Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex

Systematic interrogation of gene function requires the ability to perturb gene expression in a robust and generalizable manner. Here we describe structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci. We used these engineered Cas9 activation complexes to investigate single-guide RNA (sgRNA) targeting rules for effective transcriptional activation, to demonstrate multiplexed activation of ten genes simultaneously, and to upregulate long intergenic non-coding RNA (lincRNA) transcripts. We also synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor. The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual sgRNA and complementary DNA overexpression. A gene expression signature based on the top screening hits correlated with markers of BRAF inhibitor resistance in cell lines and patient-derived samples. These results collectively demonstrate the potential of Cas9-based activators as a powerful genetic perturbation technology.National Science Foundation (U.S.) (Graduate Research Fellowship)United States. Dept. of Energy (Computational Science Graduate Fellowship)National Institute of Mental Health (U.S.) (DP1-MH100706)National Institute of Neurological Disorders and Stroke (U.S.) (R01-NS07312401)National Science Foundation (U.S.)W. M. Keck FoundationKinship Foundation. Searle Scholars ProgramKlingenstein FoundationVallee FoundationSimons Foundatio

Prevalence and significance of DDX41 gene variants in the general population.

Germ line variants in the DDX41 gene have been linked to myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) development. However, the risks associated with different variants remain unknown, as do the basis of their leukemogenic properties, impact on steady-state hematopoiesis, and links to other cancers. Here, we investigate the frequency and significance of DDX41 variants in 454 792 United Kingdom Biobank (UKB) participants and identify 452 unique nonsynonymous DNA variants in 3538 (1/129) individuals. Many were novel, and the prevalence of most varied markedly by ancestry. Among the 1059 individuals with germ line pathogenic variants (DDX41-GPV) 34 developed MDS/AML (odds ratio, 12.3 vs noncarriers). Of these, 7 of 218 had start-lost, 22 of 584 had truncating, and 5 of 257 had missense (odds ratios: 12.9, 15.1, and 7.5, respectively). Using multivariate logistic regression, we found significant associations of DDX41-GPV with MDS, AML, and family history of leukemia but not lymphoma, myeloproliferative neoplasms, or other cancers. We also report that DDX41-GPV carriers do not have an increased prevalence of clonal hematopoiesis (CH). In fact, CH was significantly more common before sporadic vs DDX41-mutant MDS/AML, revealing distinct evolutionary paths. Furthermore, somatic mutation rates did not differ between sporadic and DDX41-mutant AML genomes, ruling out genomic instability as a driver of the latter. Finally, we found that higher mean red cell volume (MCV) and somatic DDX41 mutations in blood DNA identify DDX41-GPV carriers at increased MDS/AML risk. Collectively, our findings give new insights into the prevalence and cognate risks associated with DDX41 variants, as well as the clonal evolution and early detection of DDX41-mutant MDS/AML

Genome-wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis.

Funder: British Heart Foundation (BHF); doi: https://doi.org/10.13039/501100000274Clonal hematopoiesis (CH), the clonal expansion of a blood stem cell and its progeny driven by somatic driver mutations, affects over a third of people, yet remains poorly understood. Here we analyze genetic data from 200,453 UK Biobank participants to map the landscape of inherited predisposition to CH, increasing the number of germline associations with CH in European-ancestry populations from 4 to 14. Genes at new loci implicate DNA damage repair (PARP1, ATM, CHEK2), hematopoietic stem cell migration/homing (CD164) and myeloid oncogenesis (SETBP1). Several associations were CH-subtype-specific including variants at TCL1A and CD164 that had opposite associations with DNMT3A- versus TET2-mutant CH, the two most common CH subtypes, proposing key roles for these two loci in CH development. Mendelian randomization analyses showed that smoking and longer leukocyte telomere length are causal risk factors for CH and that genetic predisposition to CH increases risks of myeloproliferative neoplasia, nonhematological malignancies, atrial fibrillation and blood epigenetic ageing

Genome-wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis.

Funder: British Heart FoundationClonal hematopoiesis (CH), the clonal expansion of a blood stem cell and its progeny driven by somatic driver mutations, affects over a third of people, yet remains poorly understood. Here we analyze genetic data from 200,453 UK Biobank participants to map the landscape of inherited predisposition to CH, increasing the number of germline associations with CH in European-ancestry populations from 4 to 14. Genes at new loci implicate DNA damage repair (PARP1, ATM, CHEK2), hematopoietic stem cell migration/homing (CD164) and myeloid oncogenesis (SETBP1). Several associations were CH-subtype-specific including variants at TCL1A and CD164 that had opposite associations with DNMT3A- versus TET2-mutant CH, the two most common CH subtypes, proposing key roles for these two loci in CH development. Mendelian randomization analyses showed that smoking and longer leukocyte telomere length are causal risk factors for CH and that genetic predisposition to CH increases risks of myeloproliferative neoplasia, nonhematological malignancies, atrial fibrillation and blood epigenetic ageing

Multiparameter prediction of myeloid neoplasia risk.

