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. Author manuscript; available in PMC: 2018 Jun 1.
Published in final edited form as: Genes Chromosomes Cancer. 2017 Mar 31;56(6):472–483. doi: 10.1002/gcc.22451

Somatic mutations in murine models of leukemia and lymphoma: disease specificity and clinical relevance

Liat Goldberg 1, Sheryl M Gough 1, Fan Lee 1, Christine Dang 1, Robert L Walker 1, Yeulin J Zhu 1, Sven Bilke 1, Marbin Pineda 1, Masahiro Onozawa 2, Yang Jo Chung 1, Paul S Meltzer 1, Peter D Aplan 1
PMCID: PMC5399546  NIHMSID: NIHMS851975  PMID: 28196408

Abstract

Malignant transformation is a multistep process that is dictated by acquisition of multiple genomic aberrations that provide growth and survival advantage. During the post genomic era, high throughput genomic sequencing has advanced exponentially, leading to identification of countless cancer associated mutations with potential for targeted therapy. Mouse models of cancer serve as excellent tools to examine the functionality of gene mutations and their contribution to the malignant process. However, it remains unclear whether the genetic events that occur during transformation are similar in mice and humans. To address that, we chose several transgenic mouse models of hematopoietic malignancies and identified acquired mutations in these mice by means of targeted re-sequencing of known cancer-associated genes as well as whole exome sequencing. We found that mutations that are typically found in acute myeloid leukemia or T cell acute lymphoblastic leukemia patients are also common in mouse models of the respective disease. Moreover, we found that the most frequent mutations found in a mouse model of lymphoma occur in a set of epigenetic modifier genes, implicating this pathway in the generation of lymphoma. These results demonstrate that genetically engineered mouse models (GEMM) mimic the genetic evolution of human cancer and serve as excellent platforms for target discovery and validation.

Introduction

Genetically engineered animal models of human cancer have proven to be useful tools for investigating cancer initiation, progression, and therapy (Frese and Tuveson 2007; Hanahan, et al. 2007; Smith and Muller 2013). A common strategy employed in these studies is to generate mice that have one or more defined mutation(s) in the germline, and observe these mice for the onset of cancer over a defined study period. In these experiments, it is anticipated that the mutation(s) engineered into the germline have disrupted one or more critical pathways required for malignant transformation, and that complementary pathways will become activated with time, possibly due to spontaneous somatic mutations. The fact that additional mutations can collaborate with transgenes to induce malignant transformation has been shown by acceleration of disease onset in transgenic mice through the use of mutagens such as N-ethyl-N-nitrosourea (ENU) or retroviral insertions (Dang, et al. 2015; Huser, et al. 2014).

Investigators have traditionally used two approaches to identify genes and/or pathways that can complement a genetically engineered mutation. A targeted or candidate gene approach studies a limited number of genes that are known or suspected to be involved in malignant transformation, using PCR amplification and Sanger sequencing of target genes. Using this strategy, we have previously identified spontaneous mutations in one third of the leukemias that arose in NUP98-HOXD13 transgenic mice (Novak, et al. 2012; Slape, et al. 2008). These mutations most commonly involved Ras pathway genes, such as Nras, Kras, Ptpn11 and Cbl. A major limitation of this strategy however is that novel gene mutations would not be identified, as only a limited number of genes is assayed.

An alternate approach involves mutagenizing the genome, and allowing for biological selection to identify a malignant clone. One limitation of this approach is that it does not necessarily mimic malignancies that arise spontaneously, in that the complementary mutations are forced using chemicals or retroviral insertions. However, this approach has clearly been successful, as chemical mutagenesis screens implicated APC as a critical gene involved in colon cancer (Moser, et al. 1990), and retroviral insertional mutagenesis identified Hoxa9, Meis1, Nf1, and Evi1 as genes important for leukemic transformation(Largaespada, et al. 1995; Mucenski, et al. 1988; Nakamura, et al. 1996).

Over the past decade, whole genome sequencing (WGS) has emerged as a major tool for surveying the entire genome in order to identify cancer associated mutations. A complementary method, whole exome sequencing (WES), sequences only the exonic portion of the genome (~3%); these two techniques have revolutionized the search for mutations in cancer genomes (Ley, et al. 2008; Parsons, et al. 2008; Thirman, et al. 1993)

