25 Participants Needed

Personalized Drug Selection for Pancreatic Cancer

RC
Overseen ByRobert C. Grant, MD
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)
Prior Safety DataThis treatment has passed at least one previous human trial

Trial Summary

Will I have to stop taking my current medications?

The trial protocol does not specify if you need to stop taking your current medications. However, you cannot be on any other anti-cancer therapy, except for certain supportive care medications that were started at least one month before joining the study.

What data supports the effectiveness of this treatment for pancreatic cancer?

Research shows that using patient-derived models to test drug combinations can help identify personalized treatments for pancreatic cancer, as no single drug works for most patients. This approach has been effective in predicting which treatments might work best for individual patients, suggesting it could improve outcomes.12345

Is the personalized drug selection for pancreatic cancer safe for humans?

The research suggests that using patient-derived models and next-generation sequencing (NGS) can help predict drug toxicity, which is important for safety. In a study, software analysis of NGS data identified biomarkers linked to increased toxicity, which matched reported side effects in patients, indicating a potential for predicting and managing safety concerns.16789

How is the treatment using tumor models for pancreatic cancer different from other treatments?

This treatment is unique because it uses patient-derived models like xenograft mice and organoids to personalize therapy for pancreatic cancer, allowing doctors to test and select the most effective drugs for each individual patient, unlike standard treatments that are not tailored to the patient's specific tumor characteristics.17101112

What is the purpose of this trial?

The purpose of this study is to see if using Patient Derived Organoids (PDO) to choose a drug for the treatment of pancreatic cancer individually for each patient is useful. The study will look at the number of participants who have a response to their assigned drug.

Research Team

RC

Robert C. Grant, MD

Principal Investigator

Princess Margaret Cancer Centre

Eligibility Criteria

This trial is for individuals with pancreatic cancer. It's exploring if using Patient Derived Organoids (PDO) to select a personalized drug treatment can be effective. Participants must have a type of pancreatic cancer that qualifies and should meet other specific health criteria not detailed here.

Inclusion Criteria

Life Expectancy of greater than 12 weeks
I am able to get out of my bed or chair and move around.
Ability to understand and willing to sign a written informed consent form in accordance with applicable local and regulatory requirements. Each patient must sign a consent form prior to screening to document their willingness to participate.
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Exclusion Criteria

Lactating and nursing women
I do not have severe numbness or pain in my hands or feet.
Patients with a history of a severe allergic reaction attributed to compounds of similar or biologic composition to the PDO matched drug may be excluded if assessed by the investigator and determined to be unsafe to proceed
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Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants receive a drug selected based on their Patient Derived Organoids (PDO) results for either progressive disease or maintenance therapy

8-12 weeks

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

Treatment Details

Interventions

  • Using Tumor Models to Determine Treatments
Trial Overview The study tests the effectiveness of various drugs (Colchicine, Selinexor, Abemaciclib, Ponatinib, Cobimetinib, Brigatinib, Neratinib, Doxorubicin, Etoposide, Ceritinib) chosen based on PDO models for treating pancreatic cancer. The main focus is on how many patients respond positively to their tailored treatment.
Participant Groups
2Treatment groups
Experimental Treatment
Group I: Cohort BExperimental Treatment10 Interventions
Patients in this cohort will be entering the study for maintenance therapy with stable disease.
Group II: Cohort AExperimental Treatment10 Interventions
Patients in this cohort will be entering the study for treatment for progressive disease.

Find a Clinic Near You

Who Is Running the Clinical Trial?

University Health Network, Toronto

Lead Sponsor

Trials
1,555
Recruited
526,000+

Findings from Research

A study involving 14 patients with pancreatic ductal adenocarcinoma (PDAC) used next-generation sequencing (NGS) to identify mutations and pharmacogenomic biomarkers, confirming known driver mutations like KRAS and TP53.
The analysis revealed that while no changes in therapy were made, the software could predict treatment efficacy and toxicity, suggesting that integrating NGS with evidence-based software could enhance precision medicine approaches for PDAC.
Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer.Malgerud, L., Lindberg, J., Wirta, V., et al.[2018]
The ADEStrata approach, developed in this study, allows for the stratification of tumor mutations based on adverse drug events (ADEs), specifically focusing on musculoskeletal adverse events in breast cancer patients treated with aromatase inhibitors, using data from 212 patients.
By prioritizing somatic variants linked to ADEs, the study found that this method can identify high-risk patients who may experience poor outcomes, suggesting that it could help tailor cancer therapies to improve patient safety and efficacy.
Adverse Drug Event-based Stratification of Tumor Mutations: A Case Study of Breast Cancer Patients Receiving Aromatase Inhibitors.Wang, C., Zimmermann, MT., Prodduturi, N., et al.[2018]
The ADEStrata framework, previously used for breast cancer, was successfully applied to ovarian cancer patients treated with cisplatin, demonstrating its versatility in predicting adverse drug events (ADEs) and patient outcomes.
By analyzing 156 ovarian cancer patients, the study showed that understanding the molecular mechanisms of cisplatin-related ADEs can help identify tumor subtypes with varying clinical outcomes, potentially guiding personalized treatment strategies.
Adverse Drug Events-based Tumor Stratification for Ovarian Cancer Patients Receiving Platinum Therapy.Wang, C., Zimmermann, MT., Chute, CG., et al.[2020]

References

Integrated Patient-Derived Models Delineate Individualized Therapeutic Vulnerabilities of Pancreatic Cancer. [2019]
Preclinical models of pancreatic ductal adenocarcinoma: challenges and opportunities in the era of precision medicine. [2021]
Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. [2023]
Coordinated epidermal growth factor receptor pathway gene overexpression predicts epidermal growth factor receptor inhibitor sensitivity in pancreatic cancer. [2022]
Personalized Management of Pancreatic Ductal Adenocarcinoma Patients through Computational Modeling. [2023]
Bioinformatory-assisted analysis of next-generation sequencing data for precision medicine in pancreatic cancer. [2018]
Pancreas Cancer Precision Treatment Using Avatar Mice from a Bioinformatics Perspective. [2023]
Adverse Drug Event-based Stratification of Tumor Mutations: A Case Study of Breast Cancer Patients Receiving Aromatase Inhibitors. [2018]
Adverse Drug Events-based Tumor Stratification for Ovarian Cancer Patients Receiving Platinum Therapy. [2020]
10.United Statespubmed.ncbi.nlm.nih.gov
Organoid Profiling Identifies Common Responders to Chemotherapy in Pancreatic Cancer. [2022]
Multiplex Patient-Based Drug Response Assay in Pancreatic Ductal Adenocarcinoma. [2021]
Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer. [2022]
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