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ABSTRACT The mutational mechanisms underlying recurrent deletions in clonal hematopoiesis are not entirely clear. In the current study we inspect the genomic regions around recurrent

deletions in myeloid malignancies, and identify microhomology-based signatures in _CALR_, _ASXL1_ and _SRSF2_ loci. We demonstrate that these deletions are the result of double stand break

repair by a PARP1 dependent microhomology-mediated end joining (MMEJ) pathway. Importantly, we provide evidence that these recurrent deletions originate in pre-leukemic stem cells. While DNA

polymerase theta (POLQ) is considered a key component in MMEJ repair, we provide evidence that pre-leukemic MMEJ (preL-MMEJ) deletions can be generated in _POLQ_ knockout cells. In

contrast, aphidicolin (an inhibitor of replicative polymerases and replication) treatment resulted in a significant reduction in preL-MMEJ. Altogether, our data indicate an association

between POLQ independent MMEJ and clonal hematopoiesis and elucidate mutational mechanisms involved in the very first steps of leukemia evolution. SIMILAR CONTENT BEING VIEWED BY OTHERS

INHERENT GENOME INSTABILITY UNDERLIES TRISOMY 21-ASSOCIATED MYELOID MALIGNANCIES Article 20 January 2024 SINGLE-CELL RNA SEQUENCING OF A NEW TRANSGENIC T(8;21) PRELEUKEMIA MOUSE MODEL

REVEALS REGULATORY NETWORKS PROMOTING LEUKEMIC TRANSFORMATION Article Open access 14 October 2023 REMOVAL OF TREX1 ACTIVITY ENHANCES CRISPR–CAS9-MEDIATED HOMOLOGOUS RECOMBINATION Article

Open access 12 August 2024 INTRODUCTION Human aged hematopoietic stem and progenitor cells (HSPCs) are prone to clonal expansion due to the acquisition of recurrent somatic mutations1,2.

This phenomenon is known as age related clonal hematopoiesis (ARCH)3,4,5. Somatic pre-leukemic mutations (pLMs) do not usually spread randomly across the possible physical positions of a

gene, but rather occur at apparent mutational hotspots. The majority of pLMs are nonsynonymous single nucleotide variants (SNVs), however other pLMs are due to recurrent insertions or

deletions (indels)3. While the mechanistic explanation for SNVs in cancer has been studied6, the mechanisms leading to recurrent indels in cancer are less understood. While different indel

signatures were previously identified in cancer genomes6, only two main mutational processes for somatic indels in cancer are mechanistically characterized. The first of which is polymerase

slippage, that frequently occurs in repetitive elements and long repeats (microsatellite (MS) signature)7, while the second is by the error prone process of double strand break (DSB) DNA

repair8,9. In a recently published study6, mutation signatures from 4,645 whole-genome and 19,184 exome sequences from different tumor types were analyzed. While 97% of all indels identified

in hypermutated cancer genomes carried MS indel signatures in thymine mononucleotide repeats, signatures associated with defective DSB repair were less abundant and mainly reported in

BRCA-related tumors (ovarian, breast and cervical carcinomas) owing to deficiencies in the homologous recombination pathway. While other specific indel signatures are associated with tobacco

smoking, exposure to UV light and aging, the exact mechanisms underlying them remain to be elucidated. The study of ARCH and pre-leukemia has been mainly focused on the phenotypic

consequences of pLMs, whereas the mutational processes underlying indels signatures in myeloid malignancies and pre-leukemia remain poorly understood. The current study sought to identify

deletion signatures in myeloid malignancies that would shed light on the origins and mutational processes promoting these variants. In this work we demonstrate that the most common recurrent

deletions in clonal hematopoiesis are the result of PARP1 dependent repair of DSBs by a sub-pathway of the MMEJ that is POLQ independent. RESULTS MOST COMMON DELETIONS IN MYELOID

MALIGNANCIES SHARE AN MH-BASED SIGNATURE To study deletion signatures in myeloid malignancies we analyzed targeted sequencing data (COSMIC). This analysis revealed that the most common

somatic deletions in myeloid malignancies share a similar signature (Fig. 1a) in which two pre-existing identical sequences (e.g. microhomologies (MHs)) are flanking the deletions (Fig. 1b).

The most common somatic MH-based deletions were found in _CALR_, _ASXL1_ and _SRSF2_ genes (Fig. 1a). We validated these results by analyzing deletion signatures in a well-defined targeted

sequencing cohort of 1540 adult-AML samples10. In this cohort MH-based deletions in _ASXL1_ and _SRSF2_ were the most recurrent deletions in AML (Fig. 1c). In a sequencing Myeloproliferative

neoplasms (MPN) cohort11 of 2035 patients, the authors identified by PCR a _CALR_ MH-based 52 bp deletion in 16.2% of 1321 Essential thrombocytosis (ET) patients and 13.6% of 309

Myelofibrosis (MF) patients. This validates that _CALR_ MH-based 52 bp deletion is the most commonly reported deletion in these two clinical entities. Importantly, analysis of this cohort

identified a recurrent MH-based deletion in the _NFE-2_ gene (Fig. 1d). An analysis of an unbiased whole-exome sequencing dataset derived from 562 AMLs (BeatAML)12 validated the high

occurrence rates of _ASXL1_ and _SRSF2_ MH-based deletions, and exposed additional non-recurrent MH-based deletions in _TET2_, _DNMT3a_, _CEBPA_ and _RUNX1_ genes (Supplementary Fig. 1a).

