Impact of the Histologic Pattern of Residual Tumor After Neoadjuvant Chemotherapy on Recurrence and Survival in Stage I–III Breast Cancer

Laws, Alison; Pastorello, Ricardo; Dey, Tanujit; Grossmith, Samantha; King, Claire; McGrath, Monica; Schnitt, Stuart J.; Mittendorf, Elizabeth A.; King, Tari A.

doi:10.1245/s10434-022-12054-6

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…Secondly, the sample size was relatively small and data collection was carried out in a single institution. Thirdly, recent studies suggest that the distribution of residual disease, whether scattered or concerted, may impact patients' long-term survival [34,35]. Nevertheless, the distribution pattern of residual tumors was not considered in this study.…”

Section: Discussionmentioning

confidence: 91%

Impact of Neoadjuvant Chemotherapy (NAC) on Biomarker Expression in Breast Cancer

Lee,

Kim,

Lee

et al. 2024

Medicina

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Background and Objectives: This study aimed to explore biomarker change after NAC (neoadjuvant chemotherapy) and to investigate biomarker expression as a prognostic factor in patients with residual disease (RD) after NAC. Materials and Methods: We retrospectively evaluated 104 patients with invasive breast cancer, who underwent NAC and surgery at Pusan National University Hospital from 2015 to July 2022. The expression of the biomarker was assessed, and the overall survival (OS) and disease-free survival (DFS) were investigated. Results: After NAC, 24 patients (23.1%) out of 104 total patients had a pathological complete response (pCR). We found that changes in at least one biomarker were observed in 41 patients (51.2%), among 80 patients with RD. In patients with RD after NAC (n = 80), a subtype change was identified in 20 patients (25.0%). Any kind of change in the HER2 status was present 19 (23.7%) patients. The hormone receptor (HR)+/HER2+ subtype was significantly associated with better disease-free survival (DFS) (HR, 0.13; 95% CI, 0.02–0.99; p = 0.049). No change in p53 was associated with better DFS, and negative-to-positive change in p53 expression after NAC was correlated with worse DFS (p < 0.001). Negative-to-positive change in p53 was an independent, worse DFS factor in the multivariate analysis (HR,18.44; 95% CI, 1.86–182.97; p = 0.013). Conclusions: Biomarker change and subtype change after NAC were not infrequent, which can affect the further treatment strategy after surgery. The expression change of p53 might have a prognostic role. Overall, we suggest that the re-evaluation of biomarkers after NAC can provide a prognostic role and is needed for the best decision to be made on further treatment.

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Section: Discussionmentioning

confidence: 91%

Impact of Neoadjuvant Chemotherapy (NAC) on Biomarker Expression in Breast Cancer

Lee,

Kim,

Lee

et al. 2024

Medicina

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“…Machine learning models have also been used in BC prognosis prediction [24,25]. Together these data highlight the clinical utility of evaluating the treatment response to NAC, not only for prognostic information but also for guiding therapeutic management in the era of increasingly tailored treatment strategies [26]. In this setting, different adjuvant therapies, tailored based on the biologic subtype of BC, have been used to improve survival outcomes.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Different Patterns of Residual Disease on Long-Term Oncological Outcomes in Breast Cancer Patients Treated with Neo-Adjuvant Chemotherapy

Tinterri,

Fernandes,

Zambelli

et al. 2024

Cancers

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Backgrounds: The majority of breast cancer (BC) patients treated with neo-adjuvant chemotherapy (NAC) achieves a pathologic partial response with different patterns of residual disease. No clear correlation between these patterns and oncological results was described. Our aims were to define the predictive factors for different patterns of residual disease and compare the outcomes between the scattered versus the circumscribed pattern. Methods: We reviewed 219 postoperative surgical specimens. Patients were divided into two groups: scattered versus circumscribed. Disease-free survival (DFS), distant DFS (DDFS), and overall survival (OS) were analyzed. Results: The scattered and circumscribed patterns were assessed in 111 (50.7%) and 108 (49.3%) patients. Two independent predictive factors for the circumscribed pattern were identified: discontinuation of NAC cycles (p = 0.011), and tumor size post-NAC >18 mm (p = 0.022). No difference was observed in terms of DFS and DDFS. Patients with the scattered pattern exhibited a statistically significant better OS. Discontinuation of NAC cycles, tumor size >18 mm, triple-negative BC, and ypN+ were associated with increased recurrence and poorer survival. Conclusions: Discontinuation of NAC cycles and tumor size are independent factors associated with patterns of residual disease. The scattered pattern presents better survival. Understanding the relationship between NAC, the residual pattern, and differences in survival outcomes offers the potential to optimize the therapeutic approaches.

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“…Molecular tumor characterization has advanced targeted therapeutics; despite this, expression of target biomarkers in any given patient does not necessarily ensure a predictable or durable response to therapy. Further compoudning challenges to optimizing therapy include the inability, a process of escalating or de-escalating therapy when it will result in better or comparable results with similar or fewer side effects, include the inability of large-scale trials with finite enrollment criteria to adequately address individual tumor characteristics (morphology [ 2 , 3 ], vascularity [ 4 , 5 ], location [ 6 ], tumor microenvironment [ 7 ]) and deliver personalized oncologic treatment recommendations that comprehensively address these variations in individual tumor conditions resulting from tumor heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

et al. 2023

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Background Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient’s response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient’s response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform. Methods A single-blinded study was performed using a retrospective database from a single institution (9/2014–12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction). Results The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2− (35%), HR−/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10–7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% – 96.4%; Clopper–Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): − 1.99; 95% CI [− 3.96, − 0.02]; p = 0.043) and clinically assessed pCR (HR: − 1.76; 95% CI [− 3.75, 0.23]; p = 0.054). Conclusion We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

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Impact of the Histologic Pattern of Residual Tumor After Neoadjuvant Chemotherapy on Recurrence and Survival in Stage I–III Breast Cancer

Cited by 8 publications

References 22 publications

Impact of Neoadjuvant Chemotherapy (NAC) on Biomarker Expression in Breast Cancer

Impact of Neoadjuvant Chemotherapy (NAC) on Biomarker Expression in Breast Cancer

The Impact of Different Patterns of Residual Disease on Long-Term Oncological Outcomes in Breast Cancer Patients Treated with Neo-Adjuvant Chemotherapy

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

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