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      Tumor cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers

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          Abstract

          Cancer genome sequencing studies indicate that a single breast cancer typically harbors multiple genetically distinct subclones 1–4 . Since carcinogenesis involves a breakdown in the cell-cell cooperation that normally maintains epithelial tissue architecture, individual subclones within a malignant microenvironment are commonly depicted as self-interested competitors 5,6 . Alternatively, breast cancer subclones might interact cooperatively to gain a selective growth advantage in some cases. Although interclonal cooperation has been shown to drive tumorigenesis in fruitfly models 7,8 , definitive evidence for functional cooperation between epithelial tumor cell subclones in mammals is lacking. Here, we use mouse models of breast cancer to show that interclonal cooperation can be essential for tumor maintenance. Aberrant expression of the secreted signaling molecule Wnt1 generates mixed-lineage mammary tumors composed of basal and luminal tumor cell subtypes, which purportedly derive from a bipotent malignant progenitor cell residing atop a tumor cell hierarchy 9 . Using somatic HRas mutations as clonal markers, we show that some Wnt tumors indeed conform to a hierarchical configuration, but others unexpectedly harbor genetically distinct basal HRas mutant (HRasmut ) and luminal HRas wild-type (HRaswt ) subclones. Both subclones are required for efficient tumor propagation, which strictly depends on luminally-produced Wnt1. When biclonal tumors were challenged with Wnt withdrawal to simulate targeted therapy, analysis of tumor regression and relapse revealed that basal subclones recruit heterologous Wnt-producing cells to restore tumor growth. Alternatively, in the absence of a substitute Wnt source, the original subclones often evolve to rescue Wnt pathway activation and drive relapse, either by restoring cooperation or by switching to a defector strategy. Uncovering similar modes of interclonal cooperation in human cancers may inform efforts aimed at eradicating tumor cell communities. Cancer progression is known to depend on cooperation between tumor cells and neighboring host cells in the microenvironment. Some have suggested that cooperation between distinct tumor cell subsets also may contribute to the malignant phenotype 10–12 . Favoring this possibility, genetically distinct subclones cooperatively enhanced tumor growth in models engineered to recapitulate a form of tumor cell heterogeneity identified in brain cancers 13 . Similarly, phenotypically distinct tumor cell subsets cooperatively enhanced tumor invasion in a murine lung cancer model 14 . In the case of human breast cancer, recent studies highlight the phenotypic and genetic diversity present locally within individual tumors 15,16 , but whether this heterogeneity is a cause or a consequence of tumor progression remains unclear. Accordingly, we sought definitive evidence for functional cooperation between tumor cell subsets in mouse models of human breast cancer. Mammary cancers arising in the classic MMTV-Wnt1 transgenic mouse model 17 display tumor cell heterogeneity that is widely attributed to malignant transformation of a bipotent mammary progenitor cell 9,18,19 . Concordantly, MMTV-Wnt1 tumor cells partition into basal and luminal subsets which comingle, recalling the corresponding basal and luminal lineages found in the normal mammary gland (Figure 1a, b). Although mutations in Wnt pathway components are rare in human breast cancers, the transcriptional profile of Wnt1-initiated tumors resembles that of other mammary cancer models that commonly show mixed-lineage histopathology, including chemical carcinogen-induced rodent mammary cancers 20,21 . While studying cooperating oncogenic mutations in the MMTV-Wnt1 model, we found evidence suggesting some Wnt tumors harbor unexpected genetic heterogeneity. About half of all Wnt-initiated mammary tumors spontaneously acquire somatic HRas mutations that encode an activated oncoprotein 22,23 . Since HRas mutations act dominantly, HRas mutant allele fractions (MAFs) of approximately 0.5 are expected, barring copy number changes at the HRas locus. Instead, when tumor-derived HRas alleles were amplified by PCR and subjected to DNA sequencing, chromatogram peak heights often indicated smaller HRas MAFs with fractions < 0.3 detected in 4 of 10 tumors. Notably, tumors maintained their small HRas MAFs as a stable property when explanted onto the flanks of syngeneic host mice (Fig. 1c). This discrepancy could not be explained by contamination of samples with normal (non-tumor) cells since tumor cell content assessed by histopathology consistently exceeded 80%. Moreover, copy number variations leading to either HRaswt allele gain or HRasmut allele loss seemed unlikely driver events. Instead, we considered whether some Wnt tumors might harbor distinct HRasmut and HRaswt subclones, noting that biclonal tumors would adopt a mixed-lineage phenotype provided each subclone were committed to a distinct lineage. To search for lineage-restricted HRasmut and HRaswt subclones, dissociated cells prepared from Hrasmut Wnt tumors were sorted into basal and luminal subsets (Extended Data Fig. 1), then HRas MAFs were determined for each subset and for corresponding samples of unsorted cells. Half of the HRasmut Wnt tumors analyzed (5 of 10) showed negligible subset-specific enrichment in HRasmut alleles, a pattern consistent with a hierarchical configuration (Fig. 1d,e). In these cases, basal and luminal cells from the same tumor always harbored identical HRasmut alleles (Fig. 1e), suggesting they descended from a common bipotent HRasmut progenitor. In contrast, for the remaining half of tumors analyzed, HRasmut alleles were highly enriched within the basal tumor cell subset, a pattern consistent with a biclonal configuration (Fig. 1e). Basal HRasmut allele enrichment correlated with a lower overall HRas MAF, further suggesting the presence of a private, subclone-restricted mutation. Regardless of whether the distribution of HRasmut alleles fit a hierarchical or biclonal pattern, tumors showed classic mixed-lineage histopathology (Extended Data Fig. 2), and luminal tumor cells were invariably the main source of Wnt1 expression as previously reported 24 (Fig. 1f). Therefore, some Wnt tumors appeared to harbor distinct basal HRasmut /Wnt1 low and luminal HRaswt /Wnt1 high subclones, implicating interclonal cooperation in tumor maintenance. These findings recall early reports in which MMTV-associated mammary tumors initiated by activation of endogenous Wnt genes sometimes were noted to be oligoclonal 25,26 . Seeking stringent proof that some Wnt tumors are biclonal and require interclonal cooperation for maintenance, we attempted to rescue growth of basal HRasmut /Wnt1 low subclones from Wnt1 deprivation by providing access to replacement Wnt1-producing cells. For these experiments, the original MMTV-Wnt1 model (hereafter cWnt denoting constitutive Wnt1 expression) was used in combination with a closely related model engineered for doxycycline (Dox)-dependent transgene expression (MMTV-rtTA/Tet-O-Wnt1; hereafter iWnt denoting inducible Wnt1 expression). During chronic Dox treatment, iWnt mice and mammary tumors phenocopy their cWnt counterparts, but iWnt tumors regress following Dox withdrawal due to abrogation of Wnt1 transgene expression 27 . To enable tracking of cell lineages in tumor reconstitution experiments, iWnt mice were crossed with an mRFP reporter line, generating iWnt/mRFP+ mice. As expected, a subset of Dox-dependent iWnt/mRFP+ mammary tumors appeared biclonal, since they harbored a basally-restricted HRasmut subclone. After dissociating these tumors into cell suspensions, 105 unsorted cells were injected orthotopically into the mammary fat pads of two sets of Dox-treated, mRFP reporter-negative female host mice (Fig. 2a). Control hosts lacked a transgene capable of rescuing tumor cells from Wnt withdrawal (wt/mRFP−), whereas rescue hosts expressed the constitutive Wnt1 transgene (cWnt/mRFP−). During chronic Dox treatment, both control and rescue hosts developed mammary tumors in most glands injected with iWnt/mRFP+ tumor cells (Fig. 2b and Extended Data Table 1). As expected, these reconstituted tumors usually regressed when iWnt transgene expression was switched off via Dox withdrawal. On control hosts, tumor regression always was complete, and mice remained relapse-free during 6 weeks