Attenuated Total Reflection–Fourier transform infrared microspectroscopic mapping for the...

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Attenuated Total Reflection Fourier Transform Infrared(ATR-FTIR) spectral discrimination of brain tumourseverity from serum samples

James R. Hands1, Konrad M. Dorling1, Peter Abel2, Katherine M. Ashton3,Andrew Brodbelt4, Charles Davis3, Timothy Dawson3, Michael D. Jenkinson4,Robert W. Lea2, Carol Walker4, and Matthew J. Baker*; 1

1 Centre for Materials Science, Division of Chemistry, JB Firth Building, University of Central Lancashire, Preston, PR1 2HE, UK2 School of Pharmacy and Biomedical Sciences, Maudland Building, University of Central Lancashire, Preston, PR1 2HE, UK3 Department of Pathology, Lancashire Teaching Hospitals NHS Trust, Royal Preston Hospital, Sharoe Green Lane North,

Preston, Lancashire, PR2 9HT, UK4 The Walton Centre for Neurology and Neurosurgery, The Walton Centre NHS Foundation Trust, Lower Lane, Fazakerley,

Liverpool, L9 7LJ, UK

Received 16 September 2013, revised 6 November 2013, accepted 28 November 2013Published online 7 January 2014

Key words: serum, filtrate, glioma, rapid, spectroscopy, infrared, ATR-FTIR, cancer

# 2014 by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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Gliomas are the most frequent primary brain tumours inadults with over 9,000 people diagnosed each year in theUK. A rapid, reagent-free and cost-effective diagnosticregime using serum spectroscopy would allow for rapiddiagnostic results and for swift treatment planning andmonitoring within the clinical environment. We reportthe use of ATR-FTIR spectral data combined with aRBF-SVM for the diagnosis of gliomas (high-grade andlow-grade) from non-cancer with sensitivities and specifi-cities on average of 93.75 and 96.53% respectively. Theproposed diagnostic regime has the ability to reducemortality and morbidity rates.

Centrifugal filter (EMD Millipore, USA).

* Corresponding author: e-mail: [email protected], Phone: +44 1772 893209

J. Biophotonics 1–11 (2014) / DOI 10.1002/jbio.201300149

1. Introduction

Malignant gliomas are among the more lethal of hu-man cancers. In 2010, of the 9,156 newly diagnosedpatients, brain cancers accounted for 2,689 deaths inmen and 2,208 deaths in woman in the UK [1]. Inthe last three decades incidence and mortality rateshave increased by 23% and 25% for men and wo-men respectively. Brain cancer is the most commoncancer for childhood deaths after leukaemia. In2010, one-third of 217 children newly diagnosed withbrain cancer died in England [2].

More than 120 different types of tumour can befound in the brain, of which, gliomas account for 30–40% of all intracranial neoplasms [3]. The term ‘glio-ma’ refers to a primary site tumour which originatesfrom glial cells (neuroglia) within the brain and cen-tral nervous system (CNS). Glial cells outnumber neu-ron cells and occupy approximately half of the brain.The two main glial cell types in the brain and CNS areastrocytes and oligodendrocytes. Malignant gliomasare graded according to the classification of the WorldHealth Organisation (WHO). The three main types ofmalignant glioma are astrocytomas, ependymomasand oligodendrogliomas. A tumour with a mixture ofthe histological features present in the main three isknown as a mixed glioma [4]. Table 1 shows the sub-types of high-grade and low-grade gliomas [5].

Survival rates of people with glial tumours varydepending on tumour type, histopathological grad-ing, age and other patient specific diseases. On aver-age, people diagnosed with glial tumours survive: 6–8 years with low-grade astrocytomas or oligoendro-gliomas; 3 years with anaplastic astrocytomas and12–18 months for high-grade astrocytomas and Glio-blastoma multiforme [6]. Age at diagnosis of a glialtumour affects survival rate after 5-years; childrenand adolescents (0–19 years) have a relatively goodsurvival rate (58.1%) compared to young adults(20–39 years) and adults (40–59 years) where survi-val rates decrease steadily (52.8% and 19.1%, re-spectively). Older adults (60þ) have a poor survivalrate of just 4.4% [7]. The majority of new brain can-cer cases are malignant and brain cancer has highrates of mortality in adults and children [2, 8].

