Subsurface processes can be simulated at multiple scales with variable degrees of fidelity. Some microscopic (pore‐scale) features of reactive transport cannot be properly resolved in macroscopic (Darcy‐scale) models. While microscopic descriptors might be closer to reality, they are computationally unfeasible when deployed on a macroscale. Hybrid algorithms combine the physical fidelity of a microscopic model with the computational efficiency of its macroscopic counterpart. We develop a hybrid model of dynamic reactive fronts in an open fracture, with a chemical reaction occurring in the zone of contact between two dissolved species. Away from the front, both fluid flow and solute transport are described by one‐dimensional models. In the front's proximity, two‐dimensional Stokes equations are used to model fluid flow, and solute transport is described with advection‐diffusion‐reaction equations. These two descriptors are coupled via an iterative procedure, which enforces the continuity of concentrations and mass fluxes across the interface between the two models. Our numerical experiments demonstrate that the hybrid model outperforms its microscopic and macroscopic counterparts in terms of computational time and representational accuracy, respectively.
Background: Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the first and second most common histologic subtypes of breast cancer. Both IDC and ILC present distinguishing clinicopathologic features that contribute to differences in response to treatment and long-term prognosis. BCI is a validated gene expression assay that provides the risk of overall (0-10y) and late (5-10y) distant recurrence (DR) and predicts the likelihood of benefit from extended endocrine therapy (EET). In this analysis, BCI results between groups of HR+ ILC and IDC breast cancer patients were compared. Methods: The BCI Clinical Database for Correlative Studies is an IRB-approved de-identified database containing >50 clinicopathologic and molecular variables from cases submitted for BCI testing in clinical practice (N=19,126). Molecular variables include BCI Prognostic score, HOXB13/IL17BR ratio (H/I), and molecular grade index (MGI). Clinicopathologic variables were abstracted from pathology reports when available. Chi-squared tests and Kruskal-Wallis tests were used to compare categorical and numeric factors, respectively, between IDC and ILC subgroups. Kaplan-Meier survival analysis and Cox proportional hazards regression were used to analyze BCI Predictive performance in the lobular patients from IDEAL study. Results: The current study included 3814 patients submitted for BCI testing during years 4-7 post-diagnosis with available histologic subtype data (80.5% IDC; 13.2% ILC; 3.0% mixed; 3.3% other). Among those with either ductal (n=3072) or lobular (n=504) carcinomas (71% node-negative and 29% node-positive), patients with ILC were older compared to IDC (>70 y: 17% vs 12%). Clinically, ILC was generally less aggressive than IDC (Grade 3: 7% vs 21%; lymphovascular invasion: 9% vs 20%; HER2+: 2% vs 13%; Ki67 % positive stained cells: 29% vs 46%; p< 0.001 for all comparisons), with the exception that ILC had larger tumors than IDC (T2/T3: 46% vs 24%) due to its unique histology. This was consistent with BCI Prognostic results showing fewer ILC patients at high risk for late DR compared to IDC (43% vs 55%, p< 0.001). BCI (H/I) showed a similar trend with fewer patients with High Likelihood to benefit from EET (39% vs 43%) although the difference was not statistically significant (p=0.169). The combination of BCI prognostic and predictive results revealed that more ILC patients were classified as Low Risk/Low Likelihood of benefit (43% vs 38%) and fewer were called High Risk/High Likelihood of benefit (25% vs 35%) (p< 0.001) compared to IDC. The IDEAL BCI translational study included 142 ILC patients with 9.3 years of median follow-up. Similar to the BCI Clinical Database results, ILC was associated with less aggressive disease than IDC (Grade 3: 18% vs 41%; HER2+: 11% vs 24%). 39% and 61% of ILC patients were classified as BCI (H/I)-High and -Low, respectively. Given the small sample size, BCI (H/I)-High showed a non-significant absolute benefit of 11.9% (HR=0.44, 95% CI 0.09-2.14; p=0.298) and BCI (H/I)-Low showed no benefit (HR=2.63, 95% CI 0.70-9.93; p=0.138). An analysis of the 3-way interaction among treatment, biomarker and histology showed a non-significant p-value of 0.28, suggesting BCI (H/I) provides similar predictive information in ILC as in the overall population. Conclusion: BCI identified a smaller proportion of patients with ILC at High Risk of late DR and High Likelihood of benefit from EET compared to IDC. Data from the IDEAL translational study showed that while fewer patients with ILC were identified as BCI (H/I)-High, they still derived similar absolute benefit compared to the overall cohort, while those classified as BCI (H/I)-Low derived no benefit from EET. Citation Format: Otto Metzger, Neeta Parimi, Natalia Siuliukina, Yi Zhang, Kai Treuner, Gerrit-Jan Liefers. Breast Cancer Index (BCI) identifies fewer patients with high risk of late recurrence and high likelihood of benefit from extended endocrine therapy with invasive lobular compared to invasive ductal carcinoma [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-03-13.