Taken together, our analyses suggest that the most common deletions in myeloid malignancies are MH-based deletions. We hypothesized that specific mutational mechanisms may underlie these

recurrent deletions together with selective pressures. To elucidate the interplay between selective pressures and specific mutational mechanisms, we analyzed somatic mutations reported in

the _ASXL1_ gene. HIGH RECURRENCE RATES OF _ASXL1_ MH-BASED DELETION IN MYELOID MALIGNANCIES ARE DRIVEN BY SPECIFIC MUTATIONAL MECHANISMS Truncating events occur across the entire exon 12 of

the _ASXL1_ gene and have been suggested to have a gain-of-function role in promoting myeloid malignancies13. However, three truncating events were significantly more abundant than others

(Supplementary Fig. 2): the nonsense mutation p.R693*, the insertion p.G646fs*12 and the MH-based deletion p.E635fs*15. We compared the frequencies of these common events between

hematological and solid tumors. Significant differences were observed in the prevalence of the MH-based deletion (p.E635fs*15) between hematological (153/376 deletion cases) and

non-hematological (4/103 cases) cancers (_P_ < 0.00001) (Fig. 2a, b). While the MH-based deletion leads to a similar truncated ASXL1 protein as the other two variants (650–700 amino

acids), we did not observe similar differences in the frequencies of neither p.R693* nor p.G646fs*12. While we cannot rule out a selective advantage for the MH-based deletion truncated ASXL1

protein specifically in the hematopoietic system, a possible interpretation of these results is that specific mutational mechanisms contribute to leukemogenesis in the myeloid malignancies’

cell of origin. To address this hypothesis, we first aimed at identifying the recurrent MH-based deletions’ cell of origin. MULTIPOTENT HSCS ARE THE CELLS OF ORIGIN OF SOMATIC MH-BASED

DELETIONS Somatic mutations in _CALR_, _ASXL1_ and _SRSF2_ genes have been shown by others to be pre-leukemic lesions originating in early multipotent hematopoietic stem cells14,15. We

wished to validate that multipotent HSCs are the cells of origin for the three recurrent MH-based deletions in these genes. To this end, we first analyzed published sequencing data from

healthy individuals and pre-AML cases14. We identified the recurrent MH-based deletion in _ASXL1_ in three of 124 pre-AML cases 10.7, 8.8 and 1.7 years prior to AML diagnoses (Supplementary

Table 1) and none among the 676 controls14. Additionally, we purified T-cells derived from five AML samples harboring MH-based deletions in _ASXL1_ and _SRSF2_ genes (Supplementary Table 2).

Somatic MH-based deletions were identified by next-generation sequencing (NGS) in paired T-cells at low allele frequencies (Fig. 3a, Supplementary Table 3). Similarly, we identified the

recurrent MH-based _CALR_ deletion in isolated HSPCs and mature cells from two different Myelofibrosis (MF) cases (Supplementary Table 2). This deletion was identified among HSCs, more

committed progenitors, and mature myeloid and lymphoid cells (Fig. 3b). We further transplanted CD34 positive cells from one of the cases into NOD/SCID/IL-2Rgc-null (NSG) mice. After 16

weeks multi-lineage graft was observed with the _CALR_ deletion being found in both myeloid and lymphoid cells (Fig. 3c). Taken together, these data highlight the fact that recurrent

MH-based deletions in _ASXL1_, _SRSF2_ and _CALR_ genes originate in early multipotent HSCs and are part of clonal hematopoiesis. As we provide evidence that these deletions may be the

result of specific mutational mechanisms, we aimed to gain insights into these mechanisms by modeling the generation of recurrent MH-based deletions. CRISPR/CAS9 MEDIATED DSBS RECAPITULATE

RECURRENT MH-BASED DELETIONS IN MYELOID MALIGNANCIES Since MH-based deletion signatures are considered to be the result of mutagenic DSB repair8, we studied the generation of these recurrent

deletions in vitro by introducing DSBs using the CRISPR/Cas9 system. We introduced sequential DSBs along the hotspot regions of the _CALR_, _ASXL1_ and _SRSF2_ genes in K562 CML cell line.

Specific DSBs successfully recapitulated recurrent MH-based deletions in _ASXL1_ and _SRSF2_ genes in K562 cells (Fig. 4a, b). However, we were unable to recapitulate the _CALR_ recurrent

frameshift MH-based deletion in vitro at high allele frequency (Supplementary Fig. 3). We validated these results by introducing specific DSBs in four different hematologic cell lines of

different genomic and cytogenetic backgrounds. Recurrent MH-based deletions in _ASXL1_ and _SRSF2_ were successfully obtained in all of these cells (Fig. 4c, d, Supplementary Fig. 4). We

further introduced these DSBs in primary human CD34 + HSPCs isolated from six individuals between 30 and 63 years of age (Supplementary Table 4). Remarkably, high frequencies of recurrent

MH-based deletions in _ASXL1_ and _SRSF2_ genes were obtained in all six primary samples (Fig. 4e, f, Supplementary Fig. 4). Our CRISPR/Cas9 experiments of sequential DSBs along the _ASXL1_

and _SRSF2_ genes provide evidence that the relative frequencies of the different deletions (including the recurrent MH-based deletions) are dependent on specific DSB positions (Fig. 4a, b).

As we hypothesized that specific DSB repair pathways would also contribute to the obtained indel landscape, we next aimed to understand which repair machinery is involved in generating the

recurrent MH-based deletions. RECURRENT MH-BASED DELETIONS IN MYELOID MALIGNANCIES ARE THE RESULT OF PARP1 MEDIATED MMEJ REPAIR We demonstrated that specific DSBs in _ASXL1_ and _SRSF2_

genes lead to similar indel distributions across many cell lines and primary cells of different genetic backgrounds. We therefore continued to study the DSB repair machineries leading to

MH-based deletions in K562 cells. To address this, we manipulated key players in the MMEJ and the classical non-homologous end-joining (c-NHEJ) mutagenic DSB repair pathways. In vitro

inhibition of the MMEJ pathway by PARP1 inhibitor (Rucaparib camsylate) prior to DSB induction, similar to a previously descried method16, resulted in significantly reduced allele fractions

of both _ASXL1_ and _SRSF2_ recurrent MH-based deletions (Fig. 5a–f, Supplementary Fig. 5). This provides evidence that these deletions are the result of PARP1 mediated MMEJ repair. To

further validate our results, we inhibited the c-NHEJ pathway by generating K562 _LIG4_ −/− cells. _LIG4_ knockout resulted in a significant increase in the allele fractions of the recurrent

