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Molecular Biology, Pathobiology, and Genetics |
1 Ian Potter Centre for Cancer Genomics and Predictive Medicine, Departments of 2 Haematology and Medical Oncology, and 3 Pathology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, Victoria, Australia; 4 Diagnostics and Pathology, Genentech Inc., South San Francisco, California; 5 Department of Medical Oncology, St. Vincent's Hospital, Darlinghurst and School of Medicine, University of New South Wales, Sydney, Australia; 6 Cancer Research Program, Garvan Institute of Medical Research, St. Vincent's Hospital, Darlinghurst, Australia; 7 Department of Thoracic Medicine, Prince Charles Hospital, Chermside, Australia; and 8 Department of Pathology, Stanford University, Stanford, California
Requests for reprints: David Bowtell, Ian Potter Centre for Cancer Genomics and Predictive Medicine, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett Street, Melbourne, Victoria 8006, Australia. Phone: 61-3-96561356; Fax: 61-3-96561414; E-mail: d.bowtell{at}petermac.org.
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Introduction |
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In 3% to 5% of new cancer cases, the site of origin of a tumor cannot be readily identified, or a diagnosis of origin is equivocal (7). This disease manifestation is known as a cancer of unknown primary (CUP). These tumors represent a clinically diverse group, typically presenting with moderately to poorly differentiated tumors, often adenocarcinoma, involving multiple organs including liver, bone, lung, lymph nodes, pleura, and brain (8, 9). Patients with CUP represent a disproportionate fraction of cancer deaths due to their poor median survival, often measured in months (9). In many cases, patients receive a series of sequential treatments before a response, if any, is obtained. A large proportion of cases remain undiagnosed (10), with the result that therapy cannot be matched to their specific disease.
Gene expression profiling, applied as a site of origin diagnostic, seems likely to be one of the first areas of oncology where a microarray test influencing patient management could be applied, but important questions have yet to be resolved regarding design of such an assay. Recently, preexisting gene expression data sets have been combined to create very large databases that can be used to validate novel classification algorithms (11, 12). These studies are predicated on the belief that the use of an ever increasing number of samples will improve classification, although the influence of training set size has not been systematically evaluated. Likewise, previous studies have investigated major histopathologic types of tumors (1, 2) but have not investigated the importance of the inclusion of subtypes, such as histologic variants of ovarian cancer or estrogen receptor–positive and –negative breast tumors, in training sets. Finally, although metastatic tumors from known primary carcinoma have been correctly identified (1, 2, 12), no study has examined the efficacy of such tests for predicting site of origin of CUP, benchmarked against clinical diagnostic variables.
An additional consideration when developing any expression-based diagnostic is the clinical utility of the platform in terms of its ease of use and application to formalin-fixed paraffin-embedded (FFPE) tissue. Although microarray technology has matured considerably, it still has relatively lengthy protocols, with multiple enzymatic steps taking up to 3 days to complete. A number of studies have shown that accurate classification of multiple cancer types can be made using a reduced number of genes (2, 3, 11, 12). Hundreds rather than thousands of genes are therefore likely to be sufficient, indicating that a classification can be achieved using cheaper, faster, and more robust platforms for quantifying gene expression such as quantitative PCR. Quantitative PCR has several advantages over microarray, including its ability to use FFPE tissue. Several studies have reported robust expression analysis from fixed material using quantitative-PCR (13–15). Using multiplex reactions or generating low-density quantitative-PCR arrays therefore offers an attractive alternative to microarray for validation of classifier performance using existing FFPE tissue and eventual clinical application in a conventional pathology laboratory.
