Hot spots of retroviral integration in human CD34+ hematopoietic cells

doi:10.1182/blood-2007-01-068759Prepublished online May 16, 2007;

Alessandra Recchia and Fulvio MavilioManfred Schmidt, Christof von Kalle, Steve Howe, Adrian J. Thrasher, Alessandro Aiuti, Giuliana Ferrari, Claudia Cattoglio, Giulia Facchini, Daniela Sartori, Antonella Antonelli, Annarita Miccio, Barbara Cassani, Hot spots of retroviral integration in human CD34+ hematopoietic cells

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Hot spots of retroviral integration

in human CD34+ hematopoietic cells

Claudia Cattoglio, Giulia Facchini, Daniela Sartori, Antonella Antonelli, Annarita Miccio,

Barbara Cassani, Manfred Schmidt, Christof von Kalle, Steve Howe, Adrian J. Thrasher,

Alessandro Aiuti, Giuliana Ferrari, Alessandra Recchia and Fulvio Mavilio.

From the IIT Unit of Molecular Neuroscience, Istituto Scientifico H. San Raffaele, Milan,

Italy; Department of Biomedical Sciences, University of Modena and Reggio Emilia,

Modena, Italy; San Raffaele Telethon Institute for Gene Therapy, Milan, Italy; Vita-Salute

San Raffaele University, Milan, Italy; National Center for Tumor Diseases, Heidelberg,

Germany; Molecular Immunology Unit, Institute of Child Health, London, United Kingdom.

Running title: Retroviral integration hot spots

Supported by grants from Telethon Italy (GGP06101 and TIGET), the European Commission

(VI FP, CONSERT) and Fondazione Cariplo.

Corresponding author: Fulvio Mavilio Department of Biomedical Sciences University of Modena and Reggio Emilia Via Campi 287, 41100 Modena, Italy Phone: +39-059-2055392 Fax: +39-059-2055410 e-mail: [email protected]

C. Cattoglio, A. Recchia and F. Mavilio designed research and wrote the paper;

C. Cattoglio, G. Facchini, D. Sartori, A. Antonelli, A. Miccio, S. Howe, B. Cassani performed

research and analyzed data; M. Schmidt, C. von Kalle, A. Thrasher, A. Aiuti and G. Ferrari

contributed vital reagents and data sets.

Blood First Edition Paper, prepublished online May 16, 2007; DOI 10.1182/blood-2007-01-068759

Copyright © 2007 American Society of Hematology




ABSTRACT

Insertional oncogenesis is a possible consequence of the integration of gamma-retroviral

(RV) or lentiviral (LV) vectors into the human genome. RV common insertion sites

(CIS) have been identified in hematopoietic malignancies and in the non-malignant

progeny of transduced hematopoietic stem/progenitor cells (HSCs), possibly as a

consequence of clonal selection in vivo. We have mapped a large number of RV and LV

integrations in human CD34+ HSCs, transduced in vitro and analyzed without selection.

Recurrent insertion sites (hot spots) account for >21% of the RV integration events,

while they are significantly less frequent in the case of LV vectors. RV but not LV hot

spots are highly enriched in proto-oncogenes, cancer-associated CIS, and growth-

controlling genes, indicating that at least part of the biases observed in the HSC progeny

in vivo are characteristics of RV integration, already present in non-transplanted cells.

Genes involved in hematopoietic and immune system development are targeted at high

frequency and enriched in hot spots, suggesting that the CD34+ gene expression

program is instrumental in directing RV integration. The lower propensity of LV

vectors for integrating in potentially dangerous regions of the human genome may be a

factor determining a better safety profile for gene therapy applications.




Introduction

Gene therapy of genetic blood disorders requires stable genetic modification of hematopoietic

stem cells. Gene transfer vectors derived from murine gamma-retroviruses, such as the

Moloney murine leukemia virus (MLV), have been used for more than a decade to transduce

human bone marrow-derived or mobilized hematopoietic stem/progenitor cells (HSCs) in a

clinical context. Retroviral vector-mediated gene transfer has recently achieved therapeutic

efficacy, allowing correction of life-threatening diseases such as severe combined

immunodeficiencies (SCID)1-3 or chronic granulomatous disease (CGD)4. MLV-derived

vectors, however, have also raised significant safety concerns for the genotoxic risk

potentially associated with their uncontrolled integration into the human genome5-7. Indeed,

insertional activation of a T-cell proto-oncogene has been correlated with the occurrence of

lymphoproliferative disorders in three patients treated with retrovirally-transduced

hematopoietic cells for X-linked SCID (X-SCID)1. Recent studies have shown that gamma-

retroviral vectors integrate preferentially within transcribed genes and around promoters and

CpG islands8, where insertion of the viral long terminal repeat (LTR) transcriptional enhancer

has a high probability to interfere with gene regulation9. Nevertheless, no adverse event

related to viral insertion was reported in other clinical trials for X-SCID3, adenosine

deaminase-deficient SCID (ADA-SCID)2, CGD4 or graft-versus-host disease9, suggesting the

existence of specific risk factors that are incompletely understood10.

Analysis of MLV integration patterns in natural or experimentally induced

leukemias/lymphomas showed the existence of insertion sites recurrently associated with a

malignant phenotype. These “common insertion sites” (CIS) include proto-oncogenes or other

genes associated with cell growth and proliferation, the activation or deregulation of which

has a causal relationship with the establishment and/or progression of neoplasia11. Some of

these sites, such as the EVI1-MDS1 locus, have been identified at relatively high frequency

also in the non-malignant progeny of transduced hematopoietic cells in mice12 non-human

primates13 and humans4, indicating that insertion into certain genes may cause clonal

amplification of transduced progenitors in vivo. From these studies, however, it is not clear

whether clonal dominance is entirely the result of in vivo selection, or is favored by the

existence of highly preferred regions of retroviral integration that make clonal amplification

more likely to occur. This issue is highly relevant in understanding the different outcomes of




different gene therapy clinical trials, in assessing the relative safety of using MLV-derived

vectors in specific clinical applications, and in comparing the safety profile of alternative

vectors (e.g., HIV-derived lentiviral vectors) or vector designs.

We report an analysis of gamma-retroviral (RV) and lentiviral (LV) vector integration

hot spots from large collections of integration sites obtained from human cord blood- and

bone marrow-derived CD34+ HSCs transduced in vitro and analyzed without selection. Hot

spots account for >20% of the MLV integration sites, while they are significantly less

frequent in the case of HIV-derived vectors. Integration sites associated with clonal

dominance and neoplasia in both mice and humans, including LMO2, are hot spots of

gamma-retroviral but not lentiviral integration in human hematopoietic cells.

