Annals of Oncology 22: 383–389, 2011
Published online 30 July 2010
Clinical validation of an autoantibody test for lung
P. Boyle1, C. J. Chapman2, S. Holdenrieder3, A. Murray4, C. Robertson5, W. C. Wood6,
P. Maddison7, G. Healey4, G. H. Fairley4, A. C. Barnes4& J. F. R. Robertson2*
1International Prevention Research Institute, Lyon, France;2Division of Surgery, The University of Nottingham, Nottingham, UK;3Institute of Clinical Chemistry,
University Hospital Munich, Munich, Germany;4Oncimmune Ltd, Nottingham City Hospital, Nottingham;5Department of Mathematics and Statistics, University of
Strathclyde, Glasgow, UK;6Department of Surgery, Emory University School of Medicine, Atlanta, USA;7Department of Neurology, Queen’s Medical Centre,
Received 1 April 2010; revised 28 May 2010; accepted 31 May 2010
Background: Autoantibodies may be present in a variety of underlying cancers several years before tumours can be
detected and testing for their presence may allow earlier diagnosis. We report the clinical validation of an autoantibody
panel in newly diagnosed patients with lung cancer (LC).
Patients and methods: Three cohorts of patients with newly diagnosed LC were identified: group 1 (n = 145), group
2 (n = 241) and group 3 (n = 269). Patients were individually matched by gender, age and smoking history to a control
individual with no history of malignant disease. Serum samples were obtained after diagnosis but before any
anticancer treatment. Autoantibody levels were measured against a panel of six tumour-related antigens (p53,
NY-ESO-1, CAGE, GBU4-5, Annexin 1 and SOX2). Assay sensitivity was tested in relation to demographic variables
and cancer type/stage.
Results: The autoantibody panel demonstrated a sensitivity/specificity of 36%/91%, 39%/89% and 37%/90% in
groups 1, 2 and 3, respectively, with good reproducibility. There was no significant difference between different LC
stages, indicating that the antigens included covered the different types of LC well.
Conclusion: This assay confirms the value of an autoantibody panel as a diagnostic tool and offers a potential
system for monitoring patients at high risk of LC.
Key words: autoantibodies, clinical validation, lung cancer, newly diagnosed patients
Lung cancer (LC) is the worldwide leading cause of cancer-
related mortality . Tobacco smoking is estimated to cause
upwards of 90% of cases, and other recognised risk factors
include passive smoking, occupational exposure, especially to
asbestos and radon exposure . Outcomes are substantially
better with early localised disease compared with locally
advanced and metastatic disease, with 5-year survival rates of
53%, 23.7% and 3.5%, respectively .
Although the latent period of LC in smokers is reported to be
at least 20 years , ?85% of patients with LC remain
undiagnosed until the disease is symptomatic and has reached
an advanced stage . At present, there is nothing to offer for
early diagnosis, although ongoing clinical trials are
investigating the use of spiral computed tomography (CT) in
‘at-risk’ individuals [3–12]. However, the radiation dose
delivered and the substantial costs limit its widespread
application as a screening procedure . Furthermore, the
high rate of false positives (as high as 50% in a prevalence
round)  dictates that many individuals require follow-up
examinations and a substantial proportion of individuals
undergo unnecessary thoracotomy . Application of a filter
such as a blood-based marker to identify smokers at the highest
risk for LC may improve the positive predictive value (PPV)
of these screening tools [11, 15].
There is a considerable body of evidence documenting the
presence of circulating antibodies to autologous cellular
antigens [referred to as tumour-associated antigens (TAA)] in
serum samples from patients with a variety of cancers,
including LC [16–24]. Monitoring persons at increased risk of
cancer for the presence of serum autoantibodies may allow
earlier detection of the disease.
The panel of proteins selected for investigation comprised
a number of well-recognised TAA, four of which (p53,
NY-ESO-1, CAGE and GBU4-5) have been described by
ourselves in a previous publication to induce the production of
autoantibodies or immune biomarkers in LC . In brief, p53
is a tumour suppressor gene, which is often mutated in cancer
and to which autoantibodies were first described ,
*Correspondence to: Prof. J. F. R. Robertson, Division of Breast Surgery, University of
Nottingham, Nottingham City Hospital, Hucknall Road, Nottingham NG5 1PB, UK.
Tel: +44 (0)115-823-1876; Fax: +44 (0)115-823-1877;
ª The Author 2010. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
autoantibodies to this protein have also been detected in some
cases, even before the cancer diagnosis [26, 27]. NY-ESO-1 and
CAGE are both cancer testis antigens whose expression has
been described in a number of solid tumours [28, 29] and with
GBU4-5, a protein of unknown function that encodes a DEAD
box domain, have also been described as inducing
autoantibodies in LC [24, 30, 31].
