Integrated genomics of ovarian xenograft tumor progression and chemotherapy response
- Ashley Stuckey†1,
- Andrew Fischer†2,
- Daniel H Miller†2,
- Sara Hillenmeyer2,
- Kyu K Kim1,
- Anna Ritz3,
- Rakesh K Singh1,
- Benjamin J Raphael3,
- Laurent Brard4 and
- Alexander S Brodsky†2Email author
© Stuckey et al; licensee BioMed Central Ltd. 2011
Received: 10 September 2010
Accepted: 22 July 2011
Published: 22 July 2011
Ovarian cancer is the most deadly gynecological cancer with a very poor prognosis. Xenograft mouse models have proven to be one very useful tool in testing candidate therapeutic agents and gene function in vivo. In this study we identify genes and gene networks important for the efficacy of a pre-clinical anti-tumor therapeutic, MT19c.
In order to understand how ovarian xenograft tumors may be growing and responding to anti-tumor therapeutics, we used genome-wide mRNA expression and DNA copy number measurements to identify key genes and pathways that may be critical for SKOV-3 xenograft tumor progression. We compared SKOV-3 xenografts treated with the ergocalciferol derived, MT19c, to untreated tumors collected at multiple time points. Cell viability assays were used to test the function of the PPARγ agonist, Rosiglitazone, on SKOV-3 cell growth.
These data indicate that a number of known survival and growth pathways including Notch signaling and general apoptosis factors are differentially expressed in treated vs. untreated xenografts. As tumors grow, cell cycle and DNA replication genes show increased expression, consistent with faster growth. The steroid nuclear receptor, PPARγ, was significantly up-regulated in MT19c treated xenografts. Surprisingly, stimulation of PPARγ with Rosiglitazone reduced the efficacy of MT19c and cisplatin suggesting that PPARγ is regulating a survival pathway in SKOV-3 cells. To identify which genes may be important for tumor growth and treatment response, we observed that MT19c down-regulates some high copy number genes and stimulates expression of some low copy number genes suggesting that these genes are particularly important for SKOV-3 xenograft growth and survival.
We have characterized the time dependent responses of ovarian xenograft tumors to the vitamin D analog, MT19c. Our results suggest that PPARγ promotes survival for some ovarian tumor cells. We propose that a combination of regulated expression and copy number can identify genes that are likely important for chemotherapy response. Our findings suggest a new approach to identify candidate genes that are critical for anti-tumor therapy.
Epithelial ovarian cancer (EOC) is the most lethal of all the gynecologic cancers, affecting thousands of women each year . Most patients initially respond to chemotherapy, only to recur within a few years with drug-resistant metastatic disease . Thus, there is a pressing need to develop new anti-tumor therapies that can work alone, or in combination with platinum-based therapy.
Two general approaches have been pursued to address drug resistance: development of new therapeutics, and drug combinations that improve standard platinum and/or taxane based chemotherapy. The application of calcitriol/vitamin D3 has emerged as an important strategy to target the vitamin D receptor (VDR) for cancer treatment . Hypercalcemia and other toxicities have limited development of calcitriol and vitamin D analogs tested to date .
MT19c is a novel vitamin D analog based on B3CD [4, 5] that shows significant effects on EOC cell lines and xenograft tumor models. MT19c was designed to be a vitamin D receptor ligand but appears to work independently of VDR (Brard L, Lange TS, Robinson K, Kim KK, Brodsky AS, Uzun A, Padbury J, Moore R, Singh RK: Discovery of the first Ergocalciferol derived vitamin D receptor independent true non-hypercalcemic anti-cancer agent (MT19c), submitted). Here, we aimed to understand which pathways and genes may be important for MT19c action in a SKOV-3 xenograft tumor model. These data also provide insight into key pathways and genes important for tumor growth and survival.
As EOC progresses, tumors may evolve through two general mechanisms: accumulation of new mutations, or selection of specific cell types emerging from a heterogeneous mixture of cells . In the clinic, examination of tumors is typically only feasible as a snapshot at a given time with little knowledge about how a tumor is evolving during disease progression. A recent evaluation of long-term platinum treatment of a mouse lung cancer model suggested that DNA repair pathways are significantly up-regulated leading to resistance .
