‘Gain’ of supernumerary copies of the 8q24.21 chromosomal region has been shown to be common in many human cancers1–13 and is associated with poor prognosis7,10,14. The well-characterized myelocytomatosis (MYC) oncogene resides in the 8q24.21 region and is consistently co-gained with an adjacent ‘gene desert’ of approximately 2 megabases that contains the long non-coding RNA gene PVT1, the CCDC26 gene candidate and the GSDMC gene. Whether low copy-number gain of one or more of these genes drives neoplasia is not known. Here we use chromosome engineering in mice to show that a single extra copy of either the Myc gene or the region encompassing Pvt1, Ccdc26 and Gsdmc fails to advance cancer measurably, whereas a single supernumerary segment encompassing all four genes successfully promotes cancer. Gain of PVT1 long non-coding RNA expression was required for high MYC protein levels in 8q24-amplified human cancer cells. PVT1 RNA and MYC protein expression correlated in primary human tumours, and copy number of PVT1 was co-increased in more than 98% of MYC-copy-increase cancers. Ablation of PVT1 from MYC-driven colon cancer line HCT116 diminished its tumorigenic potency. As MYC protein has been refractory to small-molecule inhibition, the dependence of high MYC protein levels on PVT1 long non-coding RNA provides a much needed therapeutic target.

Highlights d Proteogenomic characterization reveals the functional impact of genomic alterations d Phosphoproteomics uncovers putative therapeutic targets downstream of KRAS d Multiomics links endothelial cell remodeling and glycolysis to immune exclusion d Proteomics and glycoproteomics reveal candidates for early detection or intervention

Purpose Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability. Methods This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer’s confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing. Results After reading the SOP, average accuracy in somatic variant identification increased by 16.7% ( p value = 0.0298) and average interreviewer agreement increased by 12.7% ( p value < 0.001). Manual review conducted after reading the SOP did not significantly increase reviewer time. Conclusion This SOP supports and enhances manual somatic variant detection by improving reviewer accuracy while reducing the interreviewer variability for variant calling and annotation.

Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consuming, costly, poorly standardized, and non-reproducible. Here, we systematized and standardized somatic variant refinement using a machine learning approach. The final model incorporates 41,000 variants from 440 sequencing cases. This model accurately recapitulated manual refinement labels for three independent testing sets (13,579 variants) and accurately predicted somatic variants confirmed by orthogonal validation sequencing data (212,158 variants). The model improves on manual somatic refinement by reducing bias on calls otherwise subject to high inter-reviewer variability.