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Preliminary evidence of different selection pressures on cancer cells as compared to normal tissues

Theoretical Biology and Medical Modelling20129:44

DOI: 10.1186/1742-4682-9-44

Received: 16 August 2012

Accepted: 3 November 2012

Published: 12 November 2012

Abstract

Background

Cancer is characterized by both a high mutation rate as well as high rates of cell division and cell death. We postulate that these conditions will result in the eventual mutational inactivation of genes not essential to the survival of the cancer cell, while mutations in essential genes will be eliminated by natural selection leaving molecular signatures of selection in genes required for survival and reproduction. By looking for signatures of natural selection in the genomes of cancer cells, it should therefore be possible to determine which genes have been essential for the development of a particular cancer.

Methods

We provide a proof of principle test of this idea by applying a test of neutrality (Nei-Gojobori Z-test of selection) to 139 cancer-related nucleotide sequences obtained from GenBank representing 46 cancer-derived genes.

Results

Among cancer associated sequences, 10 genes showed molecular evidence of selection. Of these 10 genes, four showed molecular evidence of selection in non-cancer transcripts. Among non-cancer associated sequences, eight genes showed molecular evidence of selection, with four of these also showing selection in the cancer associated sequences.

Conclusions

These results provide preliminary evidence that the same genes may experience different selection pressures within normal and cancer tissues. Application of this technique could identify genes under unique selection pressure in cancer tissues and thereby indicate possible targets for therapeutic intervention.

Introduction

Cancer cell clones evolve over the lifespan a tumour[13]. The selective pressures driving this clonal evolution are myriad and may include microenvironmental factors, immune system surveillance, competition with other cancer and somatic cells, and selective killing of cancer cells by surgery, chemotherapy and radiation[29]. Two features of cancer portend intense natural selection among cancer cells. The first is the observation that cancer cells (at least in the later stages of growth) experience a high rate of cell death[10]. The second is the greatly increased rate of mutations in cancer cells[1116]. For example, a recent large scale study identified mutations in 11% of protein coding genes examined over 756 cancer cell lines[17]. Many of these mutations, even if they change the resulting protein sequence of the gene product may be considered to be “passenger” mutations that do not contribute to oncogenesis[16] and are of no significance to the cancer cell[3, 12]. Indeed mutations in non-essential genes may even be adaptive to the cancer cell as they shed costly metabolic processes irrelevant to reproduction of the cancer cell[3].

The high mutation rate and rapid cellular turnover may be expected to form an intense environment for natural selection where mutations arise and are tested for functional importance through competition with other cells. Eventually, this environment may lead to the situation where many genes have been rendered nonfunctional by mutations and the subset of genes that have been important for the survival and multiplication of the cancer cells will have been preserved through constant selection of functional versions of these genes.

Evolutionary biologists have identified a number of methods for detecting molecular evidence of natural selection[18]. These, so-called “tests of selection” attempt to differentiate neutral evolution (i.e. genetic drift) from Darwinian selection. One commonly used method compares ratios of synonymous and non-synonymous base substitutions. This approach has the advantage of being robust with regards to population growth[18], a confounding factor particularly important in the context of cancer cell growth. Synonymous base substitutions change the exonic base pair sequence but conserve the translated amino acid sequence (because of the degenerate nature of the DNA code). In contrast, nonsynonymous base pair substitutions change both the base pair sequence as well as the translated amino acid sequence. An increased rate of synonymous to nonsynonymous base substitutions provides evidence that the base sequence in question is or has been under natural selection to conserve the amino acid sequence (purifying selection). Less commonly, a sequence may exhibit an increased rate of nonsynonymous to synonymous base substitutions, indicating the base sequence in question has been under natural selection to change the ancestral amino acid sequence (diversifying selection). Perhaps the best described example of this is the diversifying selection shaping the peptide binding grooves of MHC class I molecules[19]. We might expect that the majority of selection pressures on cancer cells would be in the form of purifying selection to maintain the function of essential genes. However it is also possible that diversifying selection also plays a role in cancer cell evolution, possibly in facilitating the exploitation of new microenvironments.

Here we test the hypothesis that due to the high mutation rates and increased cell turnover in cancer cells, genes of importance to the survival of the cancer cell should show molecular evidence of natural selection. Furthermore, we predict that in the majority of cases this selection would be in the form of purifying selection.

