Translational360

Translational360™ is a linked real-world data product from ConcertAI designed to accelerate biomarker-driven oncology research. It integrates electronic medical records (EMR), claims data, and social determinants of health (SDoH) with high-resolution molecular datasets sourced from industry-leading partners. The result is a comprehensive translational data asset that enables the creation of real-world cohorts aligned to clinical trial endpoints and molecular stratification needs, serving researchers and life sciences teams working across solid and hematologic cancers.

By combining market-leading clinical data with fit-for-purpose molecular data, Translational360™ is built to reflect the true complexity of real-world patients — including comorbidities, prior treatment histories, and health equity considerations. The platform is designed to support pre-clinical and translational research priorities, driving both discovery and validation across the oncology pipeline.

Key Benefits

• Comprehensive: Access whole exome, transcriptome, and circulating tumor data matched to curated clinical records and outcomes.
• Flexible: Build cohorts by biomarker, tumor type, therapy exposure, or resistance patterns across both solid and hematologic cancers.
• Real-World Ready: Designed to reflect patient complexity, including comorbidities, prior treatments, and equity considerations.
• Trial Accelerator: Aligns with pre-clinical and translational research priorities to support discovery and validation workflows.

Core Molecular Capabilities

• Whole Exome and Transcriptome Data: Provides DNA and RNA data for understanding drug response and resistance mechanisms, matched to full clinical and therapy history. Particularly suited for solid tumor cohort building and drug discovery.
• Liquid Biopsy Genomics: Offers ctDNA profiling from blood samples to detect early resistance and recurrence markers. Supports longitudinal patient monitoring and expands cohort reach beyond biopsy-accessible tumors, providing real-time, non-invasive molecular data.
• Heme-Malignancy Genomics: Delivers pan-heme coverage across 500+ biomarkers and 30+ hematologic diseases. Incorporates FISH, cytogenetics, NGS, and flow cytometry data, with companion diagnostic support for targeted therapies and mutation panels covering AML, MDS, MM, CLL, and additional indications.

Linked Real-World Clinical Context

• Over 200 EMR variables including ECOG performance status, laboratory results, and therapy records.
• SDoH and payer data to support health equity insights and analyses.
• Integrated line-of-therapy and comorbidity models for a complete patient picture.
• Custom curation available to match specific clinical trial designs and research requirements.

Enhanced Patient Journey Tracking — Example Use Case

• Translational360™ enables multi-modal longitudinal tracking of individual patient journeys, illustrated by an AML case study involving an NPM1+ at diagnosis and FLT3+ at relapse patient.
• The example patient (72-year-old female) was diagnosed with therapy-related AML in May 2020, treated with HMA + Venetoclax, achieved remission in mid-2021, relapsed in October 2022, and subsequently received Venetoclax plus Gilteritinib through 2023.
• Data sources used included multi-modal Transcriptome360™, structured EMR, open claims with greater than 95% overlap, and NeoGenomics NGS and karyotype data.
• Biomarker insights generated: NeoGenomics report at diagnosis identified Normal Karyotype 46,XX, NPM1+ at diagnosis, TET2+ and ASXL1- associations with increased HMA response, and FLT3+ detection at relapse.
• Clinical insights generated: EMR contributed ECOG scores (0 at diagnosis, 1 from 2021 onward), BMI, and lab results. Claims and EMR data together captured comorbidities, a transplant event in early 2023, and multiple transfusions following relapse.

Translational360™ is part of ConcertAI's broader Precision Suite of oncology data and analytics products. By connecting advanced molecular testing with deep real-world clinical context, it provides the molecular depth and patient-level detail needed to drive precision oncology research forward.