- Open Access
A molecular map of mesenchymal tumors
- Stephen R Henderson1Email author,
- David Guiliano†1, 2,
- Nadege Presneau†1,
- Sean McLean1,
- Richard Frow1, 3,
- Sonja Vujovic1,
- John Anderson4,
- Neil Sebire5,
- Jeremy Whelan6,
- Nick Athanasou7,
- Adrienne M Flanagan3 and
- Chris Boshoff1
© Henderson et al; licensee BioMed Central Ltd. 2005
- Received: 31 March 2005
- Accepted: 26 July 2005
- Published: 26 August 2005
Bone and soft tissue tumors represent a diverse group of neoplasms thought to derive from cells of the mesenchyme or neural crest. Histological diagnosis is challenging due to the poor or heterogenous differentiation of many tumors, resulting in uncertainty over prognosis and appropriate therapy.
We have undertaken a broad and comprehensive study of the gene expression profile of 96 tumors with representatives of all mesenchymal tissues, including several problem diagnostic groups. Using machine learning methods adapted to this problem we identify molecular fingerprints for most tumors, which are pathognomonic (decisive) and biologically revealing.
We demonstrate the utility of gene expression profiles and machine learning for a complex clinical problem, and identify putative origins for certain mesenchymal tumors.
- Feature Selection
- Neural Crest
- Additional Data File
- Gene Expression Microarray
- Malignant Peripheral Nerve Sheath Tumor
Tumors of bone and soft tissue are a wide spectrum of benign and malignant neoplasms (sarcoma) derived from mesenchymal precursor cells (hereafter referred to as mesenchymal tumors) [1, 2]. Many show heterogeneous patterns of differentiation or exhibit little similarity to differentiated mesenchymal tissues, while others have diverse cellular morphology (pleomorphism). Thus, specialist expertise is required for diagnosis as the histopathology of mesenchymal tumors is often overlapping or indistinct. With the introduction of neo-adjuvant cytotoxic therapies, diagnosis has become even more challenging as pathologists must rely increasingly upon needle core biopsies that produce only small quantities of tissue for immunohistochemistry and histopathological diagnosis. Furthermore, molecular therapies have been developed targeting oncogenic pathways that may transcend the current histopathological categories.
The discovery of definitive oncogenic gene fusions for certain mesenchymal tumors has aided pathologists greatly. These include the EWS-ERG or EWS-FLI1 fusion transcripts for Ewing's sarcoma (EWS) [3–5], or the SYT-SSX fusion transcript for synovial sarcoma [6, 7]. Also, reverse transcriptase polymerase chain reaction (RT-PCR) has become the gold standard for diagnosis. These 'simple sarcomas' are ideal candidates for targeted therapy, with relatively stable karyotypes and stereotypical molecular pathology . Nonetheless, chromosomal translocations are confirmatory for only a fraction of mesenchymal tumors while those with complex karyotypes remain diagnostically challenging.
There are many gene expression microarray (GEM) studies covering a range of mesenchymal tumors [9–28]. These studies have proved the general applicability of GEM for the diagnosis of mesenchymal tumors. Yet each study compares a fraction of the mesenchymal tumors in isolation. They do not address the spectra of disease nor the challenges of diagnostic pathology, which deals with many confounding diagnoses. Here we present a more comprehensive study encompassing representative tumors derived from all mesenchymal tissue types, as well as more poorly differentiated tumors.
Using a machine learning model, we assess the overall utility of GEM for the diagnosis of 19 types of mesenchymal tumors. We find both expected and unexpected relationships between certain mesenchymal tumors, and ascertain expression fingerprints for decisive diagnostic or pathognomonic features for most tumor classes. These fingerprints give clues to the etiology of several mesenchymal tumors.
Our machine learning model fits the data in two steps. The first step incorporates biological assumptions by merging related tumors into broader groups. Step two splits the broader groups into specific tumors. This latter process is derived by expert decision, rather than automated model selection. We consciously mirror the clinical diagnostic method of progressively resolving general differentiation types into specific tumors in a branch-wise (or tree-like) method. The use of a decision process structured by prior knowledge (together with a well-suited feature selection algorithm) gives our model an estimated error of about 0.1. Although further study and validation will be required to bring it to clinical use, we believe this method is a prototype for the extension of GEM and machine learning to other complex diagnostic problems. The GEM data from this study are available from the European Bioinformatics Institute (EBI) public repository ArrayExpress (accession no. E-MEXP-353) .
