Ignite Creation Date:
2025-12-24 @ 6:37 PM
Ignite Modification Date:
2025-12-28 @ 9:22 AM
Study NCT ID:
NCT06536257
Status:
RECRUITING
Last Update Posted:
2025-09-18
First Post:
2024-03-30
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Personalised Immunotherapy Platform
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008545', 'term': 'Melanoma'}, {'id': 'D002280', 'term': 'Carcinoma, Basal Cell'}, {'id': 'D015266', 'term': 'Carcinoma, Merkel Cell'}], 'ancestors': [{'id': 'D018358', 'term': 'Neuroendocrine Tumors'}, {'id': 'D017599', 'term': 'Neuroectodermal Tumors'}, {'id': 'D009373', 'term': 'Neoplasms, Germ Cell and Embryonal'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D009380', 'term': 'Neoplasms, Nerve Tissue'}, {'id': 'D018326', 'term': 'Nevi and Melanomas'}, {'id': 'D012878', 'term': 'Skin Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D012871', 'term': 'Skin Diseases'}, {'id': 'D017437', 'term': 'Skin and Connective Tissue Diseases'}, {'id': 'D002277', 'term': 'Carcinoma'}, {'id': 'D009375', 'term': 'Neoplasms, Glandular and Epithelial'}, {'id': 'D018295', 'term': 'Neoplasms, Basal Cell'}, {'id': 'D027601', 'term': 'Polyomavirus Infections'}, {'id': 'D004266', 'term': 'DNA Virus Infections'}, {'id': 'D014777', 'term': 'Virus Diseases'}, {'id': 'D007239', 'term': 'Infections'}, {'id': 'D014412', 'term': 'Tumor Virus Infections'}, {'id': 'D018278', 'term': 'Carcinoma, Neuroendocrine'}, {'id': 'D000230', 'term': 'Adenocarcinoma'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D000098412', 'term': 'Predictive Learning Models'}], 'ancestors': [{'id': 'D000098411', 'term': 'Prediction Methods, Machine'}, {'id': 'D001185', 'term': 'Artificial Intelligence'}, {'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Laboratory analysis of pretreatment tumour biopsies:\n\n* Tumour mutation burden (TMB): Qiaseq TMB IO (Qaigen) and TruSight Oncology 500 (Illumina)\n* Gene expression profiling (GEP): Pancancer 360 IO (Nanostring) and Whole Transcriptome Sequencing (Illumina)\n* Tumour Immune Profiling (mIHC): Multiplex immunohistochemistry (Akoya Biosciences)'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2021-06-08', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2037-06-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-09-12', 'studyFirstSubmitDate': '2024-03-30', 'studyFirstSubmitQcDate': '2024-07-31', 'lastUpdatePostDateStruct': {'date': '2025-09-18', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2024-08-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-06-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Assessment of the accuracy of predictive test results in melanoma', 'timeFrame': '6 months', 'description': 'Proportion of correctly predicted responders and non-responders to the immunotherapy treatment decision by the MDT, based on the 6 month progression-free survival (PFS)'}], 'secondaryOutcomes': [{'measure': 'Assessment of the inaccuracy of predictive test results', 'timeFrame': '6 months', 'description': 'Identification of patient populations for which the predictive model did not predict response (6 month PFS) to immunotherapy'}, {'measure': 'Evaluation of a test request form - completion rate', 'timeFrame': '2 years', 'description': 'Proportion of required data completed by the referring clinician'}, {'measure': 'Analysis of potential barriers to complete a test request form', 'timeFrame': '2 years', 'description': 'Identification of barriers to completion of the request form'}, {'measure': 'Evaluation of accessible data in medical records for completion of a test request form', 'timeFrame': '2 years', 'description': 'Proportion of required data that is readily available in routine patient medical records'}, {'measure': 'Evaluation of a test request form from clinician feedback via qualitative surveys', 'timeFrame': '2 years', 'description': 'Summarised feedback from clinicians in narrative format'}, {'measure': "Conduct qualitative surveys to evaluate a test result report from consumers' viewpoint", 'timeFrame': '1 year', 'description': 'Survey results from consumer responses on the comprehension of risk probability (mean score from a 7 point scale), communication efficacy (mean score from a 4 point scale), significance and actionability (mean score from a 7 point scale) of the information within the report'}, {'measure': "Qualiaitive