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Showing content from https://www.ncbi.nlm.nih.gov/pubmed/25426838 below:

Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence

. 2014 Dec 25;371(26):2477-87. doi: 10.1056/NEJMoa1409405. Epub 2014 Nov 26. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence Anna K Kähler, Robert E Handsaker, Johan Lindberg, Samuel A Rose, Samuel F Bakhoum, Kimberly Chambert, Eran Mick, Benjamin M Neale, Menachem Fromer, Shaun M Purcell, Oscar Svantesson, Mikael Landén, Martin Höglund, Sören Lehmann, Stacey B Gabriel, Jennifer L Moran, Eric S Lander, Patrick F Sullivan, Pamela Sklar, Henrik Grönberg, Christina M Hultman, Steven A McCarroll

Affiliations

Affiliation

¹ The authors' affiliations are listed in the Appendix.

PMID: 25426838
PMCID: PMC4290021
DOI: 10.1056/NEJMoa1409405

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Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence

Giulio Genovese et al. N Engl J Med. 2014.

. 2014 Dec 25;371(26):2477-87. doi: 10.1056/NEJMoa1409405. Epub 2014 Nov 26. Authors Giulio Genovese¹, Anna K Kähler, Robert E Handsaker, Johan Lindberg, Samuel A Rose, Samuel F Bakhoum, Kimberly Chambert, Eran Mick, Benjamin M Neale, Menachem Fromer, Shaun M Purcell, Oscar Svantesson, Mikael Landén, Martin Höglund, Sören Lehmann, Stacey B Gabriel, Jennifer L Moran, Eric S Lander, Patrick F Sullivan, Pamela Sklar, Henrik Grönberg, Christina M Hultman, Steven A McCarroll Affiliation

¹ The authors' affiliations are listed in the Appendix.

PMID: 25426838
PMCID: PMC4290021
DOI: 10.1056/NEJMoa1409405

Item in Clipboard

Abstract

Background: Cancers arise from multiple acquired mutations, which presumably occur over many years. Early stages in cancer development might be present years before cancers become clinically apparent.

Methods: We analyzed data from whole-exome sequencing of DNA in peripheral-blood cells from 12,380 persons, unselected for cancer or hematologic phenotypes. We identified somatic mutations on the basis of unusual allelic fractions. We used data from Swedish national patient registers to follow health outcomes for 2 to 7 years after DNA sampling.

Results: Clonal hematopoiesis with somatic mutations was observed in 10% of persons older than 65 years of age but in only 1% of those younger than 50 years of age. Detectable clonal expansions most frequently involved somatic mutations in three genes (DNMT3A, ASXL1, and TET2) that have previously been implicated in hematologic cancers. Clonal hematopoiesis was a strong risk factor for subsequent hematologic cancer (hazard ratio, 12.9; 95% confidence interval, 5.8 to 28.7). Approximately 42% of hematologic cancers in this cohort arose in persons who had clonality at the time of DNA sampling, more than 6 months before a first diagnosis of cancer. Analysis of bone marrow-biopsy specimens obtained from two patients at the time of diagnosis of acute myeloid leukemia revealed that their cancers arose from the earlier clones.

Conclusions: Clonal hematopoiesis with somatic mutations is readily detected by means of DNA sequencing, is increasingly common as people age, and is associated with increased risks of hematologic cancer and death. A subset of the genes that are mutated in patients with myeloid cancers is frequently mutated in apparently healthy persons; these mutations may represent characteristic early events in the development of hematologic cancers. (Funded by the National Human Genome Research Institute and others.).