The differentiated tumors largely cluster in groups reflecting common tissue types. Examples are the neurofibroma (NFB) and schwannoma (SWN), the alveolar (ARMS) and embryonal rhabdomyosarcoma (ERMS), and the well-differentiated liposarcoma (WLS) and lipoma (LMA). Similarities in the GEM profiles of malignant peripheral nerve sheath tumors (MPNST) and monophasic synovial sarcoma (MSS) were previously reported and this relationship is supported in our data . Likewise, the relation between the small round blue cell tumors: EWS, ARMS and ERMS is clear from this plot. An interesting finding was the similarity of the chordoma (CMA), a rare tumor arising along the midline to the chondrosarcoma (CHS). Reflecting the experience of histopathological diagnosis, the osteosarcoma (OS), pleomorphic sarcoma (PMS) and leiomyosarcoma (LMS) show the greatest diversity being dispersed throughout the other samples.
The MDS is based on distances calculated from the whole molecular signature (all genes or probesets) (Figure 1). Thus, confounding factors such as intercalating tissue, tumor site or patient age and sex may affect the resultant pattern.
The key to the success of supervised learning is to focus on a small set of strongly distinguishing features, thus ignoring confounding factors. Strong feature selection is appropriate in this study as we have sound histological evidence that there are underlying patterns to be uncovered (at least for most tumors). This reduced set of features (or genes) gives a molecular fingerprint of the tumors that is both diagnostic and descriptive. Our feature selection method is suited to this application as it selects ten genes evenly for each class. Using a simple k = nearest neighbors (k-NN) machine learning algorithm and the extended feature selection algorithm we attempted initially to create a model for all tumors simultaneously (see Materials and methods section for more detail on feature selection and k-NN algorithm). The overall cross-validation error was 0.33 compared with a random guessing error of 0.96 +/- S.E. 0.01 (see Materials and methods: classification algorithm). Therefore, even with this simplest approach, the machine learning model is fairly successful.
Errors of the simplest machine learning model
Model (n = 96)
Error +/- S.E.
Random guessing (n = 10)
0.93 +/- 0.01
Re-permuted and cross-validated (n = 10)
0.94 +/- 0.01
The majority of errors from the simple model above were as expected between the adipocytic tumors LMA, WLS and myxoid liposarcoma (MLS), between MPNST and MSS, and between LMS, PMS and de-differentiated chondrosarcoma (DCS). By considering unsupervised learning methods (MDS and hierarchical clustering) plus biological assumptions and trial-and-error, we arrived at an improved two-step method. This was designed, in part, to mirror the thought processes involved during clinical histopathological diagnosis. This two-step model is, thus, expert derived and not an automated procedure.
Frequency table of errors
Errors of two-step machine learning model
Error (absolute number)
All sarcoma (n = 96)
MSS and MPNST (n = 14)
NFB and SWN (n = 8)
ARMS and ERMS (n = 7)
LMA/WLS and MLS (n = 12)
We demonstrate here the feasibility of using GEM and machine learning to aid the diagnosis of mesenchymal tumors. In contrast to previous studies, our work encompasses a wide range of mesenchymal neoplasms and reflects the open-ended nature of histopathological diagnosis. It is clear from unsupervised methods such as MDS that there is information within the expression profile that can be further refined (Figure 1). This complex problem is surprisingly tractable firstly because of the large number of definitive gene markers associated with many of the tumors, especially the well-differentiated groups. In machine learning terminology we may state that these genes (or features) have no overlap in their class distributions, while in histopathological parlance their expression is pathognomonic. This would be rare in prognostic studies, for instance, as certain patients with good molecular signatures may still have poor outcomes due to unobserved factors. Secondly, we have used a novel machine learning strategy breaking this complex problem into soluble steps. We have implicitly admitted limitations in our model by not attempting to further decompose groups such as LMS/PMS/DCS (spindle-like tumors) and WLS/LMA. Thirdly, we have used a feature selection strategy that captures information evenly on all the tumor groups. Further validation on prospective samples is required before GEM studies are of clinical use for the diagnosis of mesenchymal tumors.
