EDITORIALS

A New Way to Look at Liver Cancer

See Article on Page 667

Hepatocellular carcinoma (HCC) exemplifies afundamental limitation of cancer pathology.Under the microscope, no two cancers look ex-

actly the same, even though they may be equivalent byestablished criteria (Fig. 1). Conversely, two tumors in thesame diagnostic category may disseminate at drasticallydifferent rates or show completely different responses totherapy. This dilemma is particularly frustrating to thepathologist, because retrospective analysis usually showssome tumor-specific feature that looks like it must havebeen important. Indeed, the concept of tumor grading isbased on finding the particular features that make a dif-ference, but grading usually sorts out the extremes—mosttumors tend to rank in the middle of a grading system.

Recent scientific and technical advances have providednew ways to look at HCC. The Genome Project andhigh-throughput cloning efforts have defined almost allhuman genes, which now number about 30,000. It ap-pears that only a few thousand more genes are hiding inthe Genome Project data, so this effort is nearly complete.High-throughput preparation of DNA clones or synthesisof oligonucleotides, combined with microprinting tech-nology, has produced high-density microarrays in whichthousands of tiny DNA spots are printed on a single glassslide. The slide is then hybridized with fluorescently la-beled RNA extracted from a tumor, and the hybridizationintensity, a direct measure of gene expression, is quanti-fied by confocal laser scanning. Within a few years, mi-croarrays have progressed from rather crudely assembledcollections of a few thousand poorly identified genes tohighly organized sets that can screen expression of the vastmajority of genes in a single analysis. Microarray studiesof tumors have produced detailed pictures of gene expres-sion, information that is not apparent in conventionalhistopathology. An array produces a very large data set for

each tumor, and the data are useful even when the rela-tionships among the tumor-expressed genes have notbeen worked out. The pattern of gene expression is anempirical but unique picture of a tumor. This utility isfortunate, because the data sets are so large and encompassso many newly described genes that it is difficult (notimpossible, but a lot of work) to find relationships ordiscriminate which expression changes are important.

In this issue of HEPATOLOGY, Lee et al.1 have combinedtwo substantial resources to change the diagnostic para-digm. They analyzed specimens from a large, 90-patientcohort who received partial hepatectomy for HCC andthen had long-term follow-up. The specimens were stud-ied with an advanced 21,000-gene oligonucleotide mi-croarray. This effort produced an extremely large set ofsignificant data that is a valuable resource for many kindsof studies. The analysis in the paper stresses only twoaspects, but these are important ones: HCC classificationand prognosis.

The first conclusion, perhaps not very surprising, isthat HCCs have a pattern of gene expression that is a lotlike normal liver. Other cancers show a similar relation-ship to their tissue of origin.2 Only 4,200 genes showed asmuch as a 2-fold difference in HCC compared to normalliver expression in at least 10% of the tumors, and al-though the authors did not elaborate this point, about3-fold more genes usually show consistent expression be-tween tumor and liver. The second conclusion, however,is unexpected: HCC are surprisingly homogeneous andfall into only two categories, “Cluster A” and “Cluster B.”This fact was determined by unsupervised hierarchicalclustering analysis—i.e., the tumors were grouped only bythe relationships of their abstract gene expression patternsto one another, not by the function of the genes thatcontributed to the patterns. Function is an importantissue that was considered later in the study.

The homogeneity of HCC distinctly contrasts withbreast cancer, for example, which shows striking diversityfrom one tumor to another.3 Such heterogeneity is ex-pected because tumors arise and progress through a highlyvaried sequence of genetic alterations. Nevertheless, theobservation of homogeneity in HCC appears to be ro-bust. A closer inspection of the data (Fig. 1) shows that theclusters have subgroups, but with only limited variation.Moreover, the HCCs were associated with three mainetiologies—hepatitis B virus, hepatitis C virus, and non-viral causes—and these etiologies were representedequally in the two clusters. HCC of other etiologies (al-cohol, hemochromatosis) also fit into the same two clus-

Abbreviations: HCC, hepatocellular carcinoma; AFP, �-fetoprotein.From the Department of Pathology, The Marion Bessin Liver Center, and The

Albert Einstein Cancer Center, Albert Einstein College of Medicine, Bronx, NY.Supported by grants from the National Cancer Institute of the National Institutes

of Health J.L.’s; expression profile studies are carried out through the AECOMcDNA Microarray Facility.

Address reprint requests to: Joseph Locker, M.D., Ph.D., Department of Pathol-ogy, Albert Einstein College of Medicine, 1300 Morris Park Ave., Bronx, NY10461. E-mail: locker@aecom.yu.edu; fax: (718) 430-3483.

Copyright © 2004 by the American Association for the Study of Liver Diseases.Published online in Wiley InterScience (www.interscience.wiley.com).DOI 10.1002/hep.20368

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ters. Finally, in case the homogeneity reflected the limitedvariety of their own patient cohort, Lee et al. showedsimilar clustering of expression in published data that rep-resented a much greater variety of specimens.

Their significantly different survival outcomes alsoconfirm that the two clusters are biologically distinct.This result is particularly striking because the clusteringanalysis was unsupervised. Most other studies have super-vised clustering with survival data to find genes associatedwith survival.4–9 In contrast, faster progression and de-creased survival are an intrinsic property of Cluster Atumors compared to those in Cluster B.

The two Clusters cannot be distinguished by his-topathological grade or etiology, so how do they differ?Lee et al. identified 406 genes for which a change in geneexpression, either positive or negative, predicted survival.This time, they used supervised analysis, although the vastmajority of these “survival” genes had already been iden-tified by unsupervised analysis. The functions of the sur-vival genes suggest why tumors in the two clusters behavedifferently. Forty-five percent of the up-regulated ClusterA survival genes were associated with cell proliferation.Many other Cluster A up-regulations were antiapoptotic.Another up-regulated gene was very important. This geneencodes hypoxia induced factor 1,10 a transcription factorthat stimulates adaptive responses to hypoxia, and pro-duction of angiogenic factors. Thus, the Cluster A tumorcell is more malignant because it divides more frequently.It is also more resistant to apoptosis, better adapted tohypoxia, and a better stimulator of angiogenesis. MightCluster A represent a later stage of progression than clus-ter B? This possibility seems to be ruled out by the retro-spective analysis of data from two published studies, sincethe genes that predicted intrahepatic metastasis9 or earlyrecurrence6 distributed into both Clusters A and B.

The survival genes do not include the usual markers ofhepatocytic differentiation—e.g., �-fetoprotein (AFP),albumin, or �1-antitrypsin. This absence of these genesfrom the survival list is unexpected, because it is generallypresumed that the highest-grade tumors will be the most“dedifferentiated” and have the least resemblance to anormal liver cell. If this presumption is correct, thenmarkers of hepatocytic differentiation ought to be sur-vival genes. The absence of AFP from the list of survivalgenes is particularly surprising, because AFP is a knownprognostic marker.11 In this case the answer is complex:Both Cluster A and Cluster B include AFP-positive and-negative tumors. In Cluster B, AFP expression had astrong negative correlation with survival, but in Cluster A,AFP expression had a positive correlation. Thus, the twoeffects cancelled each other out in the broader analysis.Perhaps, however, the AFP-negative tumors of Cluster Amight be the dedifferentiated high-grade tumors thatwould be expected to do badly.

The array data predicted that proliferation would behigher in Cluster A and apoptosis higher in Cluster B tu-mors. Lee et al. confirmed these predictions by analyzingtissue sections. They used Ki-67, a marker of cell prolifera-tion, and also directly counted apoptotic cells (see Supple-mentary Fig. 5 to Lee et al. at http://interscience.wiley.com/jpages/0270-9139/suppmat/index.html). Significantly, thecorrelation of counts and expression clusters confirmed astudy by Ito et al.,12 who reported that proliferation andapoptosis rates were strong and opposite predictors of sur-vival. The relationships might seem obvious: Tumors thatgrow faster or have less cell death should be more aggressive.In several cancers, however, increased cell proliferation andapoptotic cell death are linked together and a high apoptoticrate sometimes indicates a more aggressive cancer.13

Fig. 1. Two kinds of hepatocellu-lar carcinoma (HCC). Two moder-ately differentiated HCCs show cleardifferences from each other. The tu-mor on the right has larger cells andnuclei and forms larger acini. Suchfeatures vary from one tumor to an-other but do not predict clinical be-havior. However, one tumor has apoor-prognosis, high-proliferation,Cluster A gene expression profile,while the other has a good-progno-sis, high-apoptosis, Cluster B profile.There are two illustrated morpholog-ical features that correspond to theprofiles. The left tumor has a mitoticcell (Mi), while the right tumor hasan apoptotic cell (Ap).

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Technology is rapidly evolving, and it is already possi-ble to include expression profiling in the patient work-up.Such testing is not indicated by Lee et al.,1 because theirdata suggest that the pathologist can provide nearly thesame prognostic information by counting Ki67-positiveand apoptotic cells. Nevertheless, prognostic informationhas relatively little impact on treatment, since both Clus-ter A and Cluster B patients benefited from their resec-tions. The use of advanced testing to determine therapy isa radically different situation, and a recent study of breastcancer provides a model for this approach.4 Gene expres-sion profiles were used to predict which patients mightbenefit from chemotherapy. However, diagnosis andtreatment have not reached the point where the sameapproach would be useful for HCC.

Even if gene expression profiling is not required for man-agement of HCC patients, the research is very promising.Lee et al.1 focused on unsupervised clustering and prognosis,but there are numerous other important questions that couldbe approached with further analysis of their comprehensivedata sets. Other recent studies have characterized gene ex-pression profiles associated with progression from precancer-ous lesions,14 recurrence,6 vascular invasion,15 intrahepaticand extrahepatic metastasis,9,16,17 hepatitis B or C etiol-ogy,5,7,18–22 p53 mutation,8 and sensitivity to selected che-motherapeutic agents.23,24 Though many of these studiesshould be elaborated with more specimens and comprehen-sive microarrays, such research will provide a detailed mech-anistic model of HCC progression. Most important,expression profiling will reveal specific targets for rationaltherapy.

JOSEPH LOCKER

Albert Einstein College of MedicineBronx, NY

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