Materials and Methods

Retroviral vectors

Cord blood (CB)-derived CD34+ cells were transduced with the previously described LGS∆N

and LGS∆N-∆CAAT RV vectors14, driving the expression of GFP under an intact or a U3-

deleted MLV LTR, and of ∆LNGFR under an internal SV40 promoter. CB-derived CD34+

cells were also transduced with the self-inactivating (SIN) pRRLsin-18.pptCMV-GFPwpre

LV vector15, containing a U3-deleted HIV-1 LTR and a CMV-driven GFP cassette16, or with

the pHR2pptCMV-GFPwpre or the pHR2pptGS∆N LV vectors, retaining HIV-1 wild-type

LTRs and driving the expression of GFP or ∆LNGFR under internal CMV or SV40

promoters. To generate the pHR2pptCMVGFPwpre construct, a pptCMVGFPwpre fragment

from the pRRLsin-18.pptCMVGFPwpre vector was cloned into ClaI-EcoRI sites of

pHR2MD-NGFR17. To obtain the pHR2pptGS∆N LV construct, the pHR2pptCMVGFPwpre

vector was digested with BamHI/EcoRI and ligated to a GFP-SV40∆LNGFR cassette. Bone

marrow (BM)-derived CD34+ cells were transduced with previously described RV vectors

expressing either the ADA (GIADA12) or the γc receptor3 cDNA.

RV vector supernatants were produced by transient transfection of the amphotropic

Phoenix packaging cell line. Infectious particle titer was determined on K562 cells. VSV-G

pseudotyped LV particles were prepared by transient co-transfection of 293T cells, collected

and concentrated as already described17, and titrated on 293T cells. Transduction efficiency




was evaluated by flow cytometry. Amphotropic or GaLV-pseudotyped ADA and γc receptor

RV vectors were titered as previously described2,3.

Transduction of human CD34+ cells

CD34+ HSCs were purified from cord blood Ficoll fractions by magnetic sorting

(MiniMACS; Milthenyi, Auburn, CA) and pre-stimulated for 24-48 hours in serum-free

Iscove’s modified Dulbecco’s medium (IMDM) supplemented with 20% BIT (Stem Cell

Technologies; Vancouver, BC), 20 ng/ml human thrombopoietin, 100 ng/ml Flt-3 ligand

(PeproTech; Rocky Hill, NJ), 20 ng/ml interleukin-6, and 100 ng/ml stem cell factor (R&D

Systems Minneapolis, MN). RV transduction was performed by spinoculation (3 rounds at

1,500 rpm for 45 min) in the presence of 4 µg/ml polybrene. LV transduction was performed

by overnight incubation at a MOI of 200 in the presence of 4 µg/ml polybrene. Transduction

efficiency was evaluated by analysis of EGFP and/or ∆LNGFR expression by flow cytometry

using a mouse anti-human NGFR antibody (Becton Dickinson).

BM- or peripheral blood-derived CD34+ cells were purified from normal donors or

SCID patients by magnetic sorting, pre-stimulated for 24 hours in IMDM containing human

serum, or serum-free X-Vivo10 medium, and a cytokine cocktail (FLT3-ligand, SCF, TPO,

IL-3), and transduced by three cycle-exposure to the GIADA1 or the γc receptor RV vector

supernatant as previously described2,3.

Cloning and analysis of retroviral insertion sites

Integration sites were cloned by linker-mediated PCR (LM-PCR) or linear amplification-

mediated PCR (LAM-PCR), as described18,19. Briefly, genomic DNA was extracted from 0.5-

5 x 106 infected cells and digested with MseI and a second enzyme to prevent amplification of

internal 5’ LTR fragments (PstI for RV vectors and SacI/NarI for LV vectors). An MseI

double-stranded linker was then ligated and LM-PCR performed with nested primers specific

for the linker and the 3’ LTR (MLV: 5’- GACTTGTGGTCTCGCTGTTCCTTGG-3’ and 5’-

GGTCTCCTCTGAGTGATTGACTACC-3’; HIV: 5’- AGTGCTTCAAGTAGTGTGTGCC-

3’ and 5’- GTCTGTTGTGTGACTCTGGTAAC-3’). PCR products were shotgun-cloned

(TOPO TA cloning kit, Invitrogen; Carlsbad, CA) into libraries of integration junctions,

which were then sequenced to saturation. A valid integration contained the MLV or HIV

nested primer, the entire MLV or HIV genome up to a CA dinucleotide and the linker nested




primer. Sequences between the 3’ LTR and the linker primers were mapped onto the human

genome by the BLAT genome browser (UCSC Human Genome Project Working Draft, May

2004). Random genomic sequences originated by LM-PCR (genomic MseI-MseI, PstI-MseI,

NarI-MseI or SacI-MseI fragments) were used as controls. Sequences featuring a unique best

hit with ≥95% identity to the human genome were considered genuine integration sites, and

classified as intergenic when occurring at an arbitrarily chosen distance of >30 kb from any

Known Gene (UCSC definition), perigenic when ≤30 kb upstream or downstream of a Known

Gene, and intragenic when within the transcribed portion of at least one Known Gene. In case

of multiple transcript variants, the most represented and/or the longest isoform was chosen.

Gene density analysis was performed using the Table Browser tool of the UCSC BLAT

genome browser. For each integration, the number of Known Genes (a single isoform in case

of multiple variants) contained in a range of 1 Megabase (Mb) around the insertion site was

calculated. For all pairwise comparisons, we applied a two-sample test for equality of

proportions with continuity correction (Rweb 1.03).

A genomic region was defined as an “hot spot” for retroviral integration according to

criteria developed for defining cancer-related common insertion sites (CIS), with minor

modifications11,20. Cutoff values were set at 36 kb for 2 insertions, 56 kb for 3 insertions and

104 kb for 4 or more insertions.

Gene expression profiling

The expression profile of CD34+ cells was determined by microarray analysis. RNA was

isolated from 1-2 x 106 CB- and BM-derived CD34+ cells stimulated with cytokines according

to the same protocols used for RV (CB- and BM-derived cells) or LV (CB-derived cells)

vector transduction, transcribed into biotinylated cRNA, hybridized to Affymetrix HG-

U133A Gene Chip arrays and analyzed as previously described9. To correlate retroviral

integration and gene activity, expression values from the CD34+ cell microarrays were

divided into four classes, i.e. absent, low (below the 25th percentile in a normalized

distribution), intermediate (between the 25th and the 75th percentile) and high (above the 75th

percentile).




