Jelı́nek, Jaroslav

Leukemia cells accumulate DNA damage but altered DNA repair mechanisms protect them from apoptosis. We showed here that formaldehyde generated by serine/one-carbon cycle metabolism contributed to accumulation of toxic DNA-protein crosslinks (DPCs) in leukemia cells, especially in driver clones harboring oncogenic tyrosine kinases [OTKs: FLT3(ITD), JAK2(V617F), BCR/ABL1]. To counteract this effect, OTKs enhanced the expression of DNA polymerase theta (POLq) by ERK1/2 serine/threonine kinase-dependent inhibition of c-CBL E3 ligase-mediated ubiquitination of POLq and its proteasomal degradation. Overexpression of POLq in OTK-positive cells resulted in efficient repair of DPC-containing DNA double-strand breaks (DSBs) by POLq-mediated end-joining (TMEJ). Transforming activity of OTKs and other leukemia-inducing oncogenes, especially of those causing inhibition of BRCA1/2 -mediated homologous recombination (HR) with and without concomitant inhibition of DNA-PK -dependent non-homologous end-joining (D-NHEJ), was abrogated in Polq-/- murine bone marrow cells. Genetic and pharmacological targeting of POLq polymerase and helicase activities revealed that both activities are promising targets in leukemia cells. Moreover, OTK inhibitor or DPC-inducing drug etoposide enhanced anti-leukemia effect of POLq inhibitor (POLqi) in vitro and in vivo. In conclusion, we demonstrated that POLq plays an essential role in protecting leukemia cells from metabolically induced toxic DNA lesions triggered by formaldehyde and that it can be targeted to achieve therapeutic effect.

536 Background: DNA methylation is a heritable covalent modification that is developmentally regulated and is critical in tissue-type definition. Although genotype-phenotype correlations have been described for different subtypes of renal cell carcinoma (RCC), it is unknown if DNA methylation profiles correlate with morphological or ontology based phenotypes. Here we test the hypothesis that DNA methylation signatures can discriminate between putative precursor cells in the nephron. Methods: We performed deep profiling of DNA methylation in diverse histopathological RCC subtypes and validated DNA methylation and transcriptome signatures in The Cancer Genome Atlas Clear Cell and Chromophobe Renal Cell Carcinoma Datasets. Results: Our data provide the first mapping of methylome epi-signature and indicates that RCC subtypes can be grouped into two major epi-clusters: C1 which encompasses clear cell RCC, papillary RCC, mucinous and spindle cell carcinomas and translocation RCC; C2 which comprises oncocytoma and chromophobe RCC. Interestingly, C1 epi-cluster displayed three fold more hypermethylation as compared to C2 epi-cluster. Of note, differentially methylated regions between C1 and C2 epi-clusters occur in gene bodies and intergenic regions, instead of gene promoters. Transcriptome analysis of C1 epi-cluster suggests a functional convergence on Polycomb targets, whereas C2 epi-cluster displays DNA methylation defects. Furthermore, we find that our epigenetic ontogeny signature is associated with worse outcomes of patients with clear-cell RCC. Conclusions: Taken together, our data defines the epi-clusters that can discriminate between distinct RCC subtypes and for the first time, to our knowledge, define the epigenetic basis for proximal versus distal tubule derived kidney tumors.

Abstract Purpose: DNA methylation is a heritable covalent modification that is developmentally regulated and is critical in tissue-type definition. Although genotype–phenotype correlations have been described for different subtypes of renal cell carcinoma (RCC), it is unknown if DNA methylation profiles correlate with morphological or ontology based phenotypes. Here, we test the hypothesis that DNA methylation signatures can discriminate between putative precursor cells in the nephron. Experimental Designs: We performed deep profiling of DNA methylation and transcriptome in diverse histopathological RCC subtypes and validated DNA methylation in an independent dataset as well as in The Cancer Genome Atlas Clear Cell and Chromophobe Renal Cell Carcinoma Datasets. Results: Our data provide the first mapping of methylome epi-signature and indicate that RCC subtypes can be grouped into two major epi-clusters: C1, which encompasses clear-cell RCC, papillary RCC, mucinous and spindle cell carcinomas and translocation RCC; C2, which comprises oncocytoma and chromophobe RCC. Interestingly, C1 epi-cluster displayed 3-fold more hypermethylation as compared with C2 epi-cluster. Of note, differentially methylated regions between C1 and C2 epi-clusters occur in gene bodies and intergenic regions, instead of gene promoters. Transcriptome analysis of C1 epi-cluster suggests a functional convergence on Polycomb targets, whereas C2 epi-cluster displays DNA methylation defects. Furthermore, we find that our epigenetic ontogeny signature is associated with worse outcomes of patients with clear-cell RCC. Conclusions: Our data define the epi-clusters that can discriminate between distinct RCC subtypes and for the first time define the epigenetic basis for proximal versus distal tubule derived kidney tumors. Clin Cancer Res; 22(24); 6236–46. ©2016 AACR.