MH-based deletions, further ruling out the role of c-NHEJ in generating these variants (Fig. 5c, f, Supplementary Fig. 5). Interestingly, _LIG4_ −/− cells did not produce short deletions

and insertions close to the breakpoint, suggesting that these indels are due to LIG4 mediated c-NHEJ repair. Moreover, high dosages of rucaparib treatment reduced insertions at the

breakpoints, while short deletions were obtained. This indicates that PARP1 may not be specific to MMEJ and it may have some role in c-NHEJ repair, as was previously described17. Altogether,

our results suggest that recurrent MH-based deletions in _ASXL1_ and _SRSF2_ are the consequence of PARP1 mediated and LIG4 independent MMEJ repair. We thereafter refer to these deletions

as pre-leukemic MMEJ deletions (preL-MMEJ deletions). Of note, while _CALR_ recurrent MH-based deletion could be obtained at very low allele fractions in Wild type (WT) K562 cells, a 20-fold

increase in allele fraction was observed among _LIG4_ −/− cells (Supplementary Fig. 6). DNA Polymerase theta (POLQ) was shown to be a key participant in MMEJ8,18,19 and also to play a role

in CRISPR/Cas9 mediated MMEJ repair20. We therefore aimed to assess POLQ contribution to the preL-MMEJ deletions. PREL-MMEJ DELETIONS CAN BE OBTAINED IN _POLQ_ KNOCKOUT CELLS We generated

three distinct K562 _POLQ_ −/− cells harboring frameshift mutations at exon 14, 16 and 18 of the _POLQ_ gene. Each one of these mutations presumably leads to a premature stop codon upstream

or inside the polymerase domain, previously shown to be required for end joining repair21,22. In _SRSF2_, a significant decrease in total fractions of MH-based deletions together with an

increase in short deletions, validated a role for POLQ in MMEJ (Fig. 6f, g). In contrast, _POLQ_ knockout resulted in a mild and mostly insignificant decrease in the fractions of both

preL-MMEJ deletions (Fig. 6c, h, Supplementary Fig. 7). This suggests that POLQ has a limited role in the pathway leading to preL-MMEJ deletions. We therefore hypothesized that other DNA

polymerases may collaborate with PARP1 and be involved in the pathway leading to preL-MMEJ deletions in humans. In order to identify such an involvement, we analyzed gene expression data of

single human HSCs. INHIBITION OF REPLICATIVE DNA POLYMERASES BY APHIDICOLIN REDUCES THE FORMATION OF PREL-MMEJ DELETIONS We next studied the gene expression profiles of human single

bone-marrow (BM) progenitor cells as was previously described23. We analyzed BM CD34+ profiles from the Human Cell Atlas Consortium’s immune census dataset

(https://preview.data.humancellatlas.org/) (Supplementary Fig. 8) and focused on multipotent HSCs expressing CD34 and AVP markers, and proliferating MPPs (cells of origin of MH-based

deletions) (Supplementary Fig. 9). We noticed that as HSCs enter cell replication, they upregulate components of the c-NHEJ, MMEJ, and HR pathways (Supplementary Fig. 10). Our experimental

results demonstrated that inhibition of _PARP1_ by rucaparib camsylate resulted in a decreased production of preL-MMEJ deletions in vitro. As we also provide evidence that the preL-MMEJ

deletions originate in multipotent HSCs, we assessed for a possible correlation between the expression levels of _PARPs_ and a list of human DNA polymerases24 specifically in HSCs and MPPs,

for polymerases that are not correlated throughout all progenitor states. Among this sub-population, _PARP1_ expression levels were shown to significantly correlate only in HSCs with _POLQ_,

but also with _POLD1_, _POLE_ and _POLE4_ gene expression levels (Fig. 7a). _POLD1_ and _POLE_ genes encode for the catalytic subunits of the B-family DNA polymerases delta and epsilon

respectively, which are the major replicases that carry out DNA replication in eukaryotes25. We next aimed to experimentally assess whether these replicative DNA polymerases contribute to

the formation of preL-MMEJ deletions. To address this issue, we treated K562 WT and _POLQ_ −/− cells with low and high doses of aphidicolin, a potent inhibitor of eukaryotic replicative

B-family DNA polymerases26,27,28,29. Remarkably, aphidicolin treatment significantly reduced the fractions of preL-MMEJ deletions in both WT and _POLQ_ −/− cells (Fig. 7b–g, Supplementary

Fig. 11). The relative contribution of _POLQ_ knockout to this reduction seemed to be negligible compared to aphidicolin treatment (Fig. 7b–g). A parallel increase was observed in the

fraction of c-NHEJ associated short deletions in both genomic loci (Supplementary Fig. 11b, e). Altogether, these results suggest a sub-pathway of the MMEJ repair, leading to preL-MMEJ

deletions. This sub-pathway seemed to be mediated by PARP1, active in the absence of POLQ, and successfully inhibited by aphidicolin treatment. DISCUSSION In the current study, we establish

that the three most common pre-leukemic somatic deletions _ASXL1_ c.1900_1922del23, _SRSF2_ c.284_307del24 and _CALR_ c.1092_1143del52 (termed here preL-MMEJ deletions) share a similar

deletion signature suggesting similar underlying mutational processes (Fig. 1). We provide evidence that these hotspot-deletions occur not just due to selective advantage, but also as a

result of unique mutational mechanisms (Fig. 2) and that they originate in multipotent HSCs (Fig. 3). All three preL-MMEJ deletions were successfully recapitulated following DSBs (Fig. 4,

Supplementary Fig. 6) that are repaired by the PARP1 dependent MMEJ (Fig. 5). Knockout of _POLQ_ gene (which encodes the main polymerase involved in MMEJ) did not significantly reduced

preL-MMEJ deletions (Fig. 6). Single cell RNA-seq data of human HSCs suggest that the MMEJ pathway is activated as HSCs replicate, and exposed a correlation between the gene expression of

_PARP1_ and _POLQ, POLD1_ and _POLE_ (Fig. 7a). Finally, inhibition of the replicative polymerases and consequently cellular replication by aphidicolin resulted in a significant reduction of

preL-MMEJ deletions (Fig. 7). Collectively, our data provide insights into mutational mechanisms in HSCs and the early stages of clonal hematopoiesis. In the current study we provide

evidence that MMEJ is a major driver in early leukemia evolution. HSCs appear to use MMEJ over c-NHEJ repair as reflected by the much higher prevalence of the preL-MMEJ deletions compared to

other short deletions. Our analysis of single cell RNA-seq data together with in vitro experimental results indicate that the MMEJ pathway is active during cell replication. Our findings

demonstrate that synchronizing cells at the G1/S boundary by aphidicolin substantially reduced MMEJ, while c-NHEJ repair was relatively increased. This is in line with previous reports

demonstrating a significant elevated MMEJ activity during S and G2 cell cycle phases owing to CtIP phosphorylation as cells enter S-phase30,31. Phosphorylated CtIP is in turn stimulating the