Gliomas are primarily detected using a medicalimaging technique such as computerised tomography(CT) and magnetic resonance imaging (MRI),amongst others [9]. Evidence of a tumour being pre-sent requires confirmatory diagnosis via biopsy or tu-mour resection [10]. A biopsy specimen is firstly ac-quired by drilling into the skull before the specimenis histopathologically examined for the presence ofany malignancies [11, 12]. Histological grading doesnot provide clinicians with accurate prognostic andtherapeutic details on an individual patient basis [13].The current diagnostic regime requires histopatholo-gical interpretation of tissue specimens by a trainedneuropathologist, thus pathological diagnoses are tosome extent subjective [14]; Bruner et al. 1997 foundthat disagreements between original and review diag-noses in 214 of 500 cases, some 42.8% [15]. A studyfound that the inter-observer agreement was consid-erably better for specialised neuropathologists as op-posed to general surgical pathologists when gradingfibrillary astrocytomas, likely due to experience. Theneuropathologists all agreed or had only one discre-pant diagnosis in 86.7% versus the general surgicalpathologists who had 43.3% in all thirty reviewedcases [16]. A rapid diagnostic serum test would bebeneficial to both patients and clinicians whilst reduc-ing current diagnosis times.

A rapid serum screening regime would signifi-cantly reduce current diagnosis times and greatly in-crease the chance of successful treatment. Despitean increase in mortality rates for brain cancer, survi-val rates are also increasing due to early detection[2]. The ability to rapidly detect diseases, such asglioma, allows for treatment to be offered earlierand survival rates to increase.

Previous studies have provided evidence of thebenefits of applying spectroscopy to clinical pro-blems [17, 18]. Backhaus et al. (2010) distinguishedbetween breast cancer serum and healthy patientserum samples achieving a sensitivity of 98% and aspecificity of 95%. The study also shown the abilityto differentiate between breast cancer and other dis-ease states (e.g. Alzheimer’s disease, coronary heartdiseases and hepatitis C) using minute quantities ofpatient serum with a sensitivity of 98% and a specifi-city of 100% [19]. ATR-FTIR has been used success-fully to discriminate between whole and ultrafiltrateserum samples obtained from patients suffering fromone of the following; acute myocardial infarction, an-gina pectoris and other pains in the chest. This studyachieved a sensitivity of 88.5% and a specificity of85.1% in a blind validation test [20]. Disease statescan be distinguished between using infrared spectro-scopy; Scaglia et al. (2011) achieved a sensitivity of95.2% and a specificity of 100% when using a leave-one-out cross-validation on spectra collected fromnormal patients (no hepatic fibrosis) and various de-grees of hepatic fibrosis [21]. Our previous study has

Table 1 The sub-types of WHO grade I–IV low-gradeand high-grade brain tumors [5].

General TumourGrade

WHO Grade Grade Sub-type

Low Grade IIIII

Pilocytic astrocytomaOligodendrogliomaAstrocytoma

High Grade IIIIIIIV

Anaplastic astrocytomaOligodendrogliomaGlioblastoma

J. R. Hands et al.: Serum spectroscopy gliomas2

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shown the diagnostic ability of gliomas from non-cancer patients using serum samples with high sensi-tivities and specificities as high as 87.5% and 100%respectively [22]. Specifically to the spectroscopicgrading of cancers a number of studies have beenconducted. Steiner et al. (2003) was successfully ableto distinguish between control (non-cancer tissue)and astrocytoma and glioblastoma tissue sectionsusing infrared spectroscopy with high classificationrates approaching 90% accuracy [23]. Glioma grad-ing has also been shown to be suitable when decid-ing whether to continue or not continue with tumourresection based on infrared spectroscopic classifica-tion; Sobottka et al. (2008) found that FTIR couldbe used as an intraoperative tool in cerebral gliomasurgery to help define tumour margins and detecthigh grade tumour residues with sensitivities andspecificities as high as 100% and 96.9% respectively[24]. Gajjar et al. (2009) has shown the ability to dif-ferentiate between patients diagnosed with eitherovarian or endometrial cancer from non-cancer con-trols (including patients with known benign gynaeco-logical conditions) using blood and serum samplesapplied with ATR-FTIR spectroscopy with classifica-tion results as high as 96.7% for ovarian cancer and81.7% for endometrial cancer [25].