of monitoring. Interestingly, subclinical disease often persisted, since most control hosts subsequently relapsed after Dox re-treatment (Fig. 2b). By contrast, on cWnt rescue hosts, most reconstituted tumors only partially regressed, then relapsed spontaneously (Fig. 2b and Extended Data Fig. 3a). On control hosts, primary tumors were reconstituted almost exclusively from donor mRFP+ cells, and relapses triggered by Dox re-treatment remained mRFP+ as expected (Fig. 2c). By contrast, on rescue hosts primary tumors showed varying degrees of chimerism due to incorporation of mRFP− (host-derived) luminal cells (Extended Data Fig. 3b), and relapses arising during Dox withdrawal always showed pronounced lineage-restricted chimerism, resulting in mRFP+/basal and mRFP−/luminal subpopulations (Fig. 2c). To confirm that donor basal subclones recruit host luminal epithelium to serve as a replacement Wnt1 source at relapse, we turned to Northern hybridization analysis of tumor RNAs. Strikingly, tumors reconstituted on rescue hosts typically expressed the larger iWnt transgene prior to Dox withdrawal (pertinent exceptions discussed in Extended Data Fig. 3), then switched to expressing the smaller cWnt transgene at relapse, indicating heterologous rescue (Fig. 2d). Furthermore, the biclonal configuration evident in parental tumors was maintained in all reconstituted tumors in that basal cells were HRasmut /Wnt1 low whereas luminal cells were HRaswt /Wnt1 high (Extended Data Fig. 4). We repeated these rescue experiments twice, beginning each time with an independent, iWnt/mRFP tumor harboring a distinct, basally-restricted HRas mutation. In all cases, we observed rescue of donor-derived basal HRasmut /Wnt1 low tumor cells by cWnt host-derived luminal HRaswt /Wnt1 high cells (Fig. 3a,b and Extended Data Fig. 5). Moreover, the HRasmut allele detected in relapsed tumors was always identical to that detected in parental tumors, confirming that basal subclones found at relapse were descended from donor-derived tumor cells and were not novel clones. Examination of tumor sections by fluorescence microscopy revealed pervasive intermingling of basal mRFP+ and luminal mRFP− tumor cells within chimeric relapses, consistent with the prevailing notion that secreted Wnts provide a short-range signal to neighboring cells (Fig. 3c). Prospective analysis of a larger set of independent Wnt tumors will be required to precisely estimate the overall fraction of Wnt tumors that is biclonally configured. Notably, we cannot yet determine clonal configurations for that half of Wnt tumors that lack an HRas mutation. Whereas biclonal iWnt/mRFP+ tumors were readily reconstituted from unsorted (FACS-naïve) cells, sorted basal and luminal cells each reconstituted tumors inefficiently when injected into mammary glands (Extended Data Fig. 6a), perhaps owing in part to loss of cell viability during FACS. Remarkably, tumors that did arise after injecting a single sorted subtype always were biclonal, comprised of both basal HRasmut /Wnt1 low and luminal HRaswt /Wnt1 high subsets (7 of 7 tumors analyzed, Extended Data Fig. 6b). Given the imperfect separation achieved by FACS (95 – 98% purity), rare cells cross-contaminating each subset presumably sufficed to permit interclonal cooperation during tumor reconstitution. Consistent with this notion, the relative sizes of the basal and luminal cell populations within these tumors approximated that found in parental tumors and did not reflect the lineage enrichment achieved by sorting. We confirmed this result in an experimental context where the putative cooperating subclones were differentially labeled by the mRFP transgene. Here, tumor cells derived from chimeric relapses generated in our rescue experiment were studied prospectively. Again, neither the basal (mRFP+/HRasmut /Wnt1 low) nor the luminal (mRFP−/HRaswt /Wnt1 high) subsets reconstituted tumors efficiently, whereas a 1:1 admixture of both sorted populations reliably reconstituted biclonal tumors (Extended Data Fig. 7). Notably, every tumor reconstituted in these experiments faithfully restored the subclonal composition of the source tumor, pointing to strong selection favoring propagation of both subclones in tandem. iWnt tumors that regress upon Dox withdrawal frequently relapse weeks later as Dox-independent tumors (DITs), mirroring the clinical scenario of acquired resistance to effective targeted therapy. Next, we asked which subclone(s) contribute when biclonal tumors beget relapse. A putative biclonal iWnt tumor with an HRas MAF <0.3 was identified and propagated on host mice, generating a set of Dox-dependent tumor explants. Explants maintained the HRas MAF observed in the parental tumor, suggesting a stable biclonal configuration (Fig. 4a,b). Host mice then were subjected to Dox withdrawal and monitored until relapse, generating a set of 20 DITs. In accord with our previous work 28 , 18 of the 20 relapses (90%) occurred through one of two mutually exclusive modes of Wnt pathway reactivation. Seven DITs (35%), re-expressed the Wnt1 transgene, and all 7 had acquired one of two rtTA mutations (G138R or H100Y) previously shown to rescue mammary tumors from oncogene withdrawal by enabling aberrant, Dox-independent expression of TetO-controlled transgenes 29 . All 7 of these tumors had an HRas MAF comparable to parental tumor (Fig. 4b), strongly suggesting that rtTA mutations originating within the HRaswt sublone restored both Wnt1 expression and cooperation with HRasmut cells, culminating in biclonal relapse. Eleven DITs (55%) instead rescued oncogenic signaling by acquiring an activating mutation in β-catenin (Ctnnb1, hereafter βcat), a key downstream Wnt effector (Fig 4a,b). Compared with parental tumor, these relapses showed markedly increased HRas MAFs that were highly reproducible across the tumor set (Fig. 4b). Therefore, βcat mutations likely originated within HRasmut cells that later emerged as predominant relapse clones. By activating the Wnt pathway in a cell-autonomous manner, βcat mutations presumably obviated the need to maintain cooperation with Wnt-producing HRaswt subclones. As such, βcatmut relapse clones act like “defectors” in evolutionary game theory terms. HRas MAFs in βcatmut relapses consistently exceeded 0.5, indicating that βcatmut clones must harbor additional HRas locus aberrations; however, no gross changes in HRas gene copy number were observed (Extended Data Fig. 8), implicating copy number neutral loss-of-heterozygosity events. To further examine the clonal configuration of relapsed tumors, an iWnt/mRFP+ tumor previously confirmed as biclonal in our rescue experiments (Fig. 2) was propagated as above to derive DITs, then relapse-derived tumor cells were separated into basal and luminal subsets and analyzed. One DIT that relapsed via Wnt1 transgene re-expression was biclonal with a luminally-restricted rtTA mutation (Fig. 4c). Trophic support from this luminal rtTAmut subclone likely rescued growth of its basal rtTAwt counterpart, providing a plausible cellular mechanism whereby this rescue mutation was maintained at a low MAF. In contrast, our prior analysis indicated that βcat rescue mutations originate within basal tumor cells and obviate the need to cooperate with Wnt-producing luminal cells. Nonetheless, βcatmut relapses consistently harbored abundant luminal tumor cells (Fig. 4c and Extended Data Fig. 9). We hypothesized that acquired βcat mutations endow basally-restricted subclones with novel bipotent differentiation potential, thereby converting them to hierarchically-configured clones at relapse. Two βcatmut DITs analyzed as above showed comparable βcatmut allele prevalence in the basal and luminal subsets (Fig. 4c), consistent with a scenario in which βcatmut relapse clones acquired bipotency. (An alternative scenario in which each subclone independently acquired matching βcat mutations cannot be formally excluded, but seems less likely). In our prior experiments (Fig. 2, Extended Data Fig. 4) and in the rtTAmut relapse profiled above, this same subclone invariably behaved in a unipotent manner, remaining basally-restricted when partnered with a Wnt1-expressing luminal subclone in the context of primary and relapsed tumors. Efforts to explain how some cancers stably maintain intratumoral lineage diversity typically invoke tumor cell hierarchies. Here, we show that cooperation between lineage-restricted subclones provides an alternative mechanism for maintaining tumor cell heterogeneity. In our Wnt models, we found evidence for both hierarchically and biclonally configured tumors, yet