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A single-cell based precision medicine approach using glioblastoma patient-specific models.

ABSTRACT: Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.

SUBMITTER: Park JH

PROVIDER: S-EPMC9360428 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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A single-cell based precision medicine approach using glioblastoma patient-specific models.

Park James H JH Feroze Abdullah H AH Emerson Samuel N SN Mihalas Anca B AB Keene C Dirk CD Cimino Patrick J PJ de Lomana Adrian Lopez Garcia ALG Kannan Kavya K Wu Wei-Ju WJ Turkarslan Serdar S Baliga Nitin S NS Patel Anoop P AP

NPJ precision oncology 20220808 1

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced ...[more]

PMID: 35941215

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Project description:BACKGROUND AND PURPOSE:MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS:We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS:Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS:Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Project description:Glioblastoma (GBM) is a highly aggressive and heterogeneous form of brain cancer. Genotypic and phenotypic heterogeneity drives drug resistance and tumor recurrence. Combination chemotherapy could overcome drug resistance; however, GBM's location behind the blood-brain barrier severely limits chemotherapeutic options. Interstitial therapy, delivery of chemotherapy locally to the tumor site, via a biodegradable polymer implant can overcome the blood-brain barrier and increase the range of drugs available for therapy. Ideal drug candidates for interstitial therapy are those that are potent against GBM and work in combination with both standard-of-care therapy and new precision medicine targets. Herein we evaluated paclitaxel for interstitial therapy, investigating the effect of combination with both temozolomide, a clinical standard-of-care chemotherapy for GBM, and everolimus, a mammalian target of rapamycin (mTOR) inhibitor that modulates aberrant signaling present in >80% of GBM patients. Tested against a panel of GBM cell lines in vitro, paclitaxel was found to be effective at nanomolar concentrations, complement therapy with temozolomide, and synergize strongly with everolimus. The strong synergism seen with paclitaxel and everolimus was then explored in vivo. Paclitaxel and everolimus were separately formulated into fibrous scaffolds composed of acetalated dextran, a biodegradable polymer with tunable degradation rates, for implantation in the brain. Acetalated dextran degradation rates were tailored to attain matching release kinetics (~3% per day) of both paclitaxel and everolimus to maintain a fixed combination ratio of the two drugs. Combination interstitial therapy of both paclitaxel and everolimus significantly reduced GBM growth and improved progression free survival in two clinically relevant orthotopic models of GBM resection and recurrence. This work illustrates the advantages of synchronized interstitial therapy of paclitaxel and everolimus for post-surgical tumor control of GBM.

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A single-cell based precision medicine approach using glioblastoma patient-specific models.

Publications

A single-cell based precision medicine approach using glioblastoma patient-specific models.

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