Many mutations and chromosomal structural rearrangements have been identified in primary ovarian tumors and cell lines [8–10]. Copy number aberrations (CNAs) are a common mechanism observed to control gene expression and tumor progression . Loss of DNA is another mechanism that reduces expression of tumor suppressor genes, which inhibit tumor progression. Conversely, DNA copy number gain may increase expression of oncogenes. However, CNAs can explain a significant fraction of the variation in gene expression but not all of it, perhaps because of epigenetic mechanisms such as DNA methylation [11, 12].
The purpose of this study was to understand which genes and pathways may be important for MT19c's anti-tumor activity and to identify genes critical for tumor progression. A number of genes in the PPARγ network, including PPARγ, were enriched in MT19c treated tumors. When PPARγ is stimulated with Rosiglitazone, MT19c and cisplatin have significantly higher IC50s suggesting that PPARγ is promoting survival in at least some types of ovarian cancer cells, leading to poorer outcomes. By combining CNAs and drug induced expression changes, we observe a subset of genes that may be particularly important for MT19c action and/or tumor survival. We propose that combining copy number analysis with drug induced expression changes can identify key genes important for chemotherapeutic efficacy. These results will be relevant to plan future xenograft studies and highlight the importance of considering the changing tumor dynamics over time when evaluating gene expression and drug responses.
Cell culture and Reagents
SKOV-3 (ATCC) cells were grown DMEM (Mediatech, Manassas, VA) with 10% FBS (Hyclone, Logan, UT) with. MT19c was synthesized as described in detail elsewhere . Briefly, commercially available Ergocalciferol underwent a Diels-Alder reaction with N-methyl,1,2,4-triazolinedione to generate an adduct, which upon reaction with bromoacetic acid in the presence of DCC (N, N'-dicyclohexylcarbodiimide) generated MT19c in good yields. Rosiglitazone was purchased from Axxora (San Diego, CA). GW9662 was purchased from Sigma-Aldrich (St Louis, MO).
SKOV-3 Xenograft Tumors and Treatment
SKOV-3 cells (2 × 106 cells/inoculate) were suspended in 0.1 mL of matrigel and inoculated subcutaneously in the flank of 4-6 week-old nude mice (NU/NU; strain code 088/homozygous) (Charles River Laboratories, Wilmington, MA) two weeks before treatment. MT19c was prepared as a stock solution of 1 mM in 100% EtOH and diluted 1:40 in PBS for administration. Mice were monitored and treated intraperitoneally (IP) every other day with either vehicle control (control group, PBS/2.5% EtOH; 12 animals) or 0.3 mL (10 mg/kg bwt) of MT19c (10 animals). Tumor size was calculated using a caliper. Animal experiments were carried out in the animal facilities of Rhode Island Hospital (RIH), RI, USA with strict adherence to the guidelines of the Animal Welfare Committee of RIH and Women and Infants Hospital (IACUC protocol # 0061-07).
RNA purification and microarrays
RNA was isolated from each tumor by homogenization in Trizol (Invitrogen) using a Tissuemizer (Tekmar Company, Cincinnati, OH). RNA was purified using Qiagen RNAsy columns following manufacturer's protocol. All RNA was of high quality as assessed by an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc. Santa Clara, CA). RNA was prepared for hybridization on Affymetrix Human Gene 1.0 St arrays. All RNA processing, array hybridizations and scanning were performed by the Brown University Center for Genomics and Proteomics core facility. RNA was prepared for hybridization using Affymetrix standard protocols and applied to Human Gene 1.0 ST microarrays (Affymetrix, Santa Clara, CA). All microarrays were quantile normalized together and the Probe Logarithmic Intensity Error (PLIER) algorithm was used to generate signal estimates for all RefSeq genes. To select actual signal, we discarded those transcripts belonging to the lowest quartile of their respective datasets. Analysis of significantly changing genes was determined using R http://www.r-project.org/. Complete microarray data have been deposited at the National Center for Biotechnology Information's Gene Expression Omnibus (accession number GSE23616).
Ingenuity Pathway Analysis
The discriminating genes were used as input into Ingenuity Pathway Analysis (IPA) (Ingenuity IP 8.6-3003 http://www.ingenuity.com. The following analysis settings were used, the Ingenuity Knowledge database for genes on the Affymetrix Human Gene 1.0 St Array was used as a reference set, direct and indirect relationships were included, with a maximum of 35 molecules per network and a maximum of 25 networks per analysis. All data sources and species, and all tissues and cell lines were included. IPA uses a Fischer's exact test to determine which pathways and biological functions are significantly enriched in the input gene set relative to the reference set.