MRN complex mediated end-resection, which is a critical step in the initiation of both MMEJ and HR32. However, the full biological scenario in which DSBs occur in HSCs remains unclear and

is important to understand in order to potentially prevent preL-MMEJ deletions. One possible scenario as was previously suggested33 is that different types of physiological stress lead to

DNA damage and consequently to the exit of HSCs from dormancy. An alternative scenario is that aged HSCs carry more DSBs and Gamma H2AX foci due to altered dynamics of DNA replication

forks34. In both cases, MMEJ may be the preferred repair choice as it is available and efficient during cell replication. Future studies should shed more light on the origins of the DNA

damage leading to preL-MMEJ, either due to extrinsic physiological stress or age related replicative stress. Furthermore, as the mechanism of MMEJ underlying preL-MMEJ deletions is not fully

resolved in the current study, a more accurate description of the sub-pathway responsible for preL-MMEJ deletions is needed. Here we demonstrate that preL-MMEJ deletions are the result of

PARP1 mediated and POLQ independent sub-pathway of the MMEJ. While PARP1 is known to regulate the MMEJ pathway32 it also plays a role in c-NHEJ17 and single strand break (SSB) repair35. We

cannot rule out that human preL-MMEJ might be the result of SSB. Strand synthesis during MMEJ should require the involvement of DNA polymerases, we propose a model in which replication

associated DNA polymerases are involved in preL-MMEJ (Fig. 8). However, future studies are warranted to assess whether aphidicolin related reduction of preL-MMEJ is due to a direct

inhibition of replicative polymerases or as a consequence of cell cycle arrest. PreL-MMEJ deletions are typically identified among the elderly. An important factor contributing to DSB repair

choice, might be the age of the cell of origin in which the DSBs occur. In our CRISPR/Cas9 based model, similar frequencies of preL-MMEJ deletions were obtained in young and aged human

HSPCs. This might be due to the fact that our model system is not mimicking the exact biological context in which preL-MMEJ arise. It remains unclear whether preL-MMEJ deletions can occur in

HSCs at any age and expand due to selective advantage at older age or that preL-MMEJ deletions preferentially occur in aged HSCs. To elucidate this, the phylogenetic origins of preL-MMEJ

deletions can be studied in single cells as was previously done36 to determine the exact age in which they originate. While preL-MMEJ deletions in _ASXL1_ and _SRSF2_ are the most recurrent

deletions in AML, they are identified in a relatively small proportion of AML patients (~2%). However, these deletions signatures are not the sole hallmark of MMEJ repair. It was recently

shown that other genetic alterations such as blunt-end deletions, templated insertions37 and copy number variations (CNV)38 were also the result of POLQ mediated MMEJ. Large CNVs containing

_DNMT3a_ and _TET2_ genes can be found among healthy individuals39, however, the mutational mechanisms promoting them are underexplored. Interestingly, large numbers of AML patients harbor

recurrent somatic insertions that are templated from nearby genomic sequences. These duplications can be found along _CALR_, _ASXL1_ and _SRSF2_ hotspot regions, as well as in _NPM1_, _FLT3_

and _MLL_ genes. Altogether, it is possible that MMEJ related contribution to genetic alterations in pre-leukemia and leukemia are underestimated. In the current study, we aimed at

understanding the biological processes driving early mutations in myeloid malignancies. Our findings support the growing evidence that some cancer mutations do not occur randomly but rather

their physical positions and patterns are determined by more than the selective advantage they provide. DSBs followed by MMEJ repair might shape the mutational landscape observed in myeloid

malignancies. In line with this, recent studies demonstrated hyperactivity of the MMEJ pathway in _IDH2_40 and _FLT3-ITD_ mutated AMLs41 and the sensitivity of some AML42 and MPN cells43 to

PARP1 inhibition. Such sensitivity could be explained by the dependency of HSPCs on MMEJ and the synthetic lethality of PARP1 inhibitors. Further characterization of these findings is

required to potentially intervene with the MMEJ pathway and prevent somatic mutagenesis associated with clonal hematopoiesis. METHODS SAMPLES De-identified primary peripheral blood samples

were obtained with informed consent from Acute Myeloid Leukemia (AML) and Myelofibrosis (MF) patients through the Leukemia Tissue Bank at Princess Margaret Cancer Centre in accordance with

regulated procedures approved by the Research Ethics Board of the University Health Network (REB 01-0573-C). De-identified mobilized peripheral blood autologous transplant products were

obtained with informed consent from Non-Hodgkin Lymphoma (NHL), Multiple Myeloma (MM) and Amyloidosis patients through the Leukemia Tissue Bank at Princess Margaret Cancer Centre in

accordance with regulated procedures approved by the Research Ethics Board of the University Health Network (ethics committee protocol # 15-9633), and Weizmann institute of science (IRB

protocol #337-1). All patients provided written informed consent for the usage of their samples for research purposes and for the usage of their clinical and biological data. We complied

with all relevant ethical regulations for work with human participants. PRIMARY CD34 + ENRICHMENT AND PRE-ELECTROPORATION CULTURING CD34 + cells were isolated from mononuclear cells derived

from mobilized peripheral blood stem cells (PBSC) autologous transplant products by using EasySep Human CD34 Positive Selection Kit II (StemCell Technologies, 18056). Cell were cultured for

48 h before electroporation in StemSpan™ Serum-Free Expansion Medium II (SFEM II) (StemCell Technologies, 09605) with streptomycin (20 mg/mL), penicillin (20 unit/mL) and the following human

cytokines (all from GenScript unless stated otherwise, catalog numbers and dilution used in parentheses): FLT3L (Z02926, 100 ng/mL), G-CSF (Z02980, 10 ng/mL), SCF (Z02692, 100 ng/mL), TPO

