Taiwan Cancer Moonshot Project

Taiwan Cancer Moonshot Project: 1. Next-generation Pathway of Taiwan Cancer Precision Medicine (Subjective Aim 1- Taiwan Cancer Moonshot Project); 2. Development of Novel Treatments for Major Diseases (Subproject 5 - Multiomic Big Data-based Intelligent Navigation System for Precision Oncology)

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05248763

Enrollment

4190

Registered

2022-02-21

Start date

2022-02-21

Completion date

2035-12-31

Last updated

2022-07-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Malignant Neoplasm of Body of Stomach in Situ

Keywords

lung cancer, breast cancer, gastric cancer, pancreatic cancer, ovarian cancer

Brief summary

The aim of this study is to establish multiomic big data under strictly collected clinical samples from tumors, adjacent normal tissue, blood, and clinical data then analyze by using integrated proteomics and genetics platform.

Detailed description

Multiomic integrative analysis is an effective strategy to facilitate the investigation of molecular mechanism, cause, and early intervention of specific diseases. Gastric cancer is specifically chosen for our research target to decrease its incidence and improve survival. To increase precision diagnosis, prognosis and precision therapy, patients from Taiwan are selected as cohort subjects. The aim of this study is to establish multiomic big data under strictly collected clinical samples from tumors, adjacent normal tissue, blood, and clinical data then analyze by using integrated proteomics and genetics platform. Genome, transcriptome, genomic methylation, proteomics, and post-translational modification will be used to construct a map for determine the in-depth carcinogenesis of gastric cancer and strategies for cancer early diagnosis, prevention, and targeted treatments.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

20 Years to No maximum

Healthy volunteers

Yes

Inclusion criteria

* lung cancer, breast cancer, gastric cancer, pancreatic cancer, ovarian cancer

Exclusion criteria

* other cancer types

Design outcomes

Primary

Measure	Time frame	Description
Collected clinical samples from tumors, adjacent normal tissue, blood, and clinical data	Through study completion, an average of 2 to 3 year	Analyze collected clinical samples by using integrated proteomics and genetics platform (genome, transcriptome, genomic methylation, proteomics, and post-translational modification)

Secondary

Measure	Time frame	Description
Differential expressed genes and proteins in tumor	Through study completion, an average of 4 year	The relative difference, or fold change of expressed genes and proteins (tumors vs adjacent normal tissue)

Countries

Taiwan

Contacts

Primary ContactYu-Ju Chen, PhD

yujuchen@gate.sinica.edu.tw886-2-55728660

Outcome results