Functional clustering analysis

Functional cluster analysis of genes targeted by retroviral integrations and from control

sequences was performed using the DAVID 2.1 Functional Annotation Tool21,22

(http://david.abcc.ncifcrf.gov). In the DAVID annotation system, a Fisher exact test corrected

for multiple comparisons (DAVID’s EASE score21) is adopted to measure the level of gene-

enrichment in Gene Ontology (GO) annotation terms with respect to a background

population, and GO categories considered over-represented when yielding an EASE score

<0.05. A list of 417 cancer-associated CIS was obtained from the Mouse Retrovirus Tagged

Cancer Gene Database (http://rtcgd.ncifcrf.gov/), where murine genes were replaced with

human homologs. Genes were analyzed also by the network-based Ingenuity Pathways

Analysis tool (Ingenuity® Systems, www.ingenuity.com). Gene identifiers were uploaded

into the application, and mapped to their corresponding Focus Gene in the Ingenuity

Pathways Knowledge Base. Networks were algorithmically generated based on the direct or

indirect interaction between Focus Genes. The Functional Analysis of each network identified

the biological functions and/or diseases that were most significant to the genes in the network.

(Fischer’s exact test). A list of 596 human proto-oncogenes was compiled from the UNSW

Embryology DNA-Tumor Suppressor and Oncogene Database

(http://embryology.med.unsw.edu.au) and the Tumor Gene Database (http://www.tumor-

gene.org).

Results

Retroviral integration preferences in human CD34+ HSCs

Human CD34+ HSCs were purified from umbilical cord blood (CB) pools, bone marrow

(BM) from ADA-SCID and X-SCID patients, or peripheral blood (PB) from a normal donor.

CB CD34+ cells were transduced with MLV-derived gamma-retroviral (RV) or HIV-derived

lentiviral (LV) vectors carrying a GFP reporter gene and either a wild-type or a U3-deleted

(SIN) LTR. BM CD34+ cells were transduced with MLV-derived retroviral vectors

expressing either ADA2 or γc receptor3 from a wild-type LTR. PB CD34+ cells were

transduced with the vector expressing γc receptor3. Transduction efficiency ranged from 15%

(SIN-RV) to >90% (SIN-LV) depending on the vector and target cell type, and remained

stable throughout the culture period. DNA was obtained 1 to 12 days after infection, from




cells that underwent 1 (all BM and PB samples) to 5-6 (all CB samples) cell doublings in

culture. Vector-genome junctions were cloned and sequenced by a linker-mediated (LM) or

linear amplification-mediated (LAM) PCR approach adapted to the different vector types, and

mapped onto the human genome. Cumulatively, we mapped 1,030 RV and 849 LV

integrations in CB- or BM-derived CD34+ cells. 595 RV integrations were obtained from CB

cells transduced with wild-type (395) or SIN (200) LTR vectors expressing ∆LNGFR from an

internal promoter, and 435 from BM cells transduced with wild-type LTR vectors expressing

ADA (190) or γc receptor (245). All LV integrations were obtained from CB cells transduced

with wild-type (404) or SIN (445) LTR vectors expressing GFP or ∆LNGFR from an internal

promoter.

Among RV integrations, 172 (16.7%) were in an intergenic position, 566 (55.0%)

within the transcribed portion of at least one gene, and 292 (28.3%) at a distance of ≤30 kb

upstream or downstream of one or more genes (Table 1; the complete list of sequences is

available at GeneBank with the accession number XXXX). Among LV integrations, 148

(17.4%) were in intergenic, 609 (71.7%) in intragenic, and 92 (10.9%) in perigenic position.

Conversely, a collection of 798 control sequences randomly cloned by LM-PCR contained

369 (46.2%) intergenic, 308 (38.6%) intragenic and 121 (15.2%) perigenic sequences.

Compared to controls, RV vectors showed a preference for intragenic (two-sample test for

equality of proportions with continuity correction, p<10-11) and perigenic (p<10-10)

integration, while LV vectors showed a much higher preference for intragenic positions

(p<10-15).

The position of the integrated proviruses with respect to Known Genes is shown in

Figure 1, which considers the total number of vector-gene interactions in an interval of 30 kb

around each insertion site (1,517 and 1241 for RV and LV vectors respectively). Compared to

randomly cloned or computer-generated23 control sequences, a significant clustering around

transcription start sites was observed for RV but not LV vectors. Overall, 29.3% of the total

RV vector-gene interactions were within ±10 kb from the +1 position of Known Genes,

compared to 16.1% for LV vectors (p<10-15) (Table 1, Figure 1). The RV general integration

preferences were similar in CD34+ and HeLa cells, as indicated by the analysis of 869

insertions from a previously published collection24 (Table 1).

In CD34+ cells, RV integrations showed a significant preference for gene-dense

regions: >60% of proviruses were found in genomic regions containing 6-20 genes per Mb,




with a peak of 35% at a density of 6-10 genes/Mb, while >60% of control sequences mapped

to regions with a gene density of <5 genes/Mb (p<10-4) (Figure 2A). On the contrary, LV

integrations followed a distribution within regions of different gene density more similar to

that of the control sequences and of the human genome, and different from that of RV (p<10-

4) (Figure 2B).

To correlate vector integration with gene activity, we determined the expression

profile of >16,000 genes by microarray analysis in CB- and BM-derived CD34+ cells

activated in culture in the same conditions used for RV and LV transduction. As shown in

Figure 3, ~60% of 1,571 probesets representing 866 genes hit by a RV vector detected a

transcript in activated CD34+ cells, and among them 13% were classified as lowly abundant,

30% as intermediately abundant and 17% as highly abundant, compared to a 45-47%

“present” call on the whole microarrays (percentages were slightly different between CB- and

BM-derived cells) and a 11-12%, 23% and 11-12% breakdown in the three abundance

classes. With the exception of the lowest expression class, all differences were statistically

significant (10-15<p<10-7), indicating that RV vectors integrate preferentially into genes active

in CD34+ cells at the time of transduction, and particularly in the fraction of genes expressed

at higher level. A similar correlation with gene activity was observed, as already reported in T

cells25, for genes hit by LV vectors: ~56% of 1,346 probesets representing 757 hit genes

detected a transcript in activated CD34+ cells, with a 13%, 31% and 12% breakdown in the

three abundance classes. Compared to the whole microarray, the fraction of probesets with a

present call was significantly higher (56 vs. 46%, p<10-12), but the difference was accounted

for essentially by the intermediately abundant transcripts (31 vs. 23%, p<10-10) (Figure 3),

indicating that LV vectors tend to integrate into active genes in CD34+ cells but have no

specific preference for genes expressed at high levels when compared to RV vectors (p<10-4).