The remaining antigens SOX2, a member of the SOXB1
family of proteins that is described as inducing an autoantibody
responses in small-cell lung cancer (SCLC) [32, 33], and
Annexin I, a phospholipid-binding protein to which
autoantibodies, have also been described .
The selection of these antigens was confirmed following
screening of a panel of >20 potential antigens as being of
greatest diagnostic utility for the diagnosis of all non-small-cell
lung cancer (NSCLC) and SCLC cancer (C. Chapman,
This manuscript reports the clinical validation set for these
autoantibodies in the serum of patients with newly diagnosed
LC (before any treatment) and matched controls.
patients and methods
Findings from three separate groups of patients with newly diagnosed LC
are reported. The third group is the final validation set where the data were
run in a blinded manner. All patients with LC were as far as possible
individually matched by gender, age and smoking history to a control
individual with no previous history of malignant disease. In patients with
LC, blood samples were obtained after diagnosis but before receiving any
anticancer treatment. Demographic characteristics of the control versus the
study population are given in the Appendix 1.
Group 1 comprised 145 patients with stage I/II LC (including NSCLC
and SCLC) and 146 controls treated in centres in the United States and
Russia. All subjects in this group were smokers; baseline patient
characteristics are shown in Table 1. Group 2 comprised 241 patients with
LC treated at a single centre in Germany as part of a collaborative study
(Table 1). Tumour pathological information was available for the patients
with LC, including tumour, node, metastasis staging and NSCLC histology
(Table 2). In group 2, an additional 88 sera from unmatched individuals
(25 normal and 63 with benign lung conditions) supplied by the single
centre were analysed (Appendix 1).
Group 3 comprised 269 patients with LC treated at centres in the United
States, UK and Ukraine (Table 1). This group was assembled to validate the
calibration and control scheme for the autoantibody assay. Tumour
pathological information was available for the patients with LC (Table 2).
The timeline for collection of samples from patients is shown in
supplemental Table S1 (available at Annals of Oncology online).
Serum samples in group 1 were evaluated for autoantibodies against p53,
NY-ESO-1, CAGE and GBU4-5. Serum samples in groups 2 and 3 were
evaluated for autoantibodies against the same four antigens plus Annexin 1
and SOX2. In groups 2 and 3, samples from patients with cancers, matched
normals, benign lung disease and control sera for the assay were
interspersed in the order samples were assayed so that any batch effects
would be spread over all sample types. The laboratory staff running the
assay was blinded to the disease state of individual samples. Group 2,
therefore, was a validation set for the results seen in group 1 for four of the
antigens (i.e. p53, NY-ESO1, CAGE and GBU4-5) with the added value of
Annexin 1 and SOX2. Group 3 validated a calibrated and controlled assay
on the whole panel of six antigens.
Autoantibodies were determined by a quality-controlled, semi-automated
indirect enzyme-linked immunosorbent assay in which samples were
allowed to react with a titration series of antigen concentrations. All liquid
handling steps were carried out using an automated liquid handling system.
Briefly, purified recombinant antigens were diluted to provide a semi-log
titration series for each antigen from 160 to 1.6 nM . Control antigens
consisting of the purified BirA or NusA tags were also included to allow
subtraction of the signal due to nonspecific binding to bacterial
contaminants. Antigen dilutions were adsorbed to the surface of microtitre
plate wells in phosphate buffer at room temperature. After washing in
phosphate-buffered saline containing 0.1% Tween 20 (pH 7.6), microtitre
plates were blocked with a gelatine-based blocking buffer. Serum samples
(diluted 1 in 110 in a blocking buffer) were then added to the plates and
allowed to incubate at room temperature with shaking for 90 min.
Following incubation, plates were washed and horseradish peroxidase-
conjugated rabbit anti-human IgG (Dako, Glostrup, Denmark) was added.
After a 60-min incubation, the plates were washed and 3,3#,5,5#-
tetramethylbenzidine was added. Colour formation was allowed to proceed
for 15 min before the optical density (OD) of each well was determined
spectrophotometrically at 650 nm .
Calibration standards of known potency are not available for assays to
measure autoantibodies against TAAs. Therefore, a calibration system was
devised which utilised fluids drained from pleural or ascitic cavities of
patients with LC . The calibration system was only evaluated for group
3 samples. A reportable dilution range for each antigen, giving acceptable
calibration precision, was determined at 7.5%–92.5% of the upper
asymptote of the average calibration curve, equivalent to ?5.0 natural log
reference units (RU). These data were used to construct a calibration curve
of OD versus log dilution to which a four-parameter model plot was fitted
. The background-corrected OD value for each unknown sample was
then converted to a calibrated log RU.