We describe here the development of a highly accurate multiclass classifier designed for clinical application to CUP. A large and comprehensive data set of gene expression was obtained from microarray analysis of 229 tumor samples, representing 14 commonly recognized sites of origin in the differential diagnosis for CUP. In collating the data set, we have purposely addressed the issue relating to molecular heterogeneity of specific tumor classes by including multiple histologic subtypes. The importance of sample coverage, particularly across the more heterogeneous classes, is shown by observing the expression of gene markers across subtypes and by systematically removing specific subtypes from training. We further used the microarray data set to choose an optimized series of tumor markers to create a quantitative PCR–based low-density array and generated a classifier that achieved similarly high prediction accuracies with both fresh and FFPE tissue to that obtained by microarray. Finally, we show the utility of the multiclass classifier for identifying the site of origin for CUP representing several clinical scenarios.
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Materials and Methods |
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Unknown primary samples. Patients with CUP were referred to the study by treating oncologists at the Peter MacCallum Cancer Centre, Melbourne or St. Vincent's Hospital, Sydney. Thirteen patients with disseminated metastases and no clinically detectable sign of a primary tumor following a minimum investigation of histopathology and computed tomography imaging was identified for the study. Details of clinical evaluation for these patients are provided in Supplementary Table S2: CUP samples.
RNA extraction from fresh frozen tissue. Total RNA from fresh frozen tumor samples was isolated by phenol-chloroform extraction (Trizol; Invitrogen, Carlsbad, CA) and column chromatography (RNeasy, Qiagen, Valencia, CA) as previously described (16). Purified RNA was analyzed by agarose gel electrophoresis to assess the integrity of 28S and 18S rRNA bands.
Microarray analysis. Total RNA (3 µg) was amplified and labeled using a modified Eberwine method (17) and hybridized to cDNA microarrays containing 
10,500 elements as described previously (16). Reference RNA consisted of a pool of RNA isolated from eleven human tumor cell lines (18). Further details are provided in Supplementary Information Part 1: Methods. All MIAME compliant microarray data are available at ArrayExpress at EBI (www.ebi.ac.uk/arrayexpress; accession number: e-mexp-113).
Feature selection and support vector machine. Feature selection was done by ranking genes by absolute value of their signal-to-noise ratio (19) statistically comparing the gene expression observed within a single class against all other classes (one versus all). The top m ranked genes were selected for each class and combined for subsequent use in building a support vector machine (SVM) model. For supervised classification of cancers, we used linear SVMs (20). Individual models were trained for discrimination of each cancer class from all other classes (one versus all), therefore an n class classifier was comprised of n class models. In testing a sample, the class model with the highest score was deemed the correct prediction. A decision margin was also given to the class prediction, calculated from the difference between the highest and the second highest score as follows: the decision margin was termed high if the difference in absolute SVM score between the first and second predictions was >50, medium if the difference was between 26-50, or low if the difference was 
25. For more detailed description of SVM classification methods, refer to Supplementary Information Part 2: Generating a multiclass predictor using cDNA microarray.
Quantitative PCR using low-density arrays. Genes that effectively classified samples as gastric, colorectal, ovarian, pancreas, and breast were identified using a signal-to-noise metric (19) to analyze microarray expression data from 173 tumor samples. Twelve to fifteen of the most frequently selected genes for each class were chosen and the corresponding validated primer/probe sets were incorporated into a low-density array (Assay on Demand, Applied Biosystems, Foster City, CA). Six endogenous controls were added to the assay set, which were also used independently for quality assurance of cDNA using SYBR Green chemistry (Applied Biosystems). Twelve genes were added that represent tumor types outside of the five site differential and represented the class other to enable the identification tumors that did not belong to the tumor types present in the test. Genes selected for the quantitative-PCR low-density array are summarized in Supplementary Table S4: Low-density array gene list. See also Supplementary Table S5: Primer design for SYBR green endogenous controls.
For FFPE samples, five 10-µm sections were used for RNA extraction using a modification to the protocol previously described (13). Further information about RNA isolation, the generation of cDNA from fresh frozen and formalin-fixed material, gene selection for the quantitative-PCR low-density array, and subsequent quantitative-PCR analysis is described in Supplementary Information Part 1: Methods.