Genes regulating cell growth and proliferation are preferred targets of retroviral

integration

A functional classification by the Gene Ontology (GO) criteria26 of genes hit by RV and LV

vectors in CD34+ cells (Supplementary Tables 1 and 2) showed statistically significant biases

towards several gene categories (Figure 4A). In particular, genes involved in the

establishment and/or maintenance of chromatin architecture, signal transduction and cell cycle

were significantly more represented in the collection of genes hit by RV integrations




compared to their expected frequency in the human genome (EASE score <0.005). Genes

involved in chromatin remodeling and phosphorylation were hit at a higher than expected

frequency also by LV vectors (EASE score <0.0005 and <0.005 respectively), particularly

those with serine/threonine kinase and GTPase activity (EASE score <0.0005). Two

additional categories, i.e., transcription and apoptosis, were over-represented in genes hit by

RV and/or LV vectors, although at less significant levels (EASE score <0.05). A different

analysis, carried out by the Ingenuity® network-based pathway analysis software, indicated

that genes involved in cell signaling, cell growth/proliferation, cell death, cancer and

hematopoietic system development were significantly over-represented in the collection of

RV and/or LV integrations with respect to genes annotated in the software Pathways

Knowledge Base (0.005<p<0.05). These categories were therefore chosen to carry out a direct

frequency comparison between RV and LV target genes and our control gene list

(Supplementary Tables 1-3). Genes involved in cell signaling, growth/proliferation and death

were over-represented in both RV and LV integrations with respect to control sequences (10-

9<p<10-2, Figure 4B), while genes involved in hematopoietic and immune system

development, immune response and cancer were significantly over-represented only in RV

integrations (10-8<p<10-4, Figure 4B). The comparison was then extended to genes

specifically annotated in cancer-related databases (see methods for definitions and data

source). RV integrations hit 77 proto-oncogenes and 64 cancer-associated CIS, corresponding

to 7.5% and 6.2% respectively of the 1,030 integrations (Figure 5). Both categories were

significantly over-represented (p<10-3 and 10-4 respectively) compared to control sequences

(27 proto-oncogenes and 17 CIS out of 798 sequences). On the contrary, LV integrations hit

49 proto-oncogenes and 32 CIS out of 849 integrations (Figure 5), a borderline significant

difference compared to controls (p=0.03 and 0.07 respectively). Interestingly, HeLa cell

integrations show over-representation of proto-oncogenes but not CIS (not shown). This

finding is not surprising considering that CIS have been mostly defined in hematopoietic

malignancies.

Overall, these analyses show that both RV and LV vectors have a general tendency to

integrate into genes involved in the regulation of cell growth and proliferation, and that RV

integrations have a specific bias for genes associated with oncogenic transformation. An

Ingenuity network analysis confirmed these biases and showed, in addition, that a significant

number of genes hit by RV integrations are functionally linked in gene networks involved in




apoptosis (Supplementary Table 4; Figure 6A), signal transduction, transcriptional regulation

and cancer (Supplementary Table 4; Figure 6B).

RV but not LV vectors show a high frequency of integration hot spots

The RV and LV insertion site collections were analyzed for the presence of integrations at

recurrent sites (hot spots), using essentially the same criteria previously applied to the

definition of cancer-associated CIS (at least two independent insertions in <30 kb, three in

<50 kb and four in <100 kb11,20). Overall, 219 out of 1,030 (21.3%) RV insertion sites met

these criteria, identifying 97 hot spots in the genome of CD34+ cells (Supplementary Table 5).

109 out of 869 integrations (12.5%) met the same criteria in HeLa cells, defining 52 hot spots

(not shown). LV vectors showed a significantly lower propensity to integrate at recurrent

sites, with only 70 out of 849 (8.2%) integrations meeting the definition criteria, and

identifying 33 hot spots (Supplementary Table 5). Comparing the three collections, 1 hot spot

appeared to be a recurrent site for both RV (4 hits) and LV (3 hits) integration (Chr. 17q23.2:

55188652-55285672), while 3 hot spots were found in common between CD34+ and HeLa

cells (not shown). It is worth noting that 22 out of 798 (2.8%) control sequences also met the

hot spot definition criteria (Supplementary Table 5), defining a background level of false

positivity in the LM-PCR analysis. The different sub-groups of RV integrations contributed to

the hot spot list proportionally to their size, with no apparent bias related to the type of

transduced cell (CB, BM or PB), the vector used for transduction (wt-LTR or SIN-LTR), or

the number of cell doublings undergone in culture before harvesting (Supplementary Table 6).

In particular, non-expanded cell populations (BM- and PB-derived), which collectively

contributed less than half of the 1,030 total RV integrations, contributed with at least one

integration to 56 of the 97 (58%) RV hot spots (Supplementary Table 5).

The position of RV hot spot integrations with respects to Known Genes reflected the

RV general integration preferences, with intergenic, perigenic, and gene-dense regions over-

represented to the same extent observed in the entire collection of RV integrations, and

clustering around TSS only slightly decreased (p=0.015) (Table 1, Figures 1A and 2A). On

the contrary, LV hot spots showed a higher frequency of integration in intragenic (81.4 vs.

71.7%) and gene-dense (65.7 vs. 35.6% in the >11 genes/Mb range) regions (Table 1 and

Figure 2B). Similarly, RV hot spots occurred in the same proportion of expressed genes




observed for all RV integrations (Figure 3A), while LV hot spots contained a significantly

higher proportion of expressed genes (73.2 vs. 55.9% p=0.003) (Figure 3B).

Interestingly, the maximum distance between independent integrations defining a hot

spot was significantly lower for RV vectors compared to LV vectors and control sequences

with hot spot characteristics. Overall, 52% and 67% of the RV hot spots in CD34+ and HeLa

cells span less than 10 kb, including those containing three or four independent integrations,

compared to 36% and 27% for LV and control sequences respectively (Figure 7). One fourth

(26.0%) of the RV hot spots in CD34+ cells and almost one half (40.4%) of those in HeLa

cells contained two independent integrations in less than 2 kb, compared to only 3% of the

LV hot spots.

Proto-oncogenes and cancer-associated CIS are hot spots of RV but not LV integration

The list of RV integration hot spots in CD34+ cells includes proto-oncogenes (e.g., LYL1,

MYB), cancer-associated CIS (e.g., FLI1, EVI2A, EVI2B, NF1), and genes involved in

chromosomal translocations in hematopoietic malignancies (e.g., LMO2, MKL1, ETV6)

(Table 2), all of them occurring at frequencies significantly higher than expected (10-8<p<10-

4) and higher than in the overall list of RV integrations (Figure 5). Interestingly, non-

expanded cell populations contributed with at least one integration to 9 of the 17 (53%) hot

spots containing a proto-oncogene or a cancer-associated CIS (Table 2), again indicating the

absence of biases related to the number of cell doublings in culture. On the contrary, LV hot

spots showed little enrichment for proto-oncogenes or CIS, although in this case low numbers

make comparisons poorly significant (Figure 5). Furthermore, RV but not LV hot spots

included a very high proportion of genes belonging to the intracellular signaling cascade

category (25.3%), which were significantly over-represented using either the human genome

or the total RV integrations as a background population in a GO analysis (EASE score:

1.2x10-6 and 2.2x10-4, respectively), despite their relatively small number (i.e., 22).