The ability of genetically engineered mice to accurately model human malignancy has been hotly debated (Frese and Tuveson 2007). In this study we used several transgenic mouse models of hematologic malignancies and examined the genetic events that occur during the generation of the disease by targeted sequencing and WES. We studied two different murine models of leukemogenic NUP98 fusions, NUP98-HOXD13 and NUP98-PHF23, that were established in our lab (Gough, et al. 2014; Lin, et al. 2005). Although NUP98 fusions have been considered rare events in patients with AML, recent studies show that NUP98 fusions are present in approximately 5% of all pediatric AML patients, and as many as 15% of “normal karyotype” pediatric AML patients (Bisio, et al. 2016; Gough, et al. 2011; Ostronoff, et al. 2015; Struski, et al. 2016). A third murine model of AML that we studied was generated by expression of a CALM-AF10 transgene (Caudell, et al. 2007). CALM-AF10 fusions are associated with AML and immature T-ALL in humans; the AML in both human patients and engineered murine models are characterized by clonal rearrangements of both IG and TCR genes (Asnafi, et al. 2003; Caudell and Aplan 2008; Deshpande, et al. 2006), suggesting that the cell of origin is multipotent. Both NUP98 fusions, which have recently been shown to be dependent on MLL1 for leukemic transformation (Xu, et al. 2016), as well as CALM-AF10 fusions lead to overexpression of HOXA7/9/10 (referred to as a “HOXA” cluster), which is thought to be a final common pathway leading to poor prognosis AML in both humans and mice (Caudell and Aplan 2008; Collins and Hess 2016; Golub, et al. 1999; Gough, et al. 2011). To broaden the models of hematopoietic malignancy evaluated, we studied a murine model of peripheral T cell lymphoma, not otherwise specified (PTCL-NOS) that was induced by overexpression of Lin28b (Beachy, et al. 2012). Overexpression of LIN28B, or the closely related LIN28A, is seen in over 50% of patients with PTCL-NOS (Beachy, et al. 2012). In this report, we show that certain gene mutations are highly recurrent in both human and mouse leukemia, validating that these mouse models are relevant to the human disease. These results also provide insight into gene pathways that are recurrently involved in specific malignancies and may serve as attractive targets for therapeutics.

Methods

Genetically engineered mouse models (GEMMs)

Construction of transgenic mice expressing NUP98-HOXD13 (NHD13), NUP98-PHF23 (NP23), CALM-AF10 (CA10) and Lin28b and the diagnoses of malignancies that arise in these mice have been described elsewhere (Beachy, et al. 2012; Caudell, et al. 2007; Gough, et al. 2014; Lin, et al. 2005).

Multiplex targeted re-sequencing

High molecular weight genomic DNA was isolated from tissues by standard salting-out procedures (Chervinsky, et al. 2001) or Qiagen Blood and Tissue DNA Extraction Kit (Qiagen). Regions of interest were targeted by PCR amplification using multiplexed pools of primers tiling whole exons. Targeting primers were tailed with partial Illumina adapter sequences. Amplification and pooling of target amplicons was accomplished in a 48.48 Access microfluidic array on a Fluidigm Biomark System. Primer Wells were loaded with 5μl containing 1X Access Array Loading Reagent and 12-plex pools of primer pairs (6.25μM each). Sample Wells were loaded with 5μl containing 1X Advantage HD Buffer (Clontech), 0.2mM each dNTP, 0.025U/μl Advantage HD Polymerase, 1X Access Array Loading Reagent, 0.5X ROX Reference Dye (Invitrogen), 0.5X EvaGreen Dye (Biotium), and 25ng template DNA. Thermocycler conditions were: 98°C for 3 min., 40 cycles of 98°C for 10 sec., 55°C for 30 sec., 72°C for 15 sec., followed by 72°C for 5 min. Illumina adapter sequences were completed and sample specific indexes were added in a 50μl PCR containing: 1μl 100X diluted amplicon pool, 0.125μM each primer (Index Primer F: 5′-AAT GAT ACG GCG ACC ACC GAG ATCT ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT-3′, Index Primer R: 5′-CAA GCA AAG ACG GCA TAC GAG ATx xxx xxx xGT GAC TGG AGT TCA G-3′ where “x” indicates an eight nucleotide barcode sequence), and 1X Phusion High-Fidelity PCR Master Mix with HF Buffer (New England Biolabs). Thermocycler conditions were: 95°C for 30 sec., 18 cycles of 95°C for 10 sec., 65°C for 30 sec., 72°C for 30 sec., followed by 72°C for 5 min. Multiplex PCR Pools were sequenced on an Illumina Genome Analyzer IIx.

The raw sequences were aligned to MM9 mouse genome using BWA version 0.7.12-r1039. Reads not aligning to the location expected by the PCR reaction were removed from further analysis. From the remaining sequences, the known, constant primer sub-sequences were removed and alignment positions adjusted accordingly. The resulting BAM files were analyzed using the Strelka version 1.0.14 for somatic variant calling.

Whole exome capture and sequencing analysis

Mouse Illumina DNA libraries were prepared and captured using the Agilent SureSelectXT Mouse All Exon Kit according to the manufacturer’s instructions. In brief, DNA was fragmented on a Covaris S1 sonicator followed by end repair and phosphorylation. Blunt fragments were adenylated, ligated to Illumina Y-adapters and PCR amplified. Bait hybridization proceeded for 48 hours, followed by recovery of captured exome fragments by PCR. Captured exomes were sequenced on an Illumina HiSeq 2000.

Data processing and variant calling procedure mainly followed the Best Practices workflow recommended by the Broad Institute (http://www.broadinstitute.org/gatk/guide/best-practices). Briefly, the raw sequencing reads were mapped to mouse genome build 10 (mm10) by the Burrows-Wheeler Aligner (BWA, (Li and Durbin 2009)) followed by local realignment using the GATK suite from Broad Institute and duplicated reads were marked by Picard tools (http://picard.sourceforge.net).

Somatic variant calling was performed by comparison of tumor to wild type samples by the Strelka somatic variant caller (Saunders, et al. 2012) and germline variant calling was done with the UnifiedGenotyper from the Broad Institute (https://www.broadinstitute.org/gatk/). SnpEff (Cingolani, et al. 2012) and dbSNP 137 (NCBI) were used to annotate and predict effects of the variants.