We present the methodological studies imple-mented for ATR-FTIR diagnostic regime optimisa-tion. These studies include the drying time of serumand spectra obtained, filtration and analysis ofwhole, 100 kDa (kilodalton), 10 kDa and 3 kDa se-rum filtrate aliquots from control (non-cancer), low-grade glioma serum (e.g. astrocytoma) and high-grade glioma serum (Glioblastoma multiforme). Fi-nally, a variance study was carried out to assess theconsistency of the spectral data collected. ATR-FTIR is a rapid, cost-effective and easy-to-use spec-troscopic tool which has great potential as a screen-ing and diagnostic tool for a range of diseases withinthe clinical environment [26].

2. Methods and materials

2.1 Serum samples

Blood samples were collected from 49 patients diag-nosed with a Glioblastoma Multiforme (GBM), 23 pa-

tients with a diagnosed low-grade glioma (astrocyto-ma, oligoastrocytoma, oligodendroglioma) and 25normal (non-cancer) patients with no known healthproblems. The average age of the entire sample set is54.62 years. Table 2 provides demographic details bycancer group. Samples were obtained from the Wal-ton Research Tissue Bank and Brain Tumour NorthWest (BTNW) Tissue Bank where all patients hadgiven research consent. The research described in thispaper was performed with full ethical approval (Wal-ton Research Tissue Bank Application number:BTNW/WRTB 13_01, Applicant: M. J. Baker/BTNWApplication number: 1108, Applicant: P. Abel).

We investigated the use of ATR-FTIR spectro-scopy and human patient serum to discover a novelprocess for the rapid diagnosis of gliomas. This re-search investigated the fundamental differences inATR-FTIR spectra between serum and serum de-rived filtrate aliquots from patients diagnosed ashaving a low-grade glioma, high-grade glioma ornon-cancer. All blood samples were taken pre-op-eratively. The serum tubes were left to clot at roomtemperature for a minimum of 30 minutes and amaximum of 2 hours from blood draw to centrifuga-tion. Separation of the clot was accomplished bycentrifugation at 1,200 g for 10 minutes and 500 mlaliquots of serum dispensed into prelabelled cryo-vials. Serum samples were snap frozen using liquidnitrogen and stored at �80 �C.

2.2 Drying study

Normal human mixed pooled serum (0.2 mm sterilefiltered, CS100-100, purchased from TCSBiosciences,UK) was used in volumes of 1 mL to determine theoptimal drying time necessary for quality spectralcollection.

Spectra were collected using a JASCO FTIR-410spectrometer equipped with a Specac ATR single re-flection diamond Golden GateTM at the Universityof Central Lancashire, in the range of 4000–400 cm�1, at a resolution of 4 cm�1 and over 32 co-added scans. Prior to each spectral collection, abackground absorption spectrum was collected foratmospheric correction.

One microlitre of pooled serum was pipetted ontothe ATR-FTIR crystal and 3 spectra were collectedat each 0, 2, 4, 8, 16 and 32 minute interval to ob-

Table 2 Number, age and gender data of patient samples from each tumour grade.

Tumour Grade Number of Subjects Age range/mean age Gender

Normal (Non-cancer) 25 26–87/59.1 years 15 male, 10 femaleLow-Grade 23 19–60.3/36.9 years 11 male, 12 femaleHigh-Grade 49 24.7–78.8/60.1 years 29 male, 20 female

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serve spectral changes during the drying process. Thedried intimate serum film was removed from thecrystal using absolute ethanol (purchased from Fish-er Scientific, Loughborough, UK). The drying ex-periment was repeated multiple times to gain spectrarepresentative at specific times during drying.

