Breast cancer spatial heterogeneity in near-infrared spectra and the prediction of neoadjuvant chemotherapy response.
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ABSTRACT:
SUBMITTER: Santoro Y
PROVIDER: S-EPMC3203125 | biostudies-other | 2011 Sep
REPOSITORIES: biostudies-other
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Project description:PurposeTo assess initial breast tumor hemoglobin (Hb) content before the initiation of neoadjuvant chemotherapy, monitor the Hb changes at the end of each treatment cycle, and correlate these findings with tumor pathologic response.Materials and methodsThe HIPAA-compliant study protocol was approved by the institutional review boards of both institutions. Written informed consent was obtained from all patients. Patients who were eligible for neoadjuvant chemotherapy were recruited between December 2007 and May 2011, and their tumor Hb content was assessed by using a near-infrared imager coupled with an ultrasonography (US) system. Thirty-two women (mean age, 48 years; range, 32-82 years) were imaged before treatment, at the end of every treatment cycle, and before definitive surgery. The patients were graded in terms of their final pathologic response on the basis of the Miller-Payne system as nonresponders and partial responders (grades 1-3) and near-complete and complete responders (grades 4 and 5). Tumor vascularity was assessed from total Hb (tHb), oxygenated Hb (oxyHb), and deoxygenated Hb (deoxyHb) concentrations. Tumor vascularity changes during treatment were assessed from percentage tHb normalized to the pretreatment level. A two-sample two-sided t test was used to calculate the P value and to evaluate statistical significance between groups. Bonferroni-Holm correction was applied to obtain the corrected P value for multiple comparisons.ResultsThere were 20 Miller-Payne grade 1-3 tumors and 14 grade 4 or 5 tumors. Mean maximum pretreatment tHb, oxyHb, and deoxyHb levels were significantly higher in grade 4 and 5 tumors than in grade 1-3 tumors (P = .005, P = .008, and P = .017, respectively). The mean percentage tHb changes were significantly higher in grade 4 or 5 tumors than in grade 1-3 tumors at the end of treatment cycles 1-3 (P = .009 and corrected P = .009, P = .002 and corrected P = .004, and P < .001 and corrected P < .001, respectively).DiscussionThese findings indicate that initial tumor Hb content is a strong predictor of final pathologic response. Additionally, the tHb changes during early treatment cycles can further predict final pathologic response.
| S-EPMC3558877 | biostudies-literature
Project description:The purpose of this study is to develop a prediction model utilizing tumor hemoglobin parameters measured by ultrasound-guided near-infrared optical tomography (US-NIR) in conjunction with standard pathologic tumor characteristics to predict pathologic response before neoadjuvant chemotherapy (NAC) is given.Thirty-four patients' data were retrospectively analyzed using a multiple logistic regression model to predict response. These patients were split into 30 groups of training (24 tumors) and testing (12 tumors) for cross validation. Tumor vascularity was assessed using US-NIR measurements of total hemoglobin (tHb), oxygenated (oxyHb) and deoxygenated hemoglobin (deoxyHb) concentrations acquired before treatment. Tumor pathologic variables of tumor type, Nottingham score, mitotic index, the estrogen and progesterone receptors and human epidermal growth factor receptor 2 acquired before NAC in biopsy specimens were also used in the prediction model. The patients' pathologic response was graded based on the Miller-Payne system. The overall performance of the prediction models was evaluated using receiver operating characteristic (ROC) curves. The quantitative measures were sensitivity, specificity, positive and negative predictive values (PPV and NPV) and the area under the ROC curve (AUC).Utilizing tumor pathologic variables alone, average sensitivity of 56.8%, average specificity of 88.9%, average PPV of 84.8%, average NPV of 70.9% and average AUC of 84.0% were obtained from the testing data. Among the hemoglobin predictors with and without tumor pathological variables, the best predictor was tHb combined with tumor pathological variables, followed by oxyHb with pathological variables. When tHb was included with tumor pathological variables as an additional predictor, the corresponding measures improved to 79%, 94%, 90%, 86% and 92.4%, respectively. When oxyHb was included with tumor variables as an additional predictor, these measures improved to 77%, 85%, 83%, 83% and 90.6%, respectively. The addition of tHb or oxyHb significantly improved the prediction sensitivity, NPV and AUC compared with using tumor pathological variables alone.These initial findings indicate that combining widely used tumor pathologic variables with hemoglobin parameters determined by US-NIR may provide a powerful tool for predicting patient pathologic response to NAC before the start of treatment.ClincalTrials.gov ID: NCT00908609 (registered 22 May 2009).