evaluation of a test result report from consumers' viewpoint", 'timeFrame': '1 year', 'description': "Interviewer notes and transcriptions of consumers' answers to structured interview questions coded in software for qualitative data to identify and evaluate the most significant problems, highlight cases of poor comprehension, and assess the degree to which the reports met consumers' information needs"}, {'measure': "Conduct qualitative surveys to evaluation of a test result report from the clinicians' viewpoint", 'timeFrame': '2 years', 'description': 'Survey results from clinicians on the utility (mean score from a 7 point scale) and comprehension (mean score from a 7 point scale) of the report'}, {'measure': "Qualiaitive evaluation of a test result report from clinicians' viewpoint", 'timeFrame': '2 years', 'description': 'Summarised feedback from clinicians to structured interview questions'}, {'measure': 'Evaluation of the predictive model workflow on result completion', 'timeFrame': '2 years', 'description': 'Identification of workflow barriers from request form to result delivery'}, {'measure': 'Evaluation of the predictive model workflow on result delivery', 'timeFrame': '2 years', 'description': 'Time from request form submission to predictive test result delivery (business days)'}, {'measure': 'Evaluation of the predictive model workflow turnaround timeframes', 'timeFrame': '2 years', 'description': 'Proportion of test results received within 2 weeks (10 business days) from request'}, {'measure': 'Evaluation of the predictive model workflow delays', 'timeFrame': '2 years', 'description': 'Summary of reasons for delay in providing test result'}, {'measure': 'Evaluation of the predictive model workflow processes', 'timeFrame': '2 years', 'description': 'Identification of processes that require change (if required), the number of identified issues found and the result of implemented changes.'}, {'measure': 'Availability of suitable tissue for the predictive model testing protocol', 'timeFrame': '2 years', 'description': 'Proportion of patients who could not be tested because no suitable tissue available (and reasons why).'}, {'measure': 'Data quality for the predictive model testing protocol', 'timeFrame': '2 years', 'description': 'Proportion of patients who could not be tested because of missing clinical data (and which data and why missing)'}, {'measure': 'A cost analysis of the predictive model testing protocol', 'timeFrame': '2 years', 'description': 'Calculation of the cost per individual test (to include staff time, reagents, proportionate use of analytical equipment, assay costs, pathology service fee for sample preparation and shipping)'}, {'measure': 'Evaluation of the predictive test report in shaping clinician treatment decision making', 'timeFrame': '1 year', 'description': 'Index of the quality of team decision making using the multidisciplinary team (MDT) metric of decision making (MODe) framework'}, {'measure': 'Evaluation of the impact of predictive test result in shaping clinician treatment decision making', 'timeFrame': '2 years', 'description': 'Decision making with and without the knowledge of the predictive test report (Decision Impact Analysis) by the MDT'}, {'measure': 'Concordance of clinician treatment decision making with predictive test results', 'timeFrame': '2 years', 'description': 'Concordance of treatment recommendation(s) before and after provision of the predictive test report per patient case'}, {'measure': 'Discordance of clinician treatment decision making with predictive test results', 'timeFrame': '2 years', 'description': 'For treatment choice discordance, proportion of theoretical decisions based on the report that would be adhered to'}, {'measure': 'Exploratory evaluation of potential new biomarkers of immunotherapy response or resistance in blood and stool samples', 'timeFrame': '5 years', 'description': 'Identification of biomarkers in blood and/or stools predictive of response or resistance to immunotherapy that can be incorporated into the predictive model'}, {'measure': 'Evaluation of potential new biomarkers of immunotherapy response or resistance in blood and stool samples on the accuracy of the predictive model', 'timeFrame': '5 years', 'description': 'Impact of the inclusion of blood and/or stool biomarkers to the accuracy of the predictive