Materials and methods

As an initial test of this hypothesis we obtained cancer-derived DNA sequences from GenBank using the search parameters “carcinoma expression library", "cancer-associated transcript”, "tumour-associated transcript" and “Homo sapiens”. We did not attempt to obtain an exhaustive list of all available transcripts but rather sought a convenience sample of different genes where at least two different examples of the same gene sequence from cancer tissue could be obtained. We did not include animal model-derived sequences or experimental cell line sequences. To determine if these genes show natural selection in non-cancerous tissues, Genbank was again used to find non-cancer versions of the same genes. In cases where we could not locate two non-cancer sequences from among the GenBank entries, we isolated the relevant sequences from the NCBI reference sequences primary and alternate assemblies. The sequences used in this study are all publically available from NCBI; the sequence references are given in Table1.
Table 1

Gene sequences used in the analyses

Gene

GenBank Accession Numbers (cancer-related sequences)

Probability of Null hypothesis (Hs=Hn) in cancer-related sequences

Type of selection

P-distance

GenBank Accession Numbers (non- cancer-related sequences)

Probability of Null hypothesis (Hs=Hn) in non-cancer-related sequences

Type of selection

Gene function

EGFR

GI:998566 GI:998564

0.603

none

0.290

GI:229892268 GI:229892301 GI:229892299

0.212

none

Growth factor receptor

LNCaP

GI:429094 GI:429093 GI:429091

0.771

none

0.550

GI:19924155 GI:19924154

1.000

none

protease present in seminal plasma

HYAL1

GI:24497567 GI:24497563 GI:24497560

0.088

none

0.054

GI:386365498 GI:385648248 GI:385648249

0.751

none

candidate tumor suppressor

locus

ALDOA

GI:15488980 GI:15277570 GI:38197497

0.234

none

0.090

GI:342187210 GI:342187198 GI:342187192

0.331

none

glycolytic enzyme

PTPN3

GI:223941890 GI:223941884 GI:223941878

0.214

none

0.037

GI:223941893 GI:223941875

0.081

none

protein tyrosine phosphatase

NBL1

GI:323462168 GI:323276671 GI:323462166 GI:323462167

0.794

none

0.045

NC_000001.10 AC_000133.1

0.549

none

bone morphogenetic protein antagonist

BLCAP

GI:47939094 GI:28839694

0.385

none

0.033

GI:268370219 GI:268370215 GI:268370223 GI:268370217

0.077

none

tumour suppression gene

PRKA

GI:331284157 GI:116174749 GI:331284159

0.077

none

0.000

GI:331284154 GI:331284152 GI:331284159

0.497

none

regulates the effect of the cAMP-dependent protein kinase signaling pathway

STEAP2

GI:350276262 GI:350276256 GI:350276260

0.301

none

0.128

GI:100913195 GI:100913193 GI:100913197

0.484

none

metalloreductase

MAGED2

GI:41350319 GI:29171703 GI:29171704

0.899

none

0.015

NC_000023.10 AC_000155.1

0.034

diversifying

negative regulator of wild type p53 activity

FOLR1

GI:262331568 GI:262331573 GI:262331571 GI:262331569

0.470

none

0.007

NC_000011.9 AC_000143.1

1.000

none

folate receptor

EWSR1

GI:48734726 GI:38197249 GI:15029674 GI:13435962

0.310

none

0.072

GI:253970505 GI:253970501 GI:253970497 GI:253970503

0.171

none

involved gene expression, cell signaling, and RNA processing and transport

SHARPIN

GI:21706472 GI:19264111

0.451

none

0.003

GI:333805638 GI:118918414

0.290

none

Involved in NF-kappa-B activation and regulation of inflammation

NUDCD1

GI:13111833 GI:27694435 GI:21411491

0.806

none

0.005

GI:189571676 GI:189571678

0.071

none

tumor-associated antigen

PMEPA1

GI:51593770 GI:16198474 GI:22121998 GI:9255808

0.878

none

0.032

GI:40317615 GI:40317617 GI:40317619

0.353

none

Involved in down-regulation of the androgen receptor

CTAGE5

GI:313882513 GI:30411006 GI:39963693 GI:24659234

0.244

none

0.007

GI:134053863 GI:134053924 GI:134053890

0.002

purifying

tumor-associated antigen

IRAK3

GI:34785939 GI:46854383

0.791

none

0.003

GI:216547518 GI:216547503

1.000

none

receptor-associated kinase

PLS3

GI:34785158 GI:25058020 GI:288915540

0.255

none

0.015

GI:288915537 GI:288915538

0.243

none

actin-bundling protein

PRAME

GI:33874094 GI:25123208 GI:21328745

0.121

none

0.019

GI:46249372 GI:46249366 GI:46249370 GI:46249365

0.754

none

transcriptional repressor

TNFSF13

GI:33873809 GI:24934971 GI:211938417

0.211

none

0.162

GI:211938416 GI:310750386 GI:310750384

1.000

none

possibly involved in regulation of tumor cell growth and monocyte/macrophage-mediated immunological processes

ENOX2

GI:17939422 GI:80478559

0.070

none

0.006

GI:32528292 GI:32528290

1.000

none

growth-related cell surface protein

UQCC

GI:114108213 GI:77415336 GI:111598967 GI:296923772

0.452

none

0.008

GI:296923778 GI:296923775

1.000

none

enzyme involved in ubiquinol-cytochrome c reductase complex

ALOX15B

GI:85067502 GI:85067498 GI:39645887 GI:85067500

0.085

none

0.001

GI:182765463 GI:260166611

1.000

none

involved in the production of fatty acid hydroperoxides

TACSTD2

GI:166795235 GI:238914823 GI:14495610

0.340

none

0.014

NM_002353

AC_000133

1.000

none

cell surface receptor that transduces calcium signals

RAET1E

GI:149790140 GI:73909189

0.197

none

0.151

GI:21040248 GI:341915375 GI:343183384

0.586

none

delivers activating signals to NK cells

MAGEB1

GI:15489350 GI:164693182 GI:49456482

0.337

none

0.011

GI:284004909 GI:257796251 GI:257796250

0.0151

purifying

tumor-associated antigen

CSF1

GI:18088910 GI:166235151

0.413

none

0.142

GI:347360911 GI:166235149 GI:384475524

0.657

none

cytokine that controls the production, differentiation, and function of macrophages