| S-EPMC4303135 | biostudies-literature
Project description:We investigated whether we could identify gene expression profiles in initial core biopsies of breast cancer samples that would permit to a) predict a clinically meaningful response to Epi/Doc in terms of tumor size reduction, b) predict a profound reduction in intratumoral Ki67 protein expression, and c) predict an in vitro response to Epi/Doc in the ATP-TCA. 22 Samples from breast cancer patients with corresponding treatment response indicators
2012-10-26 | E-GEOD-29561 | biostudies-arrayexpress
Project description:We investigated whether we could identify gene expression profiles in initial core biopsies of breast cancer samples that would permit to a) predict a clinically meaningful response to Epi/Doc in terms of tumor size reduction, b) predict a profound reduction in intratumoral Ki67 protein expression, and c) predict an in vitro response to Epi/Doc in the ATP-TCA.
2012-10-26 | GSE29561 | GEO
Project description:We investigated whether we could identify gene expression profiles in initial core biopsies of breast cancer samples that would permit to a) predict a clinically meaningful response to Epi/Doc in terms of tumor size reduction, b) predict a profound reduction in intratumoral Ki67 protein expression, and c) predict an in vitro response to Epi/Doc in the ATP-TCA. 22 Samples from breast cancer patients with corresponding treatment response indicators
| S-ECPF-GEOD-29561 | biostudies-other
Project description:We analyzed DCE-MR images from 132 women with locally advanced breast cancer from the I-SPY1 trial to evaluate changes of intra-tumor heterogeneity for augmenting early prediction of pathologic complete response (pCR) and recurrence-free survival (RFS) after neoadjuvant chemotherapy (NAC). Utilizing image registration, voxel-wise changes including tumor deformations and changes in DCE-MRI kinetic features were computed to characterize heterogeneous changes within the tumor. Using five-fold cross-validation, logistic regression and Cox regression were performed to model pCR and RFS, respectively. The extracted imaging features were evaluated in augmenting established predictors, including functional tumor volume (FTV) and histopathologic and demographic factors, using the area under the curve (AUC) and the C-statistic as performance measures. The extracted voxel-wise features were also compared to analogous conventional aggregated features to evaluate the potential advantage of voxel-wise analysis. Voxel-wise features improved prediction of pCR (AUC?=?0.78 (±0.03) vs 0.71 (±0.04), p?<?0.05 and RFS (C-statistic?=?0.76 (?±?0.05), vs 0.63 (?±?0.01)), p?<?0.05, while models based on analogous aggregate imaging features did not show appreciable performance changes (p?>?0.05). Furthermore, all selected voxel-wise features demonstrated significant association with outcome (p?<?0.05). Thus, precise measures of voxel-wise changes in tumor heterogeneity extracted from registered DCE-MRI scans can improve early prediction of neoadjuvant treatment outcomes in locally advanced breast cancer.
| S-EPMC6702160 | biostudies-literature
Project description:Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.
| S-EPMC10825718 | biostudies-literature
Project description:Background: Neoadjuvant chemotherapy is increasingly being used to preoperatively shrink breast tumours prior to surgery. This approach also provides the opportunity to study the molecular changes associated with treatment and evaluate whether on-treatment sequential samples can improve response and outcome predictions over diagnostic or excision samples alone. Methods: A total of 95 samples from a cohort of 50 neoadjuvant chemotherapy-treated primary breast cancer patients (aged 29-76, 48% ER+, 20% HER2+) enrolled in the NEO trial taken before, at 2 weeks on-treatment, mid-therapy and at resection were sequenced with Ion Ampliseq transcriptome yielding expression values for 12,635 genes. Differential expression analysis was performed across 16 responders and 34 non-responders defined by pathological complete response and over treatment time to identify significantly differentially expressed genes, pathways and markers indicative of response status. Prediction accuracy was compared with estimations of established gene signatures, for this dataset and validated using data from the I-SPY1 trial. Results: AAGAB was identified as a novel on-treatment biomarker for pathological complete response, with an accuracy of 100% in the NEO training dataset and 78% accuracy in the I-SPY1 Trial. AAGAB levels on treatment were also significantly predictive of term survival (p = 0.048, p = 0.031) in the two cohorts. This single gene on-treatment biomarker, had greater predictive accuracy than established prognostic tests, Mammaprint and Pam50 risk of recurrence score, although interesting both of these tests performed better in the on-treatment rather than the accepted pre-treatment setting (accuracy improving consistently by 2-8%). Conclusion: Changes in gene expression measured in sequential samples from breast cancer patients receiving neoadjuvant chemotherapy resulted in the identification of a novel on-treatment biomarker and suggest that established prognostic tests may have greater prediction accuracy on- than before treatment. These results support the potential use and further evaluation of on- treatment testing in breast cancer to improve the accuracy of tumour response prediction.