Interestingly, genes involved in hematopoietic and immune system development and in

immune response by Ingenuity® pathway analysis were further and significantly enriched in

RV hot spots with respect to the entire list of RV integrations (p<10-2, Figure 4B).




Discussion

Retroviral integration preferences have significant consequences on the potential genotoxicity

of different families of vectors used to transfer genes into HSCs. The probability of dominant

activation of potentially cancer-causing genes, e.g., those involved in the control of stem cell

self-renewal, growth and differentiation, may in fact differ significantly between RV and LV

vectors simply based on the different frequency by which they may target those genes. Here

we report a detailed analysis of the RV and LV integration preferences in human CB-, PB-

and BM-derived CD34+ HSCs transduced in the same conditions used in clinical applications

and analyzed without selection. The general integration preferences of the two vector families

were similar to those previously described for other mammalian hematopoietic or non-

hematopoietic cells (reviewed in Bushman, 20058), and showed on average a two-fold higher

probability for RV vectors to target gene-dense regions, highly active genes and promoter-

proximal regions. However, RV but not LV integration occurs at high frequency (>20%) at

genomic locations (hot spots) that are significantly enriched in proto-oncogenes and genes

involved in the control of cell proliferation.

A high frequency of hot spots, defined by a statistical criterion previously applied to

define cancer-associated CIS11,20, appears to be a hallmark of RV integration in human CD34+

HSCs. We found that more than one fifth of the RV integrations meet the definition criteria, a

frequency more than 7-fold higher than expected from the analysis of a randomly cloned

collection of human DNA sequences, and almost 3-fold higher than that found in a collection

of LV integrations of comparable size. The average extension of RV hot spots, i.e., the

maximum distance between all insertions within each spot, was well within the definition

criteria, and significantly smaller than that of LV hot spots, spanning less than 10 kb in half of

the cases and less than 2 kb in one fourth of the cases. RV integration appears therefore to

have high preference for restricted genomic locations, which may exhibit specific chromatin

conformations or features that favor tethering of the pre-integration complexes (PICs) with

higher probability. These features do not include gene density, proximity to promoters or gene

expression per se, since hot spots integrations show exactly the same preferences observed in

the entire collection of RV integrations. Interestingly, we observed that the frequency of hot

spots increased progressively during the study, following the increase of the sample size in an

almost linear fashion. This may suggest that by analyzing a much higher number of sequences




all RV integrations could be clustered in a defined subset of genomic regions, all having the

appropriate features recognized by the PICs. Unfortunately, the molecular bases of the

interactions between RV PICs and the mammalian chromatin are poorly understood, and it is

difficult to correlate our finding with any specific mechanism. The situation was completely

different in the case of LV hot spots, the frequency of which increased only slightly with the

increase in the sample size and appeared to plateau. More importantly, insertions in LV hot

spots showed strikingly different characteristics with respect to the general LV integration

preferences, and were greatly enriched in gene-dense regions and expressed genes. These data

suggest that LV integration may happen in a much wider portion of the HSC genome, and that

hot spots are generated at low frequency by locations that are more favorable than others to

PIC interaction, and are apparently those with a high density of expressed genes. This

explanation is consistent with the available evidence that LV PICs are tethered to the human

genome by widely distributed chromatin component loosely associated with gene activity,

such as chromatin-remodeling27 or DNA-repair28 complexes, HMG29 and Polycomb-group

proteins30, and LEDGF31,32.

Previous studies carried out in patients4 as well as in animal models12,13,33 have

indicated that integrations in cancer-associated CIS and growth-controlling genes are enriched

in the progeny of RV-transduced, repopulating HSCs. The major conclusion of these studies

was that certain viral insertions lead to clonal selection of stem/progenitor cells in vivo.

However, the pre-transplantation frequency of these insertion events was never accurately

measured in the relevant cell population. Our analysis indicates that a bias towards integration

into or around certain category of genes, i.e., those involved in signal transduction, cell cycle,

chromatin remodeling and transcription, is already present in non-transplanted hematopoietic

progenitors, and particularly in integration hot spots. In particular, proto-oncogenes and

cancer-associated CIS are enriched at three- to five-fold the expected frequency in RV hot

spots, indicating a specific preference for genomic locations containing these categories of

genes. These include proto-oncogenes expressed in CD34+ hematopoietic progenitors and

involved in hematopoietic cell neoplasia, such as LMO2 and EVI2-NF1, targeted at a

frequency of ~1:350, LYL1 and MYB targeted at a frequency of ~1:500, and others (see

Table 2). Importantly, there was no significant difference in the number of integrations

contributing to oncogene-containing hot spots between non-expanded (BM- and PB-derived)




or moderately-expanded (all CB-derived) cell populations, arguing against the likelyhood of

clonal outgrowth generated in culture by insertional activation of growth-promoting genes.

A network-based pathway analysis indicates that a significant number of genes

targeted by RV integration are functionally linked in transcription-, signal transduction-

apoptosis- and tumorigenesis-related networks. Interestingly, genes involved in hematopoietic

and immune system development are targeted at uniquely high frequency by RV integrations,

and further enriched in RV hot spots, suggesting that the gene expression program of a

cycling hematopoietic cell is at least in part instrumental in directing RV PICs in certain

regions of the genome. Consistent with this hypothesis, almost none of the genes present in

CD34+ cells hot spots are found in hot spots from HeLa cell, which most likely operate

different regulatory networks. Kustikova et al.33 reached similar conclusions in compiling

their “insertional dominance database” from the progeny of serially transplanted HSCs in

mice, although they explain the observed over-representation of certain gene categories and

functional networks with in vivo selection rather than with intrinsic properties of the RV

integration machinery. Indeed, 18 to 34% of the genes present in the mouse database,

depending on the stringency of the comparison, are present also in our list, arguing against an

exclusive role for in vivo selection in determining most of the frequency biases. A notable

exception is the EVI1-MDS1 locus, which we found only once in non-transplanted cells while

it was found at exceedingly high frequencies in vivo in mice12,33, non-human primates13 and,

at least in one case, humans4. Insertional activation of the EVI1-MDS1 locus should therefore

be considered a factor favoring clonal amplification and/or selection in vivo independently

from the frequency by which it is targeted by RV integration before transplantation. It should

be noted, however, that our data come from a population of hematopoietic progenitors in

which the proportion of repopulating stem cells is admittedly low, leaving the possibility that

stem cell-specific hot spots went undetected. Unfortunately, an integration analysis in pre-

transplantation, long-term repopulating stem cells is currently impossible, and it is therefore

difficult to come to definitive conclusions as to what proportion of the biases detected in the

stem cell progeny in vivo is due to vector preferences and what proportion to in vivo

selection. We favor a predominant role of vector-specific factors, also based on our

experience with ADA-SCID patients in whom pre-transplantation and post-transplantation

integration preferences showed essentially overlapping patterns (A.A., B.C., A.R., F.M. et al.,

manuscript in preparation).