The following filtering criteria were used for germline variation calls: (1) minimum read depth is 5; (2) minimum altered read number is 3; (3) minimum fraction of altered reads is 0.01; (4) impact is ‘High’ or ‘Moderate’. The following filtering criteria were used for somatic variation calls: (1) minimum read depth is 5; (2) maximum altered read number in reference samples (‘Normal’) is 2 or maximum fraction of altered reads in reference samples (‘Normal’) is 0.01.

Additional filtering is described in the results.

Validation of sequencing results

Genomic DNA from paired tumor-normal (tail or kidney) tissues was used to PCR amplify regions where variants were identified by targeted re-sequencing or WES. Amplicons were purified and Sanger sequenced at the NCI CCR genomics core. To validate expression of variants, RNA was extracted from tumor tissue using TRIzol reagent according to the manufacturer’s guidelines (Ambion). cDNA was synthesized using SuperScript III First-Strand Synthesis System for RT-PCR (Invitrogen) and then PCR amplified and sequenced.

Results

Targeted re-sequencing of mouse hematologic malignancies identifies acquired mutations in genes commonly mutated in human cancer

We have previously characterized four GEMMs of hematologic malignancy that develop a wide spectrum of disease, including myelodysplastic syndrome (MDS), acute myeloid leukemia (AML), T-cell acute lymphoblastic leukemia T-ALL, B-cell acute lymphoblastic leukemia (B-ALL), and peripheral T-cell lymphoma (PTCL) (Beachy, et al. 2012; Caudell, et al. 2007; Gough, et al. 2014; Lin, et al. 2005). These models include mice that express NHD13, NP23, CA10, or Lin28b (Table S1) transgenes driven by Vav1 regulatory elements, which directs lineage specific expression to the hematopoietic compartment (Ogilvy, et al. 1999). We used targeted re-sequencing to search for spontaneous somatic mutations in a set of genes known to be recurrently mutated in hematologic cancers (Tables S2, S3,(Cancer Genome Atlas Research 2013; Lindqvist, et al. 2016). This gene set included the 14 genes most commonly mutated in human AML (NPM1, FLT3, DNMT3A, IDH1, IDH2, NRAS, KRAS, RUNX1, TET2, TP53, CEBPA, WT1, PTPN11 and KIT), as well as the 4 genes most commonly mutated in T-ALL (NOTCH1, PTEN, PHF6 and FBXW7), supplemented with 6 additional genes less commonly mutated in human hematopoietic malignancy (ASXL1, CBL, JAK2, EZH2, MPL and GATA1). Genomic DNA from tumor samples and healthy wild type littermates was used for multiplex PCR amplification, barcoding and sequencing. Since the GEMMs were generated in either C57Bl/6 or FVB strains, wild type (WT) controls with matching genetic backgrounds were included in the analyses to eliminate germline polymorphisms. Single nucleotide variants (SNVs) and insertions/deletions (indels) were validated using Sanger sequencing of matching tumor and normal tissues. Out of 24 gene targets, we identified and validated mutations in eight genes: Flt3, Kras, Nras, Cbl, Trp53, Ptpn11, Idh1 and Notch1 (Figure 1A, Table S4). Most of the mutations were similar to mutations previously identified in human leukemia. For instance, we found numerous mutations in both Kras and Nras at amino acid positions 12, 13, 61 and 63, which are frequently mutated in cancer patients and considered mutational hotspots. In most cases (12/18), the variant allele frequencies (VAF) of the ras mutations were close to or greater than 0.5, suggesting that this mutation is a relatively early event (Table S4). In addition, several samples had VAF >0.7, suggesting reduction to homozygosity in a tumor subclone. Conversely, most mice with mutations in Ptpn11, which encodes a phosphatase known to affect ras signaling, had a low VAF (<0.25), suggesting that the mutation was only present in a minor subclone (Table S4).

Figure 1.

Figure 1

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A. Distribution of mutations in mouse T-ALL and AML samples as detected by targeted re-sequencing. Ras pathway genes refers to Kras, Nras and Ptpn11. B. Schematic representation of the recurrent mutations in Notch1 detected by targeted re-sequencing in mouse T-ALL and PTCL samples.

Targeted re-sequencing of only 24 genes identified spontaneous mutations in more than half of the T-ALL samples and almost half of the AML samples (Figure 1A). The most common acquired mutation in the T-ALL samples involved the heterodimerization (HD) or proline, glutamic acid, serine, threonine-rich (PEST) domain of Notch1 (Figure 1B), similar to findings in human patients with T-ALL (Tzoneva and Ferrando 2012). In contrast, the most common acquired mutations identified in the murine AML samples involved Ras or tyrosine kinase signaling pathways, similar to findings in human AML in general (Scholl, et al. 2008), as well as NUP98-rearranged AML specifically (Bisio, et al. 2016; Ostronoff, et al. 2015; Struski, et al. 2016; Thol, et al. 2013).

We have previously shown that Notch1 PEST domain mutations in murine primary pre-T LBL tumors have a high occurrence at two hot spots; R2361 and S2398 (Lin, et al. 2006). Six of the 14 Notch1 PEST domain mutants identified in this screen mapped to one of those two “hotspots” for mutation.

