Phyloproteomics: What Phylogenetic Analysis Reveals about Serum

Proteomics

Mones Abu-Asab,*,† Mohamed Chaouchi,‡ and Hakima Amri§

Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland,National Oceanic and Atmospheric Administration, National Ocean Service, CO-OPS/Information Systems

Division, Silver Spring, Maryland, and Department of Physiology and Biophysics, School of Medicine,Georgetown University, Washington, D.C.

Received December 9, 2005

Phyloproteomics is a novel analytical tool that solves the issue of comparability between proteomicanalyses, utilizes a total spectrum-parsing algorithm, and produces biologically meaningful classificationof specimens. Phyloproteomics employs two algorithms: a new parsing algorithm (UNIPAL) and aphylogenetic algorithm (MIX). By outgroup comparison, the parsing algorithm identifies novel orvanished MS peaks and peaks signifying up or down regulated proteins and scores them as derived orancestral. The phylogenetic algorithm uses the latter scores to produce a biologically meaningfulclassification of the specimens.

Keywords: Cancer • dichotomous development • mass spectrometry • phylogenetics • phyloproteomics • proteomics• serum • transitional clades

Introduction

The utilization of the serum proteome to accurately diagnosecancer has been challenging, and its future continues to besurrounded by uncertainties.1 Although statistical analysis ofmass spectrometry (MS) profiles of serum proteins has gainedenormous popularity and credibility,2-6 algorithmic analysisthat produces biologically meaningful results with possibleclinical diagnosis is still lacking. It now seems very simplisticto attempt to define cancer on the basis of statistical patterns,since cancer is a multifaceted evolving and adapting cellularcondition with multiple proteomic profiles; some of theseprofiles cannot always be separated from noncancerous onesby narrowly defined statistical proteomic patterns on the basisof a limited number of spectral peaks. Cancer’s incipience ismarked by mutations that cause the malfunction of theapoptotic apparatus of the cell, and its promotion is character-ized by different phases with each having its distinct proteomicprofile.7,8 Advanced progression of cancer is marked by cellulardedifferentiation, loss of apoptosis, and metamorphosis into aprimordial status where survival, and not function, becomesthe cell’s primary mission.8 In this latter stage, many proteinsresponsible for differentiation are not produced, and thereforemissing MS peaks are as significant in defining the proteomicprofiles of cancer.

The multiphasic nature of cancer progression combined withpossible multiple developmental pathways8-11 entail the pres-ence of a large number of proteomic changes for each type ofcancer and its phases. These factors suggest that the proteomic

profile of a cancer type is a hierarchical and continuousaccumulation of proteomic change over time rather than oneor a few simple distinct proteomic patterns. For an analyticaltool to be successful in producing a clinical diagnosis, it hasto uncover the hierarchical profile of cancer and be able toplace a specimen within this profile.

In the present study, we propose that cancer can be promptlydiagnosed, even at early stages, by phylogenetic analysis of theserum proteome. Since cancer is an evolutionary condition thatinvolves genetic modifications and clonal production, it there-fore requires an evolutionary method of analysis. Such ananalysis is possible if an algorithm for sorting out the polarity(derived vs ancestral) of the MS values is available. We aredemonstrating here through our polarity assessment algorithm(UNIPAL) that this task can be performed, and MS data canbe analyzed with an evolutionary algorithm (Figure 1). Phylo-proteomics is an evolutionary analytical tool that sorts outmass-to-charge (m/z) values into derived (apomorphic) orancestral (plesiomorphic) and then classifies specimens ac-cording to the distribution pattern of their apomorphies intoclades (a group composed of all the specimens sharing thesame apomorphies). Phyloproteomics also illustrates the mul-tiphasic nature of cancer by assigning cancer specimens to ahierarchical classification with each hierarchy defined by theapomorphic protein changes that are present in its specimens.The classification is presented in a graphical display termedcladogram or tree. The assumption that all cancerous speci-mens fit into well-defined proteomic models (patterns basedon a few peaks) that distinguish them from noncancerous ones12-16 is replaced here by phylogenetically distinct clades ofspecimens with each clade sharing unique protein changes(synapomorphies) among its specimens.

* To whom correspondence should be addressed. mones@mail.nih.gov.† National Institutes of Health.‡ National Oceanic and Atmospheric Administration.§ Georgetown University.

2236 Journal of Proteome Research 2006, 5, 2236-2240 10.1021/pr0504485 CCC: $33.50 2006 American Chemical SocietyPublished on Web 07/26/2006

Methods

Proteomic Data. We used mass spectrometry (MS) data ofserum proteins generated by surface-enhanced laser desorp-tion-ionization time-of-flight (SELDI-TOF) of 460 specimensfrom three types of cancer: ovarian (143), pancreatic (70), andprostate (36), as well as from noncancerous specimens (211).All sets of data used here are available from the NCI-FDAClinical Proteomics Program (http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp) and are described and re-ferred to in a few publications.12,13,15,17,18 From the prostatecancer data set, we included only the confirmed cancerousspecimens.

Polarity Assessment and Phylogenetic Analysis. We em-ployed the continuous range of mass-to-charge ratio (m/z)values of all specimens for the analysis. For polarity assessment(apomorphic [or derived] vs plesiomorphic [or ancestral]), datawere polarized with a customized algorithm (UNIPAL) writtenby the authors that recognized novel and vanished MS peaks,as well as peaks signifying upregulated and downregulatedproteins for each specimen. Each of these events was codedas equal; however, no standardization, normalization, orsmoothing of the data was applied before or after polarityassessmentsUNIPAL does not require any of these processesto carry out the polarization. Outgroups used to carry outpolarity for each cancer type were selected from the noncan-cerous specimens; each outgroup encompassed the totalvariability within the noncancerous specimens.

UNIPAL requires a set of noncancerous specimens to beincluded in every separate data set in order to be used as anoutgroup. It determines the polarity for every m/z value amongthe noncancerous specimens and then scores each value of thestudy group as derived or ancestral. The outgroup should belarge enough to encompass all possible variations that existwithin noncancerous specimens.

For phylogenetic analysis, we used MIX, the parsimonyprogram of PHYLIP version 3.57c,19 to carry out separate

phylogenetic parsimony analysis for each cancer type and thenpooled all the specimens of the three cancer types plus thenoncancerous in a larger analysis that included all 460 speci-mens. Processing with MIX was carried out in randomized andnonrandomized inputs; however, no significant differenceswere observed between the two. Phylogenetic trees were drawnusing TreeView.20

Results and Discussion

The results of a phylogenetic analysis are best illustrated bya phylogenetic tree termed cladogram that shows the hierar-chical classification in a graphical format. Parsimony analysisproduced one most parsimonious cladogram (requiring theleast number of steps in constructing a classification ofspecimens) for each of the pancreatic and prostate specimens(Figure 2a,b), five equally parsimonious cladograms for ovarianspecimens (Figure 2c shows only one), and about 100 equallyparsimonious cladograms for the inclusive analysis (Figure 3summarizes only one). We examined all multiple equallyparsimonious cladograms and found them to be fundamentallyvery similar in topology. They differed only in the internalarrangement of some minor branches where one or twospecimens had equally plausible locations within their im-mediate clade.

A complete separation of the cancer specimens from non-cancerous ones depended on the size of the noncancerousoutgroup used to carry out polarity assessment. Polarizing them/z values with the largest size outgroups (ones encompassingthe largest amount of variation) available for each cancer typeproduced cladograms with separate groupings of cancerousand noncancerous specimens, that is, no cancer specimensgrouped with the healthy and vice versa (100% sensitivity andspecificity). However, with the use of randomly selected smalleroutgroups, sensitivity dropped to 96% and below; this illustratesthe significance of using the largest number possible foroutgroup polarity assessment.

Each of the cladograms (Figure 2a-c) showed an upperbifurcation composed of cancerous specimens, while the lowerend of the cladogram was occupied by a number of basal cladescomposed of noncancerous specimens and a central as-semblage of noncancerous clades adjacent to cancerous ones.The latter assembly formed a distinct order of well-resolvedand mostly single-specimen clades in the middle of thecladogram nested between the cancer and healthy clades(bracketed arrows in Figure 2a-c); we termed them transitionalclades (TC). The transitional clades bordered their respectivetypes (cancer or noncancer) in a tandem arrangement thatformed a transitional zone (TZ) between the noncancer andcancer clades.

When data of all specimens of the three cancer types werepooled together with noncancerous ones and processed, eachof the three cancers formed two large clades (the terminal andmiddle) and numerous small transitional clades adjacent to thenoncancerous ones (Figure 3). The pancreatic and prostateclades formed sister groups in their terminal and middle clades,and their terminal clades were nested within the ovarian clades.The ovarian specimens formed two distinct clades at the upperpart of the cladogram.

The cladograms revealed greater similarities in topologyamong cancer types. For each of the three cancer types, therewere two large recognizable clades (the terminal and themiddle) forming a major dichotomy that encompassed themajority of the specimens of each type (Figure 2a-c). This

Figure 1. Schematic representation of phyloproteomic analysis.The process involves two steps. The first is the algorithmicsorting of the m/z values into derived (exists in some but not allspecimens) and ancestral (in all specimens); the derived valuesare those signifying either novel, vanished, or up and downregulated peaks. The second step is a parsimony phylogeneticanalysis that groups the specimens on the basis of the sharedderived values.