In conclusion, this study shows previously unrecognized features of RV and LV

integration into human HSCs that may have an impact in assessing the prospective genotoxic

risk of using either vector system for human gene therapy applications. In particular, the

frequency and characteristics of integration hot spots may be substantial factors in

determining a differential safety profile for RV and LV vectors of comparable design and

content.




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Table 1. Retroviral integration site distribution in human CD34+ HSCs

Intergenic (%)

Intragenic (%)

Perigenic (%)

Total hits

±10 kb from TSS

(%)

Total vector/gene

interactions*

CD34+ cells

RV vectors 16.7 55.0 28.3 1,030 29.3 1,517

LV vectors 17.4 71.7 10.9 849 16.1 1,241

Control sequences

46.2 38.6 15.2 798 9.1 902

RV hot spots 16.0 56.6 27.4 219 22.2 302

LV hot spots 8.6 81.4 10.0 70 13.2 114

Control hot spots 36.4 59.1 4.5 22 13.0 23

HeLa cells

RV vectors 18.8 48.1 25.5 869 26.1 1,219

RV hot spots 16.5 53.2 30.3 109 27.3 165

Distribution of RV and LV integration sites unambiguously mapped in unselected CB- and BM-derived CD34+ HSCs, and RV integrations in HeLa cells from a previously published collection24. Integrations (total hits) were distributed as inside (intragenic), outside (intergenic), or at a distance of <30 kb upstream or dowstream (perigenic) from, Known Genes (UCSC annotation). Insertions at a distance of ±10 kb from transcription start sites (TSS) are indicated as percentage of the total vector/gene interactions. Control sequences were obtained from a randomly cloned library of Pst1/MseI-restricted, LM-PCR-amplified human CD34+ cell DNA. *Total number of genes within 30 kb from individual hits + intergenic hits.




Table 2. RV and LV hot spots containing at least one proto-oncogene and/or cancer-associated CIS.

Chromosome Range (bp) N° hits Gene symbol Origin* RV hot spots 14q24.3 13882 4 C14orf43, PNMA1 CB-RV (2)

CB-SIN-RV (2) 11p13 48661 3 AF116668, LMO2 BM-ADA (1)

CB-RV (1) CB-SIN-RV (1)

17q11.2 7827 3 EVI2A, EVI2B, NF1, OMG

BM-X-SCID(1) CB-RV (2)

10q25.2 1920 2 ADD3 BM-ADA (1) CB-SIN-RV (1)

11q23.2 22851 2 ZBTB16 BM-ADA (1) CB-RV (1)

11q24.3 14147 2 FLI1 BM-ADA (1) CB-RV (1)

12p13.2 7360 2 ETV6 CB-RV (2) 16p13.11 18559 2 ABCC1 BM-ADA (1) 19p13.13 137 2 BTBD14B, LYL1,

NFIX, TRMT1 BM-X-SCID(1) CB-RV (1)

20p12.3 136 2 PLCB1 CB-SIN-RV (2) 20q13.12 19100 2 C20orf121, PKIG,

SERINC3 CB-RV (2)

22q13.1 29588 2 AB051446, MKL1, RUTBC3

BM-X-SCID (1) CB-SIN-RV (1)

2p11.2 779 2 CAPG, LOC284948, RBED1

PB-ND (2)

2p21 975 2 AK025445, MGC40574, THADA, ZFP36L2

CB-RV (2)

4p14 11999 2 N4BP2, RHOH CB-RV (2) 6q23.3 9422 2 MYB CB-RV (2) 6p24.3 1991 2 RREB1 CB-RV (1)

CB-SIN-RV (1) LV hot spots 9q34.3 31043 3 AK130247, C9orf163,

INPP5E, NOTCH1, PMPCA, DCCAG3

CB-SIN-LV (2) CB-LV (1)

2p21 22106 2 THADA CB-SIN-LV (1) CB-LV (1)

20p12.3 24132 2 PLCB1 CB-LV (2) 17p13.3 25818 2 RUTBC1, SMG6, SRR,

TSR1 CB-LV (2)

Controls 6q25.1 4561 2 ESR1




Range indicates the maximun distance between hits contained in each hot spot. Proto-oncogenes or CIS are shown in bold. For the complete list of hot spots see Supplementary Table 5 * CB-RV, CB-derived CD34+ cells transduced with wt-LTR RV; CB-SIN-RV, CB-derived CD34+ cells transduced with SIN-LTR RV; BM-ADA, BM-derived CD34+ cells from ADA-SCID patients transduced with wt-LTR RV; BM-X-SCID: BM-derived CD34+ cells from X-SCID patients transduced with wt-LTR RV; PB-ND: PB-derived CD34+ cells from normal donor transduced with wt-LTR RV; CB-LV: CB-derived CD34+ cells transduced with wt-LTR LV; CB-SIN-LV, CB-derived CD34+ cells transduced with SIN-LTR LV (the number in parentheses indicates the number of hits for each category)




Figure 1. Retroviral integrations and transcription start sites. Distribution of gamma-retroviral (RV) (A) and lentiviral (LV) (B) integration sites in human CD34+ cells within an interval of ±30 kb upstream or downstream from the transcription start site (TSS) of known genes (UCSC definition, considering only one isoform/gene). The bars show the % distribution in each 5-kb interval of retroviral insertions (white bars), insertion hot spots (black bars), and control sequences (grey bars). The line shows the distribution of 65,000 computer-generated random insertion sites23. n values indicate vector-gene interactions, i.e., the total number of genes within 30 kb from individual insertions plus the intergenic insertions.

12

10

8

6

4

2

0-30 -25 -20 -15 -10 -5 +5 +10 +15 +20 +25 +30

TSS

Control (n = 902)

RV all (n = 1,517)

RV hot spots (n = 302)

12

10

8

6

4

2

0

TSS

Control (n = 902)

LV all (n = 1,241)

LV hot spots (n = 114)

-30 -25 -20 -15 -10 -5 +5 +10 +15 +20 +25 +30

vect

or-

gen

ein

tera

ctio

ns

(%)

vect

or-

gen

ein

tera

ctio

ns

(%)

distance from TSS (kb)

A

B


Random (n = 65,000)

Random (n = 65,000)

12

10

8

6

4

2

0-30 -25 -20 -15 -10 -5 +5 +10 +15 +20 +25 +30

TSS

Control (n = 902)

RV all (n = 1,517)


12

10

8

6

4

2

0

TSS

Control (n = 902)

LV all (n = 1,241)


-30 -25 -20 -15 -10 -5 +5 +10 +15 +20 +25 +30

vect

or-

gen

ein

tera

ctio

ns

(%)

vect

or-

gen

ein

tera

ctio

ns

(%)


A

B


Random (n = 65,000)

Random (n = 65,000)




