SYNERGY-AI: Artificial Intelligence Based Precision Oncology Clinical Trial Matching and Registry
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ClinicalTrials.gov Identifier: NCT03452774 |
Recruitment Status :
Recruiting
First Posted : March 2, 2018
Last Update Posted : May 25, 2021
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Condition or disease | Intervention/treatment |
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Cancer, Metastatic Cancer Cancer of Pancreas Cancer of Liver Cancer of Stomach Cancer Liver Cancer of Rectum Cancer of Kidney Cancer of Esophagus Cancer of Cervix Cancer of Colon Cancer of Larynx Cancer, Lung Cancer, Breast Cancer, Advanced Cancer Prostate Cancer of Neck Cancer of Skin Neuroendocrine Tumors Carcinoma Mismatch Repair Deficiency BRCA Gene Rearrangement Non Hodgkin Lymphoma Leukemia Non Small Cell Lung Cancer Cholangiocarcinoma Glioblastoma Central Nervous System Tumor Melanoma Urothelial Carcinoma Bladder Cancer Ovarian Cancer Endometrial Cancer Testicular Cancer Breast Cancer COVID Myelofibrosis Myeloproliferative Neoplasm Myeloproliferative Disorders Follicular Lymphoma Mantle Cell Lymphoma Marginal Zone Lymphoma Myelodysplastic Syndromes | Other: Clinical Trial Matching |
Study Type : | Observational [Patient Registry] |
Estimated Enrollment : | 5000 participants |
Observational Model: | Cohort |
Time Perspective: | Prospective |
Target Follow-Up Duration: | 36 Months |
Official Title: | SYNERGY-AI: Artificial Intelligence Based Precision Oncology Clinical Trial Matching and Registry |
Actual Study Start Date : | January 1, 2018 |
Estimated Primary Completion Date : | June 2022 |
Estimated Study Completion Date : | December 2022 |

Group/Cohort | Intervention/treatment |
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Study Group
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).
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Other: Clinical Trial Matching
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. |
- Proportion of patients Eligible for CTE versus Actual CTE [ Time Frame: Through study completion, an average of 1 year ]CTE Accrual
- Impact of CTE on Overall Survival (OS), estimated by Kaplan-Meier and Cox multivariable survival analysis [ Time Frame: 4 years ]OS
- Impact of CTE on Progression-Free Survival (PFS), estimated by Kaplan-Meier and Cox multivariable survival analysis [ Time Frame: 4 years ]PFS
- Identification of Barriers to CTE [ Time Frame: 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 [ Time Frame: 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 (%).
- Virtual Tumor Board Utilization [ Time Frame: Through study completion, an average of 1 year ]VTB Use Rate
- Time from Intervention to Actual CTE (months) [ Time Frame: Through study completion, an average of 1 year ]Time to CTE

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Ages Eligible for Study: | Child, Adult, Older Adult |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | No |
Sampling Method: | Non-Probability Sample |
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, 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, 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

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03452774
Contact: Selin Kurnaz, PhD | +1(917) 336-3319 | skurnaz@massivebio.com | |
Contact: Kristin Johnston, RN | +1(917) 336-3319 | kjohnston@massivebio.com |
United States, New York | |
Massive Bio, Inc | Recruiting |
New York, New York, United States, 10006 | |
Contact: Kristin Johnston, RN 844-627-7246 support@massivebio.com |
Principal Investigator: | Selin Kurnaz, PhD | Massive Bio, Inc. |
Responsible Party: | Massive Bio, Inc. |
ClinicalTrials.gov Identifier: | NCT03452774 |
Other Study ID Numbers: |
SYNERGY-AI |
First Posted: | March 2, 2018 Key Record Dates |
Last Update Posted: | May 25, 2021 |
Last Verified: | May 2021 |
Individual Participant Data (IPD) Sharing Statement: | |
Plan to Share IPD: | No |
Studies a U.S. FDA-regulated Drug Product: | No |
Studies a U.S. FDA-regulated Device Product: | No |
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 |
Lymphoma Carcinoma Glioblastoma Lymphoma, Mantle-Cell Endometrial Neoplasms Neuroendocrine Tumors Cholangiocarcinoma Nervous System Neoplasms Central Nervous System Neoplasms Testicular Neoplasms Breast Neoplasms Pancreatic Neoplasms Lung Neoplasms Esophageal Neoplasms Colonic Neoplasms |
Stomach Neoplasms Liver Neoplasms Rectal Neoplasms Uterine Cervical Neoplasms Kidney Neoplasms Carcinoma, Renal Cell Skin Neoplasms Head and Neck Neoplasms Laryngeal Neoplasms Myelodysplastic Syndromes Myeloproliferative Disorders Neoplasms by Histologic Type Neoplasms Lymphoproliferative Disorders Lymphatic Diseases |