Quantitative-PCR data analysis. Normalization of quantitative-PCR assays was conducted using an average Ct value for all endogenous controls. Samples were then converted to a fold change ratio described using standard 
Ct formula:
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Clustering of quantitative-PCR data was conducted by Pearson correlation using the program Cluster (21) and visualized using the program Mapletree (http://mapletree.sourceforge.net/). Analysis of quantitative-PCR data and the generation of a cross-platform model are described in detail in Supplementary Information Part 3: Translating a multiclass classifier to quantitative PCR.
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Results |
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90% of CUP tumors are thought to originate from epithelial cell types (10), we ensured a good representation of the major carcinoma types, defined by their anatomic tissue or organ of origin. Given also the histologic diversity of some carcinomas, we systematically represented histologic and molecular subtypes for some cancers [e.g., breast, ovarian, lung, gastric, and squamous cell carcinoma (SCC)]. Nonepithelial types, such as melanoma (22), seminoma (23), and mesothelioma (24), which can present with a cellular morphology and architecture indistinguishable from some poorly differentiated carcinomas, were also included. A summary of all tumors in the training set is detailed in Supplementary Table S6: Tumor classes.
Developing a site of origin classifier by machine learning. We used an SVM algorithm to create a 13-class predictor based on anatomic site of origin (combining head and neck and skin SCC types; SCCother). In practice, the performance of an SVM depends critically on the subset of features (genes) selected for modeling and tuning relative to the regularization constant C (see Supplementary Information Part 2). The classification method was very robust, with marginal dependence on the tuning variables across the range of values. The leave-one-out cross-validation (LOOCV) accuracy was between 94% and 96.5% using at least 20 genes per class, with the best performance obtained using 50 genes per class (600 nonredundant genes combined) and a C value of 10. Importantly, genes were reselected for every round of the LOOCV (25). The results of LOOCV, using optimal variables (C = 10, 50 genes/sample), are displayed for each class (Table 1), qualified by an associated decision margin (either high, medium, or low; see Materials and Methods). When the SVM prediction has a high or medium decision margin (combined into strong), there was a high likelihood of the prediction being correct: 203 of 207 cases (98.1%) called with strong decision margin were correctly classified (Table 1). In contrast, 4 of 18 (22%) low decision margin results were incorrect. Given that low decision margin predictions have a low but significant chance of being incorrect, we considered cases predicted with a low decision margin as unclassified. Considering only strong decision margin predictions, the adjusted accuracy for LOOCV was 89%. A similar high accuracy was achieved by splitting the entire data set into a two thirds training and a one third independent test set (Supplementary Information Part 2; see also Fig. 2B and C).
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Assessing the importance of training set coverage. Heterogeneous cancer subtypes, from organs such as the lung and ovary, may represent metaplastic or dedifferentiated variants that do not resemble a normal tissue counterpart or related subtypes morphologically or at the molecular level. Consistent with this, we found that despite the ability to measure expression across thousands of genes and a supervised approach to feature selection (top 20 ranked signal-to-noise ratio per class), there is a paucity of universally expressed site of origin markers for some cancer types (Fig. 1A, and see Supplementary Table S7 for gene list). For a higher-resolution view of gene expression, we selected several known markers from our refined list with previously validated tissue specificity (6, 27–37) and plotted the relative fold change for colorectal, breast, ovarian, and lung tumors. Markers can be identified which seem to be strongly and relatively uniformly expressed across the range of colorectal and breast tumors (Fig. 1B; VIL1 and NOX1 for colorectal cancer, PIP and GATA3 for breast). In contrast, the known histologic heterogeneity of ovarian and lung tumors correlates well with heterogeneity of expression of selected genes, including RBP1, WT1, and STAR (ovarian) and TITF1, ADH7, and SFTPB (lung). Our findings indicate the importance of inclusion of specific subtypes in training the classifier and the use of multiple markers for classification, particularly for heterogeneous tumor types such as lung and ovarian cancer.