Quantitative Real-Time PCR
Equal amounts of total RNA were reverse transcribed using Superscript III and random hexamers (Invitrogen, Carlsbad, CA). Resulting cDNA was renormalized using Quant-iT PicoGreen (Invitrogen) before mixing with 1× Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). Reactions were performed in an Applied Biosystems 7900HT Fast Real-Time PCR System. The fold change was calculated as described previously using calnexin as an internal control. Primer sequences are listed in Additional file 1 Table S1.
DNA purification and CGH arrays
Cell viability measurements
Cells were plated in 96-well plates and, 24 hours later, were treated with the indicated combinations of compounds. Viability was measured after 96 hours by WST-1 (Roche). All assays were done in biological triplicate, with technical triplicates done for each biological repeat.
To understand the genes and pathways that drive tumor growth and drug response, we compared MT19c and vehicle treated SKOV-3 xenografts in nude mice. SKOV-3 expresses VDR (Additional file 2 Figure S1) and is a common cell line to model EOC. We aimed to understand how a tumor evolves during treatment compared to untreated, growing tumors. Measurements of tumor size revealed that MT19c significantly reduced tumor size or limited growth (Figure 1A and 1B). Past experiments with larger numbers of mice show more statistically significant MT19c effects [15, 16]. MT19c induced DNA degradation, consistent with apoptosis, further demonstrating MT19c's anti-tumor activity (Figure 1C). MT19c is known to induce apoptosis in the SKOV-3 cell line [15, 16]. Xenograft tumors were collected at multiple time points after the initiation of treatment as indicated by the labels where the first letter indicates the treatment (T for MT19c and N for naïve), the number indicates the number of days, and the final letter indicates the replicate. For example, T8A indicates the A replicate of an 8 day MT19c treated tumor.
The T16C xenograft tumor has a notably different MT19c response compared to the other tumors. T16C appeared to grow before rapidly shrinking. Upon collection, T16C appeared to have a liquid center, which may indicate necrosis. The DNA from T16C was significantly degraded as shown in Figure 1C. Thus, MT19c appears to repress the T16C xenograft tumor effectively but differently than the other tumors. The naïve tumor, N16A, appeared not to have grown significantly during the experiment. However, clustering and other expression behaviors indicate that this xenograft tumor is more similar to the other naïve tumors compared to the MT19c treated tumors. In addition, H&E staining indicated that each xenograft tumor was not significantly infiltrated with vascularization (Additional file 3 Figure S2). Together, these observations suggest that MT19c is an effective anti-tumor molecule and that the majority of the purified nucleic acids were derived from human tumor cells in this xenograft system.
MT19c induced gene expression changes
To identify genes and pathways that are differentially expressed upon MT19c treatment and tumor progression, we took a genome-wide approach. RNA was purified from each tumor and probed with Affymetrix Human Gene St 1.0 microarrays.
Because the tumors collected at longer times appear to be outliers from the clustering analysis, we focused on the first two time points to assess treatment-dependent and time-dependent changes. To gain insight into consistent MT19c induced effects, we compared the treated and naïve tumors from the first two time points, days 8 and 16, excluding the T16C outlier. We identified the most significantly MT19c regulated transcripts by a t-test (q < 0.05) and log2 fold change > 0.6 (Additional files 4 and 5, Tables S2 and S3). MT19c up-regulated 268 and down-regulated 306 genes when comparing the six naïve tumors from the 8 day and 16 day time points to the five MT19c treated tumors from the same time points that clustered similarly (T8A, T8B, T8C, T16A, T16B) (Figure 2). To validate the data, we sampled genes by real-time qPCR and found good concordance in trend but not magnitude (Additional file 6 Figure S3). This is a common observation when comparing microarray and qPCR data, where the microarray dynamic range is reduced compared to qPCR .
MT19c regulated pathways
Gene Set Enrichment Analysis reveals MT19C down-regulation of energy metabolism and stimulation of DNA repair and apoptosis pathways.
Kegg (n = 165)
Biosynthesis of Steroids
Notch signaling pathway
Ubiquitin mediated Proteolysis
Gene Ontology (n = 1307)
Double Strand Break Repair
Increased expression of cell cycle regulators as during naïve tumor growth by GSEA.