Although we identified recurrent missense mutations and one in frame deletion in the Flt3 kinase domain (KD), we identified no internal tandem duplication (ITD) mutations in mouse AML samples using this multiplex PCR/next-gen sequencing approach. However, Flt3-ITD mutations were previously identified in two CA10 mice (Novak, et al. 2012). One of these mice (#7468) was included in this study, however, the assay did not detect the previously documented 39 bp insertion, suggesting that this assay may have missed large indels in Flt3. Therefore, to determine if these large insertions might have been missed due to alignment errors, we analyzed 22 NP23 AML samples for Flt3-ITD mutations using PCR primers that flank the previously identified murine Flt3-ITD (primers muFlt3LM For and muFlt3LM Rev (Novak, et al. 2012)) and found no ITD mutations (data not shown).

Mutations in epigenetic modifier genes, which are frequently found in AML patients (Naoe and Kiyoi 2013) were typically absent from the mouse leukemias, with the exception of mutations in Idh1 (Table S4, highlighted). An Idh1 R132G was initially identified in two primary NHD13 AML samples in addition to two AMLs that arose in mice transplanted from an NHD13 donor that showed no clinical or hematologic evidence of AML (Chung, et al. 2008) (Table S4). In the case of the two primary AML samples, the VAF of Idh1 mutations was close to 0.5, indicating these mutations were most likely early events, as was recently proposed in the context of AML patients (Chan and Majeti 2013; Shlush, et al. 2014). However, the Idh1 R132G VAF in the secondary leukemias was 0.25 or 0.36 (Table S4), suggesting that this mutation may have occurred in a subclone which arose at a later stage. This observation suggested that Idh1 mutations with low VAF might be present in other NHD13 AML samples that did not pass the designated threshold of VAF=0.1. Subsequently, we identified three additional NHD13 AML samples (1138, 1155, and 2420) in which Idh1 R132G occurred at VAF of 0.08, 0.03 and 0.02 (Figure 2A, Table S4). Using an allele specific PCR assay, we were able to confirm that the Idh1 R132G mutation was present in two of these samples (1138, 2420, Figure 2B); we suspect the frequency of the Idh1 mutation in sample 1155 was below the level of detection of the assay.

Figure 2.

Figure 2

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Detection of Idh1 mutations in NHD13 mice. A. Idh1 R132 mutations were detected by targeted re-sequencing. The table summarizes all mutations with over 3 reads and over 1% allele frequency found in NHD13, NP23 and CA10 mice (Fisher’s exact test p=0.003 for NHD13-driven AML vs non NHD13-driven AML). B. Idh1 R132G low frequency mutations were confirmed in mice 1138 and 2420 by use of allele specific PCR. Sensitivity of the assay was measured using serial dilutions of genomic DNA from AML sample of mouse 1135 (standard, VAF=0.62). Total DNA of the standard samples was adjusted to 100 ng using genomic DNA from a WT mouse. NTC, no template control.

Whole exome sequencing reveals novel somatic and germline mutations in mouse models of hematologic malignancies

In many (94/154) of the samples analyzed by targeted re-sequencing, no mutations were detected in the selected target genes (Figure 1, Table S1). Of note, only 2 out of 31 Lin28b samples contained a mutation in any of the target genes, suggesting that the selected target genes were not common targets for the Lin28b PTCL model, indicating a need for an un-biased approach to unravel the mutational processes in these malignancies. Therefore, we performed whole exome sequencing (WES) on 17 Lin28b PTCL samples and 18 AML samples from NP23 mice in order to gain new insights into the genetic events leading to these malignancies. These results were analyzed using the approach summarized in Figure 3A.

Figure 3.

Figure 3

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A. Scheme summarizing the filtering approach used for whole exome sequencing data analysis. B. Waterfall plot depicting the distribution of Tier 1 variants in Lin28b and NP23 tumor samples. Horizontal lines represent medians (solid line for Lin28b, dashed line for NP23).

Tier 1 variants were defined as non-synonymous mutations in coding regions with a VAF of >0.25 compared to a WT sibling. Figure 3B shows that the distribution of Tier I variants in NP23 and Lin28b tumors is very similar, with the median number of variants per sample being 13 for Lin28b and 10 for NP23. These values are similar to those in clinical series of WES for patients with AML or PTCL (Ley, et al. 2008; Palomero, et al. 2014). Using this approach, we identified 40 recurrent variants; 18 in Lin28b and 22 in NP23 samples, respectively (Table S5).

Since our study was based on inbred mice, we initially assumed, as have others (Kode, et al. 2014), that germline mutations would be filtered out by our analysis. However, for 28 of the 40 recurrent variants, identical SNVs were documented in several samples (Table S6). We suspected that these may be germline mutations and selected seven of the most common SNVs to validate by Sanger sequencing of non-malignant tissue. In all seven cases, we found the SNV in both the tumor and the normal tissue from the same mouse, demonstrating that these SNVs represented new germline mutations that had occurred within the colony (Table S6). All of the mutations presented in figure 5 and table 1 have been documented to be acquired mutations by sequencing germline tissue (tailor non-infiltrated kidney) from the same mouse.

Figure 5.