Acknowledgements: This work was funded by an Early Detection Project Grant from Cancer Research UK (EDDCPJT\100010) and a joint grant from the Leukemia and Lymphoma Society (RTF6006-19), and the Rising Tide Foundation for Clinical Cancer Research (CCR-18-500) awarded to G.S.V. The Cambridge Stem Cell Institute is supported by the Wellcome Trust (203151/Z/16/Z, 203151/A/16/Z) and the UKRI Medical Research Council (MC_PC_17230). W.G.D is funded by a Clinical Research Fellowship from the Cancer Research UK Cambridge Centre (CTRQQR-2021\100012). S.P.K. is supported by a UK Research and Innovation (UKRI) Future Leaders Fellowship (MR/T043202/1). L.M was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC), (grant 20125; AIRC 5x1000 project 21267); Cancer Research UK, FC AECC and AIRC under the International Accelerator Award Program (C355/A26819 and 22796). P.M.Q. is funded by the Miguel Servet Program (CP20/00130). A.S. is funded by Cancer Research UK (grant 29685) and Blood Cancer UK (grant 503). G.S.V. is supported by a Cancer Research UK Senior Cancer Fellowship (C22324/A23015) and work in his laboratory is also funded by the European Research Council, Kay Kendall Leukemia Fund, Blood Cancer UK and the Wellcome Trust. This research was conducted using the UK Biobank resource under approved application 56844. We thank the participants and investigators involved in the UK Biobank resource and in the other genome-wide association studies cited in this work who collectively made this research possible.The myeloid neoplasms encompass acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms. Most cases arise from the shared ancestor of clonal hematopoiesis (CH). Here we analyze data from 454,340 UK Biobank participants, of whom 1,808 developed a myeloid neoplasm 0-15 years after recruitment. We describe the differences in CH mutational landscapes and hematology/biochemistry test parameters among individuals that later develop myeloid neoplasms (pre-MN) versus controls, finding that disease-specific changes are detectable years before diagnosis. By analyzing differences between 'pre-MN' and controls, we develop and validate Cox regression models quantifying the risk of progression to each myeloid neoplasm subtype. We construct 'MN-predict', a web application that generates time-dependent predictions with the input of basic blood tests and genetic data. Our study demonstrates that many individuals that develop myeloid neoplasms can be identified years in advance and provides a framework for disease-specific prognostication that will be of substantial use to researchers and physicians

Multiparameter prediction of myeloid neoplasia risk

: The myeloid neoplasms encompass acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms. Most cases arise from the shared ancestor of clonal hematopoiesis (CH). Here we analyze data from 454,340 UK Biobank participants, of whom 1,808 developed a myeloid neoplasm 0-15 years after recruitment. We describe the differences in CH mutational landscapes and hematology/biochemistry test parameters among individuals that later develop myeloid neoplasms (pre-MN) versus controls, finding that disease-specific changes are detectable years before diagnosis. By analyzing differences between 'pre-MN' and controls, we develop and validate Cox regression models quantifying the risk of progression to each myeloid neoplasm subtype. We construct 'MN-predict', a web application that generates time-dependent predictions with the input of basic blood tests and genetic data. Our study demonstrates that many individuals that develop myeloid neoplasms can be identified years in advance and provides a framework for disease-specific prognostication that will be of substantial use to researchers and physicians

Multiparameter prediction of myeloid neoplasia risk.

Acknowledgements: This work was funded by an Early Detection Project Grant from Cancer Research UK (EDDCPJT\100010) and a joint grant from the Leukemia and Lymphoma Society (RTF6006-19), and the Rising Tide Foundation for Clinical Cancer Research (CCR-18-500) awarded to G.S.V. The Cambridge Stem Cell Institute is supported by the Wellcome Trust (203151/Z/16/Z, 203151/A/16/Z) and the UKRI Medical Research Council (MC_PC_17230). W.G.D is funded by a Clinical Research Fellowship from the Cancer Research UK Cambridge Centre (CTRQQR-2021\100012). S.P.K. is supported by a UK Research and Innovation (UKRI) Future Leaders Fellowship (MR/T043202/1). P.M.Q. is funded by the Miguel Servet Program (CP20/00130). A.S. is funded by Cancer Research UK (grant 29685) and Blood Cancer UK (grant 503). G.S.V. is supported by a Cancer Research UK Senior Cancer Fellowship (C22324/A23015) and work in his laboratory is also funded by the European Research Council, Kay Kendall Leukemia Fund, Blood Cancer UK and the Wellcome Trust. This research was conducted using the UK Biobank resource under approved application 56844. We thank the participants and investigators involved in the UK Biobank resource and in the other genome-wide association studies cited in this work who collectively made this research possible.The myeloid neoplasms encompass acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms. Most cases arise from the shared ancestor of clonal hematopoiesis (CH). Here we analyze data from 454,340 UK Biobank participants, of whom 1,808 developed a myeloid neoplasm 0-15 years after recruitment. We describe the differences in CH mutational landscapes and hematology/biochemistry test parameters among individuals that later develop myeloid neoplasms (pre-MN) versus controls, finding that disease-specific changes are detectable years before diagnosis. By analyzing differences between 'pre-MN' and controls, we develop and validate Cox regression models quantifying the risk of progression to each myeloid neoplasm subtype. We construct 'MN-predict', a web application that generates time-dependent predictions with the input of basic blood tests and genetic data. Our study demonstrates that many individuals that develop myeloid neoplasms can be identified years in advance and provides a framework for disease-specific prognostication that will be of substantial use to researchers and physicians

Multiparameter prediction of myeloid neoplasia risk.

The myeloid neoplasms encompass acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms. Most cases arise from the shared ancestor of clonal hematopoiesis (CH). Here we analyze data from 454,340 UK Biobank participants, of whom 1,808 developed a myeloid neoplasm 0-15 years after recruitment. We describe the differences in CH mutational landscapes and hematology/biochemistry test parameters among individuals that later develop myeloid neoplasms (pre-MN) versus controls, finding that disease-specific changes are detectable years before diagnosis. By analyzing differences between 'pre-MN' and controls, we develop and validate Cox regression models quantifying the risk of progression to each myeloid neoplasm subtype. We construct 'MN-predict', a web application that generates time-dependent predictions with the input of basic blood tests and genetic data. Our study demonstrates that many individuals that develop myeloid neoplasms can be identified years in advance and provides a framework for disease-specific prognostication that will be of substantial use to researchers and physicians