Response prediction to neoadjuvant chemotherapy
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Klinglmueller Florian
PROVIDER: S-ECPF-GEOD-29561 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
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:SUMMARY: About 10-25% of breast cancer patients achieve a pathologically confirmed complete response after neoadjuvant chemotherapy. Tissue samples of pretreatment core biopsies are a valuable resource for translational research aiming towards predictive biomarkers for selecting patients who are likely to benefit from neoadjuvant therapy. The German Breast Group (GBG) and the AGO-B Group (AGO = Working Group Gynecological Oncology) have extensive experience in conducting neoadjuvant clinical trials. Technologies as immunohistochemistry on tissue microarrays and standardized reverse transcription-polymerase chain reaction (RT-PCR) approaches on formalin-fixed paraffin-embedded samples allow high-throughput investigation of protein and mRNA biomarkers. With these approaches, we could demonstrate that molecular tumor subtypes and immunological infiltrates are valuable and independent predictors of therapy response. New biomarkers such as poly(ADPribose) polymerase (PARP) might be useful for the prediction of response to conventional and new targeted therapies. This review summarizes current research projects focusing on biomarker discovery in the neoadjuvant setting.
| S-EPMC3225210 | biostudies-other
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:We describe an algorithm to calculate an index that characterizes spatial differences in broadband near-infrared [(NIR), 650-1000 nm] absorption spectra of tumor-containing breast tissue. Patient-specific tumor spatial heterogeneities are visualized through a heterogeneity spectrum function (HS). HS is a biomarker that can be attributed to different molecular distributions within the tumor. To classify lesion heterogeneities, we built a heterogeneity index (HI) derived from the HS by weighing the HS in specific NIR absorption bands. It is shown that neoadjuvant chemotherapy (NAC) response is potentially related to the tumor heterogeneity. Therefore, we correlate the heterogeneity index obtained prior to treatment with the final response to NAC. From a pilot study of 15 cancer patients treated with NAC, pathological complete responders (pCR) were separated from non-pCR according to their HI (-44 ± 12 and 43 ± 17, p = 3 × 10(-8), respectively). We conclude that the HS function is a biomarker that can be used to visualize spatial heterogeneities in lesions, and the baseline HI prior to therapy correlates with chemotherapy pathological response.
| S-EPMC3203125 | biostudies-other
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
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: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:The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwent pre-operative NAC. We developed a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. The proposed deep learning model trained on all datasets as clinical information, T1-weighted subtraction images, and T2-weighted images shows better performance with area under the curve (AUC) of 0.888 as compared to the model using only clinical information (AUC = 0.827, P < 0.05). Our results demonstrate that the multimodal fusion approach using deep learning with both clinical information and MR images achieve higher prediction performance compared to the deep learning model without the fusion approach. Deep learning could integrate pretreatment MR images with clinical information to improve pCR prediction performance.
| S-EPMC8458289 | biostudies-literature
Project description:Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.
| S-EPMC5388850 | biostudies-literature