model on predicting response to immunotherapy at 6 months post-treatment'}, {'measure': 'Assessment of the accuracy of predictive test results in non-melanoma skin cancer and non-melanoma tumours', 'timeFrame': '6 months', 'description': 'Proportion of correctly predicted responders and non-responders to immunotherapy treatment based on the 6 month progression-free survival'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Biomarker', 'Predictive', 'Immunotherapy', 'Multi-omic', 'Tumour mutation burden', 'Gene expression', 'Tissue imaging', 'Machine learning', 'Multiplex immunofluorescence', 'Immune checkpoint inhibitors', 'Quantitative pathology'], 'conditions': ['Melanoma', 'Cutaneous Squamous Cell Carcinoma', 'Basal Cell Carcinoma', 'Merkel Cell Carcinoma', 'Solid Tumor']}, 'referencesModule': {'references': [{'pmid': '37808404', 'type': 'BACKGROUND', 'citation': 'Gide TN, Paver EC, Yaseen Z, Maher N, Adegoke N, Menzies AM, Pires da Silva I, Wilmott JS, Long GV, Scolyer RA. Lag-3 expression and clinical outcomes in metastatic melanoma patients treated with combination anti-lag-3 + anti-PD-1-based immunotherapies. Oncoimmunology. 2023 Oct 4;12(1):2261248. doi: 10.1080/2162402X.2023.2261248. eCollection 2023.'}, {'pmid': '37055772', 'type': 'RESULT', 'citation': 'Mao Y, Gide TN, Adegoke NA, Quek C, Maher N, Potter A, Patrick E, Saw RPM, Thompson JF, Spillane AJ, Shannon KF, Carlino MS, Lo SN, Menzies AM, da Silva IP, Long GV, Scolyer RA, Wilmott JS. Cross-platform comparison of immune signatures in immunotherapy-treated patients with advanced melanoma using a rank-based scoring approach. J Transl Med. 2023 Apr 13;21(1):257. doi: 10.1186/s12967-023-04092-9.'}, {'pmid': '37865395', 'type': 'RESULT', 'citation': 'Adegoke NA, Gide TN, Mao Y, Quek C, Patrick E, Carlino MS, Lo SN, Menzies AM, Pires da Silva I, Vergara IA, Long G, Scolyer RA, Wilmott JS. Classification of the tumor immune microenvironment and associations with outcomes in patients with metastatic melanoma treated with immunotherapies. J Immunother Cancer. 2023 Oct;11(10):e007144. doi: 10.1136/jitc-2023-007144.'}]}, 'descriptionModule': {'briefSummary': 'This is a non-interventional study to prospectively test a suite of predictive biomarker models of immunotherapy resistance in patients with melanoma, non-melanoma skin cancers and other solid tumours. The study will evaluate the documentation, processes, accuracy and utility of the predictive biomarker model in clinical practice.', 'detailedDescription': "The Personalised Immunotherapy Program (PIP) is a multicenter biomarker discovery and validation program of multi-omic biomarker based predictive models which aim to identify patients with immunotherapy resistant disease. PIP developed predictive models in retrospective setting, with validation within a prospective clinical observational study.\n\nImmune checkpoint inhibitors targeting the cytotoxic T-cell lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1) receptors have revolutionised the treatment of advanced melanoma, resulting in long-term durable responses and a 5-year overall survival of 52% with combination immunotherapy. However, clinical benefit is not universal, and half of these patients do not respond. Therefore, there is an urgent need for clinically validated biomarkers which can identify patients who are at high risk of not responding to standard-of-care immunotherapies and determine which emerging clinical trial agent is most appropriate for a particular patient's disease.\n\nResearchers performed mutation, gene expression and tumour immune profiling on tumour biopsies from melanoma patients treated with anti-PD-1 monotherapy or combined anti-PD-1 and anti-CTLA-4 therapy. From this dataset PIP has developed predictive models to identify patients with immunotherapy resistant disease.\n\nThe subsequent PIP-PREDICT is a prospective clinical study that enrols advanced cancer patients who are eligible to receive approved immunotherapies. PIP testing and biomarker reporting is used to screen potential patients. Each patient enrolled in the study receives an individual PIP Biomarker Report, which is presented as part of the established Biomarker Multidisciplinary Team (MDT) meeting of clinical oncologists, pathologists, molecular biologists, trials nursing, PIP, and biospecimen staff on a fortnightly basis.