(Pepro-Tech, 300-18, 25 ng/mL) and IL-6 (Z03034, 10 ng/mL). Cells were cultured at a density of 2.5*10^5 cells/ml in 96-well U-bottom plates. CELL LINES PRE-ELECTROPORATION CULTURING K562,

Marimo, MOLM-14, OCI-AML-2 and OCI-AML-3 cell lines were used in this study. All cell lines were obtained from ATCC, were authenticated by whole-exome sequencing and tested negative for

Mycoplasma contamination. All cell lines were sub-cultured 2 days before electroporation in RPMI 1640 Medium containing L-Glutamine (Biological Industries, 01-100-1 A) with 10% FBS,

streptomycin (20 mg/mL) and penicillin (20 unit/mL) at a density of 3*10^5 cells/ml. CRISPR/CAS9 EXPERIMENTS 20 bp sgRNA sequences were designed along the genomic loci of interest using

DESKGEN algorithm (https://www.deskgen.com/landing/#/login). Sequential DSBs that are described in Fig. 4a, b and Supplementary Fig. 3 were performed using the px330 plasmid system, similar

to a previously described method44,45. All relevant Px330 plasmid preparations details are described under section 4.1. Electroporation reactions using px330 plasmids were performed at 2 ug

purified plasmids per reaction. All other CRISPR/Cas9 experiments were done using sgRNAs guide 2 (_ASXL1_) and 5 (_SRSF2_) that were synthesized from IDT and are detailed under section 4.2.

PX330 PLASMID PREPARATIONS For experiments involving sequential DSBs along the _ASXL1_, _SRSF2_ and _CALR_ sequences, sense and antisense oligonucleotides for each sgRNA with overhangs

compatible to Bbsi-digested px330 were designed and ordered from IDT. Each oligos pair was further phosphorylated and annealed using T4 PNK (NEB, M0201S) and T4 Ligation Buffer (NEB,

B0202S). Phosphorylation and annealing reactions were performed at 37 °C for 30 min, followed by 95 °C for 5 min and ramping down to 25 °C at 5 °C /min. Annealed oligo pairs were then

ligated into a previously Bbsi digested px330 plasmid. Per reaction, 50 ng digested px330 was mixed with 1:250 diluted oligo duplex with 2X quick ligation buffer and quick ligase (NEB,

M2200S) at 16 °C overnight. BioSuper DH5α competent cells (Bio-lab, cat. no. 959758026600) were transformed with Px330. Bacteria was re-suspended and plated on LB agar ampicillin dishes and

incubated at 37 °C over-night. Colonies were then screened and grew in 2-3 ml LB + Ampicillin at 37 °C overnight in a shaker (250 rpm). For each colony (sgRNA), plasmid DNA was extracted

using the QIAprep Spin Miniprep standard protocol (Qiagen, cat. No. 27104). To validate the presence of the desired inserts, Sanger sequencing reactions were performed for each plasmid using

the U6 promoter primer ACTATCATATGCTTACCGTAAC. Electroporation reactions using purified px330 plasmids were done at 2ug plasmid per reaction. RNP COMPLEX PREPARATIONS All other CRISPR/Cas9

experiments were done using sgRNAs guide 2 (_ASXL1_) and 5 (_SRSF2_) that were synthesized from IDT. Lyophilized sgRNAs were re-suspended in IDTE buffer (PH 7.5) to a final concentration of

100 uM. RNP complex for each reaction were generated by mixing 1.2 ul sgRNA, 1.7 ul Cas9 protein (IDT) and 2.1 ul PBS followed by incubation for 10 min at 20 degrees. ELECTROPORATION

REACTIONS All electroporation reactions were performed using the 16-strip Lonza 4D nucleofector kit. Pre-electroporated cells were washed in PBS and spun down at 350xg for 10 min. Between

2*10^5 – 1*10^6 cells per reaction were re-suspended in 20 ul SF solution (K562, MARIMO, MOLM-14 and OCI-AML2 cell lines), SE solution (OCI-AML3) or P3 solution (primary CD34 + cells) and

added to the RNP complex or 2ug px330 plasmid. FF-120, DN-100, DP-115, DN-100, EO-100 and DZ-100 electroporation programs were used for K562, MARIMO, MOLM-14, OCI-AML2, OCI-AML3 and primary

CD34 + cells respectively. Immediately after electroporation, pre-warmed media were added and cells were cultured at the same conditions as the pre-electroporation culturing for additional 2

days (primary CD34 + ) or 4 days (all other cell lines) before they were lysed for NGS sequencing. INHIBITORS TREATMENT In experiments involving MMEJ inhibition, K562 cells were

sub-cultured 48 or 2 h before electroporation in a medium containing different dosages of rucaparib camsylate (Sigma, PZ0036) or aphidicolin (Sigma, A4487) respectively. Control cells were

sub-cultured in a medium containing vehicle (DMSO). 48 h following electroporation cells were washed with PBS and re-suspended in fresh clean media. Cells were lysed 5 days after

electroporation for subsequent NGS sequencing. K562 KNOCKOUT CELL LINES GENERATION K562 cells were electroporated using sgRNA guide targeting _LIG4_ or _POLQ_ genes followed by a sorting for

live single cells using BD FACSMelody™ Cell Sorter (BD Biosciences). Sorted cells were plated onto 96-well plates containing 100ul/well RPMI 1640 Medium with L-Glutamine (Biological

Industries, 01-100-1 A), 10% FBS, streptomycin (20 mg/mL) and penicillin (20 unit/mL). 7 days after sorting, 100 ul fresh media were added to each well. Cells were further maintained by

replacing 100ul medium from each well once a week. Cell colonies were lysed 28 days after sorting for subsequent NGS sequencing. For cell lysis, cells were spun at 2000g for 10 min, cells

pellets were mixed with 30 ul of 50 mM NaOH and heated at 99 °C for 10 min. Then, the reactions were cooled down at room temperature and 2 ul 1 M Tris PH = 8 was added to each reaction. NGS

sequencing and analysis were performed as described under sections 7 and 8. Colonies containing bi-allelic frameshift indels at the genomic loci of interest were further isolated and

expanded (Supplementary Fig. 12). NGS LIBRARY AND TARGETED SEQUENCING For all NGS libraries, we used cell lysis products that served as a template for PCR amplification and library

preparations. Dual indexed illumina Libraries were generated using two-step PCR procedure. 1st PCR primer prefix sequences and 2nd PCR primer sequences were used, similar to a previously

described method46. All relevant details are as follows: Target-specific primers were designed by Primer3plus (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi) and were

ordered with the described 5’ prefixes46 (IDT). 1st PCR was applied to target the regions of interest. The reaction mixture was composed of a PCR ready mix (using NEBNext® Ultra™ II Q5®

