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SYNERGY-AI: Artificial Intelligence Based Precision Oncology Clinical Trial Matching and Registry

ConditionsCancer, MetastaticCancerCancer of PancreasCancer of LiverCancer of StomachCancer LiverCancer of RectumCancer of KidneyCancer of EsophagusCancer of CervixCancer of ColonCancer of LarynxCancer, LungCancer, BreastCancer, AdvancedCancer ProstateCancer of NeckCancer of SkinNeuroendocrine TumorsCarcinomaMismatch Repair DeficiencyBRCA Gene RearrangementNon Hodgkin LymphomaLeukemiaNon Small Cell Lung CancerCholangiocarcinomaGlioblastomaCentral Nervous System TumorMelanomaUrothelial CarcinomaBladder CancerOvarian CancerEndometrial CancerTesticular CancerBreast CancerCOVIDMyelofibrosisMyeloproliferative NeoplasmMyeloproliferative DisordersFollicular LymphomaMantle Cell LymphomaMarginal Zone LymphomaMyelodysplastic Syndromes
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorMassive Bio, Inc.

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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.

Decision to consider clinical trial pre-screening enrollment (CTE) by primary provider and/or patient

Disqualifiers

ECOG PS > 2;

Abnormal organ function;

Hospice enrollment

Trial population

Observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider clinical trial enrollment (CTE) has already been made by their primary providers (PP).

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • Clinical Trial Matching

    Other intervention

    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.

Treatment groups

50,000 Participants
are divided into 1 treatment group
Group A: Study Group1 intervention

Trial outcomes

Primary outcomes

1

Proportion of patients Eligible for CTE versus Actual CTE

CTE Accrual

Time frame
Through study completion, an average of 1 year

Secondary outcomes

1

Impact of CTE on Overall Survival (OS), estimated by Kaplan-Meier and Cox multivariable survival analysis

OS

Time frame
4 years
2

Impact of CTE on Progression-Free Survival (PFS), estimated by Kaplan-Meier and Cox multivariable survival analysis

PFS

Time frame
4 years
3

Identification of Barriers to CTE

To identify barriers to accruals to clinical trials, as measured and reported by a questionnaire

Time frame
Through study completion, an average of 1 year
4

Real World Data Analytics

To Analyze Individual Standard of Care Chemotherapy Utilization (nominal), across treatment lines (numeric); data will be combined and aggregated to report chemotherapy utilization rate (%).

Time frame
Through study completion, an average of 1 year

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Massive Bio, Inc.

Lead sponsor

Source ClinicalTrials.gov
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