Fuzheng Yiliu-1010
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Cancer Stage Ii And Iii,Colorectal Neoplasms
PROVIDER: 2344714 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Urothelial cancer is a challenging disease with a wide tumor-biological spec-trum. Most molecular classification attempts are transcriptome-based and re-late only indirectly to the therapeutically relevant protein level. We improve preanalytical preparation of clinical samples for proteome analyses and char-acterize a comprehensive cohort of 434 samples with 242 tumors and 192 paired normal mucosae. We evaluate sample-wise tumor specificity and rank biomarkers by target relevance. We identify five tumor clusters that add prog-nostic information independent from histopathological groups. In silico drug prediction suggests efficacy of several compounds hitherto not in clinical use. Both, in silico as well as in vitro data, indicate predictive value of the proteo-mic clusters for these drugs. Comparative validation on external transcriptom-ic data confirms prognostic relevance and contextualizes proteomic and tran-scriptomic classifications. A real-world diagnostic approach based on im-munohistochemistry further validates our proteomic data but underlines the necessity of omics scale molecular analyses for personalized oncology.
2024-06-16 | PXD041733 | Pride
Project description:Urothelial cancer is a challenging disease with a wide tumor-biological spectrum. Most molecular classification attempts are transcriptome-based and relate only indirectly to the therapeutically relevant protein level. We improve preanalytical preparation of clinical samples for proteome analyses and characterize a comprehensive cohort of 434 samples with 242 tumors and 192 paired normal mucosae. We evaluate sample-wise tumor specificity and rank biomarkers by target relevance. We identify five tumor clusters that add prognostic information independent from histopathological groups. In silico drug prediction suggests efficacy of several compounds hitherto not in clinical use. Both, in silico as well as in vitro data, indicate predictive value of the proteomic clusters for these drugs. Comparative validation on external transcriptomic data confirms prognostic relevance and contextualizes proteomic and transcriptomic classifications. A real-world diagnostic approach based on immunohistochemistry further validates our proteomic data but underlines the necessity of omics scale molecular analyses for personalized oncology.
2024-06-16 | PXD041268 | Pride
Project description:This model is based on:
Reinforcement learning-based control of tumor growth under anti-angiogenic therapy
Authors: Parisa Yazdjerdi, Nader Meskin, Mohammad Al-Naemi, Ala-Eddin Al Moustafa, Levente Kovacs
Abstract:
Background and objectives: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration.
Methods: A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient.
Results: The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to ± 20% of tumor growth parameters changes.
Conclusion: The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.
2024-09-02 | BIOMD0000000821 | BioModels
Project description:Interventions: Fuzheng anti-tumor group:Taking Chinese Medicine;non-Fuzheng anti-tumor group:not-taking Chinese Medicine
Primary outcome(s): 3 yeah Disease-free Survival
Study Design: Non randomized control
| 2694844 | ecrin-mdr-crc
Project description:The protein structure prediction problem has been revolutionised by AlphaFold2, an algorithm that uses neural networks and evolutionary information to predict accurate models from the primary sequence. However, some proteins remain difficult to predict. Moreover, proteins are dynamic entities that exist in complex environments in vivo. Here, we use the noncanonical amino acid Photo-Leucine to obtain information on residue-residue contacts inside cells by crosslinking mass spectrometry. We then introduce AlphaLink, a modified version of the AlphaFold2 algorithm that synergistically incorporates experimental distance restraint information into its network architecture. AlphaLink improves on the performance of AlphaFold2 in predicting challenging targets in both synthetic and real-world scenarios by employing sparse experimental contacts as anchor points. The program can predict individual conformations of proteins based on the distance restraints provided. The noise-tolerant framework presented here for integrating data in protein structure prediction opens a path to accurate characterisation of protein structures from in-cell data.
2023-02-22 | PXD036833 | JPOST Repository
Project description:Purpose: Breast cancers with ESR1 mutations are resistant to antiestrogen therapy. We aimed to investigate the association of ESR1 mutations with resistance to CDK4/6 inhibitors (CDK4/6i) using real-world data analysis and experimental validation. Patients and Methods: A total of 3,958 patients with estrogen receptor-positive (ER+) metastatic breast cancer with DNA sequencing data were analyzed. Breast tumor DNA and circulating tumor (ct) DNA were sequenced using the Tempus xT tumor assay and Tempus xF liquid biopsy, respectively. Patients were stratified into either treated with CDK4/6i (breast tumors: 1,070; ctDNA: 1,885) or CDK4/6i naïve (breast tumors: 750; ctDNA: 253). Engineered MCF7 cells carrying ESR1 Y537S or D538G knock-in mutations were used to study antitumor efficacy of the CDK4/6i, palbociclib in vitro and in vivo. Results: In both xF and xT assays, ESR1 mutations were the only somatic alterations significantly more frequent in those who received CDK4/6i than those who did not. Knock-in of ESR1 Y537S or ESR1 D538G in MCF7 cells resulted in upregulation of cell cycle-related gene signatures upon treatment with CDK4/6i ± antiestrogen compared to cells with non-mutant ESR1. MCF7 xenografts harboring ESR1 Y537S and D538G mutations established in nude mice were resistant to palbociclib. Conclusions: We report herein real-world and preclinical evidence that ESR1 mutations, particularly Y537S and D538G, can drive resistance to CDK4/6 inhibitors.
2024-11-12 | GSE281302 | GEO
Project description:Prospective pharmacotyping of urothelial carcinoma organoids for drug sensitivity prediction – feasibility and real world experience
| PRJNA1180454 | ENA
Project description:Revealing Dominant Regulatory MicroRNA-495-3p that Governs Multiple Epigenetic Modifiers in Gastric Carcinogenesis In this study, we identified miR-495-3p targeting multiple epigenetic modifiers through comprehensive miRNA and mRNA profiling analysis with in silico target prediction in GC. Western blotting assay or quantitative real time PCR was performed to confirm the expression of miR-495-3p and targets of it. We applied miRNA mimics to ectopic overexpression in gastric cancer cells and observed tumor suppressive effects of miR-495-3p in the growth and metastasis of cancer. Also, we confirmed the status of CpG islands of miR-495-3p promoter using methylation specific PCR analysis.
2015-09-18 | E-GEOD-67354 | biostudies-arrayexpress
Project description:Despite substantial progress in lung cancer immunotherapy, the overall response rate in KRAS-mutant lung adenocarcinoma (ADC) patients remains low. Combining standard immunotherapy with adjuvant approaches that enhance adaptive immune responses—such as epigenetic modulation of anti-tumor immunity—is therefore an attractive strategy. To identify epigenetic regulators of tumor immunity, we constructed an epigenetic-focused sgRNA library, and performed an in vivo CRISPR screen in KrasG12D/P53-/- (KP) lung ADC model. Our data showed that loss of the histone chaperone Asf1a in tumor cells sensitizes tumors to anti-PD-1 treatment. Mechanistic studies revealed that tumor cell intrinsic Asf1a deficiency induced immunogenic macrophage differentiation in the tumor microenvironment by upregulating GM-CSF expression and potentiated T cell activation in combination with anti-PD-1. Our results provide a rationale for a novel combination therapy consisting of Asf1a inhibition and anti-PD-1 immunotherapy.
2019-11-26 | GSE138571 | GEO
Project description:Pan-cancer circulating tumor DNA detection in over 10,000 real-world Chinese patients
| PRJEB36993 | ENA