Figure 5

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Summary of the Acquired somatic mutations identified by either targeted resequencing or whole exome sequencing in GEMMs of hematologic malignancies. Oncoprints generated using the cBioPortal oncoprint tool (Gao et al. Sci. Signal. 2013 & Cerami et al. Cancer Discov. 2012) are presented for AML samples (A), T-ALL samples (B) and PTCL (C). Samples highlighted in Blue were sequenced only by targeted re-sequencing while the rest were sequenced either by WES or by both. Mutations marked green are missense events, and mutations marked black are truncating events.

Table 1.

Validated acquired SNVs and indels identified by WES

Sample Name POS REF ALT Effect AAChange Gene Exon VAF Functional class
C57BL6_Lin28b_6297 13:23575884 A T Missense K28M* Hist1h3d 1 0.52 Epigenetic regulation of transcription
C57BL6_Lin28b_6266 13:23752462 A T Missense K28M* Hist1h3b 1 0.222
C57BL6_Lin28b_6412 15:98851917 G A Nonsense Q2632* Mll2 32 0.597
C57BL6_Lin28b_6316 15:98854095 G A Nonsense R1998* Mll2 30 0.464
C57BL6_Lin28b_6502 16:4107517 C T splicing acceptor mutation Frame shift Crebbp 19 0.545
C57BL6_Lin28b_6206 16:4128468 G A Nonsense Q516* Crebbp 6 0.35
C57BL6_Lin28b_6220 10:80786409 A T Missense S603C Dot1l 14 0.197
C57BL6_Lin28b_6502 11:11754064 T C Missense L160P Ikzf1 5 0.426
C57BL6_Lin28b_6554 11:98467122 A T Missense V463E Ikzf3 8 0.278
C57BL6_Lin28b_6297 18:78858500 T G Missense K604Q Setbp1 4 0.431
C57BL6_Lin28b_6502 6:47532952 C T Missense G613E Ezh2 15 0.78
C57BL6_Lin28b_6206 X:8070752 A G Missense C222R Suv39h1 3 0.8
C57BL6_Lin28b_6266 2:153399445 A AG Frame shift 639 Asxl1 13 0.2
C57BL6_Lin28b_6358 4:101156405 C A Missense S1042I Jak1 22 0.196 Proliferation, survival and cytokine signaling
C57BL6_Lin28b_6297 4:101163673 C A Missense V657F Jak1 14 0.625
C57BL6_Lin28b_6208 4:148540355 C A Missense P2141Q Mtor 46 0.529
C57BL6_Lin28b_6358 4:148550170 C A Missense A2416D Mtor 53 0.467
C57BL6_Lin28b_6297 15:61988029 A T Missense S170C Myc 2 0.81
C57BL6_Lin28b_6206 3:98147049 C T Nonsense Q2343* Notch2 34 0.4
C57BL6_Lin28b_6502 7:141192012 C T Missense G159R Hras1 5 0.343
C57BL6_Lin28b_6226 11:4794482 C T Missense R158Q Nf2 7 0.442
C57BL6_Lin28b_6206 15:78565454 T C Missense N92S Rac2 4 0.495
C57BL6_Lin28b_6180 9:120952926 A C Missense K335T Ctnnb1 7 0.233
C57BL6_Lin28b_6412 10:19750816 C T Missense T196M Il20ra 4 0.273
C57BL6_Lin28b_6226 13:116305440 A T Missense F86I Isl1 3 0.364 Transcription regulation
C57BL6_Lin28b_6226 X:12048473 TC T Frame shift −1229 Bcor 8 0.645
C57BL6_Lin28b_6206 11:101523955 C T Missense D1118N Brca1 10 0.288 DNA damage repair
C57BL6_Lin28b_6502 11:84263324 G T Missense R957L Acaca 21 0.31 Fatty acid synthesis
C57BL6_NP23_653 6:145246771 C T Missense G12D Kras 2 0.816 Cell proliferation, DNA replication and cytokine signaling
C57BL6_NP23_G8 6:145234351 C T Missense E63K Kras 3 0.16
C57BL6_NP23_C10 5:147341238 T C Missense D842G Flt3 20 0.387
C57BL6_NP23_709 5:147341238 T C Missense D842G Flt3 20 0.93
C57BL6_NP23_G8 5:147341238 T C Missense D842G Flt3 20 0.11
C57BL6_NP23_856 18:61129084 A C Missense D804A Csf1r 18 0.241
C57BL6_NP23_173 X:87890154 C T Missense A28T Il1rapl1 2 0.432
C57BL6_NP23_784 17:84186771 C+38nt C Frame shift del at S133 Zfp36l2 2 0.5† Translation regulation
C57BL6_NP23_C10 17:84186580 A+40nt A Frame shift del at Y210 Zfp36l2 2 0.311
C57BL6_NP23_961C 2:67512860 G A Missense R1815K Xirp2 7 0.442 Cardiac development, function and disease
C57BL6_NP23_824 2:67511677 G T Nonsense E1421* Xirp2 7 1
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*

After post-translational removal of the initiator methionine, the mutation is in K27.

†

VAF estimated by Sanger sequencing. Variant had VAF<0.25 in WES.