Samples were judged to be positive if they fulfilled two criteria—i.e. they
showed a dose response to the antigen titration series and the measured
autoantibody signal to one or more of the antigens was above the accepted
cut-off set for that antigen assay. The autoantibody signal for a sample was
defined as above the cut-off when the result was greater than the calculated
cut-off for the control population at either of the two highest points on the
titration curve. All assays were carried out as two replicates and the mean
value taken as the overall assay measurement.
optimisation of assay cut-offs
A specificity of 90% was selected in order to produce a test which could be
used for early detection in a high-risk population and which would be
health economically viable. For all groups, cut-offs based on mean + 3
Table 1. Lung cancer patient characteristics
(n = 145)
(n = 241)
(n = 269)
Median age, years (range)
Patients >60 years, n (%)
Gender, n (%)
Smoking history, n (%)
0 (0.0) 241 (100.0)
Annals of Oncology
384 | Boyle et al.Volume 22|No. 2|February 2011
standard deviations (SDs) of the normal population were used. In addition,
for groups 2 and 3, the cut-offs were optimised using a Monte Carlo direct
search method  to find a set of antigen-specific cut-offs yielding the
maximum sensitivity for the fixed specificity of 90%.
For a set of possible cut-offs for the six panel antigens chosen by Monte
Carlo sampling over the feasible range, the specificity/sensitivity was first
estimated from the data. This was carried out 100 000 times. All
combinations with a specificity of ?90% were then extracted and the
combination yielding the maximum sensitivity used. This is a process
dependent on assay conditions and when new batches of proteins, or new
types of protein, are introduced to the panel, new cut-offs will have to be
To support the quoted specificity/sensitivity panel results, the area under
the curve (AUC) and standard error (SE) for the respective receiver
operating characteristic (ROC) curve was calculated for each group. The
ROC curve was constructed by calculating the specificity and sensitivity of
the test for a succession of deviations from the original cut-offs, with the
same deviation for each antigen in the panel.
adjustment for LCs in the control populations
The cut-offs are best set by comparing the results in a group of patients with
known LC and a group of high-risk individuals (e.g. smokers and ex-
smokers) who are known to not have the disease. However, the latter
population is difficult to identify since the CT screening studies have clearly
shown there are a percentage of smokers/ex-smokers who at any one time
are ‘harbouring’ an asymptomatic LC. In the prevalence round, the
percentage of undiagnosed occult cancers has been reported to be between
0.5% and 2.7% in heavy smokers, while in incidence rounds, it has been
reported to be up to 2.3% [3–12]. For this reason, adjusted specificity and
sensitivity values assuming some degree of occult LCs in the control
populations were also calculated.
The method used to calculate and adjust for the presence of undiagnosed
cancers in the controls used LC prediction models for which the most
important predictors are age, current smoking status and smoking history,
and family history of smoking-related cancers .
In group 1, autoantibodies to four antigens (p53, NY-ESO-1,
CAGE and GBU4-5) were measured as raw OD values. Using
cut-offs based on mean + 3 SDs gave a sensitivity of 36% with
a specificity of 91% (50 of the 137, 8 unassessable). The
sensitivities and specificities for each of these four antigens and
the reproducibility of these assays have been reported elsewhere
. The sensitivity and specificity of the panel was similar for
males and females. The ROC curve AUC was 0.71 (SE = 0.03).
In group 2, autoantibodies to six antigens (p53, NY-ESO-1,
CAGE, GBU4-5 plus Annexin 1 and SOX2) were measured as
raw OD values, with cut-offs based on mean + 3 SDs producing
sensitivity and specificity values of 34% (80 of the 234, 7
unassessable) and 91%, respectively. Again, individual
sensitivities and specificities for these six antigens have been
reported elsewhere . Using individually optimised cut-offs
for each antigen, the overall sensitivity was 39% (33%–45%)
(91 of the 234), with a specificity of 89%. In an at-risk
population of 20 LCs per 1000 population, this would result in
a PPV of 7.2% (i.e. 1 in 13.9 persons with a positive test would
have a LC) and a negative predictive value (NPV) of 98.6%.
The ROC curve AUC was 0.63 (SE = 0.03).
Table 2. Tumour stage and histology according to gender
Group 1 (n = 145)
Male (n = 81)
Group 2 (n = 241)
Male (n = 172)
Group 3 (n = 255a)
Male (n = 188) Female (n = 64)Female (n = 69)Female (n = 67)
Tumour type, n (%)
NSCLC stage, n (%)
NSCLC histology, n (%)
SCLC stage, n (%)
8 (30.8)10 (100.0)12 (100.0)
aTumour histology and stage data available for 255 of the 269 patients comprising group 3.
NSCLC, non-small-cell lung cancer; SCLC, small-cell lung cancer.
Annals of Oncology
Volume 22|No. 2|February 2011doi:10.1093/annonc/mdq361 | 385
Of the 88 unmatched sera received from the group 2 centre,
8 of the 88 (9%) were positive, of which none of the 25 (0%)
normal sera had raised autoantibodies, while 8 of the 63 (13%)
of individuals with benign lung disease had raised
autoantibodies. Follow-up data could only be obtained for one
of these eight individuals who was found to have developed
a gastric cancer, giving a specificity of at least 89% (55 of
In group 3, autoantibodies to the same six antigens as group
2 were measured as raw OD values and converted into
calibrated RU. Using the ODs and applying cut-offs based on
mean + 3 SDs gave a sensitivity of 32% (85 of the 269) with
a specificity of 91%. Using RU values with individually
optimised cut-offs for each antigen, the sensitivity was 37%
(31%–43%) (100 of the 269), with a specificity of 90%. In an
at-risk population of 20 LCs per 1000 population, this would
result in a PPV of 7.0% (i.e. 1 in 14.3) and an NPV of 98.6%.
The ROC curve AUC was 0.64 (SE = 0.02).
Individual antigen sensitivity and specificity are shown in
supplemental Table S2 (available at Annals of Oncology online).
adjustment for occult LCs within the control
Adjustment generated specific cut-offs for each antigen for the
different methods. The sensitivities for each antigen for a fixed
specificity of 90% are shown for the unadjusted and adjusted
method in Table 3. The most conservative estimate for adjusted
sensitivity is 40%, which in an at-risk group of 20 LCs per 1000
population would give a PPV of 7.5% (i.e. 1 in 13.3) and an
NPV of 98.7%.
effect of patient and disease characteristics on
autoantibody assay sensitivity and specificity
The calibrated group 3 dataset with an unadjusted sensitivity
and specificity of 37% and 90%, respectively, was used to assess
whether patient characteristics, tumour type or stage gave rise
to significant variation in the specificity/sensitivity (Figure 1).
Statistical comparison of subgroups with remaining controls
demonstrated no significant difference in sensitivity according
to patient gender, smoking status and age or tumour type or
stage (P > 0.10). There was also no significant difference in
sensitivity between those NSCLC tumours where the subtype
was known and those where it was unknown.
This report confirms a validated assay for the detection of
autoantibodies to selected cancer-associated antigens in the
peripheral blood. The value of a test for early cancer detection
is usually defined via a number of related parameters, including
sensitivity, specificity, PPV and NPV. A percentage of smokers/
ex-smokers are ‘harbouring’ an asymptomatic LC at any one
time. Even with the most conservative estimation of occult LCs,
the panel of autoantibodies can identify 40% of primary LCs,
including early stage of disease, with a specificity of 90% against
age-matched, gender-matched and smoking history-matched
controls. The specificity was similar (at least 89%) for patients
with benign disease.
Autoantibodies to p53 [26, 27, 40, 41], NY-ESO-1 [30, 31],
CAGE [29, 42], GBU4-5 , Annexin 1 [16, 18, 43] and SOX2
 have all been shown to be capable of inducing
autoantibodies in patients with LC. The data in this manuscript
further confirm the value of a panel of autoantibodies over
a single autoantibody assay [19, 23, 24, 35]. Recent publications
have reported autoantibodies to a natural form of Annexin 1
 and other antigens (e.g. 14-3-3 theta [43, 45] and LAMR1
), which are elevated in LC and up to 1 year before clinical
diagnosis. The combination of 14-3-3 theta, Annexin 1 and
LAMR1 gave an AUC on a combined ROC curve of 0.73. While
these results were based on a research assay, it is possible that
adding 14-3-3 theta and/or LAMR1 to the current panel might
increase the sensitivity.
Group 3 data confirm that there was no significant difference
between different stages of LC, although due to sample size the
confidence intervals were sometimes wide. Further evaluation
of the data was, therefore, carried out by comparing early-stage
(stage I/II NSCLC plus limited SCLC) with late-stage (stage III/
IV NSCLC plus extensive SCLC) disease, which again showed
no difference. The presence of such a signal in early-stage
disease is precisely what would be expected of an in vivo
amplification signal such as the humoural immune response.
Table 3. Comparison of performanceabefore and after adjustment for
the presence of undiagnosed occult cancers in the control population
Adjustment method Group 2Group 3
Occult cancer rate (5%)
Occult cancer rate (11%)
aSensitivity for specificity of 90% 6 1%, based on optimised cut-offs for
Figure 1. Forest plot showing the sensitivity at a fixed 90% specificity by
patient demographics, tumour characteristics and lung cancer stage. Line
shows unadjusted sensitivity of 37% (all stages of cancers) in group 3 (n =
269). NSCLC, non-small-cell lung cancer; SCLC, small-cell lung cancer.
Annals of Oncology
386 | Boyle et al. Volume 22|No. 2|February 2011
This is in contrast to cancer-associated antigens, which are
markers of tumour burden and not useful for the early
detection or screening of breast [46, 47] or colorectal cancer
Previous publications [16–24, 50] have highlighted the
potential value of a panel of autoantibodies for the early
detection of cancer. Using a panel of antigens, autoantibodies
have been reported up to 5 years before screening CT scans 
in LC and up to 4 years before screening mammography in
young women at increased risk [21, 23]. Other authors have
highlighted individual autoantibodies such as p53
autoantibodies detected before diagnosis of cancer in smokers
with chronic obstructive pulmonary disease  or in patients
with asbestosis . In the latter publication, the average lead
time (time from first positive sample to diagnosis) was 3.5 years
(range 1–12 years). There are similar publications on other
single autoantibodies [45, 51, 52]. These findings all indicate
the induction of autoantibodies happening relatively early in
the process of carcinogenesis.
This panel assay is the first to show reproducible results with
a calibration and control system and offers a potential system
for monitoring a population at high risk of LC, either alone or
in conjunction with imaging modalities (e.g. CT). The similar
sensitivities and specificities measured for these three datasets
and with different batches of proteins utilised emphasise the
robustness of these autoantibody assays and also confirm the
value of a panel of autoantibodies over a single autoantibody
At a fixed 90% specificity, the sensitivity of 40% is
a conservative estimate of the performance of the assay both in
terms of estimating the level of clinically occult LCs
(supplemental Table S2, available at Annals of Oncology online)
and also the sensitivity reported for SCLC (n = 73) in group 3.
The latter is lower than the 55% sensitivity and 90% specificity,
which the authors will report in a larger consecutive series (n =
242) from a single centre (C. J. Chapman, A. J. Thorpe,
A. Murray et al., unpublished data).
The sensitivity of 40% with a specificity of 90% are similar to
mammography in high-risk young women , while the
incidence of LCs in heavy smokers is at least three times the
incidence of breast cancer in a typical cohort of high-risk young
women [5, 7]. Therefore, in terms of absolute number of
cancers, this test should detect more LCs for every 1000 high-
risk persons tested than screening mammography would detect
breast cancers in a high-risk group of young women, even if
mammography were 100% sensitive rather than its current 40%
. This has to be seen in the context of a disease (i.e. LC),
which has a mortality rate between 85% and 95%. By way of
contrast, annual CT in the Mayo CT screening trial had
a specificity of 49% (with a sensitivity of 67%) in the prevalence
round. In an at-risk group of 20 of the 1000, CT gave a PPV of
2.5% (i.e. 1:40) and an NPV of 98.7%. The autoantibody test
with a sensitivity of 40% and a specificity of 90% would have
a PPV of 7.5% (i.e. 1 in 13.3) and an NPV of 98.7% in
a similar-risk group.
While such comparisons serve to highlight the potential
value of an autoantibody test for LC that has a specificity of
90%, the authors envisage the autoantibody technology and
imaging as being complementary.
Oncimmune Ltd and the University of Nottingham.
The authors would like to thank Petra Stieber and Joachim von
Pawel for their role in the collection of samples and data
included in this study. They also acknowledge Ce ´line Parsy-
Kowalska’s role in protein production and Jared Allen’s
contribution as the data manager. The manuscript was drafted
by the authors. However, the authors acknowledge and thank
Sandra Cusco ´, PhD, from Complete Medical Communications,
who provided final editorial assistance funded by Oncimmune
Ltd and the University of Nottingham.
CJC and JFRR are consultants to Oncimmune Ltd, a University
of Nottingham spinout company and JFRR holds stock. AM and
GH are full-time employees of Oncimmune Ltd. CR holds stock
option and is also a consultant to Oncimmune Ltd. WCW is the
scientific advisor for Oncimmune Ltd. GH is an employee of
Oncimmune Ltd. GHF is the Chairman of Oncimmune Ltd and
holds stock. ACB holds stock and options in Oncimmune Ltd
and has a significant conflict.
1. Boyle P, Levin B (eds). World Cancer Report 2008. Lyon, France: IARC 2008.
2. Horner MJ, Ries LAG, Krapcho M et al. (eds). SEER Cancer Statistics Review,
1975–2006. Bethesda, MD: National Cancer Institute 2009: http://
seer.cancer.gov/csr/1975_2006/, based on November 2008 SEER data
submission, posted to the SEER website (February 2010, date last accessed).
3. Sone S, Takashima S, Li F et al. Mass screening for lung cancer with mobile
spiral computed tomography scanner. Lancet 1998; 351: 1242–1245.
4. Henschke CI, McCauley DI, Yankelevitz DF, Altorki NK. Early lung cancer action
project: overall design and findings from baseline screening. Lancet 1999; 354:
5. Swensen SJ, Jett JR, Hartman TE et al. CT screening for lung cancer: five-year
prospective experience. Radiology 2005; 235: 259–265.
6. Sobue T, Moriyama N, Kaneko M et al. Screening for lung cancer with low-dose
helical computed tomography: anti-lung cancer association project. J Clin Oncol
2002; 20: 911–920.
7. Henschke CI, Naidich DP, Yankelevitz DF et al. Early lung cancer action project:
initial findings on repeat scanning. Cancer 2001; 92: 153–159.
8. Diederich S, Wormanns D, Semik M et al. Screening for early lung cancer with
low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 2002;
9. Nawa T, Nakagawa T, Suzushi S et al. Lung cancer screening using low-dose
spiral CT: results of baseline and 1-year follow-up studies. Chest 2002; 122:
10. Gohagan J, Marcus P, Fagerstrom R et al. Writing Committee, Lung Screening
Study Research Group. Baseline findings of a randomized feasibility trial of lung
cancer screening with spiral CT scan vs chest radiograph: the Lung Screening
Study of the National Cancer Institute. Chest 2004; 126: 114–121.
11. McWilliams A, Mayo J, MacDonald S et al. Lung cancer screening: a different
paradigm. Am J Respir Crit Care Med 2003; 168: 1167–1173.
12. Kaneko M, Eguchi K, Ohmatsu H et al. Peripheral lung cancer: screening and
detection with low-dose spiral CT versus radiography. Radiology 1996; 201:
Annals of Oncology
Volume 22|No. 2|February 2011doi:10.1093/annonc/mdq361 | 387
13. Berrington de Gonzalez A, Mahesh M, Kim K et al. Projected cancer risks from
computed tomography scans performed in the United States in 2007. Arch Intern
Med 2009; 169: 2071–2077.
14. Sone S, Li F, Yang ZG et al. Results of three-year mass screening programme for
lung cancer using mobile low-dose spiral computed tomography scanner. Br
J Cancer 2001; 84: 25–32.
15. McWilliams AM, MacAulay C, Gazdar AF, Lam S. Innovative molecular and
imaging approaches for the detection of lung cancer and its precursor lesions.
Oncogene 2002; 21: 6949–6959.
16. Tan EM. Autoantibodies as reporters identifying aberrant cellular mechanisms in
tumorigenesis. J Clin Invest 2001; 108: 1411–1415.
17. Houghton AN. Cancer antigens: immune recognition of self and altered self.
J Exp Med 1994; 180: 1–4.
18. Brichory FM, Misek DE, Yim AM et al. An immune response manifested by the
common occurrence of annexins I and II autoantibodies and high circulating
levels of IL-6 in lung cancer. Proc Natl Acad Sci U S A 2001; 98: 9824–9829.
19. Zhang J, Casiano CA, Peng X et al. Enhancement of antibody detection in cancer
using panel of recombinant tumor-associated antigens. Cancer Epidemiol Bio
and Prev 2003; 12: 136–143.
20. Nesterova M, Johnson N, Cheadle C et al. Autoantibody biomarker opens a new
gateway for cancer diagnosis. Biochimica et Biophysica Acta 2006; 1762:
21. Robertson JFR, Chapman CJ, Cheung KL et al. Auto-antibodies in early breast
cancer. J Clin Oncol 2005; 23: 549.
22. Zhong L, Coe SP, Stromberg AJ et al. Profiling tumor-associated antibodies for
early detection of non-small cell lung cancer. J Thor Oncol 2006; 1: 513–519.
23. Chapman C, Murray A, Chakrabarti J et al. Autoantibodies in breast cancer: their
use as an aid to early diagnosis. Ann Oncol 2007; 18: 868–873.
24. Chapman CJ, Murray A, McElveen JE et al. Autoantibodies in lung
cancer—possibilities for early detection and subsequent cure. Thorax 2008; 63:
25. Crawford LV, Pim DC, Bulbrook RD. The detection of antibodies against the
cellular protein p53 in sera from patients with breast-cancer. Int J Cancer 1982;
26. Li Y, Brandt-Rauf PW, Carney WP et al. Circulating anti-p53 antibodies in lung
cancer and relationship to histology and smoking. Biomarkers 1999; 4:
27. Trivers GE, DeBenedetti VM, Cawley HL et al. Anti p53 antibodies in sera from
patients with chronic obstructive pulmonary disease can predate a diagnosis of
cancer. Clin Cancer Res 1996; 2: 1767–1775.
28. Gnjatic S, Nishikawa H, Jungbluth AA et al. NY-ESO-1: review of an
immunogenic tumor antigen. Adv Cancer Res 2006; 95: 1–30.
29. Cho B, Lim Y, Lee DY et al. Identification and characterization of a novel
cancer/testis antigen gene CAGE. Biochem Biophys Res Commun 2002; 292:
30. Krause P, Tureci O, Micke P et al. SeroGRID: an improved method for the rapid
selection of antigens with disease related immunogenicity. J Immunol Methods
2003; 283: 261–267.
31. Tu ¨reci O, Mack U, Luxemberger U et al. Humoral responses of lung cancer
patients against tumor antigen NY-ESO-1. Cancer Lett 2006; 236: 64–71.
32. Vural B, Chen L-C, Saip P et al. Frequency of SOX Group B (SOX1, 2, 3) and ZIC2
antibodies in Turkish patients with small cell lung carcinoma and their correlation
with clinical parameters. Cancer 2005; 103: 2575–2583.
33. Maddison P, Thorpe A, Silcocks P et al. Autoimmunity to SOX2, clinical
phenotype and survival in patients with small-cell lung cancer. Lung Cancer
2010 April 3 [Epub ahead of print].
34. Robertson JFR, Barnes T, Murray A, Chapman C. Improved Immunoassay
Methods WO2006/126/008. Nottingham, GB: Oncimmune Ltd 2006.
35. Murray A, Chapman CJ, Healey G et al. Technical validation of an autoantibody
test for lung cancer. Ann Oncol 2010; 21: 1687–1693.
36. Robertson JFR, Murray A, Chapman C, Barnes A. Calibrator for Immunoassays
WO2009/081165. Nottingham, GB: Oncimmune Ltd 2007.
37. Motulsky H, Christopoulos A. Fitting Models to Biological Data Using Linear and
Nonlinear Regression (Chapter 41), Madison Avenue, NY: Oxford University Press
38. Geyer C. Estimation and optimization of functions. In Gilks WR, Richardson ST,
Speigelhalter DJ (eds), Markov Chain Monte Carlo In Practice. London: Chapman
and Hall 1999; 241–258.
39. Spitz MR, Hong WK, Amos CI et al. A risk model for prediction of lung cancer.
J Natl Cancer Inst 2007; 99: 715–726.
40. Sangrajrang S, Sornprom A, Chernrungroj G, Soussi T. Serum p53 antibodies in
patients with lung cancer: correlation with clinicopathologic features and
smoking. Lung Cancer 2003; 39: 297–301.
41. Li Y, Karjalainen A, Koskinen H et al. p53 Autoantibodies predict subsequent
development of cancer. Int J Cancer 2005; 114: 157–160.
42. Cho B, Lee H, Jeong S et al. Promoter hypomethylation of a novel cancer/testis
antigen gene CAGE is correlated with its aberrant expression and is seen in
premalignant stage of gastric cancer. Biochem Biophys Res Commun 2002;
43. Qiu J, Choi G, Li L et al. Occurrence of autoantibodies to annexin I, 14-3-3 theta
and LAMR1 in prediagnostic lung cancer sera. J Clin Oncol 2008; 26: 5060–5066.
44. Gure AO, Stockert E, Scanlan M et al. Serological identification of embryonic
neural proteins as highly immunogenic tumor antigens in small cell lung cancer.
Proc Natl Acad Sci U S A 2000; 97: 4198–4203.
45. Pereira-Faca SR, Kuick R, Puravs E et al. Identification of 14-3-3u as an antigen
that induces a humoral response in lung cancer. Cancer Res 2007; 67:
46. Robertson JFR, Pearson D, Price MR et al. Assessment of four monoclonal
antibodies as serum markers in breast cancer. Eur J Cancer 1990; 26:
47. Robertson JFR, Pearson D, Price MR et al. Prospective assessment of the role of
five serum markers in breast cancer. Cancer Immunol Immunother 1991; 33:
48. Laurence DJR, Stevens U, Bettelheim R et al. Role of plasma carcinoembryonic
antigen in the diagnosis of gastro-intestinal, mammary and bronchial carcinoma.
Br Med J 1972; 3: 605–609.
49. Thomas MW, Robertson JFR, Price MR, Hardcastle JD. Failure of CA 19-9 to
detect asymptomatic colorectal carcinoma. Br J Cancer 1991; 63: 975–976.
50. Robertson JFR, Graves CRL, Price MR. Tumour Markers US 7,402,403, B1.
Nottingham, GB: Oncimmune Ltd 1999.
51. Rohayem J, Diestelkoetter P, Weigle B et al. Antibody response to the tumor-
associated inhibitor of apoptosis protein surviving in cancer patients. Cancer Res
2000; 60: 1815–1817.
52. Suzuki H, Graziano DF, McKolanis J, Finn OJ. T cell-dependent antibody
responses against aberrantly expressed cyclin B1 protein in patients with cancer
and premalignant disease. Clin Cancer Res 2005; 11: 1521–1526.
53. Pisano ED, Gatsonis C, Hendrick E et al. Diagnostic performance of digital versus
film mammography for breast cancer screening. N Engl J Med 2005; 353:
demographic characteristics of the control versus
the study population
A total of 655 lung cancer (LC) sera (476 were from patients
with non-small-cell lung cancer, 165 with small-cell lung
cancer, 1 lung sarcoma and 13 of unknown histology) were
compared directly with 655 normal sera, which were analysed
as controls. In addition, sera from 88 unmatched individuals
(25 normal and 63 with benign lung conditions) supplied by
the group 2 centre were analysed as controls to check the
positivity rate in known benign lung disease. Samples were
obtained, with full informed consent, at different sites.
Controls for patients in group 1 were matched on the basis of
gender and age (64 years). As all subjects in this group were
smokers, pack-year matching was attempted, but a tight match
was prohibited by lack of information. There were 81 males and
64 females in the LC group and 83 males and 62 females in the
Annals of Oncology
388 | Boyle et al. Volume 22|No. 2|February 2011
control group. The median age (range) of the LC patients and Download full-text
controls were 66 (41–87) and 66 (41–87) years, respectively.
In group 2, there were 172 males and 69 females in the LC
group and 171 males and 69 females in the control group. The
median age (range) of the LC patients and controls were 63
(28–87) and 63 (28–87) years, respectively. Controls for group
2 were selected from a prospective collection of blood samples
taken from a larger sample set of a normal population in the
Midlands of England. Patients with LC were initially matched
to controls on the basis of gender, age (63 years) and smoking
history. In <5% of cases, these criteria could not be met, so
a choice had to be made to either extend the age-match criteria
or ignore the gender-match stipulation. Since the authors have
never observed a significant gender difference, age and smoking
history were given priority over gender. In 37 LC patients, the
exact smoking history was unknown, and in a further four
patients, age matching was >3 years.
The group 2 centre also supplied 88 unmatched samples from
individuals who were either thought to be normal (n = 25) or
have a range of benign lung diseases (n = 63), including mass/
nodule (n = 3), autoimmune lung disease (n = 10), chronic
obstructive pulmonary disease/emphysema (n = 2), benign
pleural effusion (n = 2), allergic/inflammatory/infective
conditions (n = 25) (e.g. allergic alveolitis, Wegner’s
granulomatosis, asthma, sarcoid, vasculitis, Dessler’s syndrome,
mycoplasmosis, tuberculosis) and nonspecified lung disorders
(n = 21). A set of individually matched controls for this group
of LC patients was selected from a prospective collection of
blood samples taken from a normal population in the UK.
Controls were matched on the basis of gender and age. With
the exception of one patient who was matched to 64 years,
controls were matched to patient age 62 years. Smoking
history was not known for the patients with LC, so controls
were simply selected from a population of smokers and
In group 3, there were 199 males and 70 females in the LC
group and 187 males and 82 females in the control group. The
median age (range) in the LC and control groups was 65
(38–87) and 65 (38–86) years, respectively.
The matched controls in group 3 were collected as part of
a larger sample set of the normal population (n = 766) in the
Midwest United States and demographic data included
ethnicity. Evaluation of calibrated reference unit (RU) for
autoantibody expression demonstrated that when controlled
for age, there was no significant difference between ethnic
groups [Caucasians (n = 614), African Americans (n = 108),
Hispanics (n = 27) and Native Americans (n = 17)] in terms of
calibrated RUs (data not shown). There was a further set of
samples from 125 normal individuals who were located in
Florida and age matched, gender matched and smoking history
matched to a similar number of the controls in the Midwest
United States (n = 125). The Florida samples were part of
another larger prospective collection of sera from the
normal population. Comparison of the 125 samples from each
of these two normal populations from different geographic and
ethnic backgrounds showed no significant difference in the
calibrated RU values for any of the six antigens (data not
Annals of Oncology
Volume 22|No. 2|February 2011doi:10.1093/annonc/mdq361 | 389