Machine Learning Map Reveals Hidden Paralog Vulnerabilities Across 1,005 Cancer Cell Lines

Cancer cells are masters of redundancy, and one of their most effective tricks is hiding lethal weaknesses behind duplicate genes. Now, a team of researchers at University College Dublin, working with colleagues at the Wellcome Sanger Institute, has built a machine learning framework that systematically exposes these concealed vulnerabilities, predicting where pairs of related genes, known as paralogs, become jointly essential in specific cancer contexts. The study, published in Genome Medicine, delivers a comprehensive, context-resolved catalogue of paralog synthetic lethal vulnerabilities spanning 1,005 cancer cell lines, and makes the entire dataset freely explorable through an interactive web portal designed to accelerate the discovery of combination therapies.

The biological logic behind the approach rests on a concept called synthetic lethality. When a tumour cell loses the function of one gene, it can often survive because a closely related paralog compensates for the loss. Knock out both genes simultaneously, however, and the cell dies, while normal cells that retain functional copies of both genes remain unharmed. This therapeutic window has already produced clinical successes, most famously the PARP inhibitors used against BRCA-deficient breast and ovarian cancers. Yet the authors of the new study argue that the field’s dominant discovery tool, genome-wide CRISPR screening, is structurally blind to many of these interactions. Standard dependency maps, such as the widely used Cancer Dependency Map, largely capture single-gene effects. When two paralogs back each other up, knocking out either one alone produces no detectable fitness defect, and the underlying dependency goes unnoticed.

Combinatorial CRISPR screens, in which genes are knocked out in pairs, can reveal these masked dependencies, but they are expensive, labour-intensive and therefore limited in scale. More importantly, the paralog synthetic lethal effects they uncover are highly context-dependent, varying across tumour types and genetic backgrounds. A paralog pair that is jointly essential in HER2-amplified breast cancer may be entirely dispensable in lung cancer, and vice versa. To translate paralog synthetic lethality into clinical practice, researchers need to know not just which pairs are synthetic lethal in principle, but which pairs constitute actionable vulnerabilities in which specific cancer settings. That is the gap the Dublin-led team set out to close.

The researchers, led by Narod Daldal and corresponding author Colm J. Ryan, with contributions from Hamda B. Ajmal and David J. Adams, developed a machine learning classifier trained to predict cell-line-specific synthetic lethality between paralog pairs. Rather than relying on a single data type, the model integrates features drawn from transcriptomics, genomics, gene essentiality profiles and protein-protein interaction network context. The intuition is straightforward: if two paralogs are expressed at different levels in a given cell line, if one of them already shows partial essentiality, or if their interaction partners within the cellular network suggest functional compensation, the probability that the pair is synthetic lethal in that specific context changes accordingly. The classifier learns these patterns from pairs where combinatorial screening data already exists and applies them to pairs that have never been tested.

A central methodological strength of the study lies in its evaluation framework, which was deliberately designed to test generalisation across three biologically distinct scenarios. The first asks whether the model can predict synthetic lethality for paralog pairs it has seen before, but in cell lines it has never encountered. The second asks whether it can generalise to entirely unseen paralog pairs within cell lines it knows. The third, and hardest, asks whether it can make predictions for unseen pairs in unseen cell lines. This graduated testing matters because a model that merely memorises known pairs would be of little use for discovery, whereas a model that generalises to novel combinations can genuinely guide new experiments. The results showed that the model generalised to both unseen pairs and unseen cell lines, although the combination of both unknowns remained the most challenging scenario, as expected.

Underpinning the predictions is a finding about which signals carry the most information. The cell-line-specific expression levels and essentiality profiles of the paralogs themselves, and of their interaction partners, proved to be the most informative features for predicting synthetic lethal interactions. In other words, the model does not simply ask whether two genes are related; it asks whether, in a particular tumour cell, the pattern of activity across the paralog pair and its network neighbourhood is consistent with compensatory redundancy. This context sensitivity is precisely what single-gene dependency maps lack, and it is what allows the resulting resource to be stratified by disease subtype and biomarker status.

To assess how well the predictions held up, the team applied the model to 33,419 paralog pairs across 1,005 cancer cell lines and compared its output against independent combinatorial CRISPR screening data. One of the most striking conclusions from this validation is a sobering one for the field: the agreement between predicted and experimentally observed interactions was comparable to the agreement observed between independent experimental studies of the same pairs. This suggests that the ceiling on predictive accuracy may be set as much by the reproducibility of the experiments themselves as by the performance of the model. In a discipline where different laboratories screening the same gene pairs can reach different conclusions, a computational predictor that matches the level of inter-experimental consistency is performing at a biologically meaningful standard, and the authors suggest that improving experimental reproducibility and model accuracy will need to advance together.

The practical value of the framework is illustrated through its application to HER2-amplified breast cancer, a clinically defined subtype driven by amplification of the ERBB2 receptor gene. When restricted to this context, the model successfully recovered known synergistic paralog relationships already documented in the literature, providing an internal check on its reliability. More importantly, it also predicted novel biomarker-associated vulnerabilities that had not previously been experimentally characterised, generating a ranked list of candidate paralog targets specific to this tumour type. Each such prediction represents a hypothesis that can be prioritised for targeted combinatorial screening, dramatically narrowing the search space that would otherwise need to be explored experimentally.

To make the resource usable by the wider research community, the team has released all genome-scale predictions through an interactive web portal, available at cancergenetics.github.io/paralogmap. The portal allows researchers to query specific paralog pairs, filter by cancer type or cell line, and identify context-specific dependencies relevant to their disease of interest. The authors position the resource as a tool for hypothesis generation, for guiding targeted combinatorial screening campaigns, and for facilitating the identification of clinically actionable paralog targets. Because the underlying article is open access, the full dataset and predictions are available without restriction, lowering the barrier for laboratories that lack the computational infrastructure to build such models themselves.

The broader significance of the work lies in what it says about the next generation of precision oncology. Single-gene targets are finite, and many of the most obvious dependencies have already been mapped. Combination strategies aimed at synthetic lethal pairs multiply the therapeutic search space enormously, but only a systematic, context-aware prioritisation framework can make that space tractable. By combining machine learning with the rich molecular profiles now available for hundreds of cancer models, and by validating against the gold standard of combinatorial CRISPR screening, this study offers a template for how hidden, context-specific vulnerabilities can be surfaced at scale. If the predicted vulnerabilities withstand experimental follow-up, paralog pairs could become a rich source of biomarker-guided combination therapies, extending the synthetic lethality paradigm beyond its current clinical footholds and into tumour types where redundant gene pairs have so far kept their weaknesses safely out of sight.

Subject of Research: Machine learning prediction of context-specific paralog synthetic lethal vulnerabilities in cancer

Article Title: Systematic prioritisation of context-specific paralog pair vulnerabilities in cancer

Article References: Daldal, N., Ajmal, H. B., Adams, D. J., & Ryan, C. J. (2026). Systematic prioritisation of context-specific paralog pair vulnerabilities in cancer. Genome Medicine. https://doi.org/10.1186/s13073-026-01759-y

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01759-y

Keywords: paralogs, synthetic lethality, precision oncology, machine learning, CRISPR screening, genetic dependencies, cancer cell lines, HER2-amplified breast cancer, dependency map, Genome Medicine, Systematic, prioritisation