Naïve Tumor 8 day vs. 16 day
BioCarta (n = 184)
Cell cycle pathway
Kegg (n = 165)
GenMapp (n = 107)
DNA replication reactome
TFT (n = 614)
Gene Ontology (n = 1307)
M phase of mitotic cell cycle
MT19C 8 day vs. 16 day
GenMapp (n = 107)
DNA Replication Reactome
Kegg (n = 165)
Transcription Factor Targets (n = 614)
Cell cycle regulators are more affected by MT19C at earlier times while translation regulators and ribosomes are affected at later times by GSEA.
Treat8 vs. Naive8
Biocarta (n = 184)
GenMapp (n = 107)
G1 to S Cell Cycle Reactome
Krebs TCA Cycle
Kegg (n = 165)
Transcription factor targets (n = 614)
Treat16 vs. Naive16
Genmapp (n = 107)
Aminoacyl tRNA biosynthesis
Consistent with the enriched cell cycle pathways, genes regulated by E2Fs were also expressed at higher levels in treated tumors compared to naïve tumors. E2Fs are key transcription factor regulators of the cell cycle, especially the G1-S transition . However, at later times, neither E2F regulated genes nor cell cycle associated genes including CCNE2 were significantly different between the treated and control tumors. This suggests that rapid, initial changes on proliferation and cell cycle regulators are induced by MT19c. Moreover, these observations suggest that tumors collected at different time points reflect specific responses to MT19c.
PPARγ Activity Enhances SKOV-3 Survival
A second IPA identified network, centered on insulin (Figure 4C), is observed when comparing all the naïve and MT19c treated tumors. These observations may be consistent with findings suggesting that IRS 1/2 and ERK 1/2 pathways are down-regulated by MT19c . These expression data along with probing of insulin signaling in cell culture, suggest that MT19c is down-regulating these pro-growth and survival pathways in SKOV-3 as part of its anti-tumor activity.
MT19c Regulates Genes in Copy Number Aberrations
Copy number changes often drive gene expression of key factors critical for tumor survival and growth. We hypothesized that an antitumor drug such as MT19c may select cells that can evade induced cell death, drive faster growth, and directly affect gene expression through gene dosage effects. We initially observed that GSEA analysis suggested a number of genomic loci were significantly up- and down-regulated by MT19c (Additional Files 9 and 10, Tables S4 and S5). We hypothesized that these MT19c induced changes could be due to the type of tumor cell selected by MT19c compared to naïve tumors. To test this possibility, we performed CGH analysis by purifying DNA from each tumor. DNA was competitively hybridized to Agilent 180 K microarrays with pooled female DNA. For the most degraded treated samples, day 30 xenograft tumors, the microarray signal was poor and was not considered further. The Agilent arrays were segmented using circular binary segmentation (CBS), after outliers greater than four standard deviations from the neighborhood of 20 probes were removed. Each tumor had similar copy number patterns suggesting the same genomes were selected to form the xenografts, with only relatively subtle difference such as in 17 p and 7 p (Additional File 11, Figure S5). Perhaps because of the small number of tumors and the observation that 3/8 MT19c tumors retain some copy loss, no significant trend in expression change for these genes between the treated and naïve tumors is observed.
Copy number can be a major determinant of expression levels for some genes. To determine if gene dosage drives expression of some genes in these xenografts, we mapped segmented CGH probes to each RefSeq gene, to determine a log score reflecting its copy number status. This score was calculated by averaging the log10 copy number ratios of the probes within each gene and selecting transcripts in the top quartile of all expressed genes. A global view of SKOV-3 CNAs show a modest number CNAs compared to other cell lines (Additional File 11, Figure S5) .
Many of the copy loss regions span large segments of chromosomes (Additional File 11, Figure S5), significantly complicating the identification of important genes. MT19c up-regulates some genes in copy loss regions (Figure 5). By reducing the number of genes that are expressed and change expression, we have reduced the number of candidates to consider for further experimentation and importance. The up-regulation of these genes when challenged with MT19c, suggests that they may be important to inhibit growth.
