# Pick the laboratory model that matches the patient, not the one to hand

Source: https://onco.cc/ideas/idea-bio1-model-patient-matching/  
OnCo record `idea-bio1-model-patient-matching` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Labs usually use whichever tumour models they already have. A searchable index that finds the model closest to a specific patient's tumour would make experiments more relevant.

## Summary

Thousands of characterised models exist across DepMap, the Human Cancer Models Initiative, PDX repositories and institutional banks, but selection is driven by availability and habit. A matching engine indexing molecular profiles, ancestry, treatment history and microenvironment features would return the closest available models for a given tumour profile, and quantify how much of the clinical population has any representative model at all.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- Hypothesis: Formal matching shows that a substantial share of common tumour molecular subtypes and non-European ancestries have no closely matched model, and directs derivation effort toward those gaps.
- Rationale: Model collections over-represent easily grown, historically sampled tumours from a narrow population, which is a plausible and testable contributor to translational failure and to inequitable drug performance.
- Proposed test: Build the index across public repositories, compute coverage of clinical genomic cohorts by subtype and ancestry, and publish the gap map as a derivation priority list.
- Maturity: speculative
- Actor: data

## Sources

- DepMap portal: https://depmap.org/portal/

## Connected records

- technologies: [Comprehensive genomic profiling](https://onco.cc/technologies/cgp/), [Patient-derived organoids](https://onco.cc/technologies/organoids/), [Patient-derived xenografts](https://onco.cc/technologies/pdx-models/)
- institutions: [Broad Institute of MIT and Harvard](https://onco.cc/institutions/broad-institute/), [National Cancer Institute (NIH)](https://onco.cc/institutions/nci/)
- bottlenecks: [Data silos](https://onco.cc/bottlenecks/b-data-silos/), [Lab models that fail to predict what happens in patients](https://onco.cc/bottlenecks/b-preclinical-models/), [Trials do not represent the people who get cancer](https://onco.cc/bottlenecks/b-trial-diversity/)

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