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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Pancreatic Cancer Resectable

Detailed Description

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers and currently ranks as the seventh leading cause of cancer-related deaths in China. The nonspecific symptoms of early PDAC are one of the most significant reasons for its low 5-year survival rate. Additionally, PDAC lacks highly sensitive and specific biomarkers for early detection and preventive screening. For the majority of advanced PDAC cases, pathological confirmation often requires tissue biopsies obtained through invasive procedures, and due to the scarcity of tumor cells in these biopsies, pathology may be unclear in up to 20% of cases. On the other hand, there are currently no effective and targeted treatments for PDAC, with surgical resection being the only available option. However, this is only applicable to a small fraction of early-stage PDAC patients, as more than 80% of PDAC patients are diagnosed with distant metastasis at initial diagnosis, where only adjuvant therapy is feasible.

Early diagnosis and detection of PDAC can significantly improve patient prognosis. To date, the only diagnostic biomarker for PDAC is serum Carbohydrate antigen199(CA199) levels, which are neither diagnostic nor specific. High CA199 levels are uncommon in early PDAC but most common in late-stage PDAC. Furthermore, elevated CA199 levels are frequently detected in various benign and malignant diseases, including pancreatitis, cholestasis, and cancer. Additionally, due to screening limitations, imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS) are insufficient for early detection of PDAC.

Ideally, the investigators aim to obtain non-invasive, reliable, and repeatable biological markers with clinical potential for early cancer diagnosis. Early studies have identified circulating tumor DNA (ctDNA), circulating tumor cells (CTC), extracellular vesicles (EV), plasma proteomics, and circulating tumor cells (CTCs) as promising real-time and remote tools for this purpose. Compared to other solid tumors, especially lung and breast cancer, some of these circulating biomarkers have entered clinical practice, while blood-derived biomarkers for PDAC diagnosis or monitoring are very limited (excluding CA199), and CA199 is largely underdeveloped compared to other tumors. One example is the use of EpCAM(Epithelial cell adhesion molecule) and cytokeratin for CellSearch system diagnostics, an FDA(Food and Drug Administration)-approved method for diagnosing metastatic breast, colon, and prostate cancers, which was evaluated for PDAC diagnosis, achieving an accuracy rate of 11-78.5%, indicating a wide variation in PDAC detection rates. Other molecular features for diagnosing PDAC, including KRAS mutations in CTCs, miRNA in EVs, and heparan sulfate proteoglycan glypican 1 (GPC1) in extracellular vesicles, unfortunately, exhibit significant differences in sensitivity and predictive performance across different studies, particularly between tumor and CTC status. One study found that in 97% of patients with tumors carrying mutated KRAS, only 18% of CTCs carried the wild-type KRAS allele, even from metastatic tumors. Therefore, due to genetic limitations associated with CTC enrichment and identification, ctDNA isolation, etc., using a single biomarker may only capture partial tumor biological characteristics, leading to low consistency and false negatives. Given the challenges of early diagnosis of PDAC, it is imperative to develop one or more new biological markers for early PDAC diagnosis to capture more possible biological characteristics of primary and metastatic tumors.

The immune system is an extremely important defense system in the human body. Through specific and non-specific biological processes, the immune system can detect various pathogens and harmful substances ranging from viruses to parasites, and differentiate them from healthy cells and tissues in the body under normal circumstances. Therefore, there is a close connection between the immune system and the overall health of an individual. Tumor development remains under constant surveillance by the immune system, and in the early stages of disease, the immune system responds and immune features undergo changes. However, when clinical symptoms appear, it indicates that the immune system is struggling to overcome the presence of harmful substances, and at this point, the disease has already progressed to the middle or late stage. By establishing a large-scale early pancreatic cancer clinical cohort, collecting high-quality multi-modal immune data from individuals, and integrating cutting-edge artificial intelligence models, it is possible to decode individual immune features and develop early diagnostic techniques for pancreatic cancer based on capturing immune status and response signals. This approach can help identify early risk factors for pancreatic cancer by analyzing abnormal immune responses before disease progression, enabling early diagnosis of pancreatic cancer.

• Study Type: Observational
• Enrollment (Estimated): 3000

Contacts and Locations

• Study Contact: Name: Qi Zhang, Associate professor; Phone Number: 13819137113; Email: qi.zhang@zju.edu.cn

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310003
      • First Affiliated Hospital, Medical College of Zhejiang University
    • Hangzhou, Zhejiang, China, 310009
      • the First Affiliated Hospital, School of Medicine, Zhejiang University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study 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.

Description

Inclusion Criteria:

• Sign the informed consent form;
• Initial diagnosis as patients with pancreatic cancer, patients with benign pancreatic lesions, or healthy controls.

Exclusion Criteria:

• History of other malignancies;
• Presence of organ dysfunction;
• Concurrent immunodeficiency syndrome, active tuberculosis, HIV infection, etc.;
• Allogeneic transplantation requiring immunosuppressive therapy;
• Poor follow-up compliance.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
Pancreatic Cancer; The patient is diagnosed with pancreatic cancer for the first time and has not received any tumor treatment.
Benign Pancreatic Diseases; The patient is diagnosed with a benign pancreatic disease ，such as SCN、MCN、IPMN.and has not undergone any treatment.
Healthy controls; A healthy population without any pancreatic-related diseases or other cancers.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Peripheral blood mononuclear cell	Using RNA seq technology to analyze differentially expressed genes in peripheral immune cells	During the 1-7 day period before surgery
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	During the 1-7 day period before surgery
Peripheral blood mononuclear cell	Analyzing RNA expression in individual cells using scRNA-seq	During the 1-7 day period before surgery
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	During the 1-7 day period before surgery
Peripheral blood mononuclear cell	Identification of open chromatin regions in individual cells using scATAC-seq technology	During the 1-7 day period before surgery
Peripheral blood mononuclear cell	Detecting the abundance and type of some markers for peripheral blood mononuclear cell using CYTOF technology	During the 1-7 day period before surgery
CT	Imaging data from the patient's initial visit	Within 1 month before surgery
MRI	Imaging data from the patient's initial visit	Within 1 month before surgery

Collaborators and Investigators

• Sponsor: Zhejiang University
• Collaborators: The Affiliated Hospital of the Chinese Academy of Military Medical Sciences; Nanjing Medical University

Study record dates

Study Major Dates

• Study Start (Estimated): August 1, 2024
• Primary Completion (Estimated): January 1, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: June 23, 2024
• First Submitted That Met QC Criteria: July 2, 2024
• First Posted (Actual): July 11, 2024

Study Record Updates

• Last Update Posted (Actual): July 16, 2024
• Last Update Submitted That Met QC Criteria: July 15, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Keywords: Pancreatic cancer; Artificial intelligence; Early diagnosis; Immunity decoding
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Endocrine System Diseases; Digestive System Neoplasms; Endocrine Gland Neoplasms; Pancreatic Diseases; Pancreatic Neoplasms
• Other Study ID Numbers: MULTIMODAL-1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No