Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Summary

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

Abstract

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients.

In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available.

This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

Introduction

Malignant peripheral nerve sheath tumors (MPNSTs) are highly aggressive spindle cell neoplasms that arise in association with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), sporadically in the general population and at sites of previous radiotherapy^1,2,3. NF1 patients are born with a wild-type copy of the NF1 tumor suppressor gene and a second NF1 allele with a loss-of-function mutation. This state of haploinsufficiency renders NF1 patients susceptible to a second loss-of-function mutation in their wild-type NF1 gene, which triggers tumorigenesis. When this "second hit" NF1 mutation occurs in a cell in the Schwann cell lineage, the resulting tumor is either a dermal neurofibroma arising in the skin or a plexiform neurofibroma that develops in large nerves or nerve plexuses. Although the pathology of dermal and plexiform neurofibromas is identical, their biologic behavior is quite different-although both dermal and plexiform neurofibromas are benign, only plexiform neurofibromas can undergo transformation and give rise to MPNSTs. In addition to the loss of neurofibromin, the Ras GTPase-activating protein encoded by the NF1 gene, MPNSTs carry mutations of multiple other tumor suppressor genes, including TP53^4,5,6,7, CDKN2A^8,9, and PTEN¹⁰, mutations of genes encoding components of polycomb repressive complex 2^11,12 (PRC2; the SUZ12 and EED genes) and aberrant expression of receptor tyrosine kinases^1,2. Mutations of NF1 and the other genes noted above are also present in sporadic and radiation-induced MPNSTs^11,12.

While these advances in our understanding of the genomic abnormalities in MPNSTs have been invaluable for understanding their pathogenesis, they have not yet resulted in the development of effective new therapies for MPNSTs. A major barrier impeding the development of new treatments is the fact that MPNSTs are rare cancers. Because of this, it is difficult to obtain the large number of patient samples that are required for global analyses defining key driver mutations such as those undertaken by The Cancer Genome Atlas (TCGA). In our experience, accumulating even a modest number of human MPNST specimens can take years. To overcome such limitations, many investigators studying other rare cancer types have turned to the use of cross-species comparative oncogenomics to identify essential driver gene mutations, define the essential cytoplasmic signaling pathways in their tumor of interest, and identify new therapeutic targets. Since the signaling pathways that are essential for tumorigenesis are highly conserved between humans and other vertebrate species, applying functional genomics approaches such as genome-scale shRNA screens can be an effective means of identifying these new driver mutations, signaling pathways, and therapeutic targets^{13,14,15,16,17,18,19}, particularly when studying rare human tumor types that are available in limiting numbers²⁰.

In the methodologies presented here, we describe this approach to performing genomic profiling in human MPNST cell lines and early passage MPNST cultures derived from P₀-GGFβ3 mice, a genetically engineered mouse model (GEM) in which Schwann cell-specific overexpression of the growth factor neuregulin-1 (NRG1) promotes the pathogenesis of plexiform neurofibromas and their subsequent progression to MPNSTs^21,22,23. The first step in this approach is to identify candidate driver genes in P₀-GGFβ3 MPNSTs, human MPNST cell lines, and surgically resected human MPNSTs. To functionally validate the signaling pathways affected by these mutations, we then use genome-scale shRNA screens to identify the genes required for proliferation and survival in human and mouse MPNST cell lines. After identifying the genes required for proliferation and survival, we then identify the druggable gene products within the collection of "hits" using the Drug Gene Interaction Database. We also compare the "hits" in human and mouse MPNST cells, to determine whether the GEM model and human MPNSTs demonstrate similar dependence on the same genes and signaling pathways. Identifying overlaps in the genes required for proliferation and survival and the affected signaling pathways serves as a means of validating the P₀-GGFβ3 mouse model at a molecular level. This approach also emphasizes the effectiveness of combining human and mouse screens to identify novel therapeutic targets, where the mouse model can serve as a complement to the human screens. The value of this cross-species approach is particularly apparent when looking for therapeutic targets in rare tumors, where human tumors and cell lines are difficult to obtain.

Protocol

Prior to the initiation of the studies, have animal procedures and protocols for handling viral vectors reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) and the Institutional Biosafety Committee (IBC). The procedures described here were approved by the Medical University of South Carolina's IACUC and IBC Boards and were performed by properly trained personnel in accordance with the NIH Guide for Care and Use of Laboratory Animals and MUSC's institutional animal care guidelines.

