Background: Patient derived tumor organoids (TOs) are emerging as potential models to elucidate mechanisms of tumor biology and therapeutic response. Here, we establish pan-cancer metrics for validation of genetic and transcriptomic recapitulation, and concordance of an organoid to its native tumor.

Methods/Results: We sequenced 50 tumor/TO pairs from 12 cancer types using the Tempus xT DNAseq panel and transcriptome RNAseq platforms. Concordance metrics between tumors and TOs were derived for genomic variants called by the DNA xT platform and comparative ratios were calculated for all detected somatic variants. Across all sequenced pairs, somatic variant detection concordance between any mutation identified in primary tissues and tumor organoids resulted in a mean value of 88.1%. Somatic primary tumor variant recapitulation, the percent of somatic variants identified in the primary tumors that were also detected in the TO, averaged 96.3%.
In addition to genetic concordance, DNAseq can identify and track clonal and subclonal diversity from source material to TO. In particular, we observed that >90% of source tumor/TO pairs harbor variants with allelic fractions <40% in both the sequenced TO and primary tumor tissue, suggesting intra-tumor heterogeneity in subclonal cell populations is maintained.
TOs and primary tumor transcriptomic profiles were compared by dimensionality reduction approaches (i.e. Uniform Manifold Approximation and Projection (UMAP), and Principal Components Analysis (PCA)) as well as differential expression analysis between cancer types. Overall, TOs recapitulated expected transcriptional programs of their tumor type as evidenced by UMAP and PCA as well as upregulation of defining pathways, such as estrogen receptor pathways in breast cancer TOs (ssGSEA p-values ranging from 0.004 to 5×10^-5 for 5 gene ontology estrogen response pathways when compared to non-breast cancer TOs).

Conclusion: Determining genomic and transcriptomic concordance of TOs to source tumors is essential to confirm the validity of a given patient derived model. Our approach establishes metrics through key genomic features identified from routine next-generation sequencing data and can be extended beyond model validation to tracking clonal evolution over time in the presence or absence of therapeutic selection pressures. Our metrics may also serve as a critical quality control step if TOs are utilized in the clinical setting for personalized medicine.

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