Hidden Genetic Threads Link Cancers Through Shared Regions and Pathways

Cancer has long been treated as a family of distinct diseases, each with its own risk factors, tissues of origin, and treatment landscape. Yet epidemiologists have noticed for decades that a diagnosis of one cancer can modestly raise the odds of developing another, hinting at inherited susceptibilities that cut across tumor types. A new study published in PLOS Genetics by Jiaqi Hu, Maiyier Muheyati, Leqi Xu, Andrew DeWan, and Hongyu Zhao takes one of the most systematic looks yet at this phenomenon, and its central message is subtle but striking: at the scale of the whole genome, shared genetic risk across cancers is real but limited, while at finer scales of genomic regions and biological pathways, that risk organizes itself into structured, reproducible patterns that whole-genome summaries miss entirely.

The team approached the problem with two complementary technologies drawn from statistical genetics. The first was local genetic correlation analysis, carried out with a method called SUPERGNOVA, which estimates how strongly the genetic influences on two traits align or diverge within specific segments of the genome rather than averaging across all roughly three billion base pairs. The researchers applied this tool to sixteen specific cancers plus a pan-cancer phenotype, computing pairwise local correlations for every combination. The second approach relied on polygenic risk scores, or PRSs, which condense the small effects of hundreds of thousands of common genetic variants into a single number estimating an individual’s inherited liability to a disease. By testing whether a PRS built for one cancer predicted risk of another, the researchers could ask whether shared susceptibility extends from broad genomic architecture down to the level of actual prediction.

The genome-wide results set the stage for everything that followed. When the authors computed standard genome-wide genetic correlations across all cancer pairs, they found twenty significantly correlated pairs, a number that confirms earlier reports of modest pan-cancer sharing but also underscores how much of the genome’s influence on each cancer remains disease-specific. Genetic correlation at this scale is a blunt instrument: it asks whether the constellation of variants affecting one cancer tends, on average, to affect another in the same direction. A weak overall correlation does not mean the two cancers share nothing; it may simply mean that shared effects concentrated in a handful of genomic neighborhoods are diluted by thousands of variants acting independently.

That is precisely what the local analysis revealed. Zooming into genomic regions rather than whole chromosomes, Hu and colleagues identified eighty-two distinct regions where genetic signals were shared across sixty-six different cancer pairs. In other words, the number of cancer pairs showing evidence of shared genetic influence more than tripled once the analysis moved from the genome-wide to the regional scale. These regions are not random stretches of DNA. The team annotated them using genetic correlations with non-cancer traits and known associations catalogued in the GWAS Catalog, building a functional picture of where and why cancer susceptibilities converge.

Among the eighty-two shared regions, five stood out as hubs of what the authors call mutually correlated cancer clusters. In these locations, the local genetic effects on multiple cancers were correlated with one another in a structured web, rather than in isolated pairs. When the researchers examined what these hub regions do biologically, enrichment appeared in functional domains connected to inflammatory processes. The finding fits a broader theme in cancer genetics: chronic inflammation is a recognized driver of tumor initiation and progression in many tissues, and inherited variation in immune and inflammatory regulation could plausibly tilt risk for several cancers at once. A single regulatory variant that dampens or amplifies inflammatory signaling, for example, might leave detectable fingerprints in the genetic architecture of lung, colorectal, and other inflammation-associated malignancies simultaneously.

The polygenic risk score analysis added an independent line of evidence. Testing PRSs across cancer pairs, the researchers identified five pairs of cancers with statistically significant cross-cancer associations, meaning that a genetic risk score optimized for one cancer carried predictive information for the other. This is a meaningful step beyond correlation of summary statistics, because PRS association tests whether shared genetic liability is strong enough to translate into measurable prediction in independent data. Still, five significant pairs out of the many tested reinforces the study’s headline conclusion: cross-cancer prediction from genetics exists, but it is the exception rather than the rule, and it demands careful dissection before it can be interpreted biologically.

That dissection came from the study’s most technically inventive component, the decomposition of genome-wide PRSs into functionally informed components. Rather than treating a PRS as an indivisible number, the authors split it into pleiotropy-informed parts, which emphasize variants already known to influence multiple traits, and pathway-specific parts, which weight variants according to membership in defined biological pathways. When the significant cross-cancer PRS associations were decomposed this way, a clear pattern emerged: the shared predictive signal was concentrated in specific pleiotropy groups and in immune-related pathways, not spread diffusely across the genome. The cross-cancer associations, in other words, are not a generic reflection of genetic overlap between complex traits; they trace back to identifiable biological machinery.

The immune pathway enrichment ties the two analytical strands of the study together elegantly. The regional analysis pointed to inflammatory functional domains in the hub regions of shared local genetic correlation, and the PRS decomposition pointed to immune-related pathways as carriers of cross-cancer predictive signal. Two independent methods, operating at different scales of genomic resolution, converged on the same biological story. This kind of methodological triangulation matters in a field where pleiotropy, the phenomenon of one variant influencing many traits, can arise from genuine shared biology or from statistical artifacts such as sample overlap and population stratification. Agreement between local correlation and PRS-based prediction makes a genuine biological basis for the shared risk considerably more convincing.

The implications reach into both research practice and, eventually, clinical risk assessment. For researchers, the study supplies a framework: rather than asking only whether two cancers are genetically correlated overall, investigators can map where in the genome the correlation lives, which functional categories those regions implicate, and whether the shared signal is strong enough to survive PRS-based prediction testing. For clinicians and genetic counselors, the work tempers expectations while pointing to future opportunities. Current polygenic risk scores are largely cancer-specific, and the modest number of significant cross-cancer pairs suggests that a single pan-cancer score is not on the immediate horizon. But for the specific pairs identified, particularly those anchored in immune pathways, multi-cancer risk models that borrow strength across related malignancies could eventually sharpen risk stratification, especially as PRS methods improve and datasets grow more diverse.

The study also carries a cautionary note about scale. Findings that vanish at the genome-wide level but appear at the regional level illustrate a general principle in complex-trait genetics: aggregation can obscure as much as it reveals. Eighty-two shared regions hiding inside twenty significant genome-wide correlations is a quantitative demonstration that the architecture of shared disease risk is heterogeneous, concentrated in pockets of the genome where pleiotropic variants cluster. As biobank-scale datasets expand and methods like SUPERGNOVA and PRS decomposition mature, the expectation is that more of these hidden threads connecting cancers will come into view, each one a potential clue to mechanisms that tumor types as different as melanoma and leukemia might, unexpectedly, share. For now, Hu and colleagues have shown that the genetic conversation between cancers is quieter than a pan-cancer model would hope, but far more organized than a genome-wide average would suggest.

Subject of Research: Shared polygenic genetic risk across multiple cancer types

Article Title: Identifying shared polygenic risk across cancers

Article References: Hu, J., Muheyati, M., Xu, L., DeWan, A., & Zhao, H. (2026). Identifying shared polygenic risk across cancers. PLOS Genetics, 22(9), e1012308. https://doi.org/10.1371/journal.pgen.1012308

Image Credits: AI Generated

DOI: 10.1371/journal.pgen.1012308

Keywords: polygenic risk scores, genetic correlation, cancer genetics, pleiotropy, SUPERGNOVA, immune pathways, inflammation, GWAS, cross-cancer susceptibility, genomic regions, PLOS Genetics, risk prediction