1. WES-Seq Analyses and Identification of Pathogenic Variants

1. Isolate genomic DNA from a tumor sample or sub-confluent (70%) MPNST cells grown on a 60 mm dish, using standard commercially available adsorption silica-gel-based methods (consult manufacturers' protocol for detailed steps). The general workflow is diagramed in Figure 1.
2. Submit at least 10 µL of genomic DNA at 50 ng/µL to the sequencing core unless different quantities or concentrations of genomic DNA are specified.
   1. The core facility fragments the genomic DNA by sonication and then purifies it using the preferred method. Exome capture and library construction are performed using the preferred exome sequencing kit and index tags are added to the amplified captured exome.
   2. Submit specimens to the sequencing core to have whole exome paired-end sequencing (WES; 100 bp sequenced from each end) performed.
   3. FASTQ files generated by the core are provided to the investigator. Use only FASTQ files that pass quality metrics for analysis.
3. Align and analyze the FASTQ files using commercially available software programs (e.g., DNAStar19,20, Partek21, or Varsome). Align the FASTQ files to the mouse reference genome GRCm38/mm10 using the default settings.
   NOTE: Using DNAStar and a mouse MPNST specimen as an example, the general workflow is diagramed in Figure 1 and briefly explained below.
   1. Open DNAStar software and choose the SeqMan NGen workflow.
   2. Choose Workflow by selecting Variant Analysis/Resequencing and the type of sequencing analysis NGS-Based Amplicon, gene panel, or exome. Click Next.
   3. Choose the preferred Reference Sequence by selecting Download Genome Package and the appropriate reference genome (i.e. Mus_musculus-GRCm38-dbSNP146.zip or Homo sapien-GRCh37.p13.zip). If the core facility provided a bed file, upload this auxiliary file. Click Next.
   4. Choose Input Sequences by selecting the appropriate sequencer technology, Illumina, and designate that the sequencing reads are paired-end. Then, select the experiment setup, multi sample, and upload the sequencing FASTQ files by selecting Add. If running multiple samples, designate each paired-end set with a unique experiment or sample name Tumor, Cell line or A18, A202… Click Next.
   5. Set the control dataset if there is one. Click Next and click Next again under Assembly Options. Under Analysis Options, click the appropriate Variant Detection mode as Diploid, then click Next. Under Assembly Output, name and designate the file saving location of the project; click Next. Run the assembly on the local computer or the cloud.
      NOTE: The resulting alignment files can be opened in the ArrayStar workflow for variant annotation of detected single nucleotide polymorphisms (SNPs). This workflow detects SNPs, not gene copy number gains or losses. A separate analysis called high-density SNP array detects copy number changes; whole exome sequencing does not reliably detect copy number changes. Consult the program user guide for the specific steps to be performed with the alignment program.
   6. Apply user-defined filters to the variant annotation of detected SNPs to include or exclude certain data. To obtain a condensed list of likely functionally relevant variants, apply the following filters to the variant datasets in this hierarchy: not in control (if a normal control sample is available), population frequency (gnomAD, ExAC, 1,000 genomes frequencies), allele frequency (include ≤0.001 or 0.1%), coverage depth (exclude <10 depth), pathogenicity or ClinVar class (include: pathogenic, likely pathogenic; exclude: uncertain, likely benign, benign), and if desired SNP type and coding impact (include: non-synonymous, missense, nonsense, frameshift, in-frame, splicing).
      NOTE: A known relevant cancer gene list can also be applied to the final list to pull only specific disease-relevant genes, see step 1.3.7. These filters can reduce the variant gene list down to less than 20 genes.
      1. Use the variant allele frequency to filter out sequencing error SNPs. Homozygous and heterozygous variants will be represented approximately at 100% and 50%, respectively for a pure non-contaminating cell population (an established tumor-derived cell line should represent a single population of isogenic tumor cells). In this case, apply 100-90% and 50-40% allele frequencies for variants as a filter, thereby removing any variants below that. If high-density SNP array data are also available for the same dataset, apply copy number gain or loss to SNPs with variant allele frequencies less than homozygous or heterozygous ratios (i.e., copy number gain resulting in 2 copies of allele A and 1 copy of allele B would make appropriate variant allele frequencies of 75%, 25%, respectively).
   7. After identifying pathogenic variants with ArrayStar, export and save the variant gene list as a csv, txt, or xls file and compare the genes containing these mutations to those in the cohort of P₀-GGFβ3 generated tumors to determine overlapping and unique mutated gene lists. Compare the mutated genes to known mutated genes associated with their human counterparts (i.e. Bushman Lab Cancer Gene list or a user-curated list).
      1. Optional, before applying any user-defined filters (1.3.6), export and save the annotated file as a VCF file.
         NOTE: This VCF file can be uploaded to other variant effector predictor software Varsome or VEP for comparison as a secondary analysis. Biological and pathway analysis can also be performed on the filtered variant gene lists.
   8. Perform functional classification of variant gene list via a user-preferred database to obtain protein class, pathway, pathway component, and gene family and ontology information.
      NOTE: PANTHER (pantherdp.org) is used as an example here.
      1. Upload filtered gene list with gene ID.
      2. Select the organism (Mus musculus).
      3. Select analysis (Functional classification viewed in gene list) and click submit biological and pathway analysis on filtered variant gene lists.