Master Mix, NEB, M0544L), a cell lysis product and a final primer concentration of 1uM each. PCR protocol was as follows: 98 °C for 30 s, followed by 40 amplification cycles of 98 °C for 10 

s,65 °C for 30 s and a final elongation at 65 °C for 5 min. Following dilution of the 1st PCR products with nuclease free water (1:1000), a 2nd PCR was performed using primers composed of

Illumina sequencing primers, indexes and adapters, under the same conditions as the 1st PCR with the exceptions of final primer concentration of 0.5uM each and 20 cycles of amplifications.

Full sgRNA and primer sequences that were used throughout this study are provided in Supplementary Table 5. Barcoded 2nd PCR products were pooled together at equal volume. Pooled library

sizes were selected (2% gel, BluePippin, Sage Science) and sent for 2 × 150-bp deep sequencing (Miseq System, Illumina). VARIANT CALLING 2 × 150-bp pair-end reads deep sequencing data

(~5000X depth) from Illumina platform were converted to fastq format. Minimap2.1 algorithm47 was applied for alignment of the processed fastq files to hg19 genome based targeted sequences

resulting in sam files that were further sorted and indexed using pysam 0.15.1 (https://github.com/pysam-developers/pysam). All reads from sorted bam files were assigned to new read groups

using picard 2.8.3 ‘AddOrReplaceReadGroups’ command (http://broadinstitute.github.io/picard). In order to avoid misalignments, local realignment was preformed using GATK3.7

‘RealignerTargetCreator’ and ‘IndelRealigner’ commands48. Mpileup files were generated by samtools 1.8 followed by SNVs and small indels detection using varscan2.3.9 ‘pileup2cns’ command to

generate VCF files containing consensus variant calls49. HSPCS CELL SORTING Mononuclear cells (106 cells per 100 ul) from peripheral blood samples of two myelofibrosis (MF) patients

underwent CD34 enrichment by magnetic beads (Miltneyi Inc.). Both CD34 positive and negative cell fractions underwent fluorescence-activated cell sorting as was previously described50. Cells

were stained with the following antibodies (all from BD Biosciences unless stated otherwise, catalog numbers and dilution used in parentheses): anti-CD45RA-FITC (555488, 1:25),

anti-CD38-PE-Cy7 (335790, 1:200), anti-CD10-Alexa-700 (624040, 1:10), anti-CD7-Pacific Blue (642916, 1:50), anti-CD45-V500 (560777, 1:200), anti-CD34-APC-Cy7 (custom made by BD, CD34 clone

581, 1:100), anti-CD34-PerCP-Efluor 710 (e-Bioscience 46-0344-42, 1:100), anti-CD33-PC5 (Beckman Coulter PNIM2647U, 1:100), anti-CD19-PE (340364, 1:200), anti-CD3-FITC (349201, 1:100),

anti-CD56-Alexafluor 647 (557711, 1:100), Streptavidin-QD605 (Invitrogen Q10101MP, 1:200), anti-CD8-APC-H7 (560179, 1:200), anti-light-chain lambda-V450 (561379, 1:200), anti-light-chain

kappa-V450 (561327, 1:200), and anti-CD57-APC (555518, 1:200). Subsequently, cells were sorted on a FACSAria III (BD Biosciences) to a post-sort purity of >95%. CD34 enriched cell

fraction was gated on CD45 + /CD33- and sorted into the following HSPCs subpopulations: HSC/MPP (CD38-/CD34 + /CD45RA-); MLP (CD38-/CD34 + /CD45RA + ); CMP/MEP (CD38 + /CD34 + 

/CD7-/CD10-/CD45RA-); and GMP (CD38 + /CD34 + /CD7-/CD10-/CD45RA + ) subsets. CD34 negative cell fraction was sorted into the following mature cell populations: Myeloid cells (CD45dim/CD33 +

 ); T cells (CD45high/CD3 + /CD8 + ); B cells (CD45high/CD19 + / light chains lambda or kappa + ); and NK cells (CD45high/CD56 + /CD57 + ). DNA from each sorted subpopulation was isolated

and amplified using the RepliG whole genome amplification (WGA) kit (REPLI-g Mini Kit for 16 h). XENOTRANSPLANTATION ASSAYS Animal experiments were performed in accordance to the IACUC of

the Weizmann Institute, its relevant guidelines and regulations (11790319-2) and we complied with all relevant ethical regulations for animal testing and research. Eight- to 12-week-old

female NOD/SCID/IL-2Rgc-null (NSG) mice were maintained under a 12 h dark/light cycle, at an ambient temperature of around 22 degrees and humidity of 50%. Mice were sub-lethally irradiated

(225 cGy) 24 h before transplantation. CD34 + cells were enriched from peripheral blood mononuclear cells of a myelofibrosis (MF) patient by magnetic beads (Miltneyi Inc.) and 50,000 cells

were injected into the right femur. Mice were euthanized 16 weeks following transplantation and human engraftment in the injected right femur and non-injected bone marrow (left femur,

tibias) was evaluated by flow cytometry analysis using the BD LSR II flow cytometer (BD Biosciences). The threshold for detection of engraftment was 0.1% human CD45 + cells. Human myeloid