Cross-validation of the first step of our model gives 0.09 errors. This is a useful guide but its accuracy should not be overstated. A reliable estimate of error usually requires hundreds of samples per class, and a leave-one-out estimation is likely to underestimate error . Moreover, there is the problem of statistically uncontrollable bias in our wide but shallowly sampled dataset . However, our model is not grossly over-fitted (see Table 2) and more importantly the generalization to other datasets is clear from the biology of the molecular fingerprints (arising from feature selection). These fingerprints may be useful as the basis of a custom diagnostic chip and also provide insight into the etiology and cell of origin of certain tumors (see Additional data file 2 (sheets a and b online).
Several of the fingerprints are clearly related to the metabolism or function of differentiated mesenchymal tissues. For instance, adipocytic tumors are identifiable by their lipid-associated genes such as perilipin (PLIN), lipoprotein lipase (LPL), and glycerol-3-phosphate dehydrogenase 1 (GPD1) (Figure 3a). Similarly, rhabdomyosarcoma are characterized by genes such as cholinergic receptor alpha (CHRNA1) and receptor-associated protein of the synapse (RAPSN) associated with the musculoskeletal synaptic junction (see Figure 3b and Additional data file 2 (sheet a) online for supporting references).
When resolving these classes further in a second classification step some of the fingerprints reflect degrees of differentiation. Thus, the ARMS is defined by its higher expression of tropomyosin-2 (TPM2), skeletal muscle actin (ACTA1) and myosin-light polypeptide-4 (MYL4), indicative of more maturity than the ERMS (see Figure 4a and 4b and supporting references in Additional data file 2 (sheet b) online).
Within these highly focused signatures there are clues to the origins of some of the poorly differentiated tumors. One striking similarity of gene expression is between the MPNST and the MSS (see Additional data file 1). The MSS have a simple karyotype consisting of an aberrant SYT-SSX fusion. The MPNST have a complex karyotype and are believed to arise from Schwann cell precursors (they are more malignant than SWN and frequently associated with NF1 mutations ) derived from the neural crest rather than the mesenchyme . Both MPNST and MSS are aggressive and poorly differentiated. Their composite signature (Figure 3g) contains the gene Endothelin-3 (EDN3), a key molecule in the development of the neural crest. EDN3 promotes self-renewal of multi-potent neural crest precursors or de-differentiation of matured cell types including Schwann cells [33–35].
Despite their similarity, the MPNST and MSS have distinct molecular signatures (Figure 4c and 4d) as do the NFB and SWN (Figure 4e and 4f), which are typically difficult to distinguish immunohistochemically. For instance, SWN but not NFB express the secreted glycoprotein WNT5A recently linked to metastases of OS  and melanoma . Yet SWN is a benign tumor that never metastasizes.
Some genes could make useful monoclonal antibodies for immunohistochemistry. Genes such as the myxoid liposarcoma-associated transcript-4 (MLAT4), which as its name suggests distinguishes MLS from WLS/LMA (Figure 4g and 4h), appear to have been identified in molecular screens but not pursued as markers thus far. The overall expression of MLS is intriguing as it shows similarities to the putative neural crest tumors MPNST and MSS and the small round blue cell tumors ERMS, ARMS and EWS. Similarly, the fingerprint itself contains early mesenchymal and neural development genes (EMX2, SOX11), and neural restricted genes (SHANK2; a post-synaptic molecule). The profile also confirms previous reports of the expression of the immunotherapeutic targets PRAME and CTAG-1 (also known as NY-ESO-1) . These are so-called 'germ cell antigens' because of their restriction to the testes of healthy males.