2. Genome-Scale shRNA Screens

NOTE: Several shRNA and CRISPR libraries are available that can be used for genome-scale functional screens with low passage tumor cultures. Here, we describe the use of CELLECTA DECIPHER shRNA libraries as an example. CELLECTA DECIPHER lentiviral shRNA libraries are optimized for RNAi genetic screens in pooled format. Each transcript is targeted by at least 5-6 unique shRNAs and each lentiviral shRNA vector contains a unique genetic barcode flanked by PCR primer sites. These libraries cover the majority of human and mouse disease-relevant genes but do not cover all genes in the genome. Cellecta human library plasmid DNA pools are available in three modules (Human Module I, II, III; targets 15,377 genes) while the mouse library plasmid pools are available in two modules (Mouse Modules I and II; targets 9,145 genes). These libraries are used to perform "drop out" assays in which targeted genes that are required for proliferation and/or survival are differentially expressed at different time points after viral transduction.

1. Lentiviral Packaging
   1. Day 0: Plate 10 dishes of 10 million 293T cells/15 cm dish in 30 mL/dish antibiotic-free DMEM containing 10% fetal bovine serum (FBS).
   2. Day 1: Confirm that the cells are ~80% confluent the next day and ready to transfect. In a 50 mL conical tube, mix the following in this order: 600 µL of packaging plasmid mix (0.5 µg/µL), 60 µL of plasmid barcode library, 12 mL of DMEM (no serum or antibiotics), and 600 µL of transfection reagent. Incubate at room temperature for 15 min.
   3. Place 900 µL of transfection reagent and 12 mL DMEM in a separate 15 mL conical tube and mix by vortexing. Add 12.9 mL of the transfection reagent/DMEM mix to the DNA mix and flick to mix. Incubate at room temperature for 15 minutes without further mixing. Add 2.5 mL of this mix, drop-wise, to each 15 cm dish of 293T cells and incubate overnight in a tissue culture incubator.
   4. Day 2: Replace media the following day with regular growth media containing antibiotics.
   5. Day 3: Harvest the virus by collecting the media and passing it through a 0.2 µm filtration unit and aliquoting into 15 mL conical tubes; also prepare five 1 mL aliquots of filtered virus in cryovials for use in viral titering (considered 48 h virus); store the virus in a -80 °C freezer. Replace media with 30 mL of growth media on the plates one at a time to avoid drying out of cells.
   6. Day 4: Harvest virus by collecting the media and passing it through a 0.2 µm filtration unit. Aliquot virus into 15 mL conical tubes. This is considered 72 h virus; store the virus in a -80 °C freezer.
2. Titer Lentiviral Pools
   1. Add 65 µL of cationic polymer (10 mg/mL) to 65 mL of tumor cell growth media. Pipette 1 mL/well of the polymer-containing medium into eleven 6-well tissue culture plates. Trypsinize early passage tumor cells and count cells using the preferred method so that each well receives 50,000 cells/mL/well. Thaw 1 mL aliquots of 48 h lentivirus from the freezer in a 37 °C water bath.
   2. For each viral module, prepare the infection as shown in Table 1.
   3. Place all 6-well plates in a tissue culture incubator. The following day, remove viral media and replenish with fresh growth media. At 48 h, add puromycin-containing media to all wells receiving selection. Before control cells are confluent, perform cell counts of surviving clones using the preferred method.
      NOTE: The concentration of puromycin required to kill non-transduced cells must be pre-determined empirically by testing a range of concentrations in a "kill" curve. Use the lowest concentration that uniformly induces the death of non-transduced cultures.
3. Lentiviral Infection of Target Cells
   1. Trypsinize MPNST cells and count. Plate 2.5 million cells per 15 cm dish for a total of twenty 15 cm dishes per module. Thaw the virus and transduce the cells with the virus at an MOI of 0.5 in the presence of 5 µg/mL of cationic polymer (Figure 2A).
   2. Remove virus-containing media the following morning and replace with fresh growth media. Culture cells for an additional 2 days to allow expression of the selection marker and then add puromycin to the cultures to eliminate non-transduced cells.
   3. Three days following the addition of puromycin, trypsinize the cells and centrifuge half of the cell population at 200 × g for 5 min in a tabletop centrifuge. Store the pelleted cells in the -80 °C freezer for future genomic DNA preparation; this is the reference time point, or time point 1 (T1).
   4. Replate the other half of the cell population and grow it for approximately 7 population doublings before harvesting and centrifuging as above. This cell pellet will serve as the final time point (time point 2, T2) and is stored at -80 °C for future genomic DNA isolation (Figure 2).
4. Isolating Genomic DNA from Cells Transduced with Virus Pools
   1. Thaw the cell pellet from the -80 °C freezer and re-suspend the pellet in 10 mL of resuspension buffer with RNAse added, and immediately split into two 15 mL polymethylpentene tubes (Figure 2B).
   2. Add 500 µL of 10% SDS per 5 mL, mix, and incubate at room temperature for 5 min. Next, place tubes into a DNA shearing device to sonicate the DNA for 25 cycles of 30 s on/30 s off, making sure to keep the temperature at 4 °C.
