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Response prediction to neoadjuvant chemotherapy: comparison between pre-therapeutic gene expression profiles and in vitro chemosensitivity assay.

ABSTRACT: Although the use of (neo-)adjuvant chemotherapy in breast cancer patients has resulted in improved outcome, not all patients benefit equally. We have evaluated the utility of an in vitro chemosensitivity assay in predicting response to neoadjuvant chemotherapy. Pre-therapeutic biopsies were obtained from 30 breast cancer patients assigned to neoadjuvant epirubicin 75 mg/m2 and docetaxel 75 mg/m2 (Epi/Doc) in a prospectively randomized clinical trial. Biopsies were subjected to a standardized ATP-based Epi/Doc chemosensitivity assay, and to gene expression profiling. Patients then received 3 cycles of chemotherapy, and response was evaluated by changes in tumor diameter and Ki67 expression. The efficacy of Epi/Doc in vitro was correlated with differential changes in tumor cell proliferation in response to Epi/Doc in vivo (p?=?0.0011; r?=?0.73670, Spearma?s rho), but did not predict for changes in tumor size. While a pre-therapeutic gene expression signature identified tumors with a clinical response to Epi/Doc, no such signature could be found for tumors that responded to Epi/Doc in vitro, or tumors in which Epi/Doc exerted an antiproliferative effect in vivo. This is the first prospective clinical trial to demonstrate the utility of a standardized in vitro chemosensitivity assay in predicting the individual biological response to chemotherapy in breast cancer.

SUBMITTER: Singer CF

PROVIDER: S-EPMC3691196 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Response prediction to neoadjuvant chemotherapy: comparison between pre-therapeutic gene expression profiles and in vitro chemosensitivity assay.

Singer Christian F CF Klinglmüller Florian F Stratmann Rembert R Staudigl Christine C Fink-Retter Anneliese A Gschwantler Daphne D Helmy Samir S Pfeiler Georg G Dressler Anne Catharina AC Sartori Christian C Bilban Martin M

PloS one 20130624 6

Although the use of (neo-)adjuvant chemotherapy in breast cancer patients has resulted in improved outcome, not all patients benefit equally. We have evaluated the utility of an in vitro chemosensitivity assay in predicting response to neoadjuvant chemotherapy. Pre-therapeutic biopsies were obtained from 30 breast cancer patients assigned to neoadjuvant epirubicin 75 mg/m2 and docetaxel 75 mg/m2 (Epi/Doc) in a prospectively randomized clinical trial. Biopsies were subjected to a standardized ATP ...[more]

PMID: 23826101

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Project description:BackgroundThe recommended treatment for patients with non-metastatic muscle-invasive bladder cancer (MIBC) is neoadjuvant chemotherapy (NAC) and radical cystectomy (RC). Following NAC, 20-40% of patients experience a complete pathological response (pCR) in the RC specimen and these patients have excellent long-term overall survival. Subject to debate is, however, whether patients with a pCR to NAC benefit from RC, which is a major surgical procedure with substantial morbidity, and if these patients might be candidates for close surveillance instead. However, currently it is not possible to accurately identify patients with a pCR to NAC in whom RC might be withheld. The objective of this study is to assess whether pathological response in the RC specimen after NAC can be predicted based on clinical, radiological, and histological variables and on a wide set of molecular biomarkers assessed in tissue, blood and urine.MethodsThis is a multicentre, prospective cohort study, including patients with cT2a-T4a N0-N1 M0 urothelial cell MIBC who are scheduled to undergo cisplatin-based NAC followed by RC. Prior to start of therapy, a 2-Deoxy-2-[18F] fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) is performed. Response to NAC is evaluated by CT-scan. Blood and urine, including cytology, are prospectively collected for biomarker analyses before and after NAC. Immediately before RC, participants undergo cystoscopy with bimanual examination and a re-staging transurethral resection (TUR) of all visible cancerous lesions or with biopsies from scar tissue. Subsequently, RC is performed in all patients. Tissue from the diagnostic TUR, the re-staging TUR, and the RC specimen is examined for the presence of urothelial cancer carcinoma and DNA and RNA is isolated for molecular analysis. The primary endpoint is the pathological stage (ypTN) in the RC and ePLND specimen and its association with clinical response.DiscussionIf the PRE-PREVENCYS trial shows that the absence of residual disease after NAC in patients with MIBC is accurately predicted, a randomized controlled trial is scheduled comparing the overall survival of NAC plus RC versus NAC followed by close surveillance for patients with a clinically complete response (PREVENCYS trial).Trial registrationNetherlands Trial Register: NL8678; Registered 20 May 2020 https://www.trialregister.nl/trial/8678.

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:Locally advanced breast cancer (LABC) is a severe type of cancer with a poor prognosis, despite advancements in therapy. As the disease is often inoperable, current guidelines suggest upfront aggressive neoadjuvant chemotherapy (NAC). Complete pathological response to chemotherapy is linked to improved survival, but conventional clinical assessments like physical exams, mammography, and imaging are limited in detecting early response. Early detection of tissue response can improve complete pathological response and patient survival while reducing exposure to ineffective and potentially harmful treatments. A rapid, cost-effective modality without the need for exogenous contrast agents would be valuable for evaluating neoadjuvant therapy response. Conventional ultrasound provides information about tissue echogenicity, but image comparisons are difficult due to instrument-dependent settings and imaging parameters. Quantitative ultrasound (QUS) overcomes this by using normalized power spectra to calculate quantitative metrics. This study used a novel transfer learning-based approach to predict LABC response to neoadjuvant chemotherapy using QUS imaging at pre-treatment. Using data from 174 patients, QUS parametric images of breast tumors with margins were generated. The ground truth response to therapy for each patient was based on standard clinical and pathological criteria. The Residual Network (ResNet) deep learning architecture was used to extract features from the parametric QUS maps. This was followed by SelectKBest and Synthetic Minority Oversampling (SMOTE) techniques for feature selection and data balancing, respectively. The Support Vector Machine (SVM) algorithm was employed to classify patients into two distinct categories: nonresponders (NR) and responders (RR). Evaluation results on an unseen test set demonstrate that the transfer learning-based approach using spectral slope parametric maps had the best performance in the identification of nonresponders with precision, recall, F1-score, and balanced accuracy of 100, 71, 83, and 86%, respectively. The transfer learning-based approach has many advantages over conventional deep learning methods since it reduces the need for large image datasets for training and shortens the training time. The results of this study demonstrate the potential of transfer learning in predicting LABC response to neoadjuvant chemotherapy before the start of treatment using quantitative ultrasound imaging. Prediction of NAC response before treatment can aid clinicians in customizing ineffectual treatment regimens for individual patients.

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.

Dataset Information

Response prediction to neoadjuvant chemotherapy: comparison between pre-therapeutic gene expression profiles and in vitro chemosensitivity assay.

Publications

Response prediction to neoadjuvant chemotherapy: comparison between pre-therapeutic gene expression profiles and in vitro chemosensitivity assay.

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