CKB, powered by JAX, oncology spotlight: Revealing complex clinical genomic associations
The crux of a tumor’s existence lies in its ability to evade the hosts’ intrinsic immune system. Antitumor therapeutic candidates strive to bolster what the immune system is unable to do by itself and kill the tumor. Predictive biomarker-driven trials have been employed to enhance the efficacy of antitumor drug candidates and have shown a great deal of progress in patient therapeutic success (Hofman et al., 2019). However, some treatments exhibit an apparently confounding failure despite the presence of the appropriate biomarker and target.
Tumors, a genomically-complex mass of cells, are often the product of a combination of genomic edits and alterations that lead to uncontrolled cellular proliferation. Adding to this complexity, tumors also undergo further evolution that is driven by processes that result in multiple, distinctive cell populations within the same tumor from an individual patient. Despite being related, these cellular subpopulations can differ significantly in phenotype, genotype, and therapeutic response (Turajilic and Swanton, 2017). Accounting for these variables—in addition to specific patient tumor type—requires a system capable of capturing the continuous supply of data and visualizing it in a way that is useful to oncology researchers. The Clinical Knowledgebase (CKB), powered by JAX is a relational knowledgebase that incorporates data related to cancer-associated genomic variants, therapeutic efficacy, and clinical data. In this sense, it can be used complementary to genomic repositories to further interrogate variants found in patient samples.
Using CKB: A real-world example
In a recent publication, Patterson and colleagues demonstrated the utility of the CKB to associate data with complex profiles using the example of ALK fusion with other another variant. Their data illustrate the impacts of sensitivity to various ALK inhibitors depending on the context.
ALK-receptor protein kinase belongs to the insulin receptor superfamily of protein kinases. The kinase was initially identified as a component of a fusion protein commonly observed in anaplastic large cell lymphomas. Studies have shown that ligand binding triggers ALK downstream signaling via various pathways, such as those involved in cell proliferation and survival, e.g., PI3K, PLC, and JAK/STAT. Furthermore, fusions between ALK and EML4, found in non-small cell lung cancer, or NPM1 in anaplastic large cell lymphomas, have been observed to function as oncogenic drivers. As such, several ALK inhibitors have been developed and FDA-approved. However, despite the initial therapeutic success, many patients exhibit therapeutic resistance. The resistance mechanisms include ALK missense mutations, copy number alterations, and mutations in alternative pathways which activate bypass signaling.
The authors used CKB to analyze the efficacy of data related to ALK mutations and various inhibitors. It was observed that the specific ALK complex profiles curated with a negative ALK inhibitor response demonstrated at 57% overlap between the variants found in JAX-CKB that are further associated with negative response data in EML4-ALK and NPM1-ALK.
By utilizing CKB, Patterson and colleagues were able to visually curate the complex molecular profiles associated with the ALK alterations. Instead of relying on traditional methods that can often categorize as either wholly resistant or sensitive, the authors were able to evaluate therapeutic efficacy in relation to a variant found in either EML4 or NPM1.
CKB: Implications for Therapeutic Development
The availability of a knowledgebase system such as CKB enables researchers to interpret complex mutations that may harbor therapeutic resistance. By easily visualizing a molecular profile that includes the association between therapeutic efficacy data and multiple genomic alterations simultaneously facilitates accurate clinical trial design and analysis, as well as better informed patient-specific therapy decisions.
For over 90 years, JAX has been at the forefront of mammalian genetics modeling and education, as has been a driving force in enabling scientific breakthroughs worldwide. JAX mice and service solutions—including CKB- support our commitment to innovation, reliability, and customer service.
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Hofman P, Badoual C, Henderson F, Berland L, Hamila M, Long-Mira E, Lassalle S, Roussel H, Hofman V, Tartour E, Ilié M. 2019. Multiplexed Immunohistochemistry for Molecular and Immune Profiling in Lung Cancer-Just About Ready for Prime-Time? Cancers (Basel) 11(3). E283. DOI: 10.3390/cancers11030283
Patterson SE, Statz CM, Yin T, Mockus SM. 2019. Utility of the JAX Clinical Knowledgebase in capture and assessment of complex genomic cancer data. NPJ Precis Oncol.3:2. DOI: 10.1038/s41698-018-0073-y
Turajilic S, Swanton C. 2017. Implications of cancer evolution for drug development. Nat Rev Drug Discov. 16(7):441-442. DOI: 10.1038/nrd.2017.78