   3. Add 5 mL of phenol/chloroform, pH 8.0 (stored at 4 °C), being sure to mix the phenol/chloroform well before use. After the addition of phenol/chloroform, mix well by vortexing vigorously on maximum setting for 45-60 s. Centrifuge for 60 min, -20 °C at 7,200 × g.
      NOTE: A frothy/milky appearance after vortexing will signal complete resuspension.
   4. Transfer 3 mL of the clear upper phase to a fresh 15 mL tube and add 0.5 mL of 3 M sodium acetate and 4 mL of isopropanol and mix well (Figure 2C). Centrifuge for 30 min at 20 °C at 7,200 × g.
   5. Following centrifugation, discard the supernatant and then carefully pipette off the remaining liquid. Add 0.5 mL of 70% ethanol and dislodge the pellet by pipetting up and down. Transfer the re-suspended pellet to a 1.5 mL centrifuge tube and combine both pellets from the same sample into a single 1.5 mL centrifuge tube. Centrifuge at maximum speed in a benchtop centrifuge for 5 min.
   6. Discard the supernatant and use a laboratory wipe to absorb any residual 70% ethanol. Re-suspend the pellet in 0.5 mL of distilled water, making sure not to let the pellet dry out as this will make resuspension difficult. Store samples at 4 °C before measuring the DNA concentration.
5. Nested PCR to Amplify shRNA Barcodes
   NOTE: Retrieve DNA from storage and place on ice. If the DNA concentration is lower than 100 ng/µL, then speed vacuum-dry the DNA and resuspend in an appropriate volume of distilled water. We recommend only processing one module at a time (time point 1 (T1) or time point 2 (T2)). Perform two preps of each time point which are to be pooled at the end; hence, it is important to not process replicate time points on the same day. The following protocol is for one time point of genomic DNA.
   1. Set up 7 tubes and label them as described: Negative control, positive control, 4 tubes for genomic DNA, and one for a master mix (Figure 3A). The negative control is distilled water; the positive control is the plasmid DNA used in the 293T transfection to generate lentivirus.
   2. Add water to each tube first as indicated in Table 2.
   3. Prepare a Master Mix (MM) as shown in Table 3.
   4. Add 18 µL of MM to each tube containing water. Next, add 25 µL of genomic DNA to each of the 4 template tubes. Then, add 1 µL of positive control (10 ng/µL) to the positive control tube, being careful not to contaminate other tubes with positive control DNA. Lastly, add 2 µL of polymerase to each tube, adding to the 4 sample tubes first and the positive control tube last; mix and spin down the PCR tubes (Figure 3).
   5. Perform PCR reaction under the conditions stated in Table 4.
   6. While the first PCR is running, prepare tubes for the 2^nd PCR as indicated in Table 5. Set up seven tubes: Negative control, positive control, 4 tubes for genomic DNA, and one for MM (Figure 3B).
   7. Add 22 µL of MM to each tube containing water and wait for the first round of PCR to finish before adding the DNA and polymerase.
      1. Once the first round of PCR is complete, combine 4 sample tubes into one microcentrifuge tube and mix.
      2. Add 25 µL to each sample tube containing water.
      3. Next, add 2 µL of the first PCR negative control and 2 µL of the first PCR positive control to appropriately labeled tubes.
      4. Add 2 µL of polymerase to each tube, adding to the 4 sample tubes first and the negative control last.
   8. Perform PCR reaction as stated in Table 6.
      NOTE: Upon analyzing results from the first PCR reaction by electrophoresis on a 3.5% agarose gel, if an abundance of product is obtained, the number of cycles can be reduced to 9-10 for the next repeat of this procedure; likewise, if product yield is low, cycles can be increased to 14.
   9. When the second PCR reaction is complete, combine 4 samples into one microcentrifuge tube plus 80 µL of 6x loading dye. For positive and negative controls, use 20 µL of the sample.
      1. Prepare a 3.5% agarose gel in Tris-Borate-EDTA (TBE) buffer (Figure 4A). To accommodate the large sample volume, create one large well in the gel comb by taping several wells together (if a comb to fit the volume is not available), being sure to leave two wells available for the positive and negative controls. Run the gel at 90 V for 1 h.
   10. Visualize the gel and confirm a band at approximately 250 base pairs (bp).
6. First Purification: Gel Extraction
   1. Using a clean scalpel or razor blade, excise the 250 bp band, cutting as much gel off as possible. Section the large band into 4 pieces and transfer each piece to a clean microcentrifuge tube. Measure and record each gel slice's weight.
      NOTE: Each piece should be approximately 200 mg or less per tube.
   2. Add 6 volumes of solubilization buffer (e.g., if the gel piece weighs 200 mg, add 1.2 mL of buffer). Place the tubes in a 50 °C water bath and rotate every 10-15 min until the gel slices are dissolved. Then, add 1 volume of isopropanol to each tube (e.g., 0.2 mL of isopropanol if the gel piece weighs 200 mg).
   3. Using two spin columns for purification, load the samples from all 4 tubes into the columns. Carry out multiple spins to process all the sample volume as the columns only hold 750 µL. Spin the columns at 17,900 × g for 1 min in a conventional benchtop microcentrifuge and discard the flowthrough each time.