Interestingly, one of the germline mutations we identified, a R250* in the ETS homologous factor (Ehf) gene, demonstrated a frequent, nearly complete, loss of heterozygosity in tumor tissue (Figure 4A). We sequenced a total of 20 paired NP23 tumor and normal tissues from the same founder line (C10), and found LOH in 12 tumor samples; in all cases, the WT allele was lost, while the mutant R250* allele was retained. An additional seven samples from two other founder lines (B10, G8) did not show an Ehf mutation (Figure 4B). Furthermore, NP23 mice with AML that displayed a LOH of the WT Ehf allele had a shorter survival rate compared to mice with no LOH (Figure 4C), suggesting that loss of a functional Ehf protein may provide a survival advantage.

Figure 4.

Figure 4

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Germline mutations in the Ehf gene with high frequency of LOH. Whole exome sequencing identified a R250* mutation in 8 NP23 AML samples originating from the C10 founder line. A. Sanger sequencing of both tumor and normal kidney tissue validated that these were germline mutations with loss of heterozygosity of the wild type allele in the tumor tissue. This analysis was performed on 20 NP23 C10 samples, 6 NP23 B10 samples and 1 G8 NP23 sample (B) and showed exclusivity of these mutations in the C10 founder line. C. Survival curve showing shorter survival of NP23 AML bearing mice carrying the Ehf mutation with LOH of the WT allele compared to mice with no LOH.

Given that the stated criteria of VAF>0.25 may have eliminated important mutations present in subclonal populations, we expanded our search to include VAF >0.1 for genes that are known to be important for leukemic transformation. Using this approach, we identified and validated additional variants in relevant genes such as Hist1h3b, Dot1l and Asxl1. In sum, a total of 36 somatic variants were identified by WES with VAF>0.1 and validated by Sanger sequencing of Lin28b and NP23 paired tumor and normal germline tissue (Table 1, Table S7). Using the same filtering approach, we also examined non-coding SNVs with a VAF>0.25. We found 66 and 119 unique non-coding SNVs in Lin28b and NP23 samples respectively (Table S8 and S9). Of these, a total of 12 non-coding SNVs were recurrent, identified in 2–13 samples. However, these SNVs were precisely identical across the samples and, similar to the identical coding SNVs we identified, are suspected to be germline mutations within the colony (Table S8 and S9).

Acquired mutations in both models could be placed into several large functional categories (Table 1). Within the Lin28b cohort, a large group of mutations were found in epigenetic regulators, similar to findings in lymphoma patients (Pasqualucci, et al. 2011b). In some cases, we found these mutations to occur at the same position as ones previously reported in human malignancy, such as in Hist1h3b, Hist1h3d and Asxl1 genes, while other mutations affected different residues within the same functional domains as seen in human malignancy (Table 1). It should be noted that the Hist1h3b and Hist1h3d mutations occur at an amino acid residue (K27) well-known to be a critical residue for epigenetic regulation of transcription (Rice and Allis 2001). The Asxl1 frameshift mutation was due to an expansion of a G repeat tract (Figure S1); this form of mutation (addition of a G within a G repeat tract encoding a frameshift at codon 642) is the single most common ASXL1 mutation in MDS and AML patients (Abdel-Wahab, et al. 2011; Gelsi-Boyer, et al. 2009).

According to our study, one Lin28b-driven PTCL sample had acquired a nonsense mutation (Q516*) in Crebbp (Table 1), which truncates a large portion of the protein, resulting in loss of both the DNA and protein interacting domains, and the acetyltransferase domain. It seems likely that Q516* results in a loss of function, similar to mutations found in lymphoma and ALL patients. The second Lin28b-driven PTCL sample acquired a Crebbp mutation at a splice acceptor site (Chr16: 4107517; AG > AA) upstream of exon 19 (Table 1), and would be predicted to omit exon 19 in the mature transcript. Reverse transcription PCR (RT-PCR) of exons 18–20 revealed two fragments; a wild type-sized allele (~340bp) and a second, smaller fragment (~250bp) consistent with the omission of exon 19 (Figure S2A). Sequence analysis of these two bands revealed 3 splice variants: the first deletes 6 bp of exon 19, the second deletes 18 bp of exon 19, while the third deletes exon 19 completely, resulting in a frame shift and premature stop codon (Figure S2). The amino acids missing in the first two variants are part of a domain of unknown function (DUF902) that is conserved in several transcriptional co-activators (pFAM database). The short mutant isoform encodes a truncated protein, lacking the histone acetyltransferase domain. Since Crebbp is suggested to act as a haploinsufficient tumor suppressor (Mullighan, et al. 2011; Pasqualucci, et al. 2011a), the splicing mutation in the Lin28b PTCL may contribute to tumor formation.

In summary, using targeted re-sequencing and whole exome sequencing, we identified and validated acquired mutations in 37 genes (Figure 5). Examination of the recurrent mutations revealed that some events were unique to a specific type of malignancy. This implies that such mutations collaborate with the transgene to drive leukemia/lymphoma in a specific cell context.

Discussion

We studied collaborative mutations using mouse models of hematologic malignancies in which a single genetic aberration is introduced in the mouse germline and spontaneous, complementary mutations accumulate over time to generate a malignancy (Beachy, et al. 2012; Caudell, et al. 2007; Gough, et al. 2014; Lin, et al. 2006). It seems feasible that the disease latency reflects the mutational processes required to generate cancer- the stronger the driver mutation is, the fewer additional mutations are needed and the shorter the disease latency will be. Of note, NUP98 fusions are likely to be early, initiating events in human AML (Struski, et al. 2016), similar to murine models in which transgenes engineered into the murine germline are initiating events.

