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Myeloid malignancies, including acute myeloid leukaemia (AML), arise from the expansion of haematopoietic stem and progenitor cells that acquire somatic mutations. Bulk molecular profiling has suggested that mutations are acquired in a stepwise fashion: mutant genes with high variant allele frequencies appear early in leukaemogenesis, and mutations with lower variant allele frequencies are thought to be acquired later 1 – 3 . Although bulk sequencing can provide information about leukaemia biology and prognosis, it cannot distinguish which mutations occur in the same clone(s), accurately measure clonal complexity, or definitively elucidate the order of mutations. To delineate the clonal framework of myeloid malignancies, we performed single-cell mutational profiling on 146 samples from 123 patients. Here we show that AML is dominated by a small number of clones, which frequently harbour co-occurring mutations in epigenetic regulators. Conversely, mutations in signalling genes often occur more than once in distinct subclones, consistent with increasing clonal diversity. We mapped clonal trajectories for each sample and uncovered combinations of mutations that synergized to promote clonal expansion and dominance. Finally, we combined protein expression with mutational analysis to map somatic genotype and clonal architecture with immunophenotype. Our findings provide insights into the pathogenesis of myeloid transformation and how clonal complexity evolves with disease progression. The evolution of myeloid malignancies is investigated using combined single-cell sequencing and immunophenotypic analysis.

Plasmacytoid dendritic cells (pDC) are the principal natural type I interferon producing dendritic cells. Neoplastic expansion of pDCs and pDC precursors leads to blastic plasmacytoid dendritic cell neoplasm (BPDCN) and clonal expansion of mature pDCs has been described in chronic myelomonocytic leukemia (CMML). The role of pDC expansion in acute myeloid leukemia (AML) is poorly studied. Here we characterize AML patients with pDC expansion (pDC-AML), which we observe in approximately 5% of AML. pDC-AML often possess cross-lineage antigen expression and have adverse risk stratification with poor outcome. RUNX1 mutations are the most common somatic alterations in pDC-AML (>70%) and are much more common than in AML without PDC expansion. We demonstrate that pDCs are clonally related to, and originate from, leukemic blasts in pDC-AML. We further demonstrate that leukemic blasts from RUNX1-mutated AML upregulate a pDC transcriptional program, poising the cells towards pDC differentiation and expansion. Finally, tagraxofusp, a targeted therapy directed to CD123, reduces leukemic burden and eliminates pDCs in a patient-derived xenograft model. In conclusion, pDC-AML is characterized by a high frequency of RUNX1 mutations and increased expression of a pDC transcriptional program. CD123 targeting represents a potential treatment approach for pDC-AML. Competing Interest Statement Conflict-of-interest disclosure: W.X. has received research support from Stemline Therapeutics. S.F.C. is a consultant for Imago Biosciences and has received honoraria from DAVA Oncology. A.D.G. served on advisory boards or as a consultant for Abbvie, Aptose, Celgene, Daiichi Sanyko, Genentech, received research funding from Abbvie, ADC Therapeutics, Aprea, AROG, Daiichi Sanyko, Pfizer, received Honoraria from Dava Oncology. R.K.R has received consulting fees from: Constellation, Incyte, Celgene, Promedior, CTI, Jazz Pharmaceuticals, Blueprint, Stemline, and research funding from Incyte, Constellation, and Stemline Therapeutics. M.S.T has received research funding from AbbVie, Cellerant, Orsenix, ADC Therapeutics, Biosight, Glycomimetics, Rafael Pharmaceuticals and Amgen. He also served on advisory Boards for AbbVie, BioLineRx, Daiichi-Sankyo, Orsenix, KAHR, Rigel, Nohla, Delta Fly Pharma, Tetraphase, Oncolyze, Jazz Pharma, Roche, Biosight and Novartis. He received royalties from UpToDate. R.L.L. is on the supervisory board of Qiagen and is a scientific advisor to Loxo (until 2019), Auron, Ajax, Mission Bio, Imago, C4 Therapeutics and Isoplexis, which each include an equity interest. He receives research support from and consulted for Celgene and Roche, he has received research support from Prelude Therapeutics, and he has consulted for Incyte, Novartis and Janssen. He has received honoraria from Lilly and Amgen for invited lectures and from Gilead for grant reviews.

