Dataset Information

Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas.

ABSTRACT: The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle to the advancement of therapy for high-grade gliomas, particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial (number NCT04135807), we provide first-in-human evidence that drug-releasing intratumoral microdevices (IMDs) can be safely and effectively used to obtain patient-specific, high-throughput molecular and histopathological drug response profiling. These data can complement other strategies to inform the selection of drugs based on their observed antitumor effect in situ. IMDs are integrated into surgical practice during tumor resection and remain in situ only for the duration of the otherwise standard operation (2 to 3 hours). None of the six enrolled patients experienced adverse events related to the IMD, and the exposed tissue was usable for downstream analysis for 11 out of 12 retrieved specimens. Analysis of the specimens provided preliminary evidence of the robustness of the readout, compatibility with a wide array of techniques for molecular tissue interrogation, and promising similarities with the available observed clinical-radiological responses to temozolomide. From an investigational aspect, the amount of information obtained with IMDs allows characterization of tissue effects of any drugs of interest, within the physiological context of the intact tumor, and without affecting the standard surgical workflow.

SUBMITTER: Peruzzi P

PROVIDER: S-EPMC10754230 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas.

Peruzzi Pierpaolo P Dominas Christine C Fell Geoffrey G Bernstock Joshua D JD Blitz Sarah S Mazzetti Debora D Zdioruk Mykola M Dawood Hassan Y HY Triggs Daniel V DV Ahn Sebastian W SW Bhagavatula Sharath K SK Davidson Shawn M SM Tatarova Zuzana Z Pannell Michael M Truman Kyla K Ball Anna A Gold Maxwell P MP Pister Veronika V Fraenkel Ernest E Chiocca E Antonio EA Ligon Keith L KL Wen Patrick Y PY Jonas Oliver O

Science translational medicine 20230906 712

The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle to the advancement of therapy for high-grade gliomas, particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial (number NCT04135807), we provide first-in-human evidence that drug-releasing intratumoral microdevices (IMDs) can be safely and effectively used to obtain patient-specific, high-throughput molecular and hi ...[more]

PMID: 37672566

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Project description:ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and methodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Dataset Information

Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas.

Publications

Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas.

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