Early Diagnosis of Pancreatic Cancer Via Deciphering Multi-modal Immunological Signatures

Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorZhejiang University

About this trial

Prospective inclusion of 1000 patients with pancreatic cancer (early-stage pancreatic cancer accounts for approximately 75% of cases), 1000 patients with benign pancreatic diseases, and 1000 healthy individuals as controls. Peripheral blood samples were collected from newly diagnosed pancreatic cancer patients and healthy individuals. Using techniques such as plasma TCR/BCR-seq, CyTOF, and plasma proteomics, multi-modal individual immune characteristics were obtained and analyzed along with clinical information. An artificial intelligence predictive model was built based on these multi-modal individual immune characteristics to establish an early screening technique for pancreatic cancer. The sensitivity and specificity of this artificial intelligence model for early pancreatic cancer diagnosis were evaluated using an external multicenter sample test set.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Sign the informed consent form;

Initial diagnosis as patients with pancreatic cancer, patients with benign pancreatic lesions, or healthy controls.

Disqualifiers

History of other malignancies;

Presence of organ dysfunction;

Concurrent immunodeficiency syndrome, active tuberculosis, HIV infection, etc.;

Allogeneic transplantation requiring immunosuppressive therapy;

Trial population

Prospective inclusion of 1000 patients with pancreatic cancer (early-stage pancreatic cancer accounts for approximately 75% of cases), 1000 patients with benign pancreatic diseases, and 1000 healthy individuals as controls. Peripheral blood samples were collected from newly diagnosed pancreatic cancer patients and healthy individuals. Using techniques such as plasma TCR-seq, CyTOF, and plasma proteomics, multi-modal individual immune characteristics were obtained and analyzed along with clinical information. An artificial intelligence predictive model was built based on these multi-modal individual immune characteristics to establish an early screening technique for pancreatic cancer.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Not listed

Trial groups

3,000 Participants
are grouped into 3 trial groups
Group A: Pancreatic Cancer
Group B: Benign Pancreatic Diseases
Group C: Healthy controls

Trial outcomes

Primary outcomes

1

Peripheral blood mononuclear cell

Using RNA seq technology to analyze differentially expressed genes in peripheral immune cells

Time frame
During the 1-7 day period before surgery
2

Peripheral blood mononuclear cell

TCR/BCR-seq:Using multiple amplification to obtain the CDR3(complementarities determining region3) region of TCR and BCR and analyzing the VDJ rearrangement pattern

Time frame
During the 1-7 day period before surgery
3

Peripheral blood mononuclear cell

Analyzing RNA expression in individual cells using scRNA-seq

Time frame
During the 1-7 day period before surgery
4

Peripheral blood mononuclear cell

scTCR/BCR-seq:Using multiple amplification to obtain the CDR3(complementarities determining region3) region of TCR and BCR and analyzing the VDJ rearrangement pattern in individual cells

Time frame
During the 1-7 day period before surgery

Secondary outcomes

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Zhejiang University

Lead sponsor

The Affiliated Hospital of the Chinese Academy of Military Medical Sciences

Collaborator

Nanjing Medical University

Collaborator

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