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.
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. Immune 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. Researchers 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. The 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. PIP-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.
Study Type
OBSERVATIONAL
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.
Chris O'Brien Lifehouse
Sydney, New South Wales, Australia
RECRUITINGMelanoma Institute Australia
Sydney, New South Wales, Australia
RECRUITINGWestmead Hospital
Sydney, New South Wales, Australia
RECRUITINGAssessment of the accuracy of predictive test results in melanoma
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)
Time frame: 6 months
Assessment of the inaccuracy of predictive test results
Identification of patient populations for which the predictive model did not predict response (6 month PFS) to immunotherapy
Time frame: 6 months
Evaluation of a test request form - completion rate
Proportion of required data completed by the referring clinician
Time frame: 2 years
Analysis of potential barriers to complete a test request form
Identification of barriers to completion of the request form
Time frame: 2 years
Evaluation of accessible data in medical records for completion of a test request form
Proportion of required data that is readily available in routine patient medical records
Time frame: 2 years
Evaluation of a test request form from clinician feedback via qualitative surveys
Summarised feedback from clinicians in narrative format
Time frame: 2 years
Conduct qualitative surveys to evaluate a test result report from consumers' viewpoint
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
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Enrollment
1,000
Time frame: 1 year
Qualiaitive evaluation of a test result report from consumers' viewpoint
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
Time frame: 1 year
Conduct qualitative surveys to evaluation of a test result report from the clinicians' viewpoint
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
Time frame: 2 years
Qualiaitive evaluation of a test result report from clinicians' viewpoint
Summarised feedback from clinicians to structured interview questions
Time frame: 2 years
Evaluation of the predictive model workflow on result completion
Identification of workflow barriers from request form to result delivery
Time frame: 2 years
Evaluation of the predictive model workflow on result delivery
Time from request form submission to predictive test result delivery (business days)
Time frame: 2 years
Evaluation of the predictive model workflow turnaround timeframes
Proportion of test results received within 2 weeks (10 business days) from request
Time frame: 2 years
Evaluation of the predictive model workflow delays
Summary of reasons for delay in providing test result
Time frame: 2 years
Evaluation of the predictive model workflow processes
Identification of processes that require change (if required), the number of identified issues found and the result of implemented changes.
Time frame: 2 years
Availability of suitable tissue for the predictive model testing protocol
Proportion of patients who could not be tested because no suitable tissue available (and reasons why).
Time frame: 2 years
Data quality for the predictive model testing protocol
Proportion of patients who could not be tested because of missing clinical data (and which data and why missing)
Time frame: 2 years
A cost analysis of the predictive model testing protocol
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)
Time frame: 2 years
Evaluation of the predictive test report in shaping clinician treatment decision making
Index of the quality of team decision making using the multidisciplinary team (MDT) metric of decision making (MODe) framework
Time frame: 1 year
Evaluation of the impact of predictive test result in shaping clinician treatment decision making
Decision making with and without the knowledge of the predictive test report (Decision Impact Analysis) by the MDT
Time frame: 2 years
Concordance of clinician treatment decision making with predictive test results
Concordance of treatment recommendation(s) before and after provision of the predictive test report per patient case
Time frame: 2 years
Discordance of clinician treatment decision making with predictive test results
For treatment choice discordance, proportion of theoretical decisions based on the report that would be adhered to
Time frame: 2 years
Exploratory evaluation of potential new biomarkers of immunotherapy response or resistance in blood and stool samples
Identification of biomarkers in blood and/or stools predictive of response or resistance to immunotherapy that can be incorporated into the predictive model
Time frame: 5 years
Evaluation of potential new biomarkers of immunotherapy response or resistance in blood and stool samples on the accuracy of the predictive model
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
Time frame: 5 years
Assessment of the accuracy of predictive test results in non-melanoma skin cancer and non-melanoma tumours
Proportion of correctly predicted responders and non-responders to immunotherapy treatment based on the 6 month progression-free survival
Time frame: 6 months