公开(公告)号：US20230293474A1

公开(公告)日：2023-09-21

申请号：US18093840

申请日：2023-01-06

Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

Inventor： James B. BRUGAROLAS ,  Haley HILL ,  Tao WANG

IPC: A61K31/275 ,  G01N33/50

CPC classification number: A61K31/275 ,  G01N33/5044 ,  G01N33/5011 ,  A61K45/06

Abstract: The present disclosure provides methods of identifying patients who have partial or total resistance to HIF-2 inhibitors or who develop partial or total resistance to HIF-2 inhibitors after treatment and providing suitable treatment to these patients.

公开(公告)号：US20210085634A1

公开(公告)日：2021-03-25

申请号：US17030123

申请日：2020-09-23

Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

Inventor： James B. BRUGAROLAS ,  Haley HILL ,  Tao WANG

IPC: A61K31/275 ,  G01N33/50

Abstract: The present disclosure provides methods of identifying patients who have partial or total resistance to HIF-2 inhibitors or who develop partial or total resistance to HIF-2 inhibitors after treatment and providing suitable treatment to these patients.

公开(公告)号：US20230349914A1

公开(公告)日：2023-11-02

申请号：US18029395

申请日：2021-09-30

Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

Inventor： Tianshi LU ,  Tao WANG

IPC: G01N33/68 ,  G06N3/08 ,  G16B40/20 ,  G16B15/30

CPC classification number: G01N33/6845 ,  G06N3/08 ,  G06N3/0455 ,  G16B40/20 ,  G16B15/30

Abstract: Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). To help determine the TCRs that interact with particular neoantigens, prediction models that predict TCR-binding specificities of neoantigens presented by different classes of major histocompatibility complex (MHCs) were developed. To confirm the applicability of the model to clinical settings, the prediction models were comprehensively validated by a series of analyses. The validated prediction models used a flexible transfer learning approach and differential learning schema to achieve highly accurate prediction of TCR binding specificity only using TCR sequence data, antigen sequence data, and MHC alleles.

公开(公告)号：US20240282409A1

公开(公告)日：2024-08-22

申请号：US18650820

申请日：2024-04-30

Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

Inventor： Tao WANG ,  Yi Han ,  Tianshi Lu ,  Yuqiu Yang

IPC: G16B20/30 ,  G16B25/10 ,  G16B40/00

CPC classification number: G16B20/30 ,  G16B25/10 ,  G16B40/00

Abstract: The disclosed technology relates to a computer-implemented method for predicting T cell receptor (TCR) binding specificities towards T cell antigen targets (namely, peptide-major histocompatibility complexes, pMHCs), and a set of extensions of this method, include prediction of immune-related adverse events (irAEs) using a machine learning model. The method involves obtaining genomic and proteomic data from patients, determining TCR and pMHC sequences by analyzing these data, and predicting binding interactions between T cell antigens and the TCRs. The extensions include: (a) a transfer learning model for improving the predictive performance of a pre-trained TCR-antigen binding model as a foundation model, to enhance prediction for a specific pMHC, (b) a biomarker metric defined based on the output of the TCR-pMHC binding prediction method, for diagnosis, prognosis and response prediction purposes, (c) a method, based on the output of the TCR-pMHC binding prediction method, to select optimal antigens for tumor vaccines.