\n\nPIP-PREDICT has a primary goal of determining the accuracy of biomarker predictions from PIP prospectively within oncology clinics. Secondary goals include assessing the feasibility of biomarker assay workflows within diagnostic providers, conducting a cost-benefit ratio analysis, evaluating the effect of biomarker reports on treatment selection within multidisciplinary teams (MDTs), and performing a post-implementation analysis of personalised immunotherapy biomarker reports in treatment decision making."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with melanoma, non-melanoma skin cancer, non-melanoma solid tumours eligible for treatment with immunotherapy(ies)', 'healthyVolunteers': False, 'eligibilityCriteria': 'MELANOMA:\n\nInclusion Criteria:\n\n1. Written informed consent to participation for the use of tumour tissue, blood and stool and collection of standard clinical data.\n2. Histologically confirmed resected stage II (at high risk of recurrence of disease), III or stage IV melanoma (including cutaneous, mucosal, acral, subungual, uveal or unknown primary melanoma) and unresectable Stage III or IV melanoma\n3. Eligible to receive immunotherapy\n4. Availability of a melanoma tissue sample which was obtained at surgery and where no systemic treatments (e.g. adjuvant treatment) were administered between sample procurement and proposed PIP testing\n5. Patients who have received adjuvant or neoadjuvant systemic therapy in the past are eligible if they have had recurrence after neoadjuvant or adjuvant therapy has been completed and the biopsy represents this relapsed disease\n6. RECIST version 1.1 measurable disease.\n7. Tissue sample must be representative of the whole tumour and therefore excision biopsies are preferred over core biopsies.\n8. A life expectancy over 6 months.\n9. Prior treatment with BRAF (B-Raf proto-oncogene) / MEK (mitogen-activated protein kinase) inhibitors are acceptable, providing the other eligibility criteria are met.\n10. If a patient has had prior radiotherapy for melanoma, the biopsy to be used for the biomarker test must be from an area that was not within the radiotherapy field.\n\nExclusion Criteria:\n\n1\\. Patients will be excluded if they have had a positive test result for hepatitis B virus surface antigen (HBV sAg) or hepatitis C virus ribonucleic acid (HCV antibody), indicating acute or chronic infection. If receiving treatment and from HCV for at least one year, patients are allowed to participate. No new testing is required for the sole purpose of this pilot phase. Patients will be excluded if they have known history of testing positive for human immunodeficiency virus (HIV) or known acquired immunodeficiency syndrome (AIDS). No new testing is required\n\nNON-MELANOMA:\n\nInclusion Criteria:\n\n1. Written informed consent to participation for the use of tumour tissue and collection of standard clinical data\n2. Histologically confirmed cancer and eligibility to receive immunotherapy treatment.\n3. Availability of a tissue sample where no systemic treatments were administered between sample procurement and proposed PIP testing\n4. If treatment has been administered since the last tissue sample was obtained, a new biopsy should be planned for routine testing or clinical trial screening, where a portion of the sample can be used for the predictive assay. No new biopsies are required for the sole purpose of this study.\n5. Patients who have received adjuvant or neoadjuvant systemic therapy in the past are eligible if they have had recurrence after neoadjuvant or adjuvant therapy has been completed and the biopsy represents this relapsed disease.\n6. Have clinically detectable disease defined as one of more of the following:\n\n * RECIST measurable. Lesions situated in a previously irradiated area are considered measurable if RECIST-defined disease progression since radiotherapy has been demonstrated in such lesions, OR,\n * Positron Emission Tomography (PET) avid, OR,\n * Clinically evident disease: photographically, detectable on CT or palpable, OR\n * Clinical status measured by observable and diagnosable signs or symptoms.\n7. The tissue sample must be representative of the whole tumour and therefore excision biopsies are preferred over core biopsies.\n8. A life expectancy over 6 months.\n9. Prior treatment with targeted therapies are acceptable, providing the other eligibility criteria are met.