   4. Wash the columns with 750 µL of wash buffer and centrifuge at 17,900 × g for 1 min. Following the wash, discard the flowthrough and dry the spin column by centrifuging at 17,900 × g for 3 min. Elute the DNA with 50 µL of distilled water per column and centrifuge as previously for 1 min and combine both tubes for a total volume of 100 µL of the sample.
7. Second purification
   1. This next purification step will use Binding Buffer 2 (from the PCR Purification Kit), which does not eliminate small fragments. Add 400 µL of buffer to the 100 µL of DNA, and load it onto one spin column (Figure 4B). Centrifuge the sample at 17,900 × g for 1 min.
   2. Next, wash the membrane with 650 µL of wash buffer and spin as previously performed. Dry the spin column with additional centrifugation as above for 3 min.
   3. Elute the DNA with 30 µL of distilled water and spin at max speed for 1 min. Following elution, check the concentration on a spectrophotometer; ensure that the concentration is not lower than 10 ng/µL or higher than 70-80 ng/µL. Store the DNA in a -20 °C freezer.
8. Sequencing of Amplified Barcodes
   1. For sequencing, dilute purified barcodes to 0.75 ng/µL using elution buffer (EB). To add sequence diversity, amplicons are clustered at 17 pM, including 30% (v/v) PhiX. Perform single-end (SE) clustering on an automated cluster generation system according to the manufacturer's protocol.
   2. Have the sequencing core perform a total of 36 cycles of single-end sequencing on a NextGen sequencer (Figure 4C). Add custom primer GexSeqS to the Illumina sequencing primers at 0.5 µM.
   3. Use the referenced analysis software to generate Fastq files and process them using software to trim read lengths to 18 nucleotides.
   4. Use Barcode Analyzer and Deconvoluter software to deconvolute trimmed reads. Calculate fold depletion scores for each shRNA as the ratio of the read counts at the reference time point (T1) versus the final time point (T2).
9. Analysis of shRNA Screen Results and Identification of Candidate Therapeutic Targets
   NOTE: In the Cellecta shRNA library, most genes are targeted by either 5 (67%) or 6 different shRNAs (32%). However, several housekeeping genes, which should be hits in most cells, are targeted by large numbers of shRNAs and serve as controls that define the range of scores for negatives.
   1. To prevent bias towards genes targeted by larger numbers of shRNAs, log transform the depletion scores. Perform a quantile estimation by calculating the 80th percentile for each gene from its empirical distribution.
   2. To generate a null distribution of log fold-depletion scores, assume that >95% of genes will not be depleted and that their log-quantile scores will have a normal distribution. Use the median of the empirical distribution to estimate the mean of the null distribution. Using this null distribution, classify all genes with log-fold depletion scores that are larger than the 95th percentile of the null distribution as 'hits.'
      NOTE: All hits should have at least two shRNAs with depletion scores above the cut-point (Figure 5).
   3. To assess the quality of the RNAi dropout screen data and the validity of the cut-point, use a set of genes that have been defined as 'core essential' by the COLT Cancer RNAi screening initiative^24,25 for comparison. Remove CEGs from the list of hits to produce the initial list of potential therapeutic targets.
      NOTE: Genes in the COLT set scored as a hit in >50% of the 72 cancer cell lines that were screened by COLT. The core essential gene list contains 640 genes.
   4. To identify potential therapeutic targets that are commonly or uniformly required by multiple MPNST cell lines, construct Venn diagrams of the non-CEG hits identified in screens of different MPNST cell lines or early passage cultures (Figure 6A). Prioritize non-CEG hits that are required in all or the majority of MPNST lines or cultures.
      1. Alternatively, perform pathway analyses on the non-CEG hit list from each cell line or early passage culture to identify genes whose products encode components of signaling pathways that are required for tumor cell proliferation and/or survival. Then, compare the signaling pathways that are identified in the pathway analysis of non-CEGs to the signaling pathways that were identified as affected by mutations identified with WES.
         NOTE: We find it particularly useful to identify signaling pathways that are consistently affected by mutations identified in WES and compare them to the signaling pathways identified as critical in shRNA screens.
   5. To identify druggable targets for which therapeutic agents are already available, screen the list of hits remaining after removal of the core essential genes using the Drug Gene Interaction Database (dgidb.org).
      NOTE: This database allows a list of genes to be entered and screened simultaneously and provides guidance on drugs that are currently available to target these genes.
   6. Validate the identified druggable high-interest targets first by knocking down their gene expression with shRNAs distinct from those used in the initial library screen in a single batch format (not a library-batch format just described). To knock down gene expression, use two to three distinct lentiviral shRNAs. Three to four days after infection, determine the effect that this has on tumor cell proliferation and survival as described below.

