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Cortical Brain Age from Pre-treatment to Post-chemotherapy in Patients with Breast Cancer.

ABSTRACT: Chemotherapy-related cognitive impairment and associated brain changes may reflect accelerated brain aging; however, empirical evidence for this theory is limited. The purpose of this study was to measure brain aging in newly diagnosed patients with breast cancer treated with chemotherapy (n = 43) and compare its longitudinal change to that of controls (n = 50). Brain age indices, derived from cortical measures, were compared between women with breast cancer and matched healthy controls across 3 timepoints (time 1: pre-surgery, time 2: 1 month following chemotherapy completion, and time 3: 1-year post-chemotherapy). The breast cancer group showed a significant decrease in cortical thickness across the 3 timepoints (p < .001) and a trend towards significant increase in predicted brain age especially from pre-treatment (time 1) to post-chemotherapy (time 2) compared to controls (p = 0.08). Greater increase in predicted brain age was related to several clinical factors (HER-2 status, surgery type, and history of neoadjuvant chemotherapy) and greater decrease in cortical thickness was associated with greater decrease in performance on a verbal learning task from time 1 to time 3 (r = - 0.48, p < .01). This study demonstrated evidence of increased cortical brain aging in middle-aged patients with breast cancer following chemotherapy treatment that was associated with decreased verbal memory performance.

SUBMITTER: Henneghan A

PROVIDER: S-EPMC7089817 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo develop and validate a radiomics nomogram based on pre-treatment, early treatment ultrasound (US) radiomics features combined with clinical characteristics for early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodA total of 217 patients with histological results of breast cancer receiving four to eight cycles of NAC before surgery from January 2018 to December 2020 were enrolled. Patients from the study population were randomly separated into a training set (n = 152) and a validation set (n = 65) at a ratio of 7:3. A total of 788 radiomics features were extracted from each region of interest in the US image at pre-treatment baseline (radiomic signature, RS1), early treatment (after completion of two cycles of NAC, RS2) and delta radiomics (calculated between the pre-treatment and post-treatment features, Delta RS). The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The predictive nomogram was built based on the radiomics signature combined with clinicopathological risk factors. Discrimination, calibration, and prediction performance were further evaluated in the validation set.ResultsOf the 217 breast masses, 127 (58.5%) were responsive to NAC and 90 (41.5%) were non-responsive. Following feature selection, nine features in RS1, 11 features in RS2, and eight features in Delta RS remained. With multivariate analysis, the RS1, RS2, Delta RS, and Ki-67 expression were independently associated with breast NAC response. However, the performance of the Delta RS (AUC Delta RS = 0.743) was not higher than RS1 (AUC RS1 = 0.722, PDelta vs RS1 = 0.086) and RS2 (AUC RS2 = 0.811, PDelta vs RS2 = 0.173) with the Delong test. The nomogram incorporating RS1, RS2, and Ki-67 expression showed better predictive ability for NAC response with an area under the curve (AUC) of 0.866 in validation cohorts than either the single RS1 (AUC 0.725) or RS2 (AUC 0.793) or Ki-67 (AUC 0.643).ConclusionThe nomogram incorporating pre-treatment and early-treatment US radiomics features and Ki-67 expression showed good performance in terms of NAC response in breast cancer, thereby providing valuable information for individual treatment and timely adjustment of chemotherapy regimens.

Project description:BackgroundPatient-derived xenografts (PDXs) are increasingly used in cancer research as a tool to inform cancer biology and drug response. Most available breast cancer PDXs have been generated in the metastatic setting. However, in the setting of operable breast cancer, PDX models both sensitive and resistant to chemotherapy are needed for drug development and prospective data are lacking regarding the clinical and molecular characteristics associated with PDX take rate in this setting.MethodsThe Breast Cancer Genome Guided Therapy Study (BEAUTY) is a prospective neoadjuvant chemotherapy (NAC) trial of stage I-III breast cancer patients treated with neoadjuvant weekly taxane+/-trastuzumab followed by anthracycline-based chemotherapy. Using percutaneous tumor biopsies (PTB), we established and characterized PDXs from both primary (untreated) and residual (treated) tumors. Tumor take rate was defined as percent of patients with the development of at least one stably transplantable (passed at least for four generations) xenograft that was pathologically confirmed as breast cancer.ResultsBaseline PTB samples from 113 women were implanted with an overall take rate of 27.4% (31/113). By clinical subtype, the take rate was 51.3% (20/39) in triple negative (TN) breast cancer, 26.5% (9/34) in HER2+, 5.0% (2/40) in luminal B and 0% (0/3) in luminal A. The take rate for those with pCR did not differ from those with residual disease in TN (p = 0.999) and HER2+ (p = 0.2401) tumors. The xenografts from 28 of these 31 patients were such that at least one of the xenografts generated had the same molecular subtype as the patient. Among the 35 patients with residual tumor after NAC adequate for implantation, the take rate was 17.1%. PDX response to paclitaxel mirrored the patients' clinical response in all eight PDX tested.ConclusionsThe generation of PDX models both sensitive and resistant to standard NAC is feasible and these models exhibit similar biological and drug response characteristics as the patients' primary tumors. Taken together, these models may be useful for biomarker discovery and future drug development.

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Cortical Brain Age from Pre-treatment to Post-chemotherapy in Patients with Breast Cancer.

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