545 Background: The Breast Cancer Index (BCI) is a gene expression–based signature that stratifies patients based on the risk of overall (0-10y) and late (post-5y) distant recurrence (DR) and predicted the likelihood of benefit from extended endocrine therapy in MA.17, Trans-aTTom, IDEAL, and B-42 trials. The Clinical Treatment Score post-5 years (CTS5) is an algorithm incorporating four clinicopathologic variables (nodes, age, tumor size and grade), which has been shown to be prognostic for late DR. Previous analysis of CTS5 in IDEAL showed that CTS5 was not predictive of extended endocrine therapy benefit. The current analysis compared the predictive performance of BCI (H/I) and CTS5 for the benefit of extended endocrine therapy in the same subset of patients from the IDEAL study. Methods: 818 patients from the IDEAL trial who remained recurrence free 2.5y after randomization and had both BCI (H/I) and CTS5 results available were included in this study. The primary endpoint was recurrence-free interval (RFI). Absolute benefit was measured as the difference in 10-year risk of recurrences between the two treatment arms. The likelihood ratio test from Cox regression models was used to test for interaction. Results: 818 IDEAL patients (68% ≥50y at surgery; 48% T2; 47% G2; 73% N+) were included in the analysis. CTS5 stratified 167, 346 and 305 patients into Low-, Intermediate-, and High-risk groups. The analysis revealed that no group derived significant benefit with CTS5-Low showing 6.4% absolute benefit (HR = 0.61, 95% CI: 0.20-1.86, P = 0.377), CTS5-Intermediate showing 6.7% absolute benefit (HR = 0.55, 95% CI: 0.27-1.09, P = 0.080), and CTS5-High showing 2.1% absolute benefit (HR = 0.80, 95% CI: 0.42-1.51, P = 0.482). BCI (H/I) stratified 428 and 390 patients into BCI (H/I)-Low and BCI (H/I)-High groups. Only BCI (H/I)-High patients derived a significant absolute benefit of 10.5% (HR = 0.38, 95% CI: 0.18-0.79, P = 0.007), while BCI (H/I)-Low patients did not show any absolute benefit (HR = 0.94, 95% CI: 0.55-1.60, P = 0.817). The treatment by biomarker interaction was significant for BCI (H/I) (P = 0.045), but not for CTS5 (P = 0.731). When re-stratifying CTS5 categories by BCI (H/I) or vice versa, only BCI (H/I)-High patients showed consistent absolute benefit regardless of CTS5 category (19.4%, 8.1% and 8.8% in CTS5-Low, -Intermediate and -High, respectively). In contrast, CTS5-High patients did not show any benefit (-4.4%) in the BCI (H/I)-Low group. Conclusions: These results demonstrate that CTS5 does not provide predictive information to support extended endocrine therapy decision-making. Only BCI (H/I) was a predictive biomarker of benefit from extended endocrine therapy. This study further highlights the clinical utility of BCI as an endocrine response biomarker and emphasizes that prognostic information does not equate to predictive information in guiding duration of endocrine therapy. Clinical trial information: NTR3077; BOOG 2006-05; Eudra-CT 2006-003958-16.
