Dataset Information

High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer.

ABSTRACT: In the neoadjuvant setting, changes in the proliferation marker Ki67 are associated with primary endocrine treatment efficacy, but its value as a predictor of response to chemotherapy is still controversial.We analyzed 262 patients with centralized basal Ki67 immunohistochemical evaluation derived from 4 GEICAM (Spanish Breast Cancer Group) clinical trials of neoadjuvant chemotherapy for breast cancer. The objective was to identify the optimal threshold for Ki67 using the receiver-operating characteristic curve method to maximize its predictive value for chemotherapy benefit. We also evaluated the predictive role of the defined Ki67 cutoffs for molecular subtypes defined by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2).A basal Ki67 cutpoint of 50% predicted pathological complete response (pCR). Patients with Ki67 >50% achieved a pCR rate of 40% (36 of 91) versus a pCR rate of 19% in patients with Ki67 ? 50% (33 of 171) (p = .0004). Ki67 predictive value was especially relevant in ER-HER2- and ER-HER2+ patients (pCR rates of 42% and 64%, respectively, in patients with Ki67 >50% versus 15% and 45%, respectively, in patients with Ki67 ? 50%; p = .0337 and .3238, respectively). Both multivariate analyses confirmed the independent predictive value of the Ki67 cutpoint of 50%.Basal Ki67 proliferation index >50% should be considered an independent predictive factor for pCR reached after neoadjuvant chemotherapy, suggesting that cell proliferation is a phenomenon closely related to chemosensitivity. These findings could help to identify a group of patients with a potentially favorable long-term prognosis.The use of basal Ki67 status as a predictive factor of chemotherapy benefit could facilitate the identification of a patient subpopulation with high probability of achieving pathological complete response when treated with primary chemotherapy, and thus with a potentially favorable long-term prognosis.

SUBMITTER: Alba E

PROVIDER: S-EPMC4746087 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer.

Alba Emilio E Lluch Ana A Ribelles Nuria N Anton-Torres Antonio A Sanchez-Rovira Pedro P Albanell Joan J Calvo Lourdes L García-Asenjo Jose Antonio Lopez JA Palacios Jose J Chacon Jose Ignacio JI Ruiz Amparo A De la Haba-Rodriguez Juan J Segui-Palmer Miguel A MA Cirauqui Beatriz B Margeli Mireia M Plazaola Arrate A Barnadas Agusti A Casas Maribel M Caballero Rosalia R Carrasco Eva E Rojo Federico F

The oncologist 20160119 2

<h4>Background</h4>In the neoadjuvant setting, changes in the proliferation marker Ki67 are associated with primary endocrine treatment efficacy, but its value as a predictor of response to chemotherapy is still controversial.<h4>Patients and methods</h4>We analyzed 262 patients with centralized basal Ki67 immunohistochemical evaluation derived from 4 GEICAM (Spanish Breast Cancer Group) clinical trials of neoadjuvant chemotherapy for breast cancer. The objective was to identify the optimal thre ...[more]

PMID: 26786263

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Project description:BackgroundNeoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor's disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease.MethodsWe applied our previously developed E2F4 genomic signature to predict neoadjuvant chemotherapy response in breast cancer. E2F4 individual regulatory activity scores were calculated for 1129 patient samples across 5 independent breast cancer neoadjuvant chemotherapy datasets. Accuracy of the E2F4 signature in predicting neoadjuvant chemotherapy response was compared to that of the Oncotype DX and MammaPrint predictive signatures.ResultsIn all datasets, E2F4 activity level was an accurate predictor of neoadjuvant chemotherapy response, with high E2F4 scores predictive of achieving pathologic complete response and low scores predictive of residual disease. These results remained significant even after stratifying patients by estrogen receptor (ER) status, tumor stage, and breast cancer molecular subtypes. Compared to the Oncotype DX and MammaPrint signatures, our E2F4 signature achieved similar performance in predicting neoadjuvant chemotherapy response, though all signatures performed better in ER+ tumors compared to ER- ones. The accuracy of our signature was reproducible across datasets and was maintained when refined from a 199-gene signature down to a clinic-friendly 33-gene panel.ConclusionOverall, we show that our E2F4 signature is accurate in predicting patient response to neoadjuvant chemotherapy. As this signature is more refined and comparable in performance to other clinically available gene expression assays in the prediction of neoadjuvant chemotherapy response, it should be considered when evaluating potential treatment options.

Project description:BackgroundBreast cancer with pathological non-complete response (non-pCR) after neoadjuvant chemotherapy (NAC) has a worse prognosis. Despite Neo-Bioscore has been validated as an independent prognostic model for breast cancer submitted to NAC, non-pCR carcinoma was not assessed in this setting.MethodsThis is a retrospective trial that included women with localized breast cancer who underwent NAC and had non-pCR carcinoma in surgical specimen between 01/01/2013 to 12/31/2015 with a three-year follow-up. Survival analysis was performed by Kaplan-Meier estimator and hazard ratio (HR) set by log-rank test for the primary and secondary endpoints, respectively Disease-Free Survival (DFS) and Overall Survival (OS). According to Neo-Bioscore, the proposed prognostic model named Clustered Neo-Bioscore was classified into low (0-3), low-intermediate (4-5), high-intermediate (6) and high (7) risk. The prognostic accuracy for recurrence risk was assessed by time-dependent receiver operating characteristic (time-ROC) methodology. Multivariate Cox regression assessed the menopausal status, histological grade, Ki-67, estrogen receptor, HER2, tumor subtype, pathological and clinical stages. Confidence interval at 95% (CI95%) and statistical significance at set 2-sided p-value less than 0.05 were adopted.ResultsAmong the 310 women enrolled, 267 patients (86.2%) had non-pCR carcinoma presenting size T3/T4 (63.3%), node-positive axilla (74.9%), stage III (62.9%), Ki-67???20% (71.9%) and non-luminal A (78.3%). Non-pCR carcinoma presented worse DFS-3y (HR?=?3.88, CI95%?=?1.18-11.95) but not OS-3y (HR?=?2.73, CI95%?=?0.66-11.40). Clustered Neo-Bioscore discerned the recurrence risk for non-pCR carcinoma: low (DFS-3y?=?0.86; baseline), low-intermediate (DFS-3y?=?0.70; HR?=?2.61), high-intermediate (DFS-3y?=?0.13, HR?=?14.05), and high (DFS-3y?=?not achieved; HR?=?22.19). The prognostic accuracy was similar between Clustered Neo-Bioscore and Neo-Bioscore (0.76 vs 0.78, p?>?0.05). Triple-negative subtype (HR?=?3.6, CI95%?=?1.19-10.92) and pathological stages II (HR?=?5.35, CI95%?=?1.19-24.01) and III (HR?=?6.56, CI95%?=?1.29-33.32) were prognoses for low-intermediate risk, whereas pathological stage III (HR?=?13.0, CI95%?=?1.60-106.10) was prognosis for low risk.ConclusionsClustered Neo-Bioscore represents a novel prognostic model of non-pCR carcinoma undergoing NAC with a more simplified and appropriate score pattern in the assessment of prognostic factors.

Project description:BackgroundAccurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer.MethodsFrom July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively).FindingsA total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts.InterpretationOur study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer.FundingThis study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

Dataset Information

High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer.

Publications

High Proliferation Predicts Pathological Complete Response to Neoadjuvant Chemotherapy in Early Breast Cancer.

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