differently configured tumors were indistinguishable by histopathology, acquired equivalent cooperating HRasmut alleles (albeit with differences in tumor cell compartmentalization), and were comparably Wnt1-dependent. Thus, although distinct clonal configurations evolved, they converged toward analogous malignant phenotypes. These findings highlight the difficulties associated with inferring the clonal architecture of cancers from histopathology, even in the “simplified” context of mouse models. Notably, the Wnt models described here provide an experimentally tractable system for exploring whether and how a tumor’s clonal configuration determines its clinical behavior and curability. Our study does not define when distinct subclones emerge in the course of tumor progression. Interclonal cooperation may be particularly prevalent in tumors initiated by aberrant expression of secreted signaling molecules, such as Wnt1 and PDGF 30 . In principle, germline mutations that impart a cancer predisposition also might bias tumors toward a biclonal configuration, since any subsequent cooperation-enabling mutations would necessarily accrue in a cell with mutant neighbors. As such, it will be important to determine whether interclonal cooperation can arise when initiating events originate in somatic cells or act primarily in a cell-intrinsic manner. If cooperation emerges as a common mechanism for maintaining subclone diversity in malignancies, this scenario would counter a key assumption made when interpreting cancer genome sequences. Specifically, certain mutations detected at low allelic fractions and commonly assumed to be late events in tumor progression, instead may be early events that enable interclonal cooperation. Methods Transgenic Mice Mice were housed under pathogen-free conditions in the Pennsylvania State University College of Medicine rodent facility with access to water and chow ad libitum. All experimental protocols were approved by the Pennsylvania State University College of Medicine’s Institutional Animal Care and Use Committee. The MMTV-Wnt1 (FVB.Cg-Tg(Wnt1)1Hev/J; stock #002934) and mRFP (B6.Cg-Tg(CAG-mRFP1)1F1Hadj/J; stock #005884) transgenic lines were obtained from the Jackson Labs. The MMTV-rtTA and tetO-Wnt1 transgenic lines were a gift from Dr. Lewis Chodosh. All mice either were generated in an inbred FVB/N background or were back-crossed 10 or more generations with FVB/N breeders before initiating experiments. Dox was administered by replacing standard mouse chow with chow containing 2g/kg Dox (Bio-serv). Genotyping was performed by PCR using genomic DNA isolated from tail clips and transgene specific primers (available upon request). Cell Sorting Mammary tumors were dissociated into single cell suspensions through mechanical separation and enzymatic digestion as described 22 . Dissociated tumor cells were enriched for Lin− (CD45−/ CD31−/ TER119−/ BP-1−) mammary epithelial cells with StemCell Technologies EasySep Mouse Epithelial Cell Enrichment Kits per the manufacturer’s instructions. Lin− cells were then incubated on ice for 20 min with anti-CD49f (α6 integrin) (BD Biosciences 555734) together with Alexafluor 647 (Invitrogen A21247) in PBS. Cells were spun down for 5 min at 550x g, then incubated with EpCAM-FITC conjugated antibody (Biolegend 118208) in PBS. Tumor cells were sorted on a BD FACS Aria cell sorter machine equipped with Diva software into their luminal (Lin−/ CD49flow/EpCAMhigh) and basal (Lin−/CD49fhigh/EpCAMlow) subpopulations. Sorted cells were collected into 15ml conical tubes containing PBS. Genomic DNA was collected from sorted cell populations using Qiagen Blood and Tissue DNeasy spin column kit. Total RNA was collected from sorted cell populations using Qiagen RNeasy spin column kit. RNA was reversed transcribed using Invitrogen Superscript II First Strand Synthesis kit. Tumor reconstitution and propagation In tumor reconstitution experiments, tumor cells for injection were counted using a hemocytometer and suspended at a concentration of 1000 cells/ul in a 50% Matrigel solution in PBS (BD Biosciences Growth Factor Reduced Matrigel Matrix). 105 cells in 100 ul of Matrigel solution were injected directly into intact #3 or #4 mammary fat pads of anesthetized adult female hosts, after using a small skin incision to expose the injection site. Incisions were closed with surgical clips. No randomization was performed since host mice were genetically identical, and no mice were excluded from analysis of tumor onset. Mice were monitored at least twice weekly for tumor growth by an investigator blinded to the tumor cell injection source. To generate a cohort of clonally related tumors for generating DITs, tumor fragments were explanted onto the flanks of wild-type Dox-treated host mice. Tumors were permitted to grow to a diameter of 8–10 mm, at which point mice received a single i.p. injection of N-methyl-N-nitrosourea (MNU) (Sigma 50mg/kg) to accelerate tumor relapse one week prior to Dox withdrawal. Without MNU treatment, only about a third of iWnt tumor explants relapsed during 12 months of continuous Dox withdrawal, and relapses arose after an average latency of 6 months. With MNU treatment, more than 90% of iWnt explants relapsed within 3 months of Dox withdrawal. Tumor regression and relapse was monitored at least twice weekly. Tumors were measured in two dimensions with calipers. DNA sequencing Genomic DNA or copy DNA isolated from tumor specimens or sorted tumor cell populations was amplified by PCR using gene specific primers (available upon request). PCR product was run out on an agarose gel, cut out and isolated using Qiagen QiaQuick Gel Isolation spin column kit. Samples were subjected to Sanger sequencing using gene specific primers on an ABI 3130XL Capillary sequencer machine. Sequence traces were analyzed using AB DNA Sequencing Analysis Software v5.2 and AB Sequence Scanner v1.0. Peak height (PH) on sequencing chromatograms was measured using ImageJ 1.46 software and HRas MAF was calculated using the following formula: MAF = PHMutant / (PHMutant + PHWild-type). Immunofluorescence Tumor samples were fixed in 4% paraformaldehyde on ice for 2 hrs before being paraffin embedded. Paraffin sections (5um) were stained with antibodies for smooth muscle actin (SMA) and Keratin 8, which label basal and luminal epithelial cells, respectively. Primary antibodies used were: rabbit anti-SMA (AbCAM 5694, 1:250), and rat anti-Keratin 8 (Troma-I) (Developmental Studies Hybridoma Bank, University of Iowa, 1:250). Secondary antibodies were: biotinylated rabbit-anti-rat IgG (Dako Cytomation E0468) and biotinylated rabbit IgG (Vector BA-1000). The fluorophore was a streptavidin fluorescein (Vector SA-5001). Hoechst-33342 dye (Invitrogen H1399) was used for nuclear DNA counterstaining, and slides were visualized using a Zeiss wide-field fluorescent microscope equipped with AxioVision 4.8 software. Quantitative RT-PCR RNA was reversed transcribed using Invitrogen Superscript II First Strand Synthesis kit. We used Taqman Gene Expression Assay mix containing unlabeled PCR primers and a FAM-labeled Taqman probe to detect expression of the following genes: Wnt1 transgene (Applied Biosystems Mm01300555_g1), Keratin-8 (Applied Biosystems Mm00835759_m1), Gata3 (Applied Biosystems Mm00484683_m1), Muc1 (Applied Biosystems Mm00449604_m1), Keratin-5 (Applied Biosystems Mm00503549_m1), Keratin-14 (Applied Biosystems Mm00516876_m1), P-Cadherin (Applied Biosystems Mm01249209_m1), and Vimentin (Applied Biosystems Mm01333430_m1). Relative quantification Real-time PCR (ΔΔCt method) was performed in triplicate using Agilent Technologies Stratagene Mx3005P detection system and analyzed using Stratagene MxPro software. Gene expression levels in sorted cell populations were normalized to Gapdh transcript levels (Applied Biosystems 4352339E) and compared to the unsorted sample (relative expression=1). Northern Hybridization Total RNA was isolated from snap-frozen bulk tumor pieces by CsCl Density Gradient Centrifugation. Northern hybridization was performed as previously described 28 using cDNA probes generated by RT-PCR. Primer pairs for probes are as follows: Wnt1, forward 5_-TGCGGTTCCTGTATTTTGC-3_ and reverse 5_-TGCATTCCTTTGGCGAGAGG-3_; Axin2, forward 5′-CCGAGCTCATCTCCAGGC-3′ and reverse 5′-GGACAGAGGCAGCGGACTC-3′; β-actin, forward 5′-TGAGACCTTCAACACCCCAG-3′ and reverse 5′-TGAGACCTTCAACACCCCAG-3′. After subcloning, the identity of each probe was confirmed by DNA sequence analysis.

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          The clonal evolution of tumor cell populations.