CKAP2

GI:148664243 GI:148664200 GI:187950332 GI:15012012

0.278

none

0.084

NC_000013.10

AC_000145.1

0.320

none

involved in regulating aneuploidy, cell cycling, and cell death

MUC1

GI:182252 GI:115528448 GI:324120948

0.085

none

0.389

GI:324120957 GI:324120955 GI:324120951 GI:324120950

0.716

none

adhesion and anti-adhesion protein; involved in cell signaling

TMPRSS3

GI:14709533 GI:33991397 GI:145701031

0.736

none

0.008

GI:291167774 GI:145701029 GI:291167776

1.000

none

serine protease; plays a role in hearing

FOLR2

GI:34785969 GI:166064049 GI:166064051

0.090

none

0.005

GI:166064049 GI:166064053 GI:166064055

1.000

none

folate receptor

BYF3

GI:33873803 GI:110624585 GI:83641884 GI:83641883

0.137

none

0.049

GI:224177471 GI:1435190 GI:179571 GI:179575

0.686

none

involved in transcriptional initiation

TPBG

GI:33872201 GI:262205658 GI:262205664

0.807

none

0.012

NC_000006.11 AC_000138.1

0.521

none

possible cell adhesion molecule

EMP2

GI:16307197 GI:64692933

0.298

none

0.002

NC_000016.9 AC_000148.1

1.000

none

epithelial membrane protein

TGM6

GI:33331029 GI:33331031

1.00

none

0.000

NC_000020.10 AC_000152.1

0.266

none

associated with central nervous system development and motor function

NBR1

GI:112382227

GI:112382229

GI:112382228

0.359

none

0.504

GI:33869357 GI:111120332

0.018

purifying

Function unknown

CALM2

GI:19913528 GI:16924228 GI:14250064

0.04

diversifying

0.524

GI:229577210 GI:13097164

0.026

diversifying

Mediates enzymes, ion channels and other proteins

NRG1

GI:49522882 GI:34782767 GI:33873543

0.012

purifying

0.220

GI:236460384 GI:236464355

1

none

signaling protein that mediates cell-cell interactions

SYTL2

GI:244790015 GI:244790004 GI:244790009 GI:244790019

0.012

purifying

0.097

GI:82571721 GI:34784984 GI:21951814 GI:21984183

0.482

none

Involved in vesicle trafficking and melanosome distribution

BCAP31

GI:213511729 GI:213511011 GI:213511507 GI:40807164 GI:15680022

0.048

purifying

0.055

GI:374253795 GI:374253793

0.083

none

multi-pass endoplasmic reticulum transmembrane protein

ILK

GI:3150001 GI:16306740 GI: 8648884

0.013

purifying

0.007

GI:62420874 GI:62420871 GI:62420872 GI:8308037

0.222

none

serine/threonine protein kinase

LIMS1

GI:13529136 GI:336455030

0.016

purifying

0.007

GI:164697166 GI:34528462 GI:336455029

<0.001

purifying

likely involved in integrin signaling

CHST4

GI:23273964 GI:262205557 GI:262205902

0.016

purifying

0.021

GI:262205902 GI:262205557

0.018

purifying

sulfotransferase; modifies glycan structures on ligands of the lymphocyte homing receptor L-selectin

EBAG9

GI:17389375 GI:13528905 GI:18490914

0.009

purifying

0.072

GI37694064 GI:37694063 GI:158254733

0.289

none

tumor-associated antigen

NACA

GI:76779232 GI:333033786 GI:163965363

0.002

purifying

0.186

GI:85397251 GI:85397957 GI:60116922

0.755

none

Component of nascent polypeptide-associated complex; prevents mistranslocation of proteins

OCIAD1

GI:13097314 GI:56789926

0.036

purifying

0.065

GI:269914125 GI:269914123 GI:269914126 GI:269914124 GI:269954665

0.005

purifying

tumor-associated antigen

P-distances are given for cancer-associated sequences only. See text for further explanation.

Analyses were performed using the Molecular Evolutionary Genetics Analysis (MEGA) software Version 5[20]. Following sequence alignment using the ClustalW method, the Nei-Gojobori Z-Test of Selection[21] was used to calculate the synonymous to nonsynonymous base substitution rates and the associated statistical probabilities. P-values of less than 0.05 were considered significant.