There is another clue to cellular origin within the CMA fingerprint. The overall expression pattern of CMA is closely related to the chondroblastic tumors CHS and chondroblastoma (CHB) (Figure 1). Likewise, the CMA are known to commonly contain focal regions of chondroid differentiation or more rarely chondrosarcomatous elements. As the tumor occurs solely along the midline it is proposed to originate from remnant notochord, an embryonic structure that is known to persist at least into infancy [39, 40]. The expression of the brachyury (T) which is highly expressed in developing notochord strongly supports this theory [41, 42].
Not all tumors had distinctive markers. The LMS, PMS and DCS were not distinguishable from each other. Cross-validation of our model in step one was fairly successful as our algorithm collectively recognized these tumors through a lack of specific markers, perhaps analogous to the way in which histopathologists recognize them (Figure 3l). At least part of the success of our model in step one is that it incorporated this uncertainty implicitly by compositing these tumors into a single 'spindle-like' group.
There are a number of extensions to the current study that may bring GEM technology closer to clinical use. Firstly, a larger study focusing on the poorly modeled cases (PMS/LMS/DCS) may help to elucidate categories supplementing current histological guidelines. Yet the pleomorphism and complex karyotypes of some mesenchymal tumors may confer a continuum of molecular pathology irreducible to simple categories. Such an analysis is unlikely to find adherents if it does not correspond with an improved interpretation of the histopathology or with appreciable clinical differences such as prognosis.
Secondly, by sampling all groups more thoroughly we could manage uncertainty. Our model gives a simple prediction of tumor type appropriate for the broad base and low sample numbers in our dataset. A more nuanced approach would be to calculate a probability of tumor type. This could be achieved most simply using Bayesian classifiers that implicitly calculate class membership probability. Thus in a clinical setting a sarcoma could be identified to a histopathologist as a central or classical example of its type, or be highlighted as an uncertain outlier for further immunohistochemical examination.
Thirdly, there is a large quantity of relevant molecular information that may transcend the diagnostic categories investigated here, yet may have prognostic significance. There is great interest, for instance, in both drug-resistance genes  and signatures associated with metastasis , which are common to a number of different tumor groups. Finally, a custom chip and user-friendly software that incorporates both a predictive model and such additional knowledge needs to be developed. This package might incorporate a prediction algorithm, visualization tools, plus additional housekeeping genes to aid normalization and quality controls. The molecular fingerprints shown here and fully listed in the additional data files (Figure 3 and 4 and Additional data file 2 online) could form the core of such a custom chip.
Tumor biopsies were obtained from 96 participants presenting at the London Bone and Soft Tissue Tumour Service (Royal National Orthopaedic Hospital, Stanmore and University College London Hospitals, London), Great Ormond Street Hospital, London, or the Nuffield Orthopaedic Center, Headington, Oxford, in the UK. The diagnosis was determined by pathological examination using criteria established by the World Health Organization (WHO) tumor classification of soft tissue and bone tumors . Where necessary, RT-PCR was performed to confirm common translocations (such as EWS, ARMS, MLS and synovial sarcoma). Ethical committee approval was obtained from all three treatment centers for the collection of fresh samples for this study. Clinical data such as diagnosis, site, grade and stage, age and sex are summarized in Additional data file 3 online. Patient biopsies were snap frozen with liquid nitrogen prior to RNA extraction and stored at -70°C.
Nucleic acid extraction
Frozen sections from each tumor sample (needle core or resection) were examined microscopically prior to RNA extraction to confirm that the sample was representative and contained more than 80% tumor cells. Total RNA was extracted from adjacent tissue sections with >80% tumor cell content, using TRIzol™ reagent (Invitrogen Ltd, Paisley, UK) followed by purification with RNeasy columns (Qiagen Ltd, Sussex, UK) according to the manufacturer's instructions. RNA quality and quantity was assessed using RNA 6000 Nano chips on Agilent 2100 Bioanalyser according to manufacturer's instructions (Agilent Technologies UK Ltd, Cheshire, UK). Our RNA quality-control threshold for the rRNA peak ratio was 28s/18s ≤ 2.