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A further consideration in building the classifier is the possibility of high decision margin predictions (false positive) when testing on tumor types not represented in training. To investigate the behavior of our classifier in this context, we systematically removed entire tumor types from training and then tested on these samples. This is similar to the leave-subtype-out analysis and we refer to it as a leave-type-out analysis. As expected, the number of high decision margin predictions decreased from 77% to 19.1%. Although the predictor decision margin is low for most samples when their representative class is not included in training, it seems that there may be exceptions if they share a strong molecular likeness to tumors of another class (Table 2). For example, when prostate tumors are absent from the training set, prostate test samples are exclusively predicted as breast (Table 2), revealing an association of these tumors based on the commonality of hormonal regulation (41, 42). Similarly, all mesothelioma samples were predicted as ovarian, perhaps reflecting their shared mesothelial cell lineage and expression of known markers such as mesothelin, retinol binding protein 1, and Wilms tumor 1 (43–45). Although such tumor types may rarely be involved in the same diagnostic differential, it further shows the importance of including a wide spectrum of tumor types in training the classifier for attaining high specificity.
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First, to compare data generated from cDNA microarray and quantitative PCR, 42 fresh frozen tumor samples were profiled on both platforms. A Pearson correlation was calculated for each gene between platforms showing a high concordance between the data sets (median r = 0.83). Three genes from 79 were considered discordant (r < 0.4), despite sequence analysis confirming the identity of the clone used in generating the microarray. Differences between microarray and PCR data have been reported previously (46), and may be due to cross-hybridization of targets to microarray probes or expression of splice-specific isoforms not recognized in the PCR assay. These genes were removed from further analyses.
Classification using formalin-fixed tissue. Given the value of accessing fixed tissue samples, we next analyzed RNA obtained from FFPE cancer samples collected in a routine diagnostic pathology service. Twenty-five FFPE samples, collected 1 to 4 years ago and spanning all five classes of interest, were chosen. Of these, seven FFPE samples matched a fresh frozen sample already analyzed, whereas eight samples represent matched primary and metastatic tumors from the same patient. Hierarchical clustering of normalized quantitative-PCR expression profiles from fresh frozen and FFPE tissues showed that samples extracted from both fresh frozen and formalin-fixed material can be clustered accurately corresponding to the tissue of origin, with few exceptions (Fig. 3A).
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Predictors were developed using either five or six class models. The five-class model is based on the five sites of origin: gastric, colorectal, pancreas, ovarian, and breast. The quantitative-PCR data set used for testing this model, composed of 55 samples, represented both fresh frozen and FFPE samples. The six-class model implements an additional class, others, that represents the combined signatures from cancers outside the original five-site differential. The test set used in this case was identical to that used for the five class model, except for the addition of four samples (melanoma, renal, prostate, and lung adenocarcinoma) representing the others class (n = 59). To develop cross-platform models, we normalized (rescaled) the data sets to cope with inherent differences in the data types. We developed a method of rank levels and compared this with the more rudimentary method of median normalization. Ranking consistently outperformed median normalization and enabled high-accuracy predictions of greater than 96% (Fig. 3B and Supplementary Information Part 3). The results from independently testing fresh and FFPE samples using five- and six-class models is shown in Fig. 3C, showing high accuracy is achieved for all specimen types.
Classification of cancers of unknown primary. To assess the clinical utility of our classifiers, we collected 13 cases of metastatic disease for which the primary tumor could not be unequivocally diagnosed at the time of presentation (Table 3, see also Supplementary Table S2 for detailed diagnostic workup and histology images). All cases fell into one of three categories: (a) metastatic disease without any prior history of cancer (n = 7); (b) metastatic disease with a prior history of cancer (n = 5) and; (c) presentation with two concurrent primary tumors (n = 1).