Xenograft tumor model systems are powerful tools for the evaluation and development of anti-tumor therapeutics. These models are especially useful for ovarian cancer, where only limited mouse models have been developed . SKOV-3 ovarian cancer cells are one of the more commonly used cell lines to model ovarian cancer. We aimed to understand what may drive xenograft tumor growth, which likely differs from growth in cell culture conditions, and what factors may be important for anti-tumor treatment. As a model, we tested the effects of MT19c, a vitamin D derivative that shows promising pre-clinical properties as demonstrated here and elsewhere [6, 15, 16]. We used multiple approaches to identify significantly MT19c regulated genes, pathways and networks with experimental support suggesting the functional importance of the insulin and PPARγ networks for MT19c efficacy. In particular, we found that PPARγ and PPARγ-controlled networks are up-regulated in treated tumors and stimulation of PPARγ with Rosiglitazone inhibited the chemotherapeutic efficacy in SKOV-3 cells. Finally, we propose an approach integrating copy number and expression data to identify which genes within CNAs are most likely to be important for tumor progression and chemotherapy. We propose that genes with high or low copy number, along with significant gene expression changes in response to an anti-tumor agent indicate genes important tumorigenesis.
This approach of linking copy number and drug induced expression changes may be a viable approach to identify particularly important genes for tumor progression. Although the majority of high copy number and high expression genes were affected by MT19c, many were not. A few high copy number genes such as RPL23 and RPS29 were significantly stimulated by MT19c. These high copy number genes may be up-regulated to help SKOV-3 cells survive in response to a lethal compound such as MT19c. But, their up-regulation upon MT19c treatment suggests that their high expression and high copy number may be serving a different role than down-regulated genes. Combining gene expression and copy number can reduce the number of genes to consider for further study. These data also suggest that genes regulated by dosage play an important role in cancer cells' response to chemotherapy. Together, these data provide insights into general pathways important for tumor progression and survival as well as MT19c efficacy.
Our observations suggest that in some cases the PPARγ network is stimulated to help ovarian cancer cells survive as suggested by Rosiglitazone treatment increasing the IC50s of MT19c and cisplatin. The observations suggest that stimulation of PPARγ by Rosiglitazone increases SKOV-3 chemotherapy resistance (Figure 4B). Rosiglitazone has been reported to inhibit growth and enhance cisplatin efficacy in some ovarian cancer cell lines, though SKOV-3 was not tested . These findings suggest that additional study is warranted to understand the conditions in which Rosiglitazone may be an effective chemotherapeutic and when it may actually promote survival of ovarian cancer cells.
We aimed to understand which gene expression networks may be important for SKOV-3 xenograft tumors progression and MT19c response. We found that significantly different conclusions may be made depending on how long the tumor was treated before the specimen was collected. Because xenograft tumors, much like patient tumors, are continuously evolving during growth, drug responses may differ at each captured state of the tumor at the time the specimen is collected for analysis. Genes involved in the cell cycle, energy metabolism, and DNA replication machinery are significantly affected by MT19c at the earlier, day 8 time point, while regulation of protein synthesis and ribosomes were significantly up-regulated at the later day 16 time point. No significant enrichment of cell cycle and DNA replication machinery was observed after 16 days of MT19c treatment. These pathways are often observed to be affected in tumors as control of metabolism and the cell cycle are often critical for tumor growth and survival. From gene expression data, it is difficult to conclude whether the effects are direct or indirect. These data identify candidate genes to test for their importance in mediating chemosensitivity in ovarian cancer cells as well as possible specific factors related to MT19c that can be discriminated with further study.
A significant contribution to these differences appears to originate from changes in the tumor as it progresses. This is highlighted when comparing the naïve tumors at the day 8 and 16 time points. When comparing the day 8 and 16 naïve tumors, cell cycle and DNA replication machinery is expressed higher at later times. We then observed that MT19c down-regulates the cell cycle at early times and yet not at later times when many of these genes are expressed at higher levels. Many of these genes are controlled by E2F. These observations suggest that control of the cell cycle depends on when the tumor is collected. In patients, the exact place in tumor progression/growth is always different and thus this alone can explain expression differences, as opposed to the inherent character of genomic differences of the tumor. Among many other factors, these expression changes during tumor growth may be inherent to tumors and could be a source of heterogeneity in patient samples. In probing xenograft tumors, these observations highlight the importance of examining changes at multiple time points, and not just at an arbitrarily defined endpoint.
Integrating copy number and expression measurements has proven valuable to gain insight into tumor networks and regulation . However, determining which genes are drivers of tumorigenesis and which may be passengers remains a challenging problem, typically requiring extensive experiments to test the function of identified factors . Copy number can be a mechanism driving gene expression levels. Typically this is assessed by correlating the measured expression and copy number values. Here, we propose an extension of the copy number and expression comparison, in which we identify important genes in CNAs by examining changes driven by an anti-tumor therapy such as MT19c. Alternatively, these expression changes could also indicate chromatin deregulation of these genes, suggesting that the observed differential regulation is simply a result of chance. The many loci differentially regulated by MT19c suggest extensive changes in epigenetic control by MT19c. We believe the simplest explanation is that the MT19c induced down-regulation strongly suggests that at least some of these amplified genes are important for growth.
Together, these observations suggest that some of the amplified genes are important for tumorigenesis consistent with the hypothesis that amplified genes are selected for tumor growth and survival. Similarly, genes at lower gene dosage are stimulated by MT19c to induce cell death or at least limit growth. These observations suggest that a significant fraction of these CNA genes are important for tumor growth and survival. We speculate that these loci are being deregulated epigenetically by chromatin changes to overcome the gene dosage determined by the DNA copy number at the time points tested. Thus, the combination of DNA copy number and the mRNA expression behavior in response to MT19c suggest that these genes may be among the most important for SKOV-3 xenograft progression.
MT19c has strong anti-tumor effects in multiple ovarian cancer cell lines including SKOV-3; however, the mechanism of MT19c action remains unclear. We observed many factors and pathways affected by MT19c, including some related to tumor growth and survival, such as DNA damage response, apopotic genes, insulin, and PPARγ. Similar to PPARγ, it is likely that many of these factors are general survival and pathway factors for SKOV-3 cells. Identifying specific mechanisms from expression data based on small molecule perturbations remains challenging, and requires significant additional functional studies. These data highlight how MT19c affects specific pathways, such as PPARγ and insulin signaling, that can be tested in additional cell lines and in vivo models to determine their importance. These studies could lead to the development of biomarkers to help determine which features effect MT19c efficacy in pre-clinical and clinical models.
Combining copy number and changes in gene expression approach in multiple cell lines may prove useful to identify particularly important genes in mediating survival and drug responses in ovarian cancer. The major copy number changes in the SKOV3 xenografts are observed in the SKOV-3 cell line as reported  including the amplifications on chromosomes 3 and 17 and LOH on chromosome 1.
In summary, these data characterize SKOV-3 ovarian xenograft tumors and identify candidate factors important for tumor progression and response to chemotherapy. We have characterized the time dependent expression changes of SKOV-3 xenograft tumors in response to a novel chemotherapeutic. We also identify a possible role for PPARγ and Rosiglitazone stimulation in mediating chemotherapy in some ovarian cancer cells. Further study examining the widely varying functions of PPARγ in different genetic and epigenetic backgrounds is warranted. Finally, we propose a new integrated genomics approach that combines copy number and expression data to identify candidate tumor drivers. We find a general bias for down-regulation of amplified genes by the cytotoxic agent, MT19c, highlighted by strong down-regulation of genes, such as PNMT, in the same amplicon as the ERBB2 locus. These observations suggest that at least some genes regulated by dosage are critical for chemotherapy responses. Together, these data suggest that integrated genomics can provide important insights into the behavior of xenograft tumor systems, which are commonly used to evaluate tumor progression and drug efficacy.
List of Abbreviations used
- The abbreviations used are:
EOC: epithelial ovarian cancer
peroxisome proliferator-activated receptorγ
vitamin D receptor
copy number alteration
hematoxylin and eosin
Comparative Genomic Hybridization.
We thank Christoph Schorl and the Brown University Center for Genomics and Proteomics supported by NIH grant P20RR015578 for processing Affymetrix microarrays. We thank the Microarray Facility at the Prostate Centre at Vancouver General Hospital, Vancouver, BC, Canada for the Agilent CGH array processing. This work was supported in part by a NHGRI K22 Genome Scholar Award (A.S.B), 7K22 HG002488, (A.S.B.), a Medical Ellison Foundation award (A.S.B.), and a seed award from the Brown University Office of the Vice President for Research (A.S.B. and L.B).
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