<i>In silico</i> generation of synthetic cancer genomes using generative AI
Bibliographic record
Abstract
Abstract Cancer originates from alterations in the genome, and understanding how these changes lead to disease is crucial for achieving the goals of precision oncology. Connecting genomic alterations to health outcomes requires extensive computational analysis using accurate algorithms. Over the years, these algorithms have become increasingly sophisticated, but a severe shortage of open access gold-standard datasets presents a fundamental challenge. Since genomic data is considered personal health information, only an extremely limited number of deeply sequenced legacy cancer genomes can be shared and redistributed. As a result, tool benchmarking is often conducted on the same small set of genomes sequenced with older technologies and uncertain ground truths. This is a major obstacle to the development of improved analytic tools. To address this issue, we have developed OncoGAN, a novel generative AI tool that uses a combination of generative adversarial networks and tabular variational autoencoders to generate realistic but entirely synthetic cancer genomes based on training sets derived from large-scale genomic projects. Our results demonstrate that this approach accurately reproduces the scale, distribution, and characteristics of somatic point mutations, copy number alterations and structural variants across multiple common cancer types, while protecting donors’ privacy information. OncoGAN accurately recapitulates tumor type-specific mutational signatures as well as the positional distribution of somatic mutations. To evaluate the fidelity of the simulations, we tested the synthetic genomes using DeepTumour, a software capable of identifying tumor types based on mutational patterns, and demonstrated a high level of concordance between the synthetic genome tumor type and DeepTumour’s prediction of the type. We also showed that augmenting real donor data with OncoGAN-generated synthetic data could be used to train a more accurate version of DeepTumour. This tool will allow the generation of an extensive and realistic set of training and testing cancer genomes whose ground truth is known exactly. This advance provides computational biologists with the ability to develop realistic cancer genome benchmarking sets and make them available to the research community for the testing, development and enhancement of cancer genome analysis tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".