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We compared our SVM predictions with the most likely primary site, based on evaluation of the case by a medical oncologist after review of all subsequent investigations and additional pathologic evaluation. In all cases where a consistent and strong decision margin prediction was made (11 of 13, 85%), SVM classification was consistent with either the highest possibility of the tissue of origin or among the short list of likely sites. We also noted that where available, outcome information and further clinical evidence were supportive of the prediction. For example, case P1328, predicted to have breast cancer, presented initially with a differential diagnosis of ovary, gastric, or breast cancer and then later presented with metastatic deposits in axillary lymph nodes and supraclavicular fossa, consistent with a breast primary. Similarly, patient P01698, who presented with widespread metastatic adenocarcinoma, was strongly predicted as ovarian cancer, but was thought to have a gastrointestinal type tumor based on histopathologic review conducted by several pathologists. This, however, conflicted with other clinical evidence such as a raised plasma CA125 concentration and no identifiable gastrointestinal type primary from endoscopic investigation radiological imaging, including positron emission tomography scan. Eventually it was decided to treat the patient with a regimen of taxol and carboplatin, as a broader-acting combination treatment was considered undesirable due to renal impairment. The patient had a good response to chemotherapy, consistent with the high decision margin prediction of ovarian cancer. This case represents a common diagnostic problem encountered with mucinous type tumors presenting in the ovary (47), and exemplifies the utility of the classifier for resolving such cases.
For a number of cases it can also be argued that the test would have significantly reduced the time taken to begin an appropriate chemotherapy regimen and may have given a survival benefit. For example, case P00563 had a 4-year history of ovarian cancer, and later presented with undifferentiated bone metastases. She was treated for recurrent ovarian cancer despite what would be an atypical presentation of a recurrence for this cancer type. Pathology and other diagnostic imaging did not suggest a likely alternative primary site until the patient presented 2 years later with an identifiable breast mass. Our microarray-based classifier, tested on the left neck mass, predicted breast cancer as the primary tumor, a diagnosis that would have resulted in an altered treatment plan 2 years earlier than otherwise applied. In such cases, where the patient has a previous history of cancer, it may be possible to access FFPE material from an earlier episode. Using our quantitative-PCR low-density array, we assayed FFPE tissue RNA extracts from both the 1994 ovarian tumor and the 2000 unidentified neck mass. Prediction using a cross-platform microarray/quantitative-PCR model (five- or six-class) resulted in strong decision margin predictions of ovarian and breast, respectively (see Supplementary Information Part 3).
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Discussion |
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Utilizing our heuristic confidence measure, 89% of training samples were correct and predicted with a strong decision margin. Closer examination of the histology associated with misclassified samples revealed some systematic errors were made by the classifier. First, it seems that misclassification is possible for well-differentiated tumors sharing a common histologic appearance with other classes. The most obvious of these relates to the lung SCC and SCCother type tumors. Metastatic SCC of unknown origin represents a small but significant fraction of all CUP cases (7), with the differential often including head and neck cancer or a primary lung tumor (50). Other molecular based studies have used comparative genetic alterations (i.e., allelic loss) to assist in matching the clonal origins of metachronous or synchronous SCC tumors (51); however, discordant results can occur due to genomic loss during tumor evolution (52), thus confounding the interpretation of results. Owing to the close molecular similarity of SCC type tumors, special attention is required to develop a more accurate gene expression–based classifier.
Errors were made for cases that represent a single sample of a particular subtype, consistent with the leave-subtype-out analysis, which showed that failure to represent specific histologic or molecular subtypes resulted in misclassification. This can be attributed to a paucity of gene markers that are truly universal across all subtypes, suggesting that some tumors do not retain expression of site of origin markers, but rather adopt an expression pattern underlying their new ectopic and differentiated form. This underscores the importance of representing class heterogeneity in respect to existing knowledge of cancer histopathology. As tumors may present in various states of differentiation, including mixed cell phenotypes, such examples must also be represented in training the classifier to cover the complete molecular heterogeneity that can arise from a specific site of origin. Unlike other studies (1), our classifier had no systematic difficulty in identifying poorly differentiated tumors. This may be attributed to the broad selection of subtypes we have used, some of which by definition are poorly differentiated (i.e., large cell lung tumors). Although several studies have compiled multiple data sets in an effort to increase sample coverage (11, 12), to our knowledge no other study has attempted to compile a multiclass data set specifically addressing the issues of histologic diversity.