We used targeted resequencing of selected candidate genes and WES to identify spontaneous, acquired mutations in murine hematopoietic malignancies. An important observation of this study is that, similar to findings in human patients, specific mutations are recurrently found in specific types of mouse leukemia and lymphoma (Figure 5). For instance, the most frequent mutation we identified in mouse T-ALL involved Notch1 (Figure 1); 14 of 27 T-ALL cases had Notch1 mutations in the HD or PEST domains (Table S4). Similarly, over half of T-ALL patients carry activating mutations in Notch1 (Tzoneva and Ferrando 2012), indicating that Notch1 is a major driver of both human and mouse T-ALL. Interestingly, we found a Notch1 PEST domain mutation in a CALM-AF10 AML sample (Table S4) that had a clonal rearrangement of the Tcrb and Igh loci (Caudell, et al. 2007), suggesting that this leukemia may have emerged from an immature and uncommitted progenitor cell.

A working hypothesis for collaborative mutations in AML suggests that myeloid leukemias have at least two complementary mutations; a “Class I” mutation that regulates cell proliferation and/or survival, and a “Class II” mutation that impairs differentiation or increases stem cell self-renewal (Gilliland and Tallman 2002; Naoe and Kiyoi 2013). Of note, most of the spontaneous somatic mutations identified by targeted re-sequencing in the current study are considered to be “Class I” mutations, such as those involving Ras and Flt3. Conversely, “Class II” mutations, such as those involving Npm1, Runx1 and Cebpa, as well as mutations in key epigenetic modifiers such as Dnmt3a and Tet2, were not detected in this study. The reason for this mutational pattern is most likely related to the choice of the initiating mutation, namely, the transgene. Expression of CALM-AF10 or Nup98-fusion proteins has been shown to induce expression of the HoxA gene cluster, leading to impaired differentiation (Caudell, et al. 2007; Gough, et al. 2014; Lohr, et al. 2012). Therefore, acquisition of additional differentiation-impairing mutations would likely provide little selective advantage, whereas mutations contributing to proliferation and survival are positively selected. The findings in these murine models are similar to those in NUP98-rearranged AML, in which the most frequent co-occurring mutations are in NRAS, KRAS, FLT3, WT1 and CEBPA. In addition, mutations in epigenetic modifiers, such as DNMT3A, TET2, ASXL1 and IDH1/2, as well as NPM1, are absent or rare in both human and murine NUP98-fusion AML (Bisio, et al. 2016; Ostronoff, et al. 2015; Struski, et al. 2016; Thol, et al. 2013).

An important exception to the above observation is the recurrent Idh1 mutations in NHD13 AML samples (Figure 2, Table S4). IDH genes are recurrently mutated in a wide variety of tumors, including glioblastoma multiforme and AML (Losman and Kaelin 2013; Marcucci, et al. 2010; Parsons, et al. 2008). These recurrent mutations in IDH1/2 genes lead to hypermethylation of both cytosine residues and histone lysine residues accompanied by a block in differentiation (Figueroa, et al. 2010; Lu, et al. 2012; Waterfall, et al. 2014). In addition, IDH2 mutations are thought to be early, pre-leukemic events that lead to expansion of hematopoietic stem and progenitor cells (Shlush, et al. 2014). Two NHD13-driven AML samples had clonal Idh1 mutations (VAF of ~ 0.5), indicating that these were early events in disease development. In contrast, at least four other NHD13-driven AML samples had subclonal Idh1 mutations, suggesting a late mutational event. Out of 74 AML samples taken from three mouse models (NHD13, NP23 and CA10), targeted re-sequencing identified Idh1 mutations in seven NHD13 samples, six of which were validated (Figure 2, Table S4). The exclusivity of these mutational events suggests that NHD13 and mutant Idh1 may collaborate in leukemia initiation or maintenance.

FLT3 mutations are common in unselected AML patients, and were recently found to occur in 36–63% of NUP98-rearranged AML (Bisio, et al. 2016; Struski, et al. 2016; Thol, et al. 2013). We found Flt3 mutations in the murine AML samples, but not the murine PTCLs. Interestingly, we found several missense mutations and one in-frame deletion in the TKD of Flt3, but no ITD mutations in this sample set. Of note, no ITD was identified in CA10 # 7468, a sample that we previously reported had a 39 bp Flt3-ITD identified by PCR amplification and Sanger sequencing (Novak, et al. 2012). This finding highlights a limitation of NGS in ascertainment of large indels. As mentioned above, although uncommon, Flt3-ITD mutations can be acquired in mice (Novak, et al. 2012), and a Flt3-ITD collaborates potently with NHD13 to cause leukemia in a murine model (Greenblatt, et al. 2012). Although speculative, it may be that the murine Flt3 locus is less susceptible to ITD events than the human FLT3 locus.