          P C Nowell (1976)
          It is proposed that most neoplasms arise from a single cell of origin, and tumor progression results from acquired genetic variability within the original clone allowing sequential selection of more aggressive sublines. Tumor cell populations are apparently more genetically unstable than normal cells, perhaps from activation of specific gene loci in the neoplasm, continued presence of carcinogen, or even nutritional deficiencies within the tumor. The acquired genetic insta0ility and associated selection process, most readily recognized cytogenetically, results in advanced human malignancies being highly individual karyotypically and biologically. Hence, each patient's cancer may require individual specific therapy, and even this may be thwarted by emergence of a genetically variant subline resistant to the treatment. More research should be directed toward understanding and controlling the evolutionary process in tumors before it reaches the late stage usually seen in clinical cancer.
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            Is Open Access

            Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors

            Background Global gene expression analyses of human breast cancers have identified at least three major tumor subtypes and a normal breast tissue group [1]. Two subtypes are estrogen receptor (ER)-negative with poor patient outcomes [2,3]; one of these two subtypes is defined by the high expression of HER2/ERBB2/NEU (HER2+/ER-) and the other shows characteristics of basal/myoepithelial cells (basal-like). The third major subtype is ER-positive and Keratin 8/18-positive, and designated the 'luminal' subtype. This subtype has been subdivided into good outcome 'luminal A' tumors and poor outcome 'luminal B' tumors [2,3]. These studies emphasize that human breast cancers are multiple distinct diseases, with each of the major subtypes likely harboring different genetic alterations and responding distinctly to therapy [4,5]. Further similar investigations may well identify additional subtypes useful in diagnosis and treatment; however, such research would be accelerated if the relevant disease properties could be accurately modeled in experimental animals. Signatures associated with specific genetic lesions and biologies can be causally assigned in such models, potentially allowing for refinement of human data. Significant progress in the ability to genetically engineer mice has led to the generation of models that recapitulate many properties of human cancers [6]. Mouse mammary tumor models have been designed to emulate genetic alterations found in human breast cancers, including inactivation of TP53, BRCA1, and RB, and overexpression of MYC and HER2/ERBB2/NEU. Such models have been generated through several strategies, including transgenic overexpression of oncogenes, expression of dominant interfering proteins, targeted disruption of tumor suppressor genes, and by treatment with chemical carcinogens [7]. While there are many advantages to using the mouse as a surrogate, there are also potential caveats, including differences in mammary physiologies and the possibility of unknown species-specific pathway differences. Furthermore, it is not always clear which features of a human cancer are most relevant for disease comparisons (for example, genetic aberrations, histological features, tumor biology). Genomic profiling provides a tool for comparative cancer analysis and offers a powerful means of cross-species comparison. Recent studies applying microarray technology to human lung, liver, or prostate carcinomas and their respective murine counterparts have reported commonalities [8-10]. In general, each of these studies focused on a single or few mouse models. Here, we used gene expression analysis to classify a large set of mouse mammary tumor models and human breast tumors. The results provide biological insights among and across the mouse models, and comparisons with human data identify biologically and clinically significant shared features. Results Murine tumor analysis To characterize the diversity of biological phenotypes present within murine mammary carcinoma models, we performed microarray-based gene expression analyses on tumors from 13 different murine models (Table 1) using Agilent microarrays and a common reference design [1]. We performed 122 microarrays consisting of 108 unique mammary tumors and 10 normal mammary gland samples (Additional data file 1). Using an unsupervised hierarchical cluster analysis of the data (Additional data file 2), murine tumor profiles indicated the presence of gene sets characteristic of endothelial cells, fibroblasts, adipocytes, lymphocytes, and two distinct epithelial cell types (basal/myoepithelial and luminal). Grouping of the murine tumors in this unsupervised cluster showed that some models developed tumors with consistent, model-specific patterns of expression, while other models showed greater diversity and did not necessarily group together. Specifically, the TgWAP-Myc, TgMMTV-Neu, TgMMTV-PyMT, TgWAP-Int3 (Notch4), TgWAP-Tag and TgC3(1)-Tag tumors had high within-model correlations. In contrast, tumors from the TgWAP-T 121 , TgMMTV-Wnt1, Brca1 Co/Co ;TgMMTV-Cre;p53 +/-, and DMBA-induced models showed diverse expression patterns. The p53 -/- transplant model tended to be homogenous, with 4/5 tumors grouping together, while the Brca1 +/-;p53 +/- ionizing radiation (IR) and p53 +/- IR models showed somewhat heterogeneous features between tumors; yet, 6/7 Brca1 +/-;p53 +/- IR and 5/7 p53 +/- IR were all present within a single dendrogram branch. Table 1 Summary of mouse mammary tumor models Tumor model No. of tumors Specificity of lesions Experimental oncogenic lesion(s) Strain Reference TgWAP-Myc 13 WAP* cMyc overexpression FVB [60] TgWAP-Int3 7 WAP Notch4 overexpression FVB [61] TgWAP-T 121 5 WAP pRb, p107, p130 inactivation B6D2 [37] TgWAP-T 121 2 WAP pRb, p107, p130 inactivation BALB/cJ [37] TgWAP-Tag 5 WAP SV40 L-T (pRb, p107, p130, p53, p300 inactivation, others); SV40 s-t C57Bl/6 [62] TgC3(1)-Tag 8 C3(1)† SV40 L-T (pRb, p107, p130, p53, p300 inactivation, others); SV40 s-t FVB [63] TgMMTV-Neu 10 MMTV‡ Unactivated rat Her2 overexpression FVB [64] TgMMTV-Wnt1 11 MMTV Wnt 1 overexpression FVB [65] TgMMTV-PyMT 7 MMTV Py-MT (activation of Src, PI-3' kinase, and Shc) FVB [66] TgMMTV-Cre;Brca1 Co/Co ;p53+/- 10 MMTV Brca1 truncation mutant; p53 heterozygous null C57Bl/6 [67] p53-/-transplanted 5 None p53 inactivation BALB/cJ [68] Medroxyprogesterone-DMBA-induced 11 None Random DMBA-induced FVB [69] p53+/-irradiated 7 None p53 heterozygous null, random IR induced BALB/cJ [70] Brca1 +/-;p53+/-irradiated 7 None Brca1 and p53 heterozygous null, random IR induced BALB/cJ [1] *WAP, whey acidic protein promoter, commonly restricted to lactating mammary gland luminal cells. †C3(1), 5' flanking region of the C3(1) component of the rat prostate steroid binding protein, expressed in mammary ductal cells. ‡MMTV, mouse mammary tumor virus promoter, often expressed in virgin mammary gland epithelium, induced with lactation; often expressed at ectopic sites (for example, lymphoid cells, salivary gland, others). As with previous human tumor studies [1,3], we performed an 'intrinsic' analysis to select genes consistently representative of groups/classes of murine samples. In the human studies, expression variation for each gene was determined using biological replicates from the same patient, and the 'intrinsic genes' identified by the algorithm had relatively low variation within biological replicates and high variation across individuals. In contrast, in this mouse study we applied the algorithm to groups of murine samples defined by an empirically determined correlation threshold of > 0.65 using the dendrogram from Additional data file 2. This 'intrinsic' analysis yielded 866 genes that we then used in a hierarchical cluster analysis (Figure 1 and Additional data file 3 for the complete cluster diagram). This analysis identified ten potential groups containing five or more samples each, including a normal mammary gland group (Group I) and nine tumor groups (designated Groups II-X). Figure 1 Mouse models intrinsic gene set cluster analysis. (a) Overview of the complete 866 gene cluster diagram. (b) Experimental sample associated dendrogram colored to indicate ten groups. (c) Luminal epithelial gene expression pattern that is highly expressed in TgMMTV-PyMT, TgMMTV-Neu, and TgWAP-myc tumors. (d) Genes encoding components of the basal lamina. (e) A second basal epithelial cluster of genes, including Keratin 5. (f) Genes expressed in fibroblast cells and implicated in epithelial to mesenchymal transition, including snail homolog 1. (g) A second mesenchymal cluster that is expressed in normals. See Additional data file 2 for the complete cluster diagram with all gene names. In general, these ten groups were contained within four