3. Perform Cytometer Assays of Cell Numbers and Viability in MPNST Cells Challenged with Candidate Therapeutic Agents

1. Grow MPNST cells in DMEM to 80% confluency. Rinse cells with room temperature Hanks' balanced salt solution (HBSS).
   NOTE: Do not grow cells to confluency as the growth of these cells will lag for a time after replating.
2. Detach cells from the substrate by covering cells for 30 s to 1 min with a non-enzymatic cell dissociation solution. Add 5 mL of DMEM per 1 mL of the dissociation solution and gently pipet cells up and down to detach from the substrate.
3. Count cells using a hemocytometer. Plate cells at a density of 1,200 cells per well in black-walled 96-well plates; plate at least three wells for each drug dilution that will be tested and perform three biological replicates of these experiments.
4. To determine the initial concentrations of the drug that will be tested, review the literature to assess what concentrations have been effective against other cancer cell types. In initial experiments, test a range of two orders of magnitude above and two orders of magnitude below the drug concentration used with other cancer types.
5. Prepare dilutions of the drug to be tested and add each dilution or vehicle to at least three replicate wells.
6. Assess direct cell numbers 1, 3, 5, and 7 days after the addition of drugs. Add Hoechst 33342 to a final concentration of 5 µg/mL and incubate plates for 30 min at 37 °C. Read plates on a high-throughput imaging cytometer using the direct cell count for total cell number option with 100,000 ms exposure times.
7. Analyze the reads using the software and export them into a spreadsheet and use appropriate software for statistical analyses.
8. If statistically significant reductions in cell numbers are observed in drug-treated wells, perform a "Live/Dead" assay to determine whether this reduction is due, in part, to the induction of cell death.
   1. Plate cells in black-walled 96 well plates as described in step 3.3.
   2. Prepare and add drugs as described in step 3.5.
   3. Assess cell viability and death 1, 3, 5, and 7 days after the addition of the drug. Add calcein AM to a final concentration of 1 µM and propidium iodide to a final concentration of 1 µM in each well. Incubate cells for 15 min at 37 °C.
   4. Image cells on an imaging cytometer using the Live+Dead software option. Analyze the reads using the software and export them into a spreadsheet and use appropriate software for statistical analyses.

Representative Results

Figure 5 plots display depletion scores of core essential genes (CEGs) labeled as TRUE compared to non-CEGs (labeled as FALSE) in each human cell line that was screened. Points represent log2 of fold depletion scores for individual genes, which are plotted over a boxplot representation of the overall score distribution. Student's t-test was used to test for a significant difference in the mean of depletion scores between the two groups in each cell line. The resulting p-values are indicated in each panel. Note that the average fold depletion scores are significantly higher for the CEGs than the non-CEGs. This is expected as Core Essential Genes are, by definition, consistently required for proliferation and/or survival in most cell types.

Figure 6A presents a Venn diagram of the "hits" for three human MPNST cell lines. We typically find that a large number of genes are shared between multiple lines; these hits are a high priority as they represent genes encoding proteins that are likely to be essential for the proliferation and/or survival of a large subset of MPNSTs. Note also that there are a number of genes that are hits in only one cell line. We encounter this commonly and it should not be taken as an indication that the screens are of poor quality. The genes that are common hits between multiple lines are then assessed using the Drug Gene Interaction Database to identify genes within this subset that encode proteins that are druggable with existing agents. We then select several of these and perform an initial validation by knocking down the expression of the corresponding mRNA with shRNAs. Since some shRNAs will have off-target effects, we always test multiple shRNAs targeting the same transcript. Figure 6B shows a representative result in which we have transduced MPNST cells with a non-targeting control and multiple shRNAs targeting BCL6. Cell numbers were then determined at varying times after transduction. Note that several of the BCL6 shRNAs markedly reduced cell numbers; as shown in the accompanying immunoblot, the degree of decrease in cell numbers correlates with the degree of BCL6 knockdown. Figure 6C shows a representative growth curve for an early passage P₀-GGFβ3 MPNST culture.

Figure 1: Workflow for performing whole exome sequencing of MPNST tissue or early passage MPNST cells. Schematic illustrates the general workflow of variant detection present in tumor-derived early passage cultures. Isolate DNA from early passage cultures (1) and submit quality DNA to the sequencing core according to their submission protocols (2). The sequencing core will check the quality of the submitted DNA and perform all necessary sample and genome library preparations. The core facility will provide users with FASTQ sequencing files with quality control metrics (3). Users will upload the FASTQ files to a genome alignment and variant caller program of their choice. (4) Annotated variants are filtered on user-defined criteria to remove non-relevant variants. Representative data shown compare resected human MPNST tumor sample vs. a cell line derived from the tumor²⁶. (5) Perform functional classification analysis with PANTHER. Abbreviation: MPNST = Malignant Peripheral Nerve Sheath Tumor. Please click here to view a larger version of this figure.

