotherapy remained in the RS ≥ 31 cohort (HR = 0.18; 95% CI: 0.07–0.47) and the RS > 25 cohort (HR = 0.28; 95% CI: 0.12–0.64). No benefit from chemotherapy was evident in the other RS groups. Following exclusion of HER2-positive patients based on RT-PCR expression, substantial benefit of chemotherapy remained for RS ≥ 31 as originally employed, and with RS > 25 employed in TAILORx.

528 Background: Better tools are needed to estimate the risk of local recurrence (LR; DCIS or invasive) after breast-conserving surgery (BCS) for DCIS to inform treatment decisions. The DCIS Score (DS) was validated as a predictor of LR in E5194 and Ontario DCIS Cohort (ODC) after BCS without radiation (Solin,2013; Rakovitch,2015). We performed a meta-analysis (MA) combining data from E5194 and ODC with additional follow-up from E5194 adjusting for pertinent clinico-pathologic factors to provide refined prediction estimates of LR risk after BCS alone. Methods: The MA used data from E5194 and ODC. Patients with positive margins and multifocality were excluded. Identical Cox regression models were fit including age at diagnosis ( < 50, ≥50 yr), tumor size (1cm, > 1cm), DCIS Score and year of surgery (before vs after 2000). Grade was not significant. MA was used to calculate precision-weighted estimates of 10 year LR risk by DS. Results: Combined cohort includes 773 pts (tamoxifen used in 20% E5194, 17% of ODC > 65 yr). The DS and the clinico-pathologic variables age, tumor size and year provided independent prognostic information on 10 yr LR risk (p≤.009). Hazard ratios from E5194 and ODC cohorts were similar for tumor size ≤1 vs. > 1cm (1.45, 1.47), age ≥50 vs. < 50 yr (0.61, 0.84) and surgery year after 2000 (0.67, 0.49). 10 yr LR risks by combinations of age, tumor size, and DS are detailed in Table. For patients ≥50 yr with tumors ≤1cm and low risk DS, the 10 yr LR risks range from 5.3-10.0%. A high risk DS is associated with a higher 10 yr predicted risk of LR in all subsets. 10 yr risk of contralateral BC was 5.4%. Conclusions: This MA provides refined estimates of 10 yr LR risk after BCS alone for DCIS. Adding clinico-pathologic factors to the DCIS Score provides enhanced prognostic LR risk estimates to guide individualized treatment decision-making.

In 2008, Efron showed that biological features in a high-dimensional study can be divided into classes and a separate false discovery rate (FDR) analysis can be conducted in each class using information from the entire set of features to assess the FDR within each class. We apply this separate class approach to true discovery rate degree of association (TDRDA) set analysis, which is used in clinical-genomic studies to identify sets of biomarkers having strong association with clinical outcome or state while controlling the FDR. Careful choice of classes based on prior information can increase the identification power of the separate class analysis relative to the overall analysis.