main categories that included (Figure 1b, left to right): the normal mammary gland samples (Group I) and tumors with mesenchymal characteristics (Group II); tumors with basal/myoepithelial features (Groups III-V); tumors with luminal characteristics (Groups VI-VIII); and tumors containing mixed characteristics (Groups IX and X). Group I contained all normal mammary gland samples, which showed a high level of similarity regardless of strain, and was characterized by the high expression of basal/myoepithelial (Figure 1e) and mesenchymal features, including vimentin (Figure 1g). Group II samples were derived from several models (2/10 Brca1 Co/Co ;TgMMTV-Cre;p53 +/-, 3/11 DMBA-induced, 1/5 p53 -/- transplant, 1/7 p53 +/- IR, 1/10 TgMMTV-Neu and 1/7 TgWAP-T 121 ) and also showed high expression of mesenchymal features (Figure 1g) that were shared with the normal samples in addition to a second highly expressed mesenchymal-like cluster that contained snail homolog 1 (a gene implicated in epithelial-mesenchymal transition [11]), the latter of which was not expressed in the normal samples (Figure 1f). Two TgWAP-Myc tumors at the extreme left of the dendrogram, which showed a distinct spindloid histology, also expressed these mesenchymal-like gene features. Further evidence for a mesenchymal phenotype for Group II tumors came from Keratin 8/18 (K8/18) and smooth muscle actin (SMA) immunofluorescence (IF) analyses, which showed that most spindloid tumors were K8/18-negative and SMA-positive (Figure 2l). Figure 2 Immunofluorescence staining of mouse samples for basal/myoepithelial and luminal cytokeratins. (a) Wild-type (wt) mammary gland stained for Keratins 8/18 (red) and Keratin 5 (green) shows K8/18 expression in luminal epithelial cells and K5 expression in basal/myoepithelial cells. (b-f) Mouse models that show luminal-like gene expression patterns stained with K8/18 (red) and K5 (green). (g-k) Tumor samples that show basal-like, or mixed luminal and basal characteristics by gene expression, stained for K8/18 (red) and K5 (green). (j) A subset of Brca1 Co/Co ;TgMMTV-Cre;p53+/-tumors showing nodules of K5/K8/18 double positive cells. (l) A splindloid tumor stained for K8/18 (red) and smooth muscle actin (green). The second large category contained Groups III-V, with Group III (4/11 DMBA-induced and 5/11 Wnt1), Group IV (7/7 Brca1+/-;p53 +/- IR, 4/10 Brca1 Co/Co ;TgMMTV-Cre;p53 +/-, 4/6 p53 +/- IR and 3/11 Wnt1) and Group V (4/5 p53 -/- transplant and 1/6 p53 +/- IR), showing characteristics of basal/myoepithelial cells (Figure 1d, e). These features were encompassed within two expression patterns. One cluster included Keratin 14, 17 and LY6D (Figure 1d); Keratin 17 is a known human basal-like tumor marker [1,12], while LY6D is a member of the Ly6 family of glycosylphosphatidylinositol (GPI)-anchored proteins that is highly expressed in head and neck squamous cell carcinomas [13]. This cluster also contained components of the basement membrane (for example, Laminins) and hemidesmosomes (for example, Envoplakin and Desmoplakin), which link the basement membrane to cytoplasmic keratin filaments. A second basal/myoepithelial cluster highly expressed in Group III and IV tumors and a subset of DMBA tumors with squamous morphology was characterized by high expression of ID4, TRIM29, and Keratin 5 (Figure 1e), the latter of which is another human basal-like tumor marker [1,12]. This gene set is expressed in a smaller subset of models compared to the set described above (Figure 1d), and is lower or absent in most Group V tumors. As predicted by gene expression data, most of these tumors stained positive for Keratin 5 (K5) by IF (Figure 2g-k). The third category of tumors (Groups VI-VIII) contained many of the 'homogenous' models, all of which showed a potential 'luminal' cell phenotype: Group VI contained the majority of the TgMMTV-Neu (9/10) and TgMMTV-PyMT (6/7) tumors, while Groups VII and VIII contained most of the TgWAP-Myc tumors (11/13) and TgWAP-Int3 samples (6/7), respectively. A distinguishing feature of these tumors (in particular Group VI) was the high expression of XBP1 (Figure 1c), which is a human luminal tumor-defining gene [14-17]. These tumors also expressed tight junction structural component genes, including Occludin, Tight Junction Protein 2 and 3, and the luminal cell K8/18 (Additional data file 2). IF for K8/18 and K5 confirmed that these tumors all exclusively expressed K8/18 (Figure 2b-f). Finally, Group IX (1/10 Brca1 Co/Co ;TgMMTV-Cre;p53 +/-, 4/7 TgWAP-T 121 tumors and 5/5 TgWAP-Tag tumors) and Group X (8/8 TgC3(1)-Tag) tumors were present at the far right and showed 'mixed' characteristics; in particular, the Group IX tumors showed some expression of luminal (Figure 1c), basal (Figure 1d) and mesenchymal genes (Figure 1f), while Group X tumors expressed basal (Figure 1e,f) and mesenchymal genes (Figure 1f,g). IF analyses showed that, as in humans [12,18], the murine basal-like models tended to express K5 while the murine luminal models expressed only K8/18. However, some of the murine basal-like models developed tumors that harbored nests of cells of both basal (K5+) and luminal (K8/18+) cell lineages. For example, in some TgMMTV-Wnt1 [19], DMBA-induced (Figure 2g,i), and Brca1-deficient strain tumors, distinct regions of single positive K5 and K8/18 cells were observed within the same tumor. Intriguingly, in some Brca1 Co/Co ;TgMMTV-Cre;p53 +/- samples, nodules of double-positive K5 and K8/18 cells were identified, suggestive of a potential transition state or precursor/stem cell population (Figure 2j), while in some TgMMTV-Wnt1 (Figure 2h) [19] and Brca1-deficient tumors, large regions of epithelioid cells were present that had little to no detectable K5 or K8/18 staining (data not shown). The reproducibility of these groups was evaluated using 'consensus clustering' (CC) [20]. CC using the intrinsic gene list showed strong concordance with the results sown in Figure 1 and supports the existence of most of the groups identified using hierarchical clustering analysis (Additional data file 4). However, our further division of some of the CC-defined groups appears justified based upon biological knowledge. For instance, hierarchical clustering separated the normal mammary gland samples (Group I) and the histologically distinct spindloid tumors (Group II), which were combined into a single group by CC. Groups VI (TgMMTV-Neu and PyMT) and VII (TgWAP-Myc) were likewise separated by hierarchical clustering, but CC placed them into a single category. CC was also performed using all genes that were expressed and varied in expression (taken from Additional data file 2), which showed far less concordance with the intrinsic list-based classifications, and which often separated tumors from individual models into different groups (Figure 3c, bottom most panel); for example, the TgMMTV-Neu tumors were separated into two or three different groups, whereas these were distinct and single groups when analyzed using the intrinsic list. This is likely due to the presence or absence of gene expression patterns coming from other cell types (that is, lymphocytes, fibroblasts, and so on) in the 'all genes' list, which causes tumors to be grouped based upon qualities not coming from the tumor cells [1]. Figure 3 Unsupervised cluster analysis of the combined gene expression data for 232 human breast tumor samples and 122 mouse mammary tumor samples. (a) A color-coded matrix below the dendrogram identifies each sample; the first two rows show clinical ER and HER2 status, respectively, with red = positive, green = negative, and gray = not tested; the third row includes all human samples colored by intrinsic subtype as determined from Additional data file 6; red = basal-like, blue = luminal, pink = HER2+/ER-, yellow = claudin-low and green = normal breast-like. The remaining rows correspond to murine models indicated at the right. (b) A gene cluster containing basal epithelial genes. (c) A luminal epithelial gene cluster that includes XBP1 and GATA3. (d) A second luminal cluster containing Keratins 8 and 18. (e) Proliferation gene cluster. (f) Interferon-regulated genes. (g) Fibroblast/mesenchymal enriched gene cluster. (h) The Kras2 amplicon cluster. See Additional data file 5 for the complete cluster diagram. Mouse-human combined unsupervised analysis The murine gene clusters were reminiscent of gene clusters identified previously in human breast tumor samples. To more directly evaluate these potential shared characteristics, we performed an integrated analysis of the mouse data presented here with an expanded version of our previously reported human breast tumor data. The human data were derived from 232 microarrays representing 184 primary breast tumors and 9 normal breast samples also assayed on Agilent microarrays and using a common reference strategy (combined human datasets of [21-23] plus 58 new patients/arrays). To combine the human