2019-07-02 | GSE122630 | GEO
Project description:BackgroundNeoadjuvant chemotherapy is increasingly given preoperatively to shrink breast tumours prior to surgery. This approach also provides the opportunity to study the molecular changes associated with treatment and evaluate whether on-treatment sequential samples can improve response and outcome predictions over diagnostic or excision samples alone.MethodsThis study included a total of 97 samples from a cohort of 50 women (aged 29-76, with 46% ER+ and 20% HER2+ tumours) with primary operable breast cancer who had been treated with neoadjuvant chemotherapy. Biopsies were taken at diagnosis, at 2 weeks on-treatment, mid-chemotherapy, and at resection. Fresh frozen samples were sequenced with Ion AmpliSeq Transcriptome yielding expression values for 12,635 genes. Differential expression analysis was performed across 16 patients with a complete pathological response (pCR) and 34 non-pCR patients, and over treatment time to identify significantly differentially expressed genes, pathways, and markers indicative of response status. Prediction accuracy was compared with estimations of established gene signatures, for this dataset and validated using data from the I-SPY 1 Trial.ResultsAlthough changes upon treatment are largely similar between the two cohorts, very few genes were found to be consistently different between responders and non-responders, making the prediction of response difficult. AAGAB was identified as a novel potential on-treatment biomarker for pathological complete response, with an accuracy of 100% in the NEO training dataset and 78% accuracy in the I-SPY 1 testing dataset. AAGAB levels on-treatment were also significantly predictive of outcome (p = 0.048, p = 0.0036) in both cohorts. This single gene on-treatment biomarker had greater predictive accuracy than established prognostic tests, Mammaprint and PAM50 risk of recurrence score, although interestingly, both of these latter tests performed better in the on-treatment rather than the accepted pre-treatment setting.ConclusionChanges in gene expression measured in sequential samples from breast cancer patients receiving neoadjuvant chemotherapy resulted in the identification of a potentially novel on-treatment biomarker and suggest that established prognostic tests may have greater prediction accuracy on than before treatment. These results support the potential use and further evaluation of on-treatment testing in breast cancer to improve the accuracy of tumour response prediction.
| S-EPMC6570893 | biostudies-literature
Project description:Neoadjuvant chemotherapy is the current standard of care for large, advanced, and/or inoperable tumors, including triple-negative breast cancer. Although the clinical benefits of neoadjuvant chemotherapy have been illustrated through numerous clinical trials, more than half of the patients do not experience therapeutic benefit and needlessly suffer from side effects. Currently, no clinically applicable biomarkers are available for predicting neoadjuvant chemotherapy response in triple-negative breast cancer; the discovery of such a predictive biomarker or marker profile is an unmet need. In this study, we introduce a generic computational framework to calculate a response-probability score (RPS), based on patient transcriptomic profiles, to predict their response to neoadjuvant chemotherapy. We first validated this framework in ER-positive breast cancer patients and showed that it predicted neoadjuvant chemotherapy response with equal performance to several clinically used gene signatures, including Oncotype DX and MammaPrint. Then, we applied this framework to triple-negative breast cancer data and, for each patient, we calculated a response probability score (TNBC-RPS). Our results indicate that the TNBC-RPS achieved the highest accuracy for predicting neoadjuvant chemotherapy response compared to previously proposed 143 gene signatures. When combined with additional clinical factors, the TNBC-RPS achieved a high prediction accuracy for triple-negative breast cancer patients, which was comparable to the prediction accuracy of Oncotype DX and MammaPrint in ER-positive patients. In conclusion, the TNBC-RPS accurately predicts neoadjuvant chemotherapy response in triple-negative breast cancer patients and has the potential to be clinically used to aid physicians in stratifying patients for more effective neoadjuvant chemotherapy.
| S-EPMC7476842 | biostudies-literature