The biotinylated hybridization target (biotin cRNA) was prepared from 10 μg of total RNA as previously described [46, 47]. The quality and quantity of the biotinylated cRNA was checked prior to hybridization using the RNA 6000 Nano chips on Agilent 2100 Bioanalyser according to manufacturer's instructions. A total of 20 μg of the biotinylated probe was hybridized to Affymetrix HG-U133A Human GeneChips (Affymetrix®, Santa Clara, CA, USA) according to manufacturer's recommendations. In cases where smaller amounts of total RNA were obtained (EWS and rhabdomyosarcoma <5 μg total RNA), 150 ng of total RNA was subjected to two rounds of amplification to obtain a biotinylated cRNA yield of 20 μg according to Affymetrix recommendations (Genechip® Eukaryotic small sample target labeling assay version II). Parallel hybridizations of probe synthesized from 10 μg and 150 ng (via single and double amplification respectively) for the same tumor sample were found to have similar quality-control and expression profiles (see ARMS, EWS samples in Figure 1). Hybridization of the synthesized biotinylated probes and scanning of the images were performed as previously reported according to Affymetrix recommendations [46, 47].
Data analysis was carried out using the R statistical environment and programming language . We extensively used R software packages from Bioconductor , an open source bioinformatics resource. We used the 'affy' package written to handle Affymetrix data, and specifically the 'rma' algorithm for pre-processing, normalizing and calculation of expression values [50, 51]. A modified version of the 'ipred' package was used for cross-validation and machine learning  together with the 'limma' package which was used for feature selection  (both described more fully below).
Hierarchical clustering and MDS
The Cluster and Treeview software packages were used to produce the average linkage hierarchical clustering shown in Figure 3 and 4 (correlation distance) . MDS was chosen for Figure 1 instead of hierarchical clustering as this captured the complexity of the data optimally within the space available. We used the classical MDS method (or principle coordinates method part of the R 'base' package) . The stress metric measures distortion required to plot high-dimensional data .
We chose simple k-NN pattern classification to model the data. This is a non-parametric algorithm, here based upon a table of inter-sample Euclidean distances . The identity of an unknown (or test) sample is attributed by majority vote to that of the k = 3 nearest neighbors. We chose k = 3 due to the limiting minimum of three samples in our smallest class (DCS). The k-NN method was comparably effective to other methods tested (linear models, support vector machine, Bayes classifier; results not shown). We used leave-one-out cross-validation to estimate the error of our model. This method iteratively builds a predictive model from all combinations (or folds hereafter) of the data excepting one sample. The excepted sample is used to test the model error, the error being the proportion of inaccuracies from all folds. The multiple tumor classes and the limited number of samples in each class are unsuitable for accurate estimation of the classification error. This is therefore only a useful guide as the fingerprints are more important than the error. As a guide only, we used a permutation test to calculate the baseline error that might be expected from random guessing. Simply, this involves randomly permuting the class labels of the data ten times and counting the fraction of correctly corresponding labels with the original labels. To assess potential over-fitting we repeated this random permuting of the data class labels ten times but asked the computer to model the data and cross-validate, each time counting the fraction of errors. This does not, however, demonstrate a lack of over-fitting but merely a lack of gross over-fitting.
As most expression data are uninformative, or even confounding of pathological categories, feature selection is incorporated into each fold of cross-validation. For multi-class models we used a well-suited feature selection method to capture information from each of the 19 tumor classes (not to split classes in step two). The algorithm selects 10 genes for each class that are different in expression from all others. We tested 5, 10, 20 and 30 gene sets finding 10 to be sufficient (better than 5 or 30 and as good as 20). We used the 'limma' method for each comparison . Limma uses a variant of linear models with an empirically moderated estimate of the standard error effectively borrowing information from the ensemble variance of genes to aid inference about individual genes. This gives improved statistical power for small sample sizes.
The following additional data are included with the online version of this article: an average linkage hierarchical clustering of the tumor samples using the same distance matrix as in Figure 1 (additional data file 1), a table providing the gene information for Figures 3 and 4 (additional data file 2) and a table containing clinical and pathological details of all samples used in this study (additional data file 3).
This work was supported by Cancer Research UK, The Wellcome Trust, the Adam Dealey Fund and the Royal National Orthopaedic Hospital NHS Trust Research and Development Fund. We thank Torsten Hothorn for discussion and assistance with adaptation of the 'ipred' software package.
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