Figure 2. Retroviral integration and gene density. Integration sites (white bars) and integration hot spots (black bars) of RV (A) and LV (B) vectors in CD34+ cells are plotted according to the number of Known Genes (UCSC definition, considering only one isoform/gene) contained in a range of 1 Mb around each insertion site, in intervals of 5 genes/Mb. The distribution of control sequences is indicated by light gray bars. The dark gray bars represent the frequency of 1-Mb segments in the human genome for each gene density interval. n values indicate the number of independent hits in each group.

inte

grat

ion

site

s(%

)

A

0

10

20

30

40

50

60

70

0-5 6-10 11-15 16-20 21-25 26-30 >30

Control (n = 798)

RV all (n = 1,030)


Genome

Known Genes / Mb

Control (n = 798)

LV all (n = 849)


Genome

0-5 6-10 11-15 16-20 21-25 26-30 >30

Known Genes / Mb

inte

grat

ion

site

s(%

)

B

0

10

20

30

40

50

60

70

inte

grat

ion

site

s(%

)

A

0

10

20

30

40

50

60

70

0-5 6-10 11-15 16-20 21-25 26-30 >30

Control (n = 798)

RV all (n = 1,030)


Genome

Known Genes / Mb

Control (n = 798)

LV all (n = 849)


Genome

0-5 6-10 11-15 16-20 21-25 26-30 >30

Known Genes / Mb

inte

grat

ion

site

s(%

)

B

0

10

20

30

40

50

60

70




Figure 3. Correlation between retroviral integration and gene activity in CD34+ cells. The bars show the % distribution of expression values from Affymetrix HG-U133A microarrays of cytokine-stimulated CD34+ cells. To correlate retroviral integration and gene activity, expression values from the CD34+ cell microarrays were divided into four classes, i.e. absent (black), low (i.e., below the 25th percentile in a normalized distribution, blue), intermediate (i.e., between the 25th and the 75th percentile, yellow) and high (i.e., above the 75th percentile, red). (A) The first two bars (all genes) show the distribution of the >16,000 genes on the microarray of cord blood (CB)- or bone marrow (BM)-derived CD34+ cells activated in the same conditions used for transduction with RV vectors, the other two bars represent the expression values of genes targeted by all RV integrations (RV all) or by integration hot spots (RV hot spots), derived from a weighted mean of the CB and BM microarray values. (B) The first bar (all genes) show the distribution of the >16,000 genes on the microarray of CB-derived CD34+ cells activated in the same conditions used for transduction with LV vectors, the other two bars represent the expression values of genes targeted by all LV integrations (LV all) or by integration hot spots (LV hot spots). The n values indicate the number of probesets analyzed for each group of genes.

n=22,283 n=1,571 n=195 n=959 n=65n=22,283

53

12

23

12

55

11

23

11

40

13

30

17

43

13

27

17

54

11

23

11

44

13

31

12

27

10

46

17

0

20

40

60

80

100

CB RV all RVhot spots

CBall genes

LV all LVhot spots

pro

bes

ets

(%)

High Intermediate Low Absent

BM0

20

40

60

80

100

A B

all genes

n=22,283 n=1,571 n=195 n=959 n=65n=22,283

53

12

23

12

53

12

23

12

55

11

23

11

55

11

23

11

40

13

30

17

40

13

30

17

43

13

27

17

43

13

27

17

54

11

23

11

54

11

23

11

44

13

31

12

44

13

31

12

27

10

46

17

27

10

46

17

0

20

40

60

80

100

CB RV all RVhot spots

CBall genes

LV all LVhot spots

pro

bes

ets

(%)

High Intermediate Low Absent

BM0

20

40

60

80

100

A B

all genes




35 ***

26 ***

*41

24

133

*128

795

Cell cycle

Apoptosis

Signal transduction

Transcription

Phosphorylation

Chromatinarchitecture

Genome (n = 14,959)

RV all (n = 762)

LV all (n = 633)

A

*****

**

*

**

*

**

5 10 15 20 25 300

2826245

5146

1492452

1973249

42565

55723

0 3 6 9 12 15

Protein ser/thrkinase activity

GTPase regulator

eligible genes (%)

26295

31458

*

GO Molecular Function

GO Biological Process

Genome (n = 17,847)

RV all (n = 866)

LV all (n = 712)

35 ***

26 ***

*41

24

133

*128

795

Cell cycle

Apoptosis

Signal transduction

Transcription

Phosphorylation

Chromatinarchitecture

Genome (n = 14,959)

RV all (n = 762)

LV all (n = 633)

A

*****

**

*

**

*

**

5 10 15 20 25 300

2826245

5146

1492452

1973249

42565

55723

0 3 6 9 12 15

Protein ser/thrkinase activity

GTPase regulator

eligible genes (%)

26295

31458

*

GO Molecular Function

GO Biological Process

Genome (n = 17,847)

RV all (n = 866)

LV all (n = 712)

B

Cancer

Hematopoieticsystem

Immune response

Immune/lymphaticsystem


Control (n = 268)

RV all (n= 637)

LV all (n = 514)


***

0 5 10 15 20 25 30 35

4715112705

39417

101

10622374

129020283

12

*

******

***

******

Diseases and Physiological System Development

0 5 10 15 20 25 30

21163

132

399

36

1679

77

Cell growth andproliferation

Cell signaling

Cell death

Cellular and Molecular Function

eligible genes (%)

11

12

4

4

23

******

***

***

***

**

***

*

***


Control (n = 268)

RV all (n= 637)

LV all (n = 514)


B

Cancer

Hematopoieticsystem

Immune response

Immune/lymphaticsystem


Control (n = 268)

RV all (n= 637)

LV all (n = 514)


***

0 5 10 15 20 25 30 35

4715112705

39417

101

10622374

129020283

12

*

******

***

******

Diseases and Physiological System Development

0 5 10 15 20 25 30

21163

132

399

36

1679

77

Cell growth andproliferation

Cell signaling

Cell death

Cellular and Molecular Function

eligible genes (%)

11

12

4

4

23

******

***

***

***

**

***

*

***


Control (n = 268)

RV all (n= 637)

LV all (n = 514)





Figure 4. Genes regulating cell growth and proliferation are preferential targets of retroviral integration. (A) Gene Ontology (GO) analysis of integration target genes in CD34+ cells. Genes identified as targets for RV (black bars) and LV (white bars) integration were analyzed for significant functional clusters with the DAVID 2.1 software. Functional categories are derived from the GO-Biological Process (establishment and/or maintenance of chromatin architecture, phosphorylation, transcription, signal transduction, apoptosis, cell cycle) and the GO-Molecular Function (GTPase regulator activity, protein serine/threonine kinase activity) classifications. Bars indicate the number of integration target genes annotated within the given category out of n genes eligible for each analysis. Asterisks denote the significance level of over-representation of any given category with respect to the human genome (grey bars), used as background population (***, EASE score <0.0005, **, EASE score <0.005, *, EASE score <0.05). The number of gene identifiers annotated within each functional category is indicated in the bars. (B) Functional clustering analysis comparing integration target and control gene lists. Function/disease categories were those significantly over-represented in at least one integration target gene list (0.005<p<0.05) using the Ingenuity Pathways Knowledge Base as background population and the Ingenuity analysis software. Bars represent the percentage of integration target genes belonging to each category among n genes eligible for the analysis. Asterisks denote the probability that differences observed between integration data sets (RV, LV, RV hot spots and LV hot spots) and the control data set are due to chance alone (two-sample test for equality of proportions with continuity correction; ***, p<0.0005, **, p<0.005, *, p<0.05). The number of genes annotated within each category is indicated in the bars.