(human CD45 + /CD33 + /CD19-) and B cells (human CD45 + /CD33-/CD19 + ) were sorted out of the xenografts using the following antibodies (all from BD Biosciences unless stated otherwise,

catalog numbers and dilution used in parentheses): anti-CD45-APC (340943, 1:200), anti-CD19-PE (340364, 1:200) and anti-CD33-PE-Cy5 (Beckman Coulter catalog number IM2647U, 1:200). DNA from

each sorted subpopulation was isolated and amplified using the RepliG whole genome amplification (WGA) kit (REPLI-g Mini Kit for 16 h). DDPCR ANALYSIS OF GRAFT SUBPOPULATIONS ddPCR reaction

was performed by using probes designed for _CALR_ deletion as described elsewhere51. Amplified DNA (2ul from a 1:20 dilution of a 16 h REPLI-g Mini Kit whole-genome amplification, Qiagen)

from each sorted population was tested in a 96-well plate in duplicate according to the manufacturer’s protocol. Mutant and wild-type sequences were read using a droplet reader with a

two-color fluorescein/HEX fluorescence detector (Bio-Rad). The mutant allele frequency was calculated as the fraction of mutant-positive droplets divided by total droplets containing a

target. As previously reported1 the minimum detection level was 1:1,000 (0.1%). Variants were considered present if there were at least three dots in the mutant fluorescein channel resulting

in VAF > 0.1%. T-CELLS ISOLATION AND EXPANSION FROM PRIMARY AML SAMPLES CD3 + cells were isolated from peripheral blood mononuclear cells of AML patients by using EasySep Human CD3

Positive Selection Kit II (StemCell Technologies, 17851) and re-suspended in RPMI 1640 Medium with L-Glutamine (Biological Industries, 01-100-1 A), 10% FBS, 250 IU/ml human IL-2

(ThermoFisher scientific, BMS334) and 5 ug/ml anti-CD28 antibody (clone CD28.2, ThermoFisher scientific, 16-0289-81). Re-suspended cells were added to 24-well plate that was pre-coated for 2

 h with PBS containing 5 ug/ml anti-CD3 antibody (clone OKT3, ThermoFisher scientific, 16-0037-81). Cells were cultured for 4 days before re-suspension in a fresh RPMI 1640 Medium, 10% FBS,

250 IU/ml hIL-2 and re-plating in a 6-well plate. Cells were then cultured for additional 21 days. Cells purity was assessed by flow cytometry before they were lysed for subsequent NGS

sequencing. SINGLE CELL RNA-SEQ ANALYSES HSPCs RNA-seq profiles were isolated from the HCA immune census BM data based on CD34 expression. A total of 19757 profiles were isolated from the

roughly 310,000 BM profiles from 8 different donors. To generate metacells from the profiles, we used the MetaCell package23 with parameters as specified below. Feature genes were selected

using the parameter T_vm = 0.08 and minimal total UMIs of 100, while excluding genes correlated to lateral effects such as mitochondrial genes, immunoglobulin genes, high abundance, prefix

“RP-“ genes, cell cycle, type I Interferon response and stress. The final feature genes, consisting of 527 genes, were used for the computation of the Metacell balanced similarity graph,

with parameters _k_ = 60, n_resamp = 500 and min_mc_size = 20. Outliers threshold of T_lfc = 3.5 was used, with 464 profiles deemed as outliers. Next, we annotated the metacell model using

hierarchical clustering of the metacell confusion matrix, supervised analysis of enriched genes and analysis of marker genes (Supplementary Fig. 8). The metacells and profiles were projected

and plotted in 2D using mc2d_K = 40, mc2d_T_edge = 0.02 with a max degree of 6, and colored using thresholds on metacells log enrichment scores (lfp values) for marker genes chosen from

common markers and the above annotation process. For studying DNA polymerases genes expression correlations to _PARPs_ in HSPCs, we calculated the Pearson correlations using metacells e_gc

values calculated once using only HSPCs metacells, and once for all progenitors metacells (Fig. 7a). DATA ANALYSIS ANALYSES OF CRISPR/CAS9 DATA All indels generated by CRISPR/Cas9 system

were called using varscan2.3.9. Substitutions and short indels identified in both edited and control samples in _ASXL1_, _SRSF2_ and _CALR_ loci were excluded. Allele percent was calculated

as the number of modified reads associated with each variant divided by the sum of all modified reads per experiment. Overall CRISPR/Cas9 modification frequencies were calculated as the sum

of modified reads divided by the mean depth per experiment. We discriminated between three deletion signatures throughout all CRISPR/Cas9 experiments: ≥5 bp deletions with flanking MHs≥2 bp,

≥5 bp deletions with flanking MHs<2 bp and short deletions of <5 bp. ANALYSES OF PUBLICLY AVAILABLE DATASETS Somatic mutation cohorts were downloaded from publicly available web-links

as described under the “Data Availability” section. Only deletions were included in our analyses, duplicate samples were removed from all cohorts. For COSMIC dataset only deletions with

available genomic coordinates were analyzed. Additionally, for COSMIC, myeloid deletions were obtained by filtering the ‘Primary site’ to include’haematopoietic and lymphoid’ tissue

following by the exclusion of the letters ‘lymph’ from the ‘Primary histology’ column. Identical deletions with multiple’Mutation CDS’ values due to multiple isoforms were combined under a

uniform name (for example _ASXL1__c.1888_1910del23 and c.1900_1922del23 were combined under the name _ASXL1_ c.1900_1922del23). Deletions from COSMIC dataset that contained common SNPs at

adjacent genomic loci, were located at intronic or intergenic regions and those that were reported in a single publication were excluded. To exclude common SNPs from all sequencing cohorts,

minor allele frequencies (MAF) for each deletion were identified using Annovar tool (https://github.com/WGLab/doc-ANNOVAR) according to the following datasets: AF, ExAC_ALL, Kaviar_AF,

ExAC_nonpsych_ALL and AF_popmax. Variants with MAF of 0.0001 or above in at least one dataset were filtered out. Deletions with no available data in any of these datasets were included. For

signature detection, deletion coordinates with flanking 20 bp from both deletion ends (e.g start-20bp, end+20 bp) were generated and used as an input for bedtools getfasta command to

generate Fasta files for all deletions flanking sequences. ‘MH signature’ detection was performed using an in-house matlab code by analyzing each deletion’s flanking sequences for

microhomologies (MHs). Specifically, we discriminated between three deletion signatures throughout all data analyses: ≥5 bp deletions with flanking MHs≥2 bp, ≥5 bp deletions with flanking

