Dataset Information

A preclinical model for predicting drug response in soft-tissue sarcoma with targeted AAVP molecular imaging.

ABSTRACT: Human sarcomas are rare but diverse malignant tumors derived from mesenchymal tissue. Clinical response to therapy is currently determined by the modified World Health Organization (WHO) criteria or the Response Evaluation Criteria in Solid Tumors (RECIST), but these standards correlate poorly with sarcoma patient outcome. We introduced ligand-directed particles with elements of AAV and phage (AAVP) to enable integration of tumor targeting to molecular imaging. We report drug-response monitoring and prediction in a nude rat model of human sarcoma by AAVP imaging. As a proof-of-concept, we imaged Herpes simplex thymidine kinase in a clinic-ready setting with PET to show that one can a priori predict tumor response to a systemic cytotoxic. Given the target expression in patient-derived sarcomas, this platform may be translated in clinical applications. Sarcoma-specific ligands and promoters may ultimately lead to an imaging transcriptome.

SUBMITTER: Hajitou A

PROVIDER: S-EPMC2393798 | biostudies-other | 2008 Mar

REPOSITORIES: biostudies-other

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A preclinical model for predicting drug response in soft-tissue sarcoma with targeted AAVP molecular imaging.

Hajitou Amin A Lev Dina C DC Hannay Jonathan A F JA Korchin Borys B Staquicini Fernanda I FI Soghomonyan Suren S Alauddin Mian M MM Benjamin Robert S RS Pollock Raphael E RE Gelovani Juri G JG Pasqualini Renata R Arap Wadih W

Proceedings of the National Academy of Sciences of the United States of America 20080312 11

Human sarcomas are rare but diverse malignant tumors derived from mesenchymal tissue. Clinical response to therapy is currently determined by the modified World Health Organization (WHO) criteria or the Response Evaluation Criteria in Solid Tumors (RECIST), but these standards correlate poorly with sarcoma patient outcome. We introduced ligand-directed particles with elements of AAV and phage (AAVP) to enable integration of tumor targeting to molecular imaging. We report drug-response monitoring ...[more]

PMID: 18337507

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Project description:ObjectivesTo construct effective prediction models for neoadjuvant radiotherapy (RT) and targeted therapy based on whole-tumor texture analysis of multisequence MRI for soft tissue sarcoma (STS) patients.MethodsThirty patients with STS of the extremities or trunk from a prospective phase II trial were enrolled for this analysis. All patients underwent pre- and post-neoadjuvant RT MRI examinations from which whole-tumor texture features were extracted, including T1-weighted with fat saturation and contrast enhancement (T1FSGd), T2-weighted with fat saturation (T2FS), and diffusion-weighted imaging (DWI) sequences and their corresponding apparent diffusion coefficient (ADC) maps. According to the postoperative pathological results, the patients were divided into pathological complete response (pCR) and non-pCR (N-pCR) groups. pCR was defined as less than 5% of residual tumor cells by postoperative pathology. Delta features were defined as the percentage change in a texture feature from pre- to post-neoadjuvant RT MRI. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance.ResultsFive of 30 patients (16.7%) achieved pCR. The Delta_Model (AUC 0.92) had a better predictive ability than the Pre_Model (AUC 0.78) and Post_Model (AUC 0.76) and was better than AJCC staging (AUC 0.52) and RECIST 1.1 criteria (AUC 0.52). The Combined_Model (pre, post, and delta features) had the best predictive performance (AUC 0.95).ConclusionWhole-tumor texture analysis of multisequence MRI can well predict pCR status after neoadjuvant RT and targeted therapy in STS patients, with better performance than RECIST 1.1 and AJCC staging.Key points• MRI multisequence texture analysis could predict the efficacy of neoadjuvant RT and targeted therapy for STS patients. • Texture features showed incremental value beyond routine clinical factors. • The Combined_Model with features at multiple time points showed the best performance.

Dataset Information

A preclinical model for predicting drug response in soft-tissue sarcoma with targeted AAVP molecular imaging.

Publications

A preclinical model for predicting drug response in soft-tissue sarcoma with targeted AAVP molecular imaging.

Similar Datasets

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