DNA mutations are known cancer drivers. Here we investigated whether mRNA events that are upregulated in cancer can functionally mimic the outcome of genetic alterations. RNA sequencing or 3′-end sequencing techniques were applied to normal and malignant B cells from 59 patients with chronic lymphocytic leukaemia (CLL)1,2,3. We discovered widespread upregulation of truncated mRNAs and proteins in primary CLL cells that were not generated by genetic alterations but instead occurred by intronic polyadenylation. Truncated mRNAs caused by intronic polyadenylation were recurrent (n = 330) and predominantly affected genes with tumour-suppressive functions. The truncated proteins generated by intronic polyadenylation often lack the tumour-suppressive functions of the corresponding full-length proteins (such as DICER and FOXN3), and several even acted in an oncogenic manner (such as CARD11, MGA and CHST11). In CLL, the inactivation of tumour-suppressor genes by aberrant mRNA processing is substantially more prevalent than the functional loss of such genes through genetic events. We further identified new candidate tumour-suppressor genes that are inactivated by intronic polyadenylation in leukaemia and by truncating DNA mutations in solid tumours4,5. These genes are understudied in cancer, as their overall mutation rates are lower than those of well-known tumour-suppressor genes. Our findings show the need to go beyond genomic analyses in cancer diagnostics, as mRNA events that are silent at the DNA level are widespread contributors to cancer pathogenesis through the inactivation of tumour-suppressor genes.
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All 3′-seq and RNA-seq data generated and analysed for this study have been deposited in the Gene Expression Omnibus (GEO) database under accession numbers GSE111310 and GSE111793. The code to analyse the data are available at https://bitbucket.org/leslielab/apa_2018/ and the processed data are available in Supplementary Table 1 (for Figs. 1b–d, 2a, 4a, Extended Data Figs. 3 and 4) and Supplementary Table 2 (for Extended Data Fig. 8a), and in the Source Data files (for Figs. 1e, 2c, e, 3a, c, 4b–d, g, Extended Data Figs. 2c, 6j, 7c and 8a). Data on DNA mutations from patients with CLL were provided by D. A. Landau and need to be requested from him. The mutation data on solid cancers were obtained through the MSK cbio portal. The data can be accessed at http://www.cbioportal.org.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was funded by the NCI grant U01-CA164190 (to C.M. and C.S.L.), a Starr Cancer Foundation grant (to C.M. and C.S.L.), the Innovator Award of the Damon Runyon-Rachleff Cancer Foundation and the Island Outreach Foundation (DRR-24-13; to C.M.), the NIH Director’s Pioneer Award (DP1-GM123454, to C.M.), the Pershing Square Sohn Cancer Research Alliance (to C.M.) and the MSK Core Grant (P30 CA008748). We are grateful to V. K. Modi for access to lymphatic tissue, to D. A. Landau for providing CLL RNA-seq data and sample identities to validate our findings, and to C. Wu and D. Neuberg for clinical outcome analyses. We thank J. Mendell and V. Narry Kim for providing the V5-DICER construct and the DICER knockout cells, J. Chaudhuri for critical reading of the manuscript, and the members of the Mayr laboratory for discussions.
Nature thanks M. Muschen and the other anonymous reviewer(s) for their contribution to the peer review of this work.