Brandon Minh Pham Bui

All Publications

• VCAN in the extracellular matrix drives glioma recurrence by enhancing cell proliferation and migration. Frontiers in neuroscience Wei, R., Xie, H., Zhou, Y., Chen, X., Zhang, L., Bui, B., Liu, X. 2024; 18: 1501906

  Abstract

  Gliomas are the most prevalent primary malignant intracranial tumors, characterized by high rates of therapy resistance, recurrence, and mortality. A major factor contributing to the poor prognosis of gliomas is their ability to diffusely infiltrate surrounding and even distant brain tissues, rendering complete total resection almost impossible and leading to frequent recurrences. The extracellular matrix (ECM) plays a key role in the tumor microenvironment and may significantly influence glioma progression, recurrence, and therapeutic response.In this study, we first identified the ECM and the Versican (VCAN), a key ECM protein, as critical contributors to glioma recurrence through a comprehensive analysis of transcriptomic data comparing recurrent and primary gliomas. Using single-cell sequencing, we revealed heterogeneous distribution patterns and extensive intercellular communication among ECM components. External sequencing and immunohistochemical (IHC) staining further validated that VCAN is significantly upregulated in recurrent gliomas and is associated with poor patient outcomes.Functional assays conducted in glioma cell lines overexpressing VCAN demonstrated that VCAN promotes cell proliferation and migration via the PI3K/Akt/AP-1 signaling pathway. Furthermore, inhibiting the PI3K/Akt pathway effectively blocked VCAN-mediated glioma progression.These findings provide valuable insights into the mechanisms underlying glioma recurrence and suggest that targeting both VCAN and the PI3K/Akt pathway could represent a promising therapeutic strategy for managing recurrent gliomas.

  View details for DOI 10.3389/fnins.2024.1501906

  View details for PubMedID 39554845

  View details for PubMedCentralID PMC11565936

• Glioma actively orchestrate a self-advantageous extracellular matrix to promote recurrence and progression BMC CANCER Wei, R., Zhou, J., Bui, B., Liu, X. 2024; 24 (1): 974

  Abstract

  The intricate interplay between cancer cells and their surrounding microenvironment has emerged as a critical factor driving the aggressive progression of various malignancies, including gliomas. Among the various components of this dynamic microenvironment, the extracellular matrix (ECM) holds particular significance. Gliomas, intrinsic brain tumors that originate from neuroglial progenitor cells, have the remarkable ability to actively reform the ECM, reshaping the structural and biochemical landscape to their advantage. This phenomenon underscores the adaptability and aggressiveness of gliomas, and highlights the intricate crosstalk between tumor cells and their surrounding matrix.In this review, we delve into how glioma actively regulates glioma ECM to organize a favorable microenvironment for its survival, invasion, progression and therapy resistance. By unraveling the intricacies of glioma-induced ECM remodeling, we gain valuable insights into potential therapeutic strategies aimed at disrupting this symbiotic relationship and curbing the relentless advance of gliomas within the brain.

  View details for DOI 10.1186/s12885-024-12751-3

  View details for Web of Science ID 001287535300009

  View details for PubMedID 39118096

  View details for PubMedCentralID PMC11308147

• Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns. JCO clinical cancer informatics Swaminathan, A., Ren, A. L., Wu, J. Y., Bhargava-Shah, A., Lopez, I., Srivastava, U., Alexopoulos, V., Pizzitola, R., Bui, B., Alkhani, L., Lee, S., Mohit, N., Seo, N., Macedo, N., Cheng, W., Wang, W., Tran, E., Thomas, R., Gevaert, O. 2024; 8: e2300091

  Abstract

  Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM).Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method.Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days.We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

  View details for DOI 10.1200/CCI.23.00091

  View details for PubMedID 38857465

• Selective prediction for extracting unstructured clinical data. Journal of the American Medical Informatics Association : JAMIA Swaminathan, A., Lopez, I., Wang, W., Srivastava, U., Tran, E., Bhargava-Shah, A., Wu, J. Y., Ren, A. L., Caoili, K., Bui, B., Alkhani, L., Lee, S., Mohit, N., Seo, N., Macedo, N., Cheng, W., Liu, C., Thomas, R., Chen, J. H., Gevaert, O. 2023

  Abstract

  While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction.We trained selective classifiers (logistic regression, random forest, support vector machine) to extract 5 variables from clinical notes: depression (n = 1563), glioblastoma (GBM, n = 659), rectal adenocarcinoma (DRA, n = 601), and abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601) of adenocarcinoma. We varied the cost of false positives (FP), false negatives (FN), and abstained notes and measured total misclassification cost.The depression selective classifiers abstained on anywhere from 0% to 97% of notes, and the change in total misclassification cost ranged from -58% to 9%. Selective classifiers abstained on 5%-43% of notes across the GBM and colorectal cancer models. The GBM selective classifier abstained on 43% of notes, which led to improvements in sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier and when compared to structured proxy variables.We showed that selective classifiers outperformed both non-selective classifiers and structured proxy variables for extracting data from unstructured clinical notes.Selective prediction should be considered when abstaining is preferable to making an incorrect prediction.

  View details for DOI 10.1093/jamia/ocad182

  View details for PubMedID 37769323