Figure 2: Workflow for performing viral transduction of the shRNA libraries into MPNST cells and isolating genomic DNA from the cells at time point 1 and time point 2. (A) Target cells are infected at a low MOI of 0.3 with barcoded lentiviral particles and selected for 72 h. Cells are passaged for 5-7 population doublings (approximately 7 days). Cell pellets at Day 0 and Day 7 are stored at -80 °C for genomic DNA isolation. Day 0 is referred to as Time point 1 (T1) and Day 7 is referred to as Time point 2 (T2). (B) Genomic DNA isolation begins with resuspension of cell pellets in resuspension buffer that are then split into two 15 mL tubes. To facilitate cell lysis, 10% SDS is added to each tube and sonicated for 25 cycles of 30 s on/30 s off. Following sonication, phenol/chloroform is added to each tube and vortexed vigorously for 45-60 s. Tubes are then centrifuged. (C) A clear upper phase is pipetted off and added to a clean tube with the addition of sodium acetate/isopropanol and mixed well. Tubes are centrifuged again. This time, the supernatant is discarded, and the pellet is dislodged with the addition of 70% ethanol. Combine the resuspended pellets into one tube and spin at maximum speed in a benchtop centrifuge. Discard the supernatant and resuspend the pellet in distilled water. Please click here to view a larger version of this figure.

Figure 3: Workflow for amplifying barcode sequences from lentiviral shRNA vectors in preparation for quantification of barcodes with next-generation sequencing. (A) Representation of how to set up tubes for the first nested PCR reaction (7 tubes: one for negative control, one for positive control, and the remaining 4 tubes for genomic DNA. The last tube will serve as the master mix tube). Following the first PCR reaction, combine the genomic DNA tubes into one tube and mix. (B) The products from the first nested PCR reaction will serve as templates for the second nested PCR reaction. Following the second PCR, combine genomic DNA tubes into one tube and mix. Please click here to view a larger version of this figure.

Figure 4: Workflow for purifying the amplified shRNA barcodes and sequencing barcodes to quantify their representation at time point 1 and time point 2. (A) Pour a 3.5% agarose gel. Because the volume of the pooled DNA exceeds the limit for one well, tape 4-6 teeth of a gel comb together to produce one large well. Prepare PCR products with 6x loading dye and load the positive control, negative control, and pooled DNA into the gel. Following electrophoresis, a large band at approximately 250 base pairs should appear in the pooled DNA lane. Using a clean scalpel, excise the entire band, and then cut into 4 gel slices. Solubilize the gel pieces and then combine them into two spin columns to elute DNA. Combine the eluted DNA into one tube. (B) The DNA is purified through a second purification step. Add Binding Buffer 2 to the tube of pooled DNA, and then pipette onto a spin column. Wash the membrane and then elute the DNA in distilled water. (C) Submit the purified DNA to the sequencing core. Please click here to view a larger version of this figure.

Figure 5: Representative examples of the distribution of Core Essential Genes after analysis as described in the protocol. In this example, three human MPNST cell lines (S462, T265, and 2XSB) cells were screened with Cellecta DECIPHER shRNA libraries. For each human MPNST cell line, a boxplot was created to compare gene-level depletion scores for genes in the list of Core Essential Genes (CEG; True box plot)²⁵ to that of genes that are not found in the list of CEGs (False box plot). Individual data points are layered on top of each box plot. P-values are from a standard t-test comparing gene-level depletion scores of CEGs to non-CEGs. Abbreviations: CEG = core essential gene; MPNST = Malignant Peripheral Nerve Sheath Tumor. Please click here to view a larger version of this figure.

Figure 6: Validation of screen results. (A) Representative Venn diagram of overlapping hits in three human MPNST cell lines. (B) S462 human MPNST cells transduced with a non-targeting (NT) lentiviral vector and lentivirus expressing four different BCL6 shRNAs (shRNA1, shRNA2, shRNA3, and shRNA4). Cells were transduced with lentivirus and then treated with a selection agent (puromycin) for 3 days. Cell numbers were then assessed over the next seven days. (C) Western blot analysis showing protein levels of BCL6 following transduction with NT,shRNA1, shRNA2, shRNA3, and shRNA4 lentivirus. Please click here to view a larger version of this figure.

Table 1: Plate layout for lentiviral titering. Please click here to download this Table.

Table 2: Initial setup of first PCR reactions. Please click here to download this Table.

Table 3: Preparation of master mix for first PCR reaction. Please click here to download this Table.

Table 4: Cellecta first PCR parameters. Please click here to download this Table.

Table 5: Initial setup of second PCR reaction. Please click here to download this Table.

Table 6: Cellecta second PCR parameters. Please click here to download this Table.

Discussion

The detailed methods presented here were developed to study peripheral nervous system neoplasia and MPNST pathogenesis. Although we have found these methods to be effective, it should be recognized that there are some potential limitations to the methods we describe here. Below, we discuss some of those limitations and potential strategies for overcoming them in other model systems.

We have found that whole exome sequencing effectively identifies mutations of interest in P₀-GGFβ3 mice. It should be recognized, though, that whole exome sequencing itself has limitations. First, whole exome sequencing is not an effective approach to identifying fusion gene products. This is because the majority of chromosomal breaks and subsequent fusion predominantly involve intergenic regions and introns as those regions represent the majority of the genome. We have instead found that RNA-Seq with paired-end reads much more effectively identifies fusion genes. There is also the question of how effectively whole exome sequencing identifies relatively large regions of chromosomal loss.