Figure 5. CIS and proto-oncogenes are over-represented in RV integrations and integration hot spots. Comparative analysis of the frequency of genes annotated in the CIS and proto-oncogene databases (see methods for definitions and data source) between integration target and control gene lists. Bars represent the % of RV and LV integrations, RV and LV integration hot spots, and control sequences targeting at least one proto-oncogene or CIS. The n values indicate the number of independent hits in each group. Asterisks denote the level of enrichment with respect to control data set (two-sample test for equality of proportions with continuity correction; ***, p<0.0005, *, p<0.05).

0 2 4 6 8 10 12

Proto-oncogenes

CIS

Control (798)

RV all (1,030)

LV all (849)

RV hot spots (219)

LV hot spots (70)

176419325

277724494

integration sites (%)

******

*

*

******

0 2 4 6 8 10 12

Proto-oncogenes

CIS

Control (798)

RV all (1,030)

LV all (849)

RV hot spots (219)

LV hot spots (70)

176419325

277724494

integration sites (%)

******

*

*

******




Figure 6. Genes hit by retroviral integration are functionally linked in gene networks. Representative networks originated by Ingenuity analysis of RV target genes (see Supplementary Table 4 for a complete list). Both networks are made of 35 target genes, with an Ingenuity score ≥42. The color code indicates the most significant biological functions associated to each network (p<0.0005). (A) RV network 1; (B) RV network 4 (networks are identified in Supplemetary Table 4).

Apoptosis (21 genes, p =1.8x10-8)

A

PMAIP1

TPT1

PARP1

CHGB

BCL2L1

MADD

CFLAR

RASGRP3

ITPR1*

TUBB

CRADD*

RTN4IP1

SLC1A6

ATXN1

AHCYL1

CASP10

TUBA8PRKCQ

DYNLL1

RTN4

TUBB1

TNFRSF7FAS

NOSIP

ALOX5AP

SNW1

ALK

MKL1*MAD1L1

DDB2

BRE*

CASP8

BCL2*

MYB*

MLX*

Transcription (25 genes, p =2.0x10-14)Cell proliferation (17 genes, p =1.5x10-5)Tumorigenesis (12 genes, p =1.3x10-4)

B

CXXC5

KLF13

MAP3K14

RFX2

ATP2B4ENG

KLF6

TNIP1

SND1

CDC27*

RUNX1

TCF7L2

JUND

EVI1RPL30RXRB

SPP1

FOXP1*

SMAD3

TOB1

PCAF

TGFBR1

SURB7

PPP3CA

FRAT1

TADA2L

RREB1*

HHEX

CTBP1

RFX1

E2F5

FOLR2

CCL18

RARG

NR1H3

Apoptosis (21 genes, p =1.8x10-8)

A

PMAIP1

TPT1

PARP1

CHGB

BCL2L1

MADD

CFLAR

RASGRP3

ITPR1*

TUBB

CRADD*

RTN4IP1

SLC1A6

ATXN1

AHCYL1

CASP10

TUBA8PRKCQ

DYNLL1

RTN4

TUBB1

TNFRSF7FAS

NOSIP

ALOX5AP

SNW1

ALK

MKL1*MAD1L1

DDB2

BRE*

CASP8

BCL2*

MYB*

MLX*

PMAIP1

TPT1

PARP1

CHGB

BCL2L1

MADD

CFLAR

RASGRP3

ITPR1*

TUBB

CRADD*

RTN4IP1

SLC1A6

ATXN1

AHCYL1

CASP10

TUBA8PRKCQ

DYNLL1

RTN4

TUBB1

TNFRSF7FAS

NOSIP

ALOX5AP

SNW1

ALK

MKL1*MAD1L1

DDB2

BRE*

CASP8

BCL2*

MYB*

MLX*

Transcription (25 genes, p =2.0x10-14)Cell proliferation (17 genes, p =1.5x10-5)Tumorigenesis (12 genes, p =1.3x10-4)

B

CXXC5

KLF13

MAP3K14

RFX2

ATP2B4ENG

KLF6

TNIP1

SND1

CDC27*

RUNX1

TCF7L2

JUND

EVI1RPL30RXRB

SPP1

FOXP1*

SMAD3

TOB1

PCAF

TGFBR1

SURB7

PPP3CA

FRAT1

TADA2L

RREB1*

HHEX

CTBP1

RFX1

E2F5

FOLR2

CCL18

RARG

NR1H3

CXXC5

KLF13

MAP3K14

RFX2

ATP2B4ENG

KLF6

TNIP1

SND1

CDC27*

RUNX1

TCF7L2

JUND

EVI1RPL30RXRB

SPP1

FOXP1*

SMAD3

TOB1

PCAF

TGFBR1

SURB7

PPP3CA

FRAT1

TADA2L

RREB1*

HHEX

CTBP1

RFX1

E2F5

FOLR2

CCL18

RARG

NR1H3




Figure 7. Schematic representation of the maximum distance between individual hits within RV and LV hot spots. Symbols represent single hot spots originated from 2 (black), 3 (grey) or 4 (white) hits in the genome of CD34+ HSCs (1,030 RV and 849 LV integrations) and HeLa cells (869 RV integrations), plotted according to the maximum distance between individual integrations (in base pairs, log scale). Also shown are “false positive” hot spots generated by applying the definition criteria to a library of LM-PCR-amplified random sequences of human CD34+ DNA (798 sequences). 26.0% of the 97 RV hot spots in CD34+ cells and almost one half (40.4%) of the 52 RV hot spots in HeLa cells contained two independent integrations in less than 2 kb, compared to only one of the 33 LV hot spots.

100 1.000 10.000 100.000

HeLa RV

CD34 RV

CD34 LV

Controls

2 hits 3 hits 4 hits2 kb

40%

26%

3%

9%

bp100 1.000 10.000 100.000

HeLa RV

CD34 RV

CD34 LV

Controls

2 hits 3 hits 4 hits2 kb

40%

26%

3%

9%

bp




Hot spots of retroviral integration in human CD34+ hematopoietic cells

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