\n10. If a patient has had prior radiotherapy for melanoma, the biopsy to be used for the biomarker test must be from an area that was not within the radiotherapy field\n\nExclusion Criteria:\n\n1\\. Patients will be excluded if they have had a positive test result for hepatitis B virus surface antigen (HBV sAg) or hepatitis C virus ribonucleic acid (HCV antibody), indicating acute or chronic infection. If receiving treatment and from HCV for at least one year, patients are allowed to participate. No new testing is required for the sole purpose of this pilot phase. Patients will be excluded if they have known history of testing positive for human immunodeficiency virus (HIV) or known acquired immunodeficiency syndrome (AIDS). No new testing is required'}, 'identificationModule': {'nctId': 'NCT06536257', 'acronym': 'PIP-PREDICT', 'briefTitle': 'Personalised Immunotherapy Platform', 'organization': {'class': 'OTHER', 'fullName': 'Melanoma Institute Australia'}, 'officialTitle': 'Personalised Immunotherapy Platform (PIP) - Implementation of a Predictive Model of Response to Immunotherapies in Melanoma', 'orgStudyIdInfo': {'id': 'MIA2020/283'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Melanoma cohort', 'description': 'Patients with melanoma who are yet to receive immunotherapy will undergo predictive biomarker testing and biomarker reporting.', 'interventionNames': ['Other: Predictive model']}, {'label': 'Non-melanoma skin cancer cohort', 'description': 'Patients with non-melanoma skin cancers (basal cell carcinoma, Merkel cell carcinoma, cutaneous squamous cell carcinoma) who are yet to receive immunotherapy will have tumour tested using the predictive model.', 'interventionNames': ['Other: Predictive model']}, {'label': 'Solid tumour cohort', 'description': 'Patients with non-melanoma, non-skin cancer, solid tumours who are yet to receive immunotherapy will have tumour tested using the predictive model.', 'interventionNames': ['Other: Predictive model']}], 'interventions': [{'name': 'Predictive model', 'type': 'OTHER', 'description': 'Patient who have not had immunotherapy will have tumour tested using the predictive model. This determines whether patients are likely to respond, or not to respond to immunotherapy. Results of the predictive model will be compared with the actual response to immunotherapy when this has been completed.', 'armGroupLabels': ['Melanoma cohort', 'Non-melanoma skin cancer cohort', 'Solid tumour cohort']}]}, 'contactsLocationsModule': {'locations': [{'zip': '2050', 'city': 'Sydney', 'state': 'New South Wales', 'status': 'RECRUITING', 'country': 'Australia', 'contacts': [{'name': 'Michael Boyer, MBBS', 'role': 'CONTACT', 'email': 'michael.boyer@lh.org.au'}, {'name': 'Jenny Lee, MBBS', 'role': 'SUB_INVESTIGATOR'}], 'facility': "Chris O'Brien Lifehouse", 'geoPoint': {'lat': -33.86785, 'lon': 151.20732}}, {'zip': '2065', 'city': 'Sydney', 'state': 'New South Wales', 'status': 'RECRUITING', 'country': 'Australia', 'contacts': [{'name': 'Georgina Long, MBBS', 'role': 'CONTACT', 'email': 'georgina.long@sydney.edu.au'}, {'name': 'Ines Silva, MBBS', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Richard Scolyer, MBBS', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Alexander Menzies, MBBS', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Serigne Lo, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Anne Cust, PhD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Rachael Morton, PhD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Melanoma Institute Australia', 'geoPoint': {'lat': -33.86785, 'lon': 151.20732}}, {'zip': '2145', 'city': 'Sydney', 'state': 'New South Wales', 'status': 'RECRUITING', 'country': 'Australia', 'contacts': [{'name': 'Matteo Carlion, MBBS', 'role': 'CONTACT', 'email': 'matteo.carlino@sydney.edu.au'}], 'facility': 'Westmead Hospital', 'geoPoint': {'lat': -33.86785, 'lon': 151.20732}}], 'centralContacts': [{'name': 'Personalised Immunotherapy Program Manager', 'role': 'CONTACT', 'email': 'PIP@melanoma.org.au', 'phone': '+61 2 9911 7200'}], 'overallOfficials': [{'name': 'James Wilmott, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Melanoma Institute Australia'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Melanoma Institute Australia', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}