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Figures
Figure 1. Clonal Expansion and Allelic Fractions

Figure 1. Clonal Expansion and Allelic Fractions

Panel A shows a model for the expansion…

Figure 1. Clonal Expansion and Allelic Fractions

Panel A shows a model for the expansion of a single hematopoietic stem cell into a clonal population, under the influence of a somatic mutation (yellow circle), and the potential conversion of the clone into a hematologic cancer through subsequent mutation (black circle with red rim). Mutations present in the founder cell would be present at an appreciable allelic fraction (though <50%) in blood-derived genomic DNA. Panel B shows the distribution of allelic fractions observed in sequencing data for high-confidence, ultra-rare variants ascertained in the 12,380 study participants; the small bump at the left of this distribution represents putative somatic mutations.

Figure 2. Candidate Driver Somatic Mutations

Panel…

Figure 2. Candidate Driver Somatic Mutations

Panel A shows all genes identified as carrying a…

Figure 2. Candidate Driver Somatic Mutations

Panel A shows all genes identified as carrying a significant excess of disruptive (nonsense, frame-shift, and splice-site) somatic mutations among 11,845 participants with sequencing data of sufficient quality for the detection of somatic mutations. To control for potential systematic sequencing artifacts due to sequence context, somatic mutations observed in multiple participants were counted only once. Panel B shows the contribution of individual genes to the total number of candidate driver somatic mutations that were observed. Panel C shows a comutation plot for participants with multiple candidate driver somatic mutations. Panel D shows estimates for participants with clonal hematopoiesis with candidate drivers (carrying at least one candidate driver mutation), those with clonal hematopoiesis with unknown drivers (carrying three or more detectable somatic mutations and no candidate drivers), and those with clonal hematopoiesis and candidate or unknown drivers. The shaded bands represent 95% confidence intervals.

Figure 3. Risk of Hematologic Cancer for…

Figure 3. Risk of Hematologic Cancer for Participants with Clonal Hematopoiesis

Panels A and D…

Figure 3. Risk of Hematologic Cancer for Participants with Clonal Hematopoiesis

Panels A and D show Kaplan–Meier plots of the proportions of participants who did not receive a diagnosis of hematologic cancer (Panel A) and for surviving participants (Panel D). Panels B and E show hazard ratios for hematologic cancer (Panel B) and death (Panel E) for participants with exactly one putative somatic mutation and no candidate drivers (one mutation), those with exactly two putative somatic mutations and no candidate drivers (two mutations), those with clonal hematopoiesis and unknown drivers (CH-UD), those with clonal hematopoiesis and candidate drivers (CH-CD), and those having clonal hematopoiesis with candidate or unknown drivers (CH), all compared with participants with no candidate drivers and no putative somatic mutations (no mutations). Panel C shows the proportions of participants who had clonal hematopoiesis with candidate or unknown drivers among 31 participants in whom hematologic cancers were diagnosed at least 6 months after DNA sampling, as compared with the proportions in a group of age-matched persons without hematologic cancer over a similar follow-up period.

Figure 4. Hematopoietic Clones and Evolution in…

Figure 4. Hematopoietic Clones and Evolution in Three Patients Who Subsequently Received a Diagnosis of…
Figure 4. Hematopoietic Clones and Evolution in Three Patients Who Subsequently Received a Diagnosis of Myeloid Cancer

Panels A and B show the allelic fraction and coverage for rare heterozygous variants ascertained through whole-genome sequencing in Participant 1 and Participant 2, respectively, each of whom received a diagnosis of myeloid cancer 2 months after DNA sampling. Blue shading indicates the strength of evidence that a mutation was somatically acquired, with the negative log₁₀ P value for the mutation being at an allelic fraction of less than 50% according to a binomial test. Mutations in black were initially ascertained through whole-exome sequencing. Mutations in red are candidate driver mutations. The histograms show the overall distribution of allelic fractions, with the candidate driver mutations indicated in red. Panel C shows the progression from clonal hematopoiesis to myeloid cancer in Participant 3, in whom DNA was sampled 34 months before diagnosis and again at the time of diagnosis. Shading represents cell populations defined by specific combinations of mutations as shown; the percentages refer to the estimated representation of each cell population in the sample, at initial DNA sampling and then at the time of diagnosis (34 months later).