Background: The Breast Cancer Index (BCI) is a validated gene expression assay that provides a quantitative individualized risk of late recurrence and predicts the likelihood of benefit from extended endocrine therapy (EET) in HR+ early-stage breast cancer. The objective of this analysis was to assess the impact of BCI on clinical decision-making regarding EET in the BCI Registry Study. Methods: The BCI Registry Study is an ongoing, prospective, large-scale study to investigate the long-term clinical outcome, decision impact, and medication adherence in HR+ early-stage breast cancer patients receiving BCI testing as part of routine clinical care. Per protocol, physicians and patients completed pre- and post-BCI test questionnaires to assess the following: physician decision-making regarding EET; physician confidence with decision-making; patient preferences for EET; patient concerns about the cost, side effects, drug safety and benefit of EET; and patient satisfaction regarding treatment recommendation. Pre- and post-BCI responses were compared using McNemar’s test and Wilcoxon signed rank test. Results: Pre- and post-BCI test physician and patient questionnaires were completed for 843 and 823 patients, respectively. For the patients included in this analysis, mean age at enrollment was 65y and 88.4% of patients were postmenopausal. 74.7% of tumors were T1, 53.4% were G2, 75.3% N0, and 13.8% HER2-positive. Following BCI testing, physicians changed treatment recommendations for EET in 40.1% (338/843) of patients (p< 0.0001). In cases in which physicians recommended EET prior to BCI testing, 44.7% (214/479) changed their recommendation not to extend endocrine therapy. Of these cases, 98.1% (210/214) were BCI Prognostic low risk and/or BCI (H/I) Predictive low likelihood of benefit from EET. In cases in which physicians did not recommend EET prior to BCI testing, 34.6% (124/358) changed their recommendation in favor of EET. In such cases, 87.1% (108/124) of cases were BCI Prognostic high risk and/or BCI (H/I) Predictive high likelihood of benefit from EET. Further, following BCI testing, 38.8% (327/843) of physicians felt more confident in their recommendation (p< 0.0001). The percentage of physicians having high confidence levels (confident or strongly confident) increased from 58.1% (490/843) before BCI testing to 80.5% (679/843) after BCI testing. The percentage of physicians having low confidence levels (not at all confident, not confident, or ambivalent) decreased from 39.1% (330/843) before BCI testing to 18.7% (158/843) after BCI testing. In addition, 40.5% (341/843) of patients felt more comfortable with their EET decision (p< 0.0001) following BCI testing. Notably, changes in patient preferences for EET correlated with BCI test results. In BCI (H/I)-Low patients, 46.9% (241/514) showed a decreased preference for EET (p< 0.0001) while in BCI (H/I)-High patients, 28.2% (87/309) showed an increased preference for EET (p< 0.0001). Compared with baseline, after BCI testing, significantly more patients were less concerned about cost (20.9%, p< 0.0001), drug safety (25.4%, p=0.0014), and the benefits of EET (29.3%, p=0.0002). No significant change in concern regarding side-effects was observed (p=0.1486). Conclusions: This first analysis in a large patient cohort of the BCI Registry confirms previous findings on the significant clinical decision-making impact of BCI for extending adjuvant endocrine therapy. Incorporating BCI into clinical practice resulted in changes in physician recommendations for EET in over 40% of cases, while at the same time increasing physician confidence in their recommendations. Knowledge of the BCI result also improved patient satisfaction and reduced patient concerns regarding cost, drug safety and benefit of EET. Citation Format: Tara B. Sanft, Jenna Wong, Brandon O’Neal, Natalia Siuliukina, Rachel C. Jankowitz, Mark Pegram, Jenny Fox, Yi Zhang, Kai Treuner, Joyce O’Shaughnessy. Prospective assessment of the decision-making impact of the Breast Cancer Index in the BCI Registry Study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-03-02.
e12513 Background: The Breast Cancer Index (BCI) is a gene expression-based biomarker that reports two results: BCI Predictive [BCI (H/I)], which predicts benefit from extended endocrine therapy (EET), and BCI Prognostic, which combines the Molecular Grade Index (MGI) with BCI (H/I) to provide individualized risk of both late (5-10 years) and overall (0-10 years) distant recurrence. The NCCN clinical practice guideline recommends use of BCI for prediction of benefit from EET in early stage, hormone receptor positive (HR+) breast cancer patients. Methods: The BCI Registry study was designed to prospectively evaluate the long-term clinical outcome, medication adherence and quality of life in HR+ early-stage breast cancer patients receiving BCI testing as part of routine clinical care. The BCI registry will enroll approximately 3,000 patients after completion of 4-7 years of primary adjuvant endocrine therapy. Patients will be followed for disease recurrence for at least 10 years from diagnosis. Patients extending endocrine therapy will be assessed annually for medication adherence. To evaluate the impact of BCI results on EET decision making, both the physician and patient will complete Decision Impact Questionnaires. Clinicopathological data are collected within the ClinCapture electronic data capture system. The BCI Registry Study is registered on ClinicalTrials.gov under NCT04875351. Results: The BCI Registry Study completed enrollment of the first 1,000 patients in January 2022. Clinical data were available for 942 patients (76.8% T1; 51.3% grade II; 75.4% N0). BCI test results were reported for 842 patients. 315 patients (37.4%) were classified as BCI (H/I)-High and 527 patients (62.6%) as BCI (H/I)-Low. Further, in node-negative (N0) patients, BCI classified 314 N0 patients (47.9%) with high risk for late distant recurrence and 342 patients (52.1%) with low risk. In node-positive (N1) patients, BCI classified 138 patients (74.2%) as high risk for late distant recurrence and 48 patients (25.8%) as low risk. The associated clinicopathological factors are shown in the table. Conclusions: The analysis of the first 1,000 BCI Registry patients confirms the expected distribution of clinicopathological factors. BCI by H/I status and prognostic risk groups are consistent with previous clinical validation studies. Clinical trial information: NCT04875351. [Table: see text]
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