Bladder Cancer, BRCA Gene Rearrangement, Breast Cancer, Cancer, Cancer, Advanced, Cancer, Breast, Cancer Liver, Cancer, Lung, Cancer, Metastatic, Cancer of Cervix, Cancer of Colon, Cancer of Esophagus, Cancer of Kidney, Cancer of Larynx, Cancer of Liver, Cancer of Neck, Cancer of Pancreas, Cancer of Rectum, Cancer of Skin, Cancer of Stomach, Cancer Prostate, Carcinoma, Central Nervous System Tumor, Cholangiocarcinoma, COVID, Endometrial Cancer, Follicular Lymphoma, Glioblastoma, Leukemia, Mantle Cell Lymphoma, Marginal Zone Lymphoma, Melanoma, Mismatch Repair Deficiency, Myelodysplastic Syndromes, Myelofibrosis, Myeloproliferative Disorders, Myeloproliferative Neoplasm, Neuroendocrine Tumors, Non Hodgkin Lymphoma, Non Small Cell Lung Cancer, Ovarian Cancer, Testicular Cancer, Urothelial Carcinoma
Conditions
Keywords
artificial intelligence, virtual tumor board, clinical trial, clinical trial matching, electronic medical record, machine learning, cost of care, targeted therapy, immunotherapy, precision medicine, precision oncology, cancer, value based care, real world data, data analytics
Brief summary
International registry for cancer patients evaluating the feasibility and clinical utility of an Artificial Intelligence-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate clinical trial enrollment (CTE), as well as the financial impact, and potential outcomes of the intervention.
Detailed description
The SYNERGY Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CT. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into the registry, which is non-interventional with no protocol-mandated tests/procedures - all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as \ 36 mo (\ 24-mo enrollment followed by 12 mo of data collection, to occur every 3 mo). The primary analysis will be performed 12 mo after last pt enrolled. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥50,000 patients.
Interventions
Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CT. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching.
Sponsors
Study design
Eligibility
Inclusion criteria
* Pts with solid and hematological malignancies; * Pts cancer-related biomarkers, gene variants, fusion and rearrangements (by immunohistochemistry, PCR, FISH or NGS): PD-L1, MSI (MMR), Claudin18.2, HER2/Neu, Tumor mutational burden/load (TMB), ABL1, ACVR1B, AKT1, AKT2, AKT3, ALK, APC, AR, ATM, ATRX, AURKA, AURKB, BAP1, BCL2, BCL6, BRAF, BRCA1, BRCA2, BTK, CCND1, CCND2, CCND3, CDK4, CDK6, CDKN1A/B, CEBPA, CHEK1, CHEK2, CSF1R, CTNNB1, DAXX, DDR1/2, DNMT3A, EGFR, ERBB2, ERBB3, ERBB4, ERCC4, ER, ESR1, FANCA, FAS, FBXW7, FGFR1, FGFR2, FGFR3, FGFR4, FLT3, GATA3, GATA6, GNAS, HDAC1, HGF, HRAS, IDH1, IDH2, IGF1R, JAK1, JAK2, JAK3, KDR (VEGFR2), KIT, KRAS, MAP2K2 (MEK2), MAP3K1, MCL1, MDM2, MDM4, MEN1, MET, MSH2, MSH3, MSH6, MTOR, MUTYH, MYC, MYCL (MYCL1), NF1, NF2, NOTCH1, NPM1, NRAS, NTRK1, NTRK2, NTRK3, PALB2, PARP1, PARP2, PARP3, PBRM1, PDCD1 (PD1), PDCD1LG2 (PD-L2), PDGFRA, PDGFRB, PIK3C, PMS2, POLD1, POLE, PRDM1, PTCH1, PTEN, RAF1, RB1, RET, RICTOR, ROS1, RPTOR, SDHA/B/C, SMAD, SMARC, SMO, STK11, TGFBR2, TP53, TSC1, TSC2, VEGFA, VHL, WT1, ZNF217, ZNF703, CEACAM, NRG1, among others. These biomarkers should be determined by local laboratory, external vendor, or next generation sequencing platform * Decision to consider clinical trial pre-screening enrollment (CTE) by primary provider and/or patient
Exclusion criteria
* ECOG PS \> 2; * Abnormal organ function; * Hospice enrollment
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Proportion of patients Eligible for CTE versus Actual CTE | Through study completion, an average of 1 year | CTE Accrual |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Time from Intervention to Actual CTE (months) | Through study completion, an average of 1 year | Time to CTE |
| Impact of CTE on Overall Survival (OS), estimated by Kaplan-Meier and Cox multivariable survival analysis | 4 years | OS |
| Virtual Tumor Board Utilization | Through study completion, an average of 1 year | VTB Use Rate |
| Identification of Barriers to CTE | Through study completion, an average of 1 year | To identify barriers to accruals to clinical trials, as measured and reported by a questionnaire |
| Real World Data Analytics | Through study completion, an average of 1 year | To Analyze Individual Standard of Care Chemotherapy Utilization (nominal), across treatment lines (numeric); data will be combined and aggregated to report chemotherapy utilization rate (%). |
| Impact of CTE on Progression-Free Survival (PFS), estimated by Kaplan-Meier and Cox multivariable survival analysis | 4 years | PFS |
Countries
Puerto Rico, United States