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dichotomy persisted in the inclusive cladogram as well (Figure3), with each of the cancers having two clades.

The use of mass spectrometry (MS) of serum proteins toproduce clinically useful profiles has proved to be challengingand has generated some controversy.21-23 Although severalmethods have been published thus far,13-16 they all either hadcancer type-specific sorting algorithms that produced below95% specificity and did not apply well across other cancer types,did not utilize all potentially useful variability within the data,or were not widely tested.16,24 Furthermore, their relativesuccess has been limited to diagnosis without any of thepredictive conclusions potentially offered by phyloproteomics.Since cancer is an evolutionary condition produced by a set ofmutations,7 its study should include evolutionary sound meth-ods of analysis. Phylogenetics reveals both relatedness anddiversity through a hypothesis of relationships among thespecimens on the basis of the parsimonious distribution ofnovel m/z values of their proteomes.

This is the first report on the application of a phylogeneticalgorithm to MS serum proteomic data for cancer analysis. Bydeveloping and applying an algorithm for polarity assessmentand then using a parsimony phylogenetic algorithm for clas-sifying specimens of three cancer types (ovarian, pancreatic,and prostate), we demonstrated that phylogenetics can suc-cessfully be applied to MS serum proteomic data for canceranalysis, diagnosis, typing, and possibly susceptibility assess-ment. Additionally, phyloproteomics points out the presenceof distinct trends within cancer proteomic profiles.

Despite the good number of algorithms used for MS serumanalysis,13-16 reproducibility and comparability of proteomicanalyses are unattainable because of the lack of broadlyacceptable universal methods of analysis. Phyloproteomics iscomposed of two algorithms that are applicable to MS data ofany cancer (Figure 1). The first algorithm, UNIPAL, is a newpolarity assessment program that we designed to work withMS data to produce a listing of novel derived values in a coded

Figure 2. Phyloproteomic cladograms of three cancers: (A) pancreatic, (B) prostate, and (C) ovarian. The nodes of major clades aremarked as follows: b, terminal cancer clade; O, middle cancer clade; 0, middle healthy clade; and 9, basal healthy clade. Transitionalzones (TZ) are marked by bracketed arrows.

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format, and the second algorithm is a popular phylogeneticparsimony program, MIX of the PHYLIP package,19 that usesthe values generated by the first algorithm to classify thespecimens. MIX is a robust analytical package that has beentested by scientists for the past 16 years, and is probably themost cited in phylogenetic studies. An added benefit to thisapproach is that it makes possible the comparison amongresults from different data sets and the evaluation of competinganalytical tools.

Phylogenetics has the intrinsic ability to reveal meaningfulbiological patterns by grouping together truly related specimensbetter than any other known methods (Table 1). Proteomicvariability encompasses ancestral and derived variations, andonly derived m/z intensity values are useful in classifying cancertypes and subtypes into a meaningful hierarchy that reflectsthe phylogeny and ontogeny of their proteomic profiles. Whileclustering techniques use the presence of common peaks(without resolving their polarity) in order to create distinctpatterns and then fit a specimen within a pattern,12,14,16

phylogenetics requires polarity assessment to sort out m/zintensities into derived and ancestral at first and then uses thedistribution pattern of derived values among the specimensto produce their classification (i.e., the cladogram). Using onlycommon intensity peaks without polarity assessment for pat-tern modelling has not been the most reliable means ofclassification.12,14 This is because clustering usually involves

ancestral values and does not resolve multiple origins of acharacter (parallelisms), and both result in polyphyletic group-ing (having unrelated specimens). Furthermore, phylogeneticscan resolve the position of a novel specimen with newvariations by placing it in a group that comprises its closestrelatives on the basis of the number of apomorphic mutationsit shares with them (Table 1).

Phyloproteomics has a potential for cancer predictivity.Predictivity here is defined as the capacity of the classificationto predict the characteristics of a specimen by determining thespecimen’s location within a cladogram. By using an amplenumber of well-characterized cancer specimens in an analysis,the unknown characters of a new specimen will be forecastedwhen it assembles within a clade in the cladogram. Thespecimen’s location in a cladogram is always based on the typeof mutations it carries and shares with the clade members,which will determine the diagnosis, cancer type, or possiblythe susceptibility to developing cancer. Cladogram topologyshows a hierarchical accumulation of novel serum proteinchanges across a continuum spanning from the transitionalnoncancerous specimens to the cancerous ones, with the latterhaving the highest number of apomorphic mutations.

Cladograms also revealed that the three types of cancer havefundamentally similar topologies; they all have one majordichotomy that indicates two lineages within each type (rep-resented on the cladograms by the terminal clade and themiddle clade [Figures 2-3]). If this typification holds true foradditional cancer types, then it is possible that ontogeneticallyall types of cancers undergo two major common pathways intheir development. There are only a few recent reports thatsupport a dichotomous pattern of development8 in colorectalcancer,9 glioblastomas,10 and pancreatic carcinoma.11 Dichoto-mies may arise in cancer because of the selective advantagesof cells harboring various mutations; the surviving mutationscan be genetic or chromosomal,8,9 point mutation or amplifica-tion,10 or differential expression of alleles.11

Noncancerous transitional clades, present in all cladogramsand mostly composed of individual specimens, are the closestsister groups to cancer clades. Because of their proximity tocancer clades, we hypothesize that these specimens, assumedto be from cancer-free individuals, represent the early stagesof cancer development that cannot yet be morphologically ormicroscopically diagnosed as cancerous. For diagnostic pur-poses, cancerous and noncancerous transitional specimens willalways be challenging to classify by other techniques. Oc-casionally, these specimens are distinct from one another byonly very few apomorphies. The mostly single specimencomposition of the transitional clades attests to their unique-ness.

Current diagnosis of cancer is not based on the number of

Figure 3. A phyloproteomic analysis showing dichotomousdistribution of cancers into two clades. A schematic cladogramof a comprehensive phyloproteomic analysis composed of 460specimens representing ovarian, pancreatic, and prostate cancersas well as noncancerous specimens. Specimens of every cancertype are classified into two clades: a terminal and middle, aswell as transitional clades. Healthy specimens are classified intoa major healthy clade and transitional clades.

Table 1. The Advantages of Phylogenetic Analysis over Statistical Cluster Analysis

phylogenetic analysis cluster analysis

9 produces a classification based on shared derivedsimilarities and reflects phyletic relationships

9 produces a classification based on overallsimilarity and may not reflect phyletic relationship

9 uses one algorithm for the analysisof all types of cancers

9 may require a specific algorithmfor each cancer type

9 discriminates between ancestral and derived states;uses only derived character states (apomorphies)

9 does not discriminate between ancestraland derived character states; uses both

9 resolves issues of parallelism(multiple independent origins) by parsimonyor maximum likelihood

9 does not resolve issues of parallelism

9 offers predictivity 9 does not offer predictivity.

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mutations or synapomorphies; therefore, the determination ofthe status of a transitional specimen is still subjective unless aclear definition that is based on derived mutations is estab-lished by pathologists. Till then we suggest that the positionof a transitional specimen within the transitional zone deter-mines its diagnosis; if a specimen is on the upper end of thetransitional zone (i.e., bordering cancer clades), then it is acancerous specimen, and those occurring in the middle andlower end of the transitional zone are to be called high riskspecimens.

So far, we have not yet carried out any correlations betweenspecimens on the cladograms and patients’ survival. Therefore,it is uncertain at this stage of the analysis if the terminal cladesof cancers represent the advanced stages of cancer progressionor if the two major clades have any prediction on prognosis.

Searching for biomarkers is a challenging process in bio-medical research, and phyloproteomics offers the capacity touncover many possible ones. The phylogenetic program, MIX,lists the shared derived m/z intensity values (synapomorphies)of each clade it produces, and each synapomorphy is a possiblebiomarker. In other words, the cladogram serves as a mapshowing the apomorphic m/z values of all potential biomarkersand their effective levels of groupings. A synapomorphy mayrepresent a novel protein, a disappeared protein, or an up/down regulated protein; thus, these proteins corresponding tothe apomorphic m/z values need to be identified if they are tobe explored as biomarkers. Since the cladograms have hierar-chical arrangement (i.e., presenting various levels of groupings)one can look for biomarkers at various levels of the cladogram.An apomorphic protein (we would like to call it apotein) thatdefines a clade will serve as a potential biomarker for the clade,while another apotein defining a nested subclade within theclade will be restricted as biomarker to the subgroup withinthe clade.

Conclusion

Phyloproteomics offers a new paradigm in cancer analysisthat reveals relatedness and diversity of cancer specimens in aphylogenetic sense; its predictive power is a useful tool fordiagnosis, characterizing cancer types, discovering biomarkers,and identifying universal characteristics that transcend severaltypes of cancer. The implications of the new paradigm are ofvaluable clinical, academic, and scientific value.

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