Myeloid malignancies, including acute myeloid leukemia (AML), arise from the proliferation and expansion of hematopoietic stem and progenitor cells which acquire somatic mutations. Bulk molecular profiling studies on patient samples have suggested that somatic mutations are obtained in a step-wise fashion, where mutant genes with high variant allele frequencies (VAFs) are proposed to occur early in disease development and mutations with lower VAFs are thought to be acquired later in disease progression 1-3. Although bulk sequencing informs leukemia biology and prognostication, it cannot distinguish which mutations occur in the same clone(s), accurately measure clonal complexity and clone size, or offer definitive evidence of mutational order. To elucidate the clonal framework of myeloid malignancies, we performed single cell mutational profiling on 146 samples from 123 patients. We found AML is most commonly comprised of a small number of dominant clones, which in many cases harbor co-occurring mutations in epigenetic regulators. Conversely, mutations in signaling genes often occur more than once in distinct subclones consistent with increasing clonal diversity. We also used these data to map the clonal trajectory of each patient and found that specific mutation combinations (FLT3-ITD + NPM1c) synergize to promote clonal expansion and dominance. We combined cell surface protein expression with single cell mutational analysis to map somatic genotype and clonal architecture with immunophenotype. Our studies of clonal architecture at a single cell level provide novel insights into the pathogenesis of myeloid transformation and how clonal complexity contributes to disease progression.

Cancer evolution is a multifaceted process involving the acquisition of somatic mutations and progressive epigenetic dysregulation of cellular fate. Both cell-intrinsic mechanisms and environmental interactions provide selective pressures capable of promoting clonal evolution and expansion, with single-cell and bulk DNA sequencing offering increased resolution into this process1-4. Advances in genome editing, single-cell biology and expressed lentiviral barcoding have enabled new insights into how transcriptional/epigenetic states change with clonal evolution5,6. Despite the extensive catalog of genomic alterations revealed by resequencing studies7,8, there remain limited means to functionally model and perturb this evolutionary process in experimental systems9. Here we integrated multi-recombinase (Cre, Flp, and Dre) tools for modeling reversible, sequential mutagenesis from premalignant clonal hematopoiesis to acute myeloid leukemia. We demonstrate that somatic acquisition of Flt3 activating mutations elicits distinct phases of acute and chronic activation resulting in differential cooperativity with Npm1 and Dnmt3a disease alleles. We next developed a generalizable allelic framework allowing for the reversible expression of oncogenic mutations at their endogenous loci. We found that reversal of mutant Flt3 resulted in rapid leukemic regression with distinct alterations in cellular compartments depending upon co-occurring mutations. These studies provide a path to model sequential mutagenesis and deterministically investigate mechanisms of transformation and oncogenic dependency in the context of clonal evolution.

DNA double-strand breaks (DSBs) are repaired through homology-directed repair (HDR) or non-homologous end joining (NHEJ). BRCA1/2-deficient cancer cells cannot perform HDR, conferring sensitivity to poly(ADP-ribose) polymerase inhibitors (PARPi). However, concomitant loss of the pro-NHEJ factors 53BP1, RIF1, REV7–Shieldin (SHLD1–3) or CST–DNA polymerase alpha (Pol-α) in BRCA1-deficient cells restores HDR and PARPi resistance. Here, we identify the TRIP13 ATPase as a negative regulator of REV7. We show that REV7 exists in active ‘closed’ and inactive ‘open’ conformations, and TRIP13 catalyses the inactivating conformational change, thereby dissociating REV7–Shieldin to promote HDR. TRIP13 similarly disassembles the REV7–REV3 translesion synthesis (TLS) complex, a component of the Fanconi anaemia pathway, inhibiting error-prone replicative lesion bypass and interstrand crosslink repair. Importantly, TRIP13 overexpression is common in BRCA1-deficient cancers, confers PARPi resistance and correlates with poor prognosis. Thus, TRIP13 emerges as an important regulator of DNA repair pathway choice—promoting HDR, while suppressing NHEJ and TLS.Clairmont et al. find that the TRIP13 ATPase regulates REV7–Shieldin dissociation to promote homology-directed repair and suppress non-homologous end joining, and show the importance of PARPi resistance in BRCA1-deficient cancers.

Coconut Chia Seed Pudding Ingredients Pudding: 2 cups coconut milk (full fat works best) 1/3 cup chia seeds 1 1/2 tbsp. sweetener of choice (raw honey, maple syrup, etc.) 1/3 tsp. ground cinnamon Modification/Topping 1: 1 ripe mango (peeled and cubed/sliced) 1 handful fresh mint (minced) 1 pinch chia seeds 1 pinch sesame seeds 1 tsp. sweetener of choice (raw honey, maple syrup, etc.), or to taste Modification/Topping 2: 2 tbsp. cacao or cocoa 1 cup diced strawberries 1 cup blueberries 1 tsp. sweetener of choice (raw honey, maple syrup, etc.), or to taste Directions 1.

Individually, members also teach at the Hartt School Community Division, Bay Path College, Three Rivers Community College, Greater Hartford Academy of the Arts, The David Einfeldt Chamber Music Seminar at Hartt, and Strings by the Sea in San Diego.

Dairy is also extremely high in fats and promotes high levels of insulin, which can cause our bodies to store high amounts of fat. [...]when we consume dairy, we are taking in whatever the cows previously ate: nutrient-poor diets of corn, soy, grains, hormones, and antibiotics.