2.3 Variance study

Normal human mixed pooled serum (0.2 mm sterilefiltered, CS100-100, purchased from TCSBiosciences,UK) was used in the variance study. A volume of1 mL was pipetted on to the ATR-FTIR single reflec-tion diamond crystal and dried for 8 minutes, atwhich time 3 spectra were collected. Three spectrawere collected per 1 mL and 50 different 1 mL spotswere analysed. The dried serum spot was removedthe crystal using absolute ethanol between each var-iance repeat with absolute ethanol. In total, 150ATR-FTIR spectra were collected.

2.4 ATR-FTIR spectral diagnostic model

All whole serum samples were thawed prior to spec-tral collection and 100 kDa, 10 kDa and 3 kDa filtra-tion aliquots were prepared using Amicon Ultra-0.5 mL centrifugal filters (purchased from MilliporeLimited, UK) [Figure 1]. Centrifugal filters filter outcomponents of the serum above the cut-off point ofthe filters membrane (i.e. 100 kDa), allowing compo-nents below the filter membrane cut-off point topass through.

Each whole serum sample (high-grade, low-gradeand control) had a filtration aliquot prepared by pi-petting 0.5 mL of the whole serum in to the filtrationdevice and centrifuging at 14,000 rpm for; 10 minutes,15 minutes, and 30 minutes for 100 kDa, 10 kDa and3 kDa filter devices respectively.

Spectra were collected in a random order withinthe serum sample sets. For each sample, a 1 mL serumspot was dried for 8 minutes on the ATR-FTIR crys-tal, at which time 3 spectra were collected. This proce-dure was repeated three times per sample. As a result,for each sample 9 spectra were collected. Prior to spec-

tral collection, a background absorption spectrum wascollected (for atmospheric correction) before the 1 mLwas pipetted onto the ATR-FTIR crystal, thus a back-ground was collected per serum replicate. The driedserum film was washed off the crystal in between eachprocedure using Virkon� disinfectant (purchasedfrom Antec Int., Suffolk, UK) and absolute ethanol.

Spectra were acquired in the range of 4000–400 cm�1, at a resolution of 4 cm�1 and averaged over32 co-added scans. In total, 3375 ATR-FTIR spectrawere collected from all whole and filtration serumsamples. Table 3 shows the total number of spectraand patients in each serum grade and filtration cate-gory. The number of patients reduces as serum filtratealiquots are prepared due to serum availability.

2.5 Pre-processing selection data

For each whole and filtration serum sample set, anidentical approach was used to pre-process the spec-tral data, and to analyse using multivariate analysismethods. Firstly, to remove any bias from analysismodels, the technical replicates from each samplewere averaged so that each serum sample set con-tained three spectra from each patient; one averagespectrum from each patient spot. Outliers were thenremoved from the spectral sets using a quality test.The fingerprint region (1800–900 cm�1) was selected

Figure 1 Centrifugal filter (EMD Millipore, USA).

Table 3 The number of spectra collected and number of patients (in brackets) for each filtrate composition for the rangeof cancer serum severities being analysed.

Whole Serum 100 kDa Serum 10 kDa Serum 3 kDa Serum

High-Grade Serum 441 (49) 423 (47) 423 (47) 405 (45)Low-Grade Serum 207 (23) 207 (23) 198 (22) 198 (22)Normal (Non-cancer) Serum 225 (25) 225 (25) 225 (25) 198 (22)Total 873 (97) 855 (95) 846 (94) 801 (89)


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for multivariate analysis. A principal componentbased noise reduction, using the first 30 principalcomponents of the data, was performed on the spec-tra to improve the signal-to-noise ratio. Followingthis, all spectra were vector normalised and meancentred. The spectral data was also analysed usingsecond derivative spectra of the data, but best over-all results for PCA and SVMs were achieved usingthe noise reduction, vector normalisation and meancentring process.

Principal component analysis (PCA) was per-formed on the pre-processed spectra, giving an unsu-pervised classification from which the loadings couldbe interpreted. Using LIBSVM code in MATLAB[27], an automatic n-fold cross validation was per-formed (where n ¼ 3) on the training data to findthe best values for the cost and gamma functions.These values were then used to train the SVM inone-versus-rest mode using a randomly selectedtraining set consisting of two thirds of the patient-as-sociated spectral data. The remainder of the data,making up the blind test set, was then projected intothe model, and confusion matrices were calculatedgiving an overall SