Space-time clustering analyses of acute lymphoblastic leukaemia in children, by immunophenotype, were carried out using a population-based registry. Significant evidence was found of space-time clustering for cases of the precursor B-cell sub-type, in the childhood peak, based on time and location at birth

EG Knox

F E Alexander

J M Birch

JM Birch

L Kinlen

LJ Kinlen

M Smith

MF Greaves

PJ Diggle

R J Q McNally

RJQ McNally

English

PubMed

Crossref

Space-time clustering analyses of childhood acute lymphoblastic leukaemia by immunophenotype

Space-time clustering analyses of acute lymphoblastic leukaemia in children, by immunophenotype, were carried out using a population-based registry. Significant evidence was found of space-time clustering for cases of the precursor B-cell sub-type, in the childhood peak, based on time and location at birth. © 2002 Cancer Research UK

Mcnally, Richard

McNally, Richard J Q

Alexander, F. E.

Birch, J. M.

The University of Manchester - Institutional Repository

Mcnally, R J Q

Alexander, F E

Birch, J M

Edinburgh Research Explorer

     Edinburgh Research Explorer                                      Space-time clustering analyses of childhood acute lymphoblasticleukaemia by immunophenotypeCitation for published version:Mcnally, RJQ, Alexander, FE & Birch, JM 2002, 'Space-time clustering analyses of childhood acutelymphoblastic leukaemia by immunophenotype' British Journal of Cancer, vol 87, no. 5, pp. 513-515. DOI:10.1038/sj.bjc.6600498Digital Object Identifier (DOI):10.1038/sj.bjc.6600498Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordPublished In:British Journal of CancerPublisher Rights Statement:Available under Open AccessGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 28. Apr. 2017Short CommunicationSpace-time clustering analyses of childhood acute lymphoblasticleukaemia by immunophenotypeRJQ McNally*,1, FE Alexander2 and JM Birch11Cancer Research UK Paediatric & Familial Cancer Research Group, Stancliffe, Central Manchester and Manchester Children’s University Hospitals NHS Trust,Hospital Road, Manchester M27 4HA, UK; 2Department of Public Health Sciences, The University of Edinburgh Medical School, Teviot Place, Edinburgh EH89AG, UKSpace-time clustering analyses of acute lymphoblastic leukaemia in children, by immunophenotype, were carried out using apopulation-based registry. Significant evidence was found of space-time clustering for cases of the precursor B-cell sub-type, inthe childhood peak, based on time and location at birth.British Journal of Cancer (2002) 87, 513 – 515. doi:10.1038/sj.bjc.6600498 www.bjcancer.comª 2002 Cancer Research UKKeywords: acute lymphoblastic leukaemia; children; aetiology; infection; immunophenotype; space-time clusteringWe have previously reported significant evidence of space-timeclustering in childhood leukaemia, diagnosed during 1954 –1985, and based on place of birth and time of diagnosis, parti-cularly among cases of acute lymphoblastic leukaemia [ALL]aged 0 – 4 years (Birch et al, 2000). The finding of space-timeclustering is consistent with the involvement of infectious agentsin the aetiology. There have been several hypotheses, supportedby indirect epidemiological evidence, proposing such a role forinfections. Kinlen (1988) demonstrated that situations of unusualrural population mixing, which promoted contacts betweensusceptible and infected individuals, are associated with excessesof ALL, whilst Greaves (1988) has proposed that delayed expo-sure to common infections would lead to a greater chance ofthe occurrence of precursor B-cell (common) ALL. Smith(1997) suggested that the relevant infectious exposures occurin utero. Our previous findings were consistent with Greaves’model and we hypothesised that the clustering cases wouldmainly involve precursor B-cell ALL. It has not been possiblepreviously to test this hypothesis directly, because no popula-tion-based data on ALL, by immunophenotype, were availableover a sufficiently long time period. We have now been ableto perform such an analysis, using cases of ALL included inthe Manchester Children’s Tumour Registry (MCTR), during1980 – 2001, for which full data on immunophenotype are avail-able. The aim of this new study was to test the prediction thatspace-time clustering is concentrated among cases of the precur-sor B-cell sub-type.PATIENTS AND METHODSAll cases, diagnosed with ALL between the 1st January 1980 and31st December 2001, and registered by the MCTR were analysed.Ordnance Survey [OS] eight-digit grid references were allocatedto each case with respect to addresses at time of birth and diag-nosis, locating each address to within 0.1 kilometre. Thefollowing diagnostic groups were specified a priori for analysis:(1) all precursor B-cell (common) ALL; (2) precursor B-cellALL, aged 18 – 54 months; (3) all non-precursor B-cell ALL;(4) non-precursor B-cell ALL, aged 18 – 54 months; (5) totalALL.There are four possible space-time interactions between: (1)times and places of diagnosis; (2) times and places of birth; (3)time of diagnosis and place of birth; and (4) time of birth andplace of diagnosis. The interpretation of these interactions willdepend on the extent of migration between birth and diagnosisamong cases (Birch et al, 2000).Knox (1964) tests were applied to the data with thresholds fixed,a priori, as: close in space, less than 5 km, and close in time, lessthan 1 year apart. One-sided tests were used to detect a significantinteraction. The strength of interactions (S) was indicated by calcu-lating ((O7E)/E)6100 counts of pairs which are close in spaceand close in time (O, observed number of close pairs, and E,expected number of close pairs). To adjust for the effects of differ-ent population densities, the tests were repeated replacinggeographical distance thresholds by distance to the Nth nearestneighbour, using all locations of all the cases in the data set exceptaddresses for the same child at a different time. N was chosen suchthat the mean distance was 5 km (N=40 for birth locations and 41for diagnosis locations).As previously discussed, two problems are apparent with theKnox test: boundary problems and the arbitrariness of the thresh-olds chosen (McNally et al, 2002). To overcome these a secondorder procedure based on K-functions (Diggle et al, 1995) is used,employing both geographical distance and nearest neighbour (NN)approaches as described above.Data were also analysed by examining clustering pairs whichcontained at least one male case (‘male: any’) and clustering pairswhich contained at least one female case (‘female: any’). Usinginternal methods, addresses were classified as being located in amore densely populated area, or a less densely populated area(McNally et al, 2002). Analysis by population density was underta-ken by considering clustering pairs which contained at least onecase from a more densely populated area (‘more densely populated:any’) and clustering pairs which contained at least one case from aless densely populated area (‘less densely populated: any’). TheEpidemiologyReceived 9 April 2002; revised 27 May 2002; accepted 29 May 2002*Correspondence: Dr Richard JQ McNally;E-mail: richard.mcnally@man.ac.ukBritish Journal of Cancer (2002) 87, 513 – 515ª 2002 Cancer Research UK All rights reserved 0007 – 0920/02 $25.00www.bjcancer.commethods are given in more detail in Birch et al (2000) and McNallyet al (2002).RESULTSThe study included 512 cases of ALL, comprising 395 precursor B-cell ALL, 63 T-cell, 36 Null-ALL, 9 B-cell, 1 non-B, non-T cell andeight unclassified. Based on place and time of birth the only statis-tically significant evidence of space-time clustering was for cases ofprecursor B-cell ALL, aged 18 – 54 months (P=0.05, using the NNthreshold version of the K-function method, Table 1a). Examina-tion of cases of precursor B-cell ALL, aged 18 – 54 months, bygender and population density (Table 1b) shows that the space-time clustering was confined to clustering pairs of cases whichcontained at least one female case (P50.1, using all four methods).There was no association with population density level. There wasno evidence for space-time clustering in any group based on timeand place of diagnosis, time of diagnosis/place of birth nor on timeof birth/place of diagnosis.DISCUSSIONOur previous study (Birch et al, 2000) showed striking space-timeclustering among a proportion of cases of ALL aged 0 – 4 years,which was based on time of diagnosis/place of birth. In contrast,the present study found limited evidence for clustering based ontime of birth/place of birth only. However, even though the resultswere statistically less significant than in the previous larger study,the clustering for the 18 – 54 month age group (the childhoodpeak) of precursor B-cell ALL clearly stood out. This is the firststudy to demonstrate that space-time clustering for cases of ALLin the childhood incidence peak is specifically due to the precursorB-cell sub-type. The results are consistent with a role for infections,but with exposure occurring pre-natally, or around the time ofbirth.The clustering was only apparent among space-time pairs thatinvolved at least one female case. This differs from our previousstudy, which found an excess of male cases over females involvedin space-time pairs. Interestingly, our recent study of incidenceEpidemiologyTable 1 Space-time clustering tests for ALL, in children aged 0 – 14 years from North West England, based on time and place of birth, and diagnosedduring the period 1980 – 2001KNOX TEST [Observed space-time pairsa,expected space-time pairs, strengthb, K-FUNCTION ANALYSISfP-valuec] [Observed integralg, P-valuehGeographical GeographicalDistanced NN Thresholde Distancei NN Thresholdja. Analyses by Disease group and AgePrecursor B-cell ALL O=212; E=208.0 O=106; E=107.4 I=71.08 I=70.07(aged 0 – 14 years) S=2.0% S=71.3% (P=0.52) (P=0.50)(P=0.40) (P=0.53)Precursor B-cell ALL O=77; E=70.8 O=45; E=35.3 I=18.61 I=29.76(aged 18 – 54 months) S=8.7% S=27.6% (P=0.09) (P=0.05)(P=0.25) (P=0.06)Non-precursor B-cell ALL O=17; E=14.2 O=6; E=7.5 I=1.25 I=713.02(aged 0 – 14 years) S=19.8% S=719.8% (P=0.44) (P=0.79)(P=0.26) (P=0.62)Non-precursor B-cell ALL O=1; E=0.9 O=1; E=0.8 I=71.78 I=0.04(aged 18 – 54 months) S=13.3% S=27.5% (P=0.66) (P=0.42)(P=0.59) (P=0.54)All ALL O=325; E=332.6 O=167; E=175.8 I=716.53 I=714.03(aged 0 – 14 years) S=72.3% S=75.0% (P=0.89) (P=0.76)(P=0.65) (P=0.73)b. Analyses of precursor B-cell ALL aged 18 – 54 months by gender and population density‘Male: any’ clustering pairs O=58; E=59.0 O=32; E=27.8 I=5.48 I=13.50S=71.7% S=15.3% (P=0.41) (P=0.25)(P=0.52) (P=0.23)‘Female: any’ clustering pairs O=53; E=43.9 O=35; E=23.6 I=20.96 I=36.83S=20.9% S=48.6% (P=0.067) (P=0.028)(P=0.098) (P=0.016)‘More densely populated: O=58; E=59.1 O=25; E=21.4 I=6.45 I=12.69any’ clustering pairs S=71.9% S=17.1% (P=0.32) (P=0.24)(P=0.52) (P=0.24)‘Less densely populated: O=27; E=24.1 O=24; E=21.3 I=11.43 I=18.28any’ clustering pairs S=12.0% S=12.9% (P=0.16) (P=0.13)(P=0.30) (P=0.30)aCases are close in time if dates of diagnosis/birth differ by less than 1 year. bStrength (S)={(Observed – Expected)/Expected}6100 counts of pairs which are close in time andspace. c1-sided P-value derived from the Poisson distribution. dWhen using geographical distance cases are close in space if their locations are 55 km apart. eWhen using nearestneighbour (NN) thresholds cases are close in space if the locations of one (or both) was nearer than the other’s 41st NN in the total data set, for diagnosis, or 40th NN for birth.fCases are close in time if dates differ by 5t where t is in the range 1 – 15 months. gI ¼ R R(s,t) ds dt, where R(s,t)=[K(s,t)-K1(s)K2(t)]/p [K1(s)K2(t)] K(s,t)=proportion of pairswhose distance apart is 4t in time and 4s in space, K1(s)=proportion of pairs whose distance apart is 4s, and K2(t)=proportion of pairs whose distance apart is 4t. hP-valueobtained by simulation (999 runs) with dates of birth randomly re-allocated to the cases in the analysis. iCases are close in space if distances between their locations differ by 5swhere s is in the range 0.5 – 7.5 km. jCases are close in space if either is within the distance to the Nth nearest neighbour of the other (in the total data set) where N is in therange 34 – 48, for diagnosis, or 33 – 47 for birth.Space-time clustering in childhood leukaemiaRJQ McNally et al514British Journal of Cancer (2002) 87(5), 513 – 515 ª 2002 Cancer Research UKtrends in ALL (McNally et al, 2000), showed that the observedincrease was attributable to the precursor B-cell sub-type, and indi-cated a faster recent rate of increase amongst females.The present data appear to be more consistent with Smith’shypothesis (Smith, 1997) that the childhood peak is due to anin-utero exposure to infection, rather than with Greaves’ hypoth-esis of delayed exposure to common infections (Greaves, 1988).The data are also consistent with Kinlen’s hypothesis of populationmixing (Kinlen, 1995), particularly in relation to one study whichdemonstrated a marked excess of leukaemia in children under 1year of age suggesting an infection during pregnancy (Kinlen andHudson, 1991). The differences between males and females maybe indicative of an immune modulation hypothesis, with femalesbeing more susceptible to leukaemogenic events resulting fromexposure to infection. Relative to our earlier findings the currentevidence would suggest that there is greater exposure to the aetio-logical agent at an earlier age, or a greater prevalence of the agent,or a new aetiological agent. Whichever of these applies we wouldhypothesise that the agent would be an infection. Finally, it shouldbe stressed that a direct comparison between this study and theprevious one is not possible, because data on immunophenotypewere not available in the previous analysis.ACKNOWLEDGEMENTSThe Manchester Children’s Tumour Registry is supported byCancer Research UK. Jillian M Birch is Cancer Research UKProfessorial Fellow at the University of Manchester. We thankMr DP Cairns, Mrs EA Dale, Mrs DA Elliott, Mrs JF Hogg andMr C Nikolaisen for all their hard work on data processing andverification.REFERENCESBirch JM, Alexander FE, Blair V, Eden OB, Taylor GM, McNally RJQ (2000)Space-time clustering patterns in childhood leukaemia support a role forinfection. Br J Cancer 82: 1571 – 1576Diggle PJ, Chetwynd AG, Haggvist R, Morris SE (1995) Second-order analysisof space-time clustering. Stat Methods Med Res 4: 124 – 136Greaves MF (1988) Speculations on the cause of childhood acute lymphoblas-tic leukaemia. Leukemia 2: 120 – 125Kinlen L (1988) Evidence for an infective cause of childhood leukaemia:comparison of a Scottish New Town with nuclear reprocessing sites inBritain. Lancet 2: 1323 – 1327Kinlen LJ, Hudson C (1991) Childhood leukaemia and poliomyelitis in rela-tion to military encampments in England and Wales in the period ofnational military service, 1950 – 63. Br Med J 303: 1357 – 1362Kinlen LJ (1995) Epidemiological evidence for an infective basis in childhoodleukaemia. Br J Cancer 71: 1 – 5Knox EG (1964) The detection of space-time interactions. Applied Stats 13:25 – 29McNally RJQ, Birch JM, Taylor GM, Eden OB (2000) Incidence of childhoodprecursor B-cell acute lymphoblastic leukaemia in north-west England.Lancet 356: 485 – 486McNally RJQ, Cairns DP, Eden OB, Alexander FE, Taylor GM, Kelsey AM,Birch JM (2002) An infectious aetiology for childhood brain tumours?Evidence from space-time clustering and seasonality analyses. Br J Cancer86: 1070 – 1077Smith M (1997) Considerations on a possible viral etiology for B-precursoracute lymphoblastic leukemia of childhood. J Immunother 20: 89 – 100EpidemiologySpace-time clustering in childhood leukaemiaRJQ McNally et al515ª 2002 Cancer Research UK British Journal of Cancer (2002) 87(5), 513 – 515

Space-time clustering patterns in childhood leukaemia support a role for infection.

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Space-time clustering analyses of childhood acute lymphoblastic leukaemia by immunophenotype

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