Although several algorithms have been developed for identifying such losses, the term "whole exome sequencing" is itself misleading because the capture of the exome even in good runs often misses up to 5-10% of exonic regions. Because of this, we routinely complement whole exome sequencing with other approaches such as array comparative genomic hybridization (aCGH). After identifying gains and losses, we examine the genes within these intervals and compare them to the known driver mutations that are associated with their human counterparts. However, the mouse genome is more stable than the human genome²⁷. Consequently, mouse tumors typically do not show chromothripsis analogous to what is seen in human neoplasms. The pattern in mouse tumors is instead much simpler, tending towards whole chromosome or chromosome gains or losses with relatively few focal deletions that tend to occur under strong selective pressure^22,23.

There are some potential pitfalls that we have encountered when performing genome-scale shRNA screens. One of the most common problems that we encounter is the relatively poor transduction of the lentiviral vectors into the target cells. We most often find that the problem arose from improper titering of the packaged lentiviral pools. Because early passage mouse tumor cell cultures are a limiting resource, many investigators will instead attempt to titer their lentivirus using another established cell line that is more readily available. The problem with this approach, though, is that the efficiency of lentiviral transduction can vary considerably from cell type to cell type. It is for this reason that we recommend titering lentivirus on the actual cells that will be used in the experiment. We have also encountered problems with relatively low viral titers. That problem most often reflects poor transfection of 293T cells when producing the packaged virus.

It is possible to obtain false positive hits when performing genome-scale shRNA screens. Because of this, once we have identified the potentially druggable targets that are of the most interest to us, we always validate the results of our shRNA screens. Typically, we will use two different approaches to validate high-interest targets. First, we knock down gene expression using two or more shRNAs distinct from those used in the initial screen and determine the effect that this has on tumor cell proliferation and survival. Second, we obtain the drug(s) identified in the Drug Gene Interaction Database and determine the effect that this has on tumor cell proliferation and survival. We use both approaches in tandem because we have encountered circumstances in which the shRNAs work and the drug does not. In at least some of those instances, examination of the whole exome sequence dataset has shown that the targeted protein is produced by a gene that has a mutation potentially affecting drug-protein interactions.

The approaches outlined above will provide the investigator with an applicable means of identifying potential driver mutations that occur in rare neoplasms, functionally identifying the signaling pathways required for proliferation and survival, and prioritizing targets for therapeutic development. We hope that other investigators will find these approaches useful for identifying key therapeutic targets in other human cancers. The reader should be aware, though, that there are other functional genomic approaches that can be used to identify genes involved in tumor pathogenesis and genes encoding potential therapeutic targets. As an example, CRISPR libraries are available that can be used in a manner analogous to that we describe for shRNA libraries. Functional screens can also be performed in vivo to identify genes promoting tumorigenesis. As an example of this, the Sleeping Beauty transposon-based somatic mutagenesis system has been previously used to target Schwann cells and their precursors, resulting in the identification of several hundred genes involved in MPNST pathogenesis²⁸. As these systems approach functional genomics in distinct ways, we recommend that the investigator carefully consider the goals of their planned experiments and base their selection of a functional genomics methodology on those goals.

Materials

Name	Company	Catalog Number	Comments
Bioruptor Sonication System	Diagenode	UCD-600
CASAVA 1.8.2
Cbot	Illumina, San Diego, CA	N/A
Celigo Image Cytometer	Nexcelom	N/A
Cellecta Barcode Analyzer and Deconvoluter software
Citrisolve Hybrid	Decon Laboratories	5989-27-5
Corning 96-well Black Microplate	Millipore Sigma	CLS3603
Diagenode Bioruptor 15ml conical tubes	Diagenode	C30010009
dNTP mix	Clontech	639210
Eosin Y	Thermo Scientific	7111
Elution buffer	Qiagen	19086
Ethanol (200 Proof)	Decon Laboratories	2716
Excel	Microsoft
FWDGEX 5’-CAAGCAGAAGACGGCATACGAGA-3’
FWDHTS 5’-TTCTCTGGCAAGCAAAAGACGGCATA-3’
GexSeqS (5’ AGAGGTTCAGAGTTCTACAGTCCGAA-3’			HPLC purified
GraphPad Prism	Dotmatics
Harris Hematoxylin	Fisherbrand	245-677
Illumina HiScanSQ	Illumina, San Diego, CA	N/A
Paraformaldehyde (4%)	Thermo Scientific	J19943-K2
PLUS Transfection Reagent	Thermo Scientific	11514015
Polybrene Transfection Reagent	Millipore Sigma	TR1003G
PureLink Quick PCR Purification Kit	Invitrogen	K310001
Qiagen Buffer P1	Qiagen	19051
Qiagen Gel Extraction Kit	Qiagen	28704
RevGEX 5’-AATGATACGGCGACCACCGAGA-3’
RevHTS1 5’-TAGCCAACGCATCGCACAAGCCA-3’
Titanium Taq polymerase	Clontech	639210
Trimmomatic software		www.usadellab.org