In our WES studies, control DNA was obtained from wild type littermates of the inbred transgenic mice. This approach decreases the cost of WES, as WES data is not obtained from germline tissue of all mice with malignancy, but only a single wild-type mouse. However, a limitation of this approach is that a germline mutation within the colony will be mis-interpreted as a somatic, acquired SNV. We suspected that some of the tier 1 mutations may arise from germline mutations within the colony, as precisely identical SNVs were present in numerous independent animals. PCR amplification and Sanger sequence analysis of matched tumor and normal tissues demonstrated that almost all identical, recurrent SNVs were spontaneous germline mutations, such as the Ehf mutation in NP23 AML samples (Figure 4, Table S6). Exceptions to this generalization were found in well-known “hot spot” mutations of established cancer genes, such as Idh1, Flt3, Nras, and Kras. Acquired SNVs were previously noted within a colony of congenic mice that had been backcrossed 10+ generations (Wartman, et al. 2011). This and the current report highlight the importance of sequencing non-malignant tissue, even when studying inbred mice, in order to distinguish somatic mutations from germline mutations that can arise within a mouse colony. Of note, some germline mutations are associated with predisposition to cancer and may be functionally relevant (Godley 2014; Noetzli, et al. 2015).

WES of murine Lin28b PTCL samples revealed recurrent mutations in genes that are involved in epigenetic regulation of transcription, such as Hist1h3b, Hist1h3d, Asxl1, Mll2, Dot1l, Ezh2 and Crebbp (Table 1). Mutations in epigenetic modifiers have been widely reported in patients with hematopoietic malignancies, and, in the case of Asxl1 and histone genes, are identical to those found in murine Lin28b PTCL (Abdel-Wahab, et al. 2011; Gelsi-Boyer, et al. 2009; Rice and Allis 2001).

In summary, parallels between the mutational evolution of mouse and human malignancies supports the sequencing of mouse tumors as a discovery tool for collaborative mutations relevant to human disease. In this study, we identified at least one likely collaborative mutation in >60% of our samples. We have shown that several epigenetic factors are subject to mutation in Lin28b-driven PTCL, identifying them as candidate genes that may play a role in human PTCL. This important finding indicates that aberrant chromatin remodeling is a critical process during development of hematopoietic malignancies, and highlights epigenetic regulatory proteins as promising targets for therapeutic intervention.

Supplementary Material

Supp Fig S1

Figure S1: Validation of Asxl1 frame shift mutation in Lin28b mouse 6266. Genomic DNA was prepared from lymph node tumor and germline (Kidney) tissue taken from Lin28b mouse 6266 diagnosed with PTCL. DNA was then used to PCR amplify exon 12 in the Asxl1 gene, amplicons were purified and Sanger sequenced. Chromatograms show Asxl1 sequence of amino acids 634– 643.

Supp Fig S2

Figure S2: Crebbp splice acceptor mutation. A. RT-PCR amplifying Crebbp exons 18–20 was performed on RNA extracted from lymph nodes of Lin28b mice, 6502 and 6222. The splice acceptor mutation in mouse 6502 generates a short transcript lacking exon 19. B. Schematic Illustration of the different splice variants generated due to the splice acceptor mutation in Lin28b mouse 6502. When exon 19 is completely skipped (Variant 3), a frame shift occurs that generates a premature stop codon. This may lead to non-sense RNA decay, which could account for the low expression level of this transcript. Variants 1 and 2 are formed by an alternate splice acceptor, leading to skipping of 2 and 6 amino acids (respectively).

Supp TableS1
Supp TableS2
Supp TableS3
Supp TableS4
Supp TableS5
Supp TableS6
Supp TableS7
Supp TableS8
Supp TableS9

Acknowledgments

The authors thank Michael Kuehl, Ross Levine, and current and former members of the Aplan lab for insightful discussions. We also thank the NCI Sequencing core for Sanger sequencing, the NCI Flow Cytometry core for assistance with immunophenotype analysis, the NCI Transgenic Core for generation of transgenic mice, and Maria Jorge for excellent animal husbandry. This work was supported by the Intramural Research Program of the National Cancer Institute, National Institutes of Health.

Footnotes

The authors have no competing interests in relation to the work described in this manuscript.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp Fig S1

Figure S1: Validation of Asxl1 frame shift mutation in Lin28b mouse 6266. Genomic DNA was prepared from lymph node tumor and germline (Kidney) tissue taken from Lin28b mouse 6266 diagnosed with PTCL. DNA was then used to PCR amplify exon 12 in the Asxl1 gene, amplicons were purified and Sanger sequenced. Chromatograms show Asxl1 sequence of amino acids 634– 643.

NIHMS851975-supplement-Supp_Fig_S1.tif (998.3KB, tif)
Supp Fig S2

Figure S2: Crebbp splice acceptor mutation. A. RT-PCR amplifying Crebbp exons 18–20 was performed on RNA extracted from lymph nodes of Lin28b mice, 6502 and 6222. The splice acceptor mutation in mouse 6502 generates a short transcript lacking exon 19. B. Schematic Illustration of the different splice variants generated due to the splice acceptor mutation in Lin28b mouse 6502. When exon 19 is completely skipped (Variant 3), a frame shift occurs that generates a premature stop codon. This may lead to non-sense RNA decay, which could account for the low expression level of this transcript. Variants 1 and 2 are formed by an alternate splice acceptor, leading to skipping of 2 and 6 amino acids (respectively).

NIHMS851975-supplement-Supp_Fig_S2.tif (1.9MB, tif)
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