MHs<2 bp and short deletions of <5 bp. Of note, our detection algorithm did not discriminate between short deletion that are located in microsatellite repeats and those that are not,

as this was beyond the scope of the current manuscript. Matlab code is available as described under the “Code Availability” section. All other analyses were performed using R (version

3.5.2). REPORTING SUMMARY Further information on research design is available in the Nature Research Reporting Summary linked to this article. DATA AVAILABILITY Raw Illumina sequencing reads

associated with CRISPR/Cas9 cell line experiments have been deposited in the NCBI Short Read Archive under bioproject ID PRJNA707245. Publicly available datasets used in this study are

available in the following web links: https://cancer.sanger.ac.uk/cosmic/download (COSMIC dataset), https://www.nejm.org/doi/full/10.1056/nejmoa1516192 (1540 adult-AML dataset10),

https://www.nejm.org/doi/full/10.1056/NEJMoa1716614 (2045 MPN dataset11), http://www.vizome.org/aml/ (BeatAML dataset12). For 1540 adult-AML and 2045 MPN datasets, we used annotated

mutational data that are open-access and available for download as part of the supplementary appendixes of these papers (table s5 in AML paper10 and table s4 in MPN paper11). Full data

containing the deletion signatures from the publicly available datasets as well as CRISPR/Cas9 indel data are provided as a Source Data file. All relevant data are also available from the

corresponding author upon reasonable request. Source data are provided with this paper. CODE AVAILABILITY MMEJ deletion matlab code is documented on GitHub

(https://github.com/ShlushLab/MMEJ_detection) and is publicly available under the MIT license from the following Zenodo repository (https://doi.org/10.5281/zenodo.4555395)52. For the

Metacell analysis, we used the previously published MetaCell package23 with parameters specified under the “Methods” section. Metacell code is documented on GitHub

(https://github.com/tanaylab/metacell) and is publicly available under the MIT license from the following Zenodo repository (https://doi.org/10.5281/zenodo.3334525)53. REFERENCES * Shlush,

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https://doi.org/10.5281/zenodo.3334525. (2019). Download references ACKNOWLEDGEMENTS The authors wish to thank Prof. John Dick, and Dr. Ayal Hendel for fruitful discussion and support. All

primary patient samples that were used in this study were generously provided by Dr. Mark Minden and through the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health

Network. L.S. is the incumbent of The Ruth and Louis Leland career development chair. This research was supported by the EU horizon 2020 grant project MAMLE ID: 714731, LLS and rising tide

foundation Grant ID: RTF6005-19, ISF-NSFC 2427/18, ISF-IPMP-Israel Precision Medicine Program 3165/19, BIRAX 713023, the Ernest and Bonnie Beutler Research Program of Excellence in Genomic

Medicine, awarded to LIS. LIS is an incumbent of the Ruth and Louis Leland career development chair. N.K. is an incumbent of the Applebaum Foundation Research Fellow Chair. This research was

also supported by the Sagol Institute for Longevity Research, the Barry and Eleanore Reznik Family Cancer Research Fund, Steven B. Rubenstein Research Fund for Leukemia and Other Blood

Disorders, the Rising Tide Foundation and the Applebaum Foundation. AUTHOR INFORMATION Author notes * These authors contributed equally: Akhiad Bercovich, Yoni Moskovitz. AUTHORS AND

AFFILIATIONS * Department of Immunology, Weizmann Institute of Science, Rehovot, Israel Tzah Feldman, Yoni Moskovitz, Noa Chapal-Ilani, Tamir Biezuner, Nathali Kaushansky & Liran I.

Shlush * Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel Akhiad Bercovich & Amos Tanay * Princess Margaret Cancer Centre,

University Health Network (UHN), Toronto, ON, Canada Amanda Mitchell, Jessie J. F. Medeiros, Mark D. Minden, Vikas Gupta & Liran I. Shlush * Department of Molecular Genetics, University

of Toronto, Toronto, ON, Canada Jessie J. F. Medeiros * Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada Mark D. Minden * Department of Medicine, University of

Toronto, Toronto, ON, Canada Mark D. Minden & Vikas Gupta * Division of Medical Oncology and Hematology, University Health Network, Toronto, ON, Canada Mark D. Minden & Vikas Gupta *

Department of Pathology, Tel-Aviv University, Tel-Aviv, Israel Michael Milyavsky * Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel Michael Milyavsky * Department of

Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel Zvi Livneh * Division of Hematology, Rambam Healthcare Campus, Haifa, Israel Liran I. Shlush Authors * Tzah Feldman View

author publications You can also search for this author inPubMed Google Scholar * Akhiad Bercovich View author publications You can also search for this author inPubMed Google Scholar *

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can also search for this author inPubMed Google Scholar * Mark D. Minden View author publications You can also search for this author inPubMed Google Scholar * Vikas Gupta View author

publications You can also search for this author inPubMed Google Scholar * Michael Milyavsky View author publications You can also search for this author inPubMed Google Scholar * Zvi Livneh

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Liran I. Shlush View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS T.F. designed and developed the study, performed CRISPR/Cas9 experiments,

cells culture and maintenance, deep targeted sequencing, analyzed sequencing data, performed variant calling, analyzed the publicly available datasets and wrote the manuscript. A.B.

performed single-cell RNA analysis. Y.M. performed CRISPR/Cas9 experiments, cells culture and maintenance, deep targeted sequencing. N.I.C. provided bioinformatics support and wrote the

matlab code for MMEJ detection. N.K. revised the paper and contributed to data interpretation. T.B. provided sequencing and technical support. A.M. and J.M. Performed xenotransplantation

experiments, cell sorting and ddPCR. M.D.M. and G.V. enabled sample acquisition, M.M. and Z.L. helped with the knockout experiments, A.T. supervised single-cell RNA analysis L.I.S. designed

and supervised the study and wrote the manuscript. CORRESPONDING AUTHOR Correspondence to Liran I. Shlush. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests.

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hematopoiesis are driven by microhomology-mediated end joining. _Nat Commun_ 12, 2455 (2021). https://doi.org/10.1038/s41467-021-22803-y Download citation * Received: 23 June 2020 *

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