The ability to accurately classify tumors using a refined number of gene markers suggests that translation of an expression-based classifier from microarray to quantitative PCR is possible. As a proof of principle, we have focused on a common differential diagnosis for CUP in women (7) covering the five sites of ovarian, gastric, colorectal, pancreas, and breast. A set of 79 site-specific markers translated to quantitative PCR allowed measurement of gene expression from either fresh or FFPE tissue. The robust quantification of mRNA from formalin-fixed tissue is consistent with several previous studies (13–15) but this is the first time to our knowledge where an accurate classifier has been generated using machine learning. Furthermore, the use of ranking to generate a classifier from microarray data, another novel feature of this work, negated the requirement to construct an entirely new training set using quantitative-PCR.
Our classifier was applied to a cohort of metastatic tumors in which the primary site could not be unequivocally identified at initial presentation, despite extensive clinical investigation. Although not all tumors in our series fit the classic definition of CUP, they represent a spectrum of real clinical scenarios where there was difficulty in diagnosing the origin of the tumor and determining clinical management. Predictions with strong decision margins could be made for all tumors, excluding the two cases of metastatic SCC. For several cases, compelling evidence became apparent during the course of the disease, which further validated the classifier predictions, albeit not until after the patient had endured extensive investigative procedures. It is difficult to obtain a definitive accuracy score for the classifier when testing on CUP tumors. This relates to the nature of these samples and that the origin of the majority of such tumors remains truly unknown (10).
Despite the bleak situation for patients with advanced stage cancer, treatments are becoming increasingly specific with approaches varying significantly depending on the cellular origin of the cancer. With recent advances in chemotherapy, specific regimens have led to improvements in survival and quality of life even in cancers that have traditionally been regarded as relatively chemoresistant (e.g., non–small-cell lung, colorectal, and pancreatic tumors; refs. 53–55). It is likely that there will be cost savings from more directed clinical evaluation of patients, enabled by a molecular genomics test. The average cost for diagnostic evaluation of CUP patients at a major US cancer center was $18,000 when a large series was considered (56) whereas a test similar to that described here is likely to cost under $1,000. Whereas a PCR or microarray-based classifier would not be expected to obviate the need for clinical investigation, it could allow much more focused testing, resulting in reduced cost, patient morbidity, and improved outcome.
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Acknowledgments |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
We are grateful to many people for their assistance with this work. In particular, we would like to thank Bianca Locandro, Kate Belfrage, and Dileepa Diyagama from the Peter Mac Microarray Core Facility for assistance with printing and hybridizing microarrays; Lisa Devereux, Justine Biggs, and Sam Coates from the Peter Mac Tissue Bank for collecting tissue samples and patient histories; Connie Mascarenhas, Patricia Goncalves, Melanie Trivett, and Neil O'Callaghan from the Peter Mac Department of Pathology and Peter Russell from the Royal Prince Alfred Hospital, Sydney, for assistance with review of patient specimens; and the members of the Centre for Cancer Genomics and Predictive Medicine, in particular Anna Tinker, David Thomas, Alan Christiansen, Ken Mitchelhill, Grant Macarthur, Sian Fereday, and Nadia Trafficante for helpful discussion. We are also grateful to Pat Brown, Matt van der Rijn, and David Botstein at Stanford University for sharing unpublished results. We would especially like to thank the many patients who donated tumor samples for this project.
We would like to dedicate this work to the memory of Professor Neil Della, a compassionate physician, talented researcher and valued friend, whose personal battle with cancer of unknown primary was the inspiration for this study.
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Footnotes |
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Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).
Received 10/12/04. Revised 2/14/05. Accepted 2/25/05.
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polypeptide; SFTBP1, surfactant, pulmonary-associated protein B.