and mouse datasets, we first used the Mouse Genome Informatics database to identify well-annotated mouse and human orthologous genes. We then performed a distance weighted discrimination correction, which is a supervised analysis method that identifies systematic differences present between two datasets and makes a global correction to compensate for these global biases [24]. Finally, we created an unsupervised hierarchical cluster of the mouse and human combined data (Figure 3 and Additional data file 5 for the complete cluster diagram). This analysis identified many shared features, including clusters that resemble the cell-lineage clusters described above. Specifically, human basal-like tumors and murine Brca1 +/-;p53 +/-;IR, Brca1 Co/Co ;TgMMTV-Cre;p53 +/-, TgMMTV-Wnt1, and some DMBA-induced tumors were characterized by the high expression of Laminin gamma 2, Keratins 5, 6B, 13, 14, 15, TRIM29, c-KIT and CRYAB (Figure 3b), the last of which is a human basal-like tumor marker possibly involved in resistance to chemotherapy [25]. As described above, the Brca1 +/-;p53 +/-;IR, some Brca1 Co/Co ;TgMMTV-Cre;p53 +/, DMBA-induced, and TgMMTV-Wnt1 tumors stained positive for K5 by IF, and human basal-like tumors tend to stain positive using a K5/6 antibody [1,12,18,26], thus showing that basal-like tumors from both species share K5 protein expression as a distinguishing feature. The murine and human 'luminal tumor' shared profile was not as similar as the shared basal profile, but did include the high expression of SPDEF, XBP1 and GATA3 (Figure 3c), and both species' luminal tumors also stained positive for K8/18 (Figure 2 and see [18]). For many genes in this luminal cluster, however, the relative level of expression differed between the two species. For example, some genes were consistently high across both species' tumors (for example, XBP1, SPDEF and GATA3), while others, including TFF, SLC39A6, and FOXA1, were high in human luminal tumors and showed lower expression in murine tumors. Of note is that the human luminal epithelial gene cluster always contains the Estrogen-Receptor (ER) and many estrogen-regulated genes, including TFF1 and SLC39A6 [22]; since most murine mammary tumors, including those profiled here, are ER-negative, the apparent lack of involvement of ER and most ER-regulated genes could explain the difference in expression for some of the human luminal epithelial genes that show discordant expression in mice. Several other prominent and noteworthy features were also identified across species, including a 'proliferation' signature that includes the well documented proliferation marker Ki-67 (Figure 3e) [1,27,28] and an interferon-regulated pattern (Figure 3f) [27]. The proliferation signature was highest in human basal-like tumors and in the murine models with impaired pRb function (that is, Group IX and X tumors). Currently, the growth regulatory impact of interferon-signaling in human breast tumors is not understood, and murine models that share this expression feature (TgMMTV-Neu, TgWAP-Tag, p53 -/- transplants, and spindloid tumors) may provide a model for future studies of this pathway. A fibroblast profile (Figure 3g) that was highly expressed in murine samples with spindloid morphology and in the TgWAP-Myc 'spindloid' tumors was also observed in many human luminal and basal-like tumors; however, on average, this profile was expressed at lower levels in the murine tumors, which is consistent with the relative epithelial to stromal cell proportions seen histologically. Through these analyses we also discovered a potential new human subtype (Figure 3, top line-yellow group, and Additional data file 6). This subtype, which was apparent in both the human only and mouse-human combined dataset, is referred to as the 'claudin-low' subtype and is characterized by the low expression of genes involved in tight junctions and cell-cell adhesion, including Claudins 3, 4, 7, Occludin, and E-cadherin (Figure 3d). These human tumors (n = 13) also showed low expression of luminal genes, inconsistent basal gene expression, and high expression of lymphocyte and endothelial cell markers. All but one tumor in this group was clinically ER-negative, and all were diagnosed as grade II or III infiltrating ductal carcinomas (Additional data file 7 for representative hematoxylin and eosin images); thus, these tumors do not appear to be lobular carcinomas as might be predicted by their low expression of E-cadherin. The uniqueness of this group was supported by shared mesenchymal expression features with the murine spindloid tumors (Figure 3g), which cluster near these human tumors and also lack expression of the Claudin gene cluster (Figure 3d). Further analyses will be required to determine the cellular origins of these human tumors. A common region of amplification across species The murine C3(1)-Tag tumors and a subset of human basal-like tumors showed high expression of a cluster of genes, including Kras2, Ipo8, Ppfibp1, Surb, and Cmas, that are all located in a syntenic region corresponding to human chromosome 12p12 and mouse chromosome 6 (Figure 3h). Kras2 amplification is associated with tumor progression in the C3(1)-Tag model [29], and haplo-insufficiency of Kras2 delays tumor progression [30]. High co-expression of Kras2-linked genes prompted us to test whether DNA copy number changes might also account for the high expression of Kras2 among a subset of the human tumors. Indeed, 9 of 16 human basal-like tumors tested by quantitative PCR had increased genomic DNA copy numbers at the KRAS2 locus; however, no mutations were detected in KRAS2 in any of these 16 basal-like tumors. In addition, van Beers et al. [31] reported that this region of human chromosome 12 is amplified in 47% of BRCA1-associated tumors by comparative genomic hybridization analysis; BRCA1-associated tumors are known to exhibit a basal-like molecular profile [3,32]. In cultured human mammary epithelial cells, which show basal/myoepithelial characteristics [1,33], both high oncogenic H-ras and SV40 Large T-antigen expression are necessary for transformation [34]. Taken together, these findings suggest that amplification of KRAS2 may either influence the cellular phenotype or define a susceptible target cell type for basal-like tumors. Mouse-human shared intrinsic features To simultaneously classify mouse and human tumors, we identified the gene set that was in common between a human breast tumor intrinsic list (1,300 genes described in Hu et al. [21]) and the mouse intrinsic list developed here (866 genes). The overlap of these two lists totaled 106 genes, which when used in a hierarchical clustering analysis (Figure 4) identifies four main groups: the leftmost group contains all the human basal-like, 'claudin-low', and 5/44 HER2+/ER- tumors, and the murine C3(1)-Tag, TgWAP-Tag, and spindloid tumors. The second group (left to right) contains the normal samples from both humans and mice, a small subset (6/44) of human HER2+/ER- and 10/92 luminal tumors, and a significant portion of the remaining murine basal-like models. By clinical criteria, nearly all human tumors in these two groups were clinically classified as ER-negative. Figure 4 Cluster analysis of mouse and human tumors using the subset of genes common to both species intrinsic lists (106 total genes). (a) Experimental sample associated dendrogram color coded according to human tumor subtype and with a matrix below showing murine tumor origins. (b) The complete 106 gene cluster diagram. (c) Close-up of genes known to be important for human basal-like tumors. (d) Close-up of genes known to be important for human luminal tumors, including ER. (e) Expression pattern of HER2/ERBB2/NEU. The third group contains 33/44 human HER2+/ER- tumors and the murine TgMMTV-Neu, MMTV-PyMT and TgWAP-Myc samples. Although the human HER2+/ER- tumors are predominantly ER-negative, this comparative genomic analysis and their keratin expression profiles as assessed by immunohistochemistry, suggests that the HER2+/ER- human tumors are 'luminal' in origin as opposed to showing basal-like features [18]. The fourth and right-most group is composed of ER-positive human luminal tumors and, lastly, the mouse TgWAP-Int3 (Notch4) tumors were in a group by themselves. These data show that although many mouse and human tumors were located on a large dendrogram branch that contained most murine luminal models and human HER2+/ER- tumors, none of the murine models we tested showed a strong human 'luminal' phenotype that is characterized by the high expression of ER, GATA3, XBP1 and FOXA1. These analyses suggest that the murine luminal models like MMTV-Neu showed their own unique profile that was a relatively weak human luminal phenotype that is missing the ER-signature. Presented at the bottom of Figure 4 are biologically important genes discussed here, genes previously shown to be human basal-like tumor markers (Figure 4c), human luminal