Results

A total of 46 cancer-derived genes represented by 139 sequences were identified (Table1). No sequences were derived from propagated cell lines. However, we were unable to determine what proportion of examples were from primary tumors vs metastatic tumors. Of the 46 genes, nine genes showed evidence of purifying selection and 1 showed evidence of diversifying selection (Table1). Six genes showed molecular evidence of selection only in cancer associated sequences (all in the form of purifying selection), four genes showed evidence of selection only in non-cancer associated sequences (three cases of purifying selection and one case of diversifying selection), and finally four genes showed molecular evidence of selection in both cancer and non-cancer associated sequences (three cases of purifying selection and one case of diversifying selection; Table1). Table1 also gives the GenBank accession numbers for all sequences used as well as sequence divergence estimates (p-distances) and the results of the Nei-Gojobori Z-tests of selection.

If signatures of selection become more common as mutations accumulate in a cancer-associated sequence, we might expect to see greater nucleotide divergence estimates in examples showing significant selection. To test this, we compared p-distances in the 10 examples showing molecular evidence of selection in the cancer associated sequences with the 36 examples not showing evidence of selection in the cancer associated sequences. The mean p-distance of sequences showing evidence of selection was 0.125, while the mean p-distance of sequences not showing evidence of selection was 0.082 (unpaired t-test, p=0.398).

Discussion

We describe a proof of principle test of a method of identifying molecular signatures of natural selection in cancer-derived gene sequences. We also show that in a sample of 46 genes the cancer and non-cancer derived sequences show different patterns of selection.

As a cancer grows and evolves and different genes come under selection pressure, natural selection may be expected to record evidence of this selection in the proportion of synonymous to nonsynonymous base substitutions as we have discussed here. Even if that particular gene later becomes non-functional through further mutations, evidence of prior selection pressure would be expected to persist. Thus a list of genes showing molecular evidence of selection only in cancer cells could be considered to be those genes which have been important to the survival of the cancer cell up to that point on time. In essence, this provides us with a method to determine which genes have been integral to the survival the cancer cell.

There are several potential weaknesses to our study. First, a different number of sequences were available for the various genes we examined. With a greater number of sequences we may expect a greater power to detect signatures of selection. To test such an effect we compared the mean number of sequences from genes which showed selection (3.17) to the mean number of sequences from genes which did not show selection (3.27). The difference was not statistically significant (p=0.134, unpaired t-test). Therefore, although this is a potential theoretical concern, we can find no evidence of this in our data.

Second, we do not have information about the geographic or racial origins of the individuals from whom the cancer and non-cancer gene sequences were derived. It is possible that increased variability noted for some genes could be due to these factors.

Third and perhaps most importantly, the choice of the model to calculate dN/dS as well as the test interpretation are both potentially controversial. The Nei-Gojobori method is perhaps less conservative than a maximum likelihood model but at the same time if the majority of sites in a protein evolve under purifying selection (as we might expect in a functionally essential gene in a tumour) the dN/dS statistic has reduced sensitivity to detect positive selection[22]. Moreover, the behaviour of dN/dS statistics when applied to polymorphisms within a population may behave differently than when applied to fixed mutations between species[23]. Whether cancer cells from the same tumour and/or from tumours from different individuals are sufficiently diverged to be considered analogous to different species[24] is a critical unanswered question. Therefore, because of these uncertainties, we decided to use the simple Nei-Gojobori statistic for this preliminary analysis. As major cancer sequencing initiatives begin producing whole genome sequences from paired cancer/normal samples from the same patient, this question will become more important. Further work should critically examine the optimal statistic to be used for these analyses.

Although we could not detect a statistically significant difference in the mean p-distances between cancer associated sequences showing evidence of selection and those that did not, there was a trend toward greater p-distances among the sequences showing selection and so our inability to demonstrate a difference may be a factor of the limited sample size.

Parenthetically, the process postulated here, where relentless mutation in cancer cells results in either mutational inactivation of genes or positive selection to maintain their function gives a functional explanation for why more advanced cancers invariably show what pathologists refer to as “de-differentiation”; as Mueller’s ratchet[25] removes all but the reproductively essential genes.

It will be obvious that the ability of gene sequences to display evidence of natural selection is based both on a high cancer cell mutation rate and an increased cancer cell proliferative rate which together provide the raw material on which selection can act. As these conditions likely are greater in more advanced cancers, we would expect to see greater molecular evidence of selection in later stage cancer cells. Indeed, comparison of early and later stage cancer cells could provide a roadmap of when particular genes experience selection pressure and therefore when these genes are important for tumorigenesis. Furthermore, because the molecular signatures of selection would be expected to persist for many generations of cancer cells, late stage cancers would be expected to contain a molecular record of genes conserved at essentially any stage of the clonal evolution of the cancer cell, even if that gene is no longer under selection pressure or even is no longer functional. By this line of reasoning, genes which are epigenetically silenced would be shielded from selection and may be expected to eventually be subject to loss of function mutations, even if they maintain molecular evidence of prior natural selection during tumorigenesis.

We caution that our results with regards to specific genes should be interpreted as preliminary only. Our sample was based only on publicly available sequences and encompassed a number of different malignancies making any conclusions about gene function based on these findings premature. Furthermore, this approach may not distinguish between driver genes which promote oncogenesis and non-driver genes nevertheless essential for cancer cell growth and reproduction. However, the application of previously described methods could be used to distinguish these[16, 17].

As new databases of cancer genomes become available[14, 1727], a future direction for this work will be to apply these techniques to whole genome sequences of cancer cells. This could be performed at the level of the tumour as a whole to look at genes important across a sample of tumours of the same type or it could be applied to single cells to explore the genes of importance in particular microenvironments such as metastatic deposits. This approach, combined with oncogenetic reconstruction of cancer clonal lineages using the same sequencing data could provide a powerful new tool to identify candidate genes of functional significance for potential targeted therapies as well as providing new insights into the evolutionary mechanisms of cancer cell clonal evolution.

Conclusions

Genes may be under different selection pressures within a cancer as compared to normal tissues. In this paper we proposed a method to answer the question of what genes are important to a cancer cell. The high mutation rates and rapid cell division present in cancer suggests that functionally important genes will show evidence of selection. We could therefore, in an indirect manner, observe what genes a cancer cell needs to survive. The genes that are important could then form a list of possible targets for therapeutic intervention.

Declarations

Authors’ Affiliations

(1)
Bachelor of Health Sciences Program, Faculty of Medicine, Room G503, O’Brien Centre for the BHSc
(2)
Calgary Laboratory Services and Department of Pathology and Laboratory Medicine, University of Calgary, C414, Diagnostic and Scientific Centre

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© Ovens and Naugler; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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