The behind the Cancer Digital Twin science
We model each patient from the data they actually have.
Genomics
Somatic variants, copy number, and structural changes from sequencing or panels.
Transcriptomics
Pathway activity and tumor micro-environment data from RNA expression.
Pathology
IHC, histology, and report findings read into structured biomarkers.
Radiology
Imaging-derived disease extent and metastatic sites.
Clinical record
Diagnosis, staging, prior lines, comorbidities, and performance status.
Many data inputs.
One reasoned answer out.
Every modality a patient has is integrated into a single in-silico model to be read, questioned, and acted on.
Inputs and outputs shown; internal processing is proprietary. Built only on publicly available, openly-licensed knowledge.
A full tumor board built in minutes.
Six specialized agents converge on a single patient. Each runs independent analysis and contributes one part of the personalized evidence battery.
Calls variants, derives signatures (HRD, MSI), computes pathway-level scores against curated knowledge bases.
Calibrates every claim. Builds confidence intervals from actual cohorts supporting each evidence piece and flags fragile claims
Reads whole-slide imaging, scores receptor and biomarker assays, identifies tumour microenvironment features.
Extracts radiomic features, tracks lesion volumetrics, computes RE- CIST trajectories, and surfaces sub-clinical change early.
Maps the patient against guideline pathways (NCCN, ESMO, NCG), reconciles with molecular reality, and surfaces deviations.
Continuously matches the patient’s evolving feature set against open trials with strong biomarker-anchored cases.
Clinical Oncologist
Bioinformation
Biostatistician
Trial Specialist
Radiologist
Pathologist
Personalized tumor biology
weighed against real-world evidence.
The patient's molecular and tumor biology
Strength of published, real-world evidence
Alignment with clinical guidelines
Safety against history and comorbidities
One patient's journey:
Meet Jane Doe.
A 54-year-old with HR+/HER2− breast cancer. Before reasoning about what comes next, her twin rebuilds the full disease chronology from every report on file.
What her oncologist receives
Her twin ranks every therapy on the same scale, shows the reasons behind each, and carries the evidence so the rationale can be checked line by line
Illustrative example. Scores, levels, and rankings shown are for demonstration only and do not represent a clinical recommendation.
A pathway, not just a label.
For Jane, the report does not stop at “PIK3CA mutated.” It lays out the actionable flow:
gate on the result, branch, act, and plan the next move.
Confirm PIK3CA activating mutation
tumor or ctDNA
PI3K-targeted therapy + endocrine backbone
biomarker-matched, on-label after progression
Guideline endocrine-based alternative
next-line option per current guidelines
Re-biopsy; reassess for ESR1 and resistance
plan the following line before it is needed
Monitor tolerance against comorbidities
safety tracked as the line proceeds
Confirm PIK3CA activating mutation
tumor or ctDNA
PI3K-targeted therapy + endocrine backbone
biomarker-matched, on-label after progression
Guideline endocrine-based alternative
next-line option per current guidelines
Re-biopsy; reassess for ESR1 and resistance
plan the following line before it is needed
Monitor tolerance against comorbidities
safety tracked as the line proceeds
More than a marker list.
Conventional reports hand back biomarkers and stop. The Digital Twin Report shows its reasoning, and lets you check it.
Decision pathway
An explicit if/then flow: not just what to do,but what next, and when.
Patient timeline
The full disease chronology,
reconstructed and placed in context.
What was ruled out
Every therapy considered is shown, ranked or demoted, each with the reason why.
Cross-modal synthesis
Where DNA, RNA, pathology and clinical data agree or conflict, surfaced explicitly.
Every claim cited
Each recommendation carries its evidence and a confidence level, so rationale can be checked line by line.
Resistance anticipation & gaps
Emerging resistance is surfaced, and what’s missing is named as a diagnostic gap with the test that would close it.
A closed loop that recalibrates
as the patient’s data changes.
Patient context
All available patient data harmonized and structured for the Digital Twin.
Digital Twin
Representing the tumor’s biology and the patient’s constraints.
Discovery + Scoring
Plausible therapies proposed, re-searched, and scored.
Rankings Delivered
An evidence-graded list delivered to the oncologist for decision support.
The twin is rebuilt as new evidence and outcomes arrive.
Peer-reviewed, and honest about the edges.
The reasoning approach — a constrained, evidence-first agent — outperformed agents equipped with 200+ tools across five cancer types in a peer-reviewed study.
Decision support for a qualified oncologist, not autonomous decision-making.
Built only on publicly available, openly-licensed knowledge sources.
Coverage depends on the data a patient has; gaps are flagged, never hidden.
Bring the science to your clinic.
Request a demo, or explore how each part of the platform delivers the Digital Twin into your workflow.