tumor markers, including ER (Figure 4d), and HER2/ERBB2/NEU (Figure 4e). A comparison of gene sets defining human tumors and murine models We used a second analysis method called gene set enrichment analysis (GSEA) [35] to search for shared relationships between human tumor subtypes and murine models. For this analysis, we first performed a two-class unpaired significance analysis of microarray (SAM) [36] analysis for each of the ten murine groups defined in Figure 1, and obtained a list of highly expressed genes that defined each group. Next, we performed similar analyses using each human subtype versus all other human tumors. Lastly, the murine lists were compared to each human subtype list using GSEA, which utilizes both gene list overlap and gene rank (Table 2). We found that the murine Groups IX (p = 0.004) and X (p = 0.001), which comprised tumors from pRb-deficient/p53-deficient models, shared significant overlap with the human basal-like subtype and tended to be anti-correlated with human luminal tumors (p = 0.083 and 0.006, respectively). Group III murine tumors (TgMMTV-Wnt1 mostly) significantly overlapped human normal breast samples (p = 0.008), possibly due to the expression of both luminal and basal/myoepithelial gene clusters in both groups. Group IV (Brca1-deficient and Wnt1) showed a significant association (p = 0.058) with the human basal-like profile. The murine Group VI (TgMMTV-Neu and TgMMTV-PyMT) showed a near significant association (p = 0.078) with the human luminal profile and were anti-correlated with the human basal-like subtype (p = 0.04). Finally, the murine Group II spindloid tumors showed significant overlap with human 'claudin-low' tumors (p = 0.001), which further suggests that this may be a distinct and novel human tumor subtype. Table 2 Gene set enrichment analysis of the ten murine groups versus five human subtypes Basal-like Luminal HER2+/ER- Normal Claudin-low Mouse class No. of genes p value p value p value p value p value p value p value p value p value p value Is class I 1,882 - - 0.4625 0.8755 0.5388 0.9137 0.1659 0.5628 0.0048 0.1028 II 912 - - - - 0.5867 0.9609 - - 0.0021 0.001 III 143 0.5289 0.9048 - - 0.5285 0.9047 0 0.008 - - IV 1,019 0 0.0581 - - - - - - - - V 34 - - 0.8492 0.998 0.9324 0.999 - - 0.0427 0.09274 VI 820 - - 0.0062 0.0783 0.3536 0.7864 0.8653 0.9769 - - VII 851 0.1258 0.3768 - - 0.5616 0.9137 - - - - VIII 236 0.1449 0.6098 0.3483 0.8205 - - 0.01878 0.2349 - - IX 462 0.0019 0.004 - - 0.56 0.9509 - - - - X 338 0 0.001 - - 0.9275 0.998 - - - - Is not class I 1,882 0.0128 0.1662 - - - - - - - - II 912 0.3996 0.8348 0.8601 0.999 - - 0.3602 0.7655 - - III 143 - - 0.3178 0.7259 - - - - 0.7628 0.991 IV 1,019 - - 0.1833 0.6516 0.398 0.8427 0.2241 0.7255 0.1453 0.6116 V 34 0.86 1 - - - - 0.0656 0.1653 - - VI 820 0 0.04 - - - - - - 0.1043 0.4444 VII 851 - - 0.1733 0.5151 - - 0.5403 0.9128 0.1628 0.5215 VIII 236 - - - - 0.1131 0.5305 - - 0.6427 0.961 IX 462 - - 0.04305 0.0833 - - 0.022 0.037 0.2612 0.5936 X 338 - - 0.02236 0.0682 - - 0.1313 0.3717 0.5437 0.9489 Statistically significant findings are highlighted in bold. NOM = nominal. We also performed a two-class unpaired SAM analysis using each mouse model as a representative of a pathway perturbation using the transgenic 'event' as a means of defining groups. Models that yielded a significant gene list (false discovery rate (FDR) = 1%) were compared to each human subtype as described above (Additional data file 8). The models based upon SV40 T-antigen (all C3(1)-Tag and WAP-Tag tumors) shared significant overlap with the human basal-like tumors (p = 0.002) and were marginally anti-correlated with the human luminal class. The BRCA1 deficient models (all Brca1+/-;p53 +/- IR and Brca1 Co/Co ;TgMMTV-Cre;p53 +/- tumors) were marginally significant with human basal-like tumors (p = 0.088). The TgMMTV-Neu tumors were nominally significant (before correction for multiple comparisons) with human luminal tumors (p = 0.006) and anti-correlated with human basal-like tumors (p = 0.027). The two most important human breast tumor biomarkers are ER and HER2; therefore, we also analyzed these data relative to these two markers. Of the 232 human tumors assayed here, 137 had ER and HER2 data assessed by immunohistochemistry and microarray data. As has been noted before [3,18,21], there is a very high correlation between tumor intrinsic subtype and ER and HER2 clinical status (p 30. The log2 ratio of Cy5/Cy3 was then reported for each gene. In the final dataset, only genes that reported values in 70% or more of the samples were included. The genes were median centered and then hierarchical clustering was performed using Cluster v2.12 [56]. For the murine unsupervised analysis, and human-mouse unsupervised cluster analyses, we filtered for genes that varied at least three-fold or more, in at least three or more samples. Average linkage clustering was performed on genes and arrays and cluster viewing and display was performed using JavaTreeview v1.0.8 [57]. Mouse Intrinsic gene set analysis Intrinsic 'groups' of experimental samples were chosen based upon having a Pearson correlation value of 0.65 or greater from the unsupervised clustering analysis of the 122 murine samples. The analysis was performed using the Intrinsic Gene Identifier v1.0 by Max Diehn/Stanford University [1]. Technical replicates were removed from the file and the members of every highly correlated node were given identical class numbers, giving every sample that fell outside the 0.65 correlation cut-off a class of their own. Using these criteria, 16 groups of samples were identified (see Additional data file 1 for these groups) and a list of 866 'intrinsic' genes was selected using the criteria of one standard deviation below the mean intrinsic gene value. A human intrinsic list of 1,300 genes was created using a subset of 146 of the 232 samples used here, and is described in Hu et al. [21]. Consensus clustering CC [20] was performed locally using Gene Pattern 1.3.1 (built Jan 6, 2005), which was downloaded from the Broad Institute distribution website [58]. Analyses were performed on the mouse dataset with all genes, and just with intrinsic genes separately. Ranges for the number of K clusters (or the focused number of classes) were from 2 to 15 to evaluate a wide range of possible groups. Using a Euclidian distance measure with average linkage, we re-sampled 1,000 times with both column and row normalization. Combining murine and human expression datasets Orthologous genes were reported by Mouse Genome Informatics (MGI 3.1) of The Jackson Laboratory. For both the human and murine datasets, Locus Link IDs assigned to Agilent oligo probe ID numbers were used to assign to MGI ID numbers. In cases where a single gene was represented by multiple probes, the median value of the redundant probes was used. This led to a total of orthologous pairings of 14,680 Agilent probes. Prior to combining the two datasets, each was column standardized to N(0,1), row median centered, and probe identifiers were converted to MGI IDs. The intersection of mouse and human MGI identifiers from genes that passed filters (same as used above) in both datasets yielded 7,907 orthologous genes in the total combined dataset. This dataset was next corrected for systemic biases using distance weighted discrimination [24]. Finally, the combined dataset was used for an average linkage hierarchical clustering analysis. Gene set enrichment analysis We took the 232 human samples and classified them as basal-like, luminal, HER2+/ER-, claudin-low, and normal breast-like according to a clustering analysis of the human dataset only (Additional data file 6), using the new intrinsic/UNC human gene list developed in Hu et al. [21]. Second, the murine samples were also classified based upon their clustering pattern in Figure 1 that used the mouse intrinsic gene list, and were assigned to Groups I-X. Two-class unpaired SAM analysis was performed for each murine class separately versus all other classes using an FDR of 1% [36], resulting in 10 class-specific gene lists. Using only the set of highly expressed genes that were associated with each analysis (and ignoring the genes whose low expression correlated with a given class), GSEA [35] was performed in R (v. 2.0.1) using the GSEA R package [59]. The ten murine gene sets were then compared to each human subtype-ranked gene set and significant enrichments reported. For statistical strength of these enrichments, GSEA uses family wise error rate (FWER) to correct for multiple testing and FDR to reduce false positive reporting. The parameters used for all GSEA were: nperm = 1,000, weighted.score.type = 1, nom.p.val.threshold = -1, fwer.p.val.threshold = -1, fdr.q.val.threshold = 0.25, topgs = 12, adjust.FDR.q.val = FALSE, gs.size.threshold.min = 25, gs.size.threshold.max = 2,000, reverse.sign = FALSE, preproc.type = 0, random.seed = 3,338, perm.type = 0, fraction = 1, replace = FALSE. Immunofluorescence Paraffin-embedded sections (5 μm thick) were processed using standard immunostaining methods. The antibodies and their dilution were α-cytokeratin 5 (K5, 1:8,000, PRB-160P, Covance, Berkeley, CA, USA), and α-cytokeratins 8/18 (Ker8/18, 1:450, GP11, Progen Biotecknik, Heidelberg, Germany). Briefly, slides were deparaffinized and hydrated through a series of xylenes and graded ethanol steps. Heat-mediated epitope retrieval was performed in boiling citrate buffer (pH 6.0) for 15 minutes, then samples cooled to room temperature for 30 minutes. Secondary antibodies for immunofluorescence were conjugated with Alexa Fluor-488 or -594 fluorophores (1:200, Molecular Probes, Invitrogen, Carlsbad, CA, USA). IF samples were mounted with VectaShield Hardset with DAPI mounting media (Vector, Burlingame, CA, USA). Human KRAS2 amplification assay We performed real-time quantitative PCR and fluorescent melting curve analyses using genomic DNAs from 16 basal-like tumors, a normal breast tissue sample, 2 leukocyte DNA, and 3 luminal tumors. DNA was extracted using the DNAeasy kit (Qiagen) and amplification was performed on the LightCycler using the following temperature parameters: 95°C, 8 minutes; 50 cycles of 57°C, 6 s; 72°C, 6 s; 95°C, 2 s; followed by cooling to 60°C and a 0.1°C/s ramp to 97°C. Each PCR reaction contained 7.5 ng template DNA in a 10 μl reaction using the LightCycler Faststart DNA Master SYBR Green I kit (Roche Applied Science, Indianapolis, IN, USA). Relative DNA copy number for each gene was determined by importing an external efficiency curve and using a 'normal' breast sample for a within-run calibrator. For each sample, the copy number for KRAS2 was divided by the average copy number of ACTB and G1P3. Amplification in any tumor was called if the relative fold change was greater than three standard deviations above the average of five control samples (two normal leukocyte samples and three luminal tumors). Additional data files The following additional data are available with the online version of this paper. Additional data file 1 is a table listing mouse tumor and normal sample associated data, including source, transgene and promoter information. Additional data file 2 is a complete unsupervised cluster diagram of all mouse tumors. Samples are colored according to mouse model from which they were derived, and the genes were selected using a variation filter of three-fold or more on three or more samples. Additional data file 3 ia a complete mouse models cluster diagram using the 866 gene murine intrinsic gene list. Additional data file 4 provides CC analyses applied to the mouse models. (a) CC matrices generated using the 866 gene mouse intrinsic list, by cluster numbers K = 2 through K = 15. (b) Empirical cumulative distribution (CDF) plot corresponding to the consensus matrices in the range K = 2 to 15. (c) CC directly compared to the hierarchical clustering-based results. The dendrogram from Figure 1 (using the intrinsic gene set) is shown and immediately below is a colored matrix showing sample assignments based upon the various number of K clusters from the CC. By comparison, the analysis performed on the mouse dataset using all genes (bottom matrix) is presented. Additional data file 5 is a complete unsupervised cluster diagram of the combined gene expression patterns of 232 human breast tumor samples and 122 mouse mammary tumor samples. This unsupervised cluster analysis is based upon the orthologous gene overlap between the human and mouse microarrays, and then we selected for the subset of genes that varied three-fold or more on three or more arrays. Additional data file 6 shows a cluster analysis of the 232 human samples using the human intrinsic/UNC gene set from Hu et al. [21]. This analysis was used to determine a human samples subtype (basal-like, luminal, HER2+/ER-, and so on), which was then used in the various SAM and GSEA analyses. Samples are colored according to their subtype: red = basal-like, blue = luminal, pink = HER2+/ER-, yellow = claudin-low and green = normal breast-like. Additional data file 7 shows a histological characterization of six different human 'claudin-low' tumors using hematoxylin and eosin sections. Additional data file 8 shows GSEA of murine pathway models versus five human subtypes. Additional data file 9 shows GSEA of ten murine classes versus clinical ER status and HER2 status in ER negative patients. Additional data file 10 shows GSEA of murine pathway models versus clinical ER status and HER2 status in ER negative patients. Supplementary Material Additional data file 1 Mouse tumor and normal sample associated data including source, transgene and promoter information. Click here for file Additional data file 2 Samples are colored according to mouse model from which they were derived, and the genes were selected using a variation filter of three-fold or more on three or more samples. Click here for file Additional data file 3 Complete mouse models cluster diagram using the 866 gene murine intrinsic gene list. Click here for file Additional data file 4 (a) CC matrices generated using the 866 gene mouse intrinsic list, by cluster numbers K = 2 through K = 15. (b) Empirical cumulative distribution (CDF) plot corresponding to the consensus matrices in the range K = 2 to 15. (c) CC directly compared to the hierarchical clustering-based results. The dendrogram from Figure 1 (using the intrinsic gene set) is shown and immediately below is a colored matrix showing sample assignments based upon the various number of K clusters from the CC. By comparison, the analysis performed on the mouse dataset using all genes (bottom matrix) is presented. Click here for file Additional data file 5 This unsupervised cluster analysis is based upon the orthologous gene overlap between the human and mouse microarrays, and then we selected for the subset of genes that varied three-fold or more on three or more arrays. Click here for file Additional data file 6 This analysis was used to determine a human samples subtype (basal-like, luminal, HER2+/ER-, and so on), which was then used the various SAM and GSEA analyses. Samples are colored according to their subtype: red = basal-like, blue = luminal, pink = HER2+/ER-, yellow = claudin-low and green = normal breast-like. Click here for file Additional data file 7 Histological characterization of six different human 'claudin-low' tumors using hematoxylin and eosin sections. Click here for file Additional data file 8 GSEA of murine pathway models versus five human subtypes. Click here for file Additional data file 9 GSEA of ten murine classes versus clinical ER status and HER2 status in ER negative patients. Click here for file Additional data file 10 GSEA of murine pathway models versus clinical ER status and HER2 status in ER negative patients. Click here for file
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              Genome Remodeling in a Basal-like Breast Cancer Metastasis and Xenograft

              Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumor progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumor, a brain metastasis, and a xenograft derived from the primary tumor. The metastasis contained two de novo mutations and a large deletion not present in the primary tumor, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumor mutations, and displayed a mutation enrichment pattern that paralleled the metastasis (16 of 20 genes). Two overlapping large deletions, encompassing CTNNA1, were present in all three tumor samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared to the primary tumor suggest that secondary tumors may arise from a minority of cells within the primary.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                19 May 2014
                3 April 2014
                03 October 2014
                : 508
                : 7494
                : 113-117
                Affiliations
                [1 ]Jake Gittlen Laboratories for Cancer Research, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
                [2 ]Penn State Hershey Cancer Institute, Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
                [3 ]Department of Medicine (Hematology/Oncology), Pennsylvania State University College of Medicine, Hershey, PA 17033 USA
                Author notes
                [* ]To whom correspondence should be addressed: Edward J. Gunther, M.D., Associate Professor of Medicine and Jake Gittlen Laboratories for Cancer Research, Pennsylvania State University College of Medicine, Biomedical Research Building, H059, 500 University Drive, Hershey, PA 17033, Telephone: (717) 531-7022, Fax: (717) 531-5634, ejg12@ 123456psu.edu
                Article
                NIHMS571501
                10.1038/nature13187
                4050741
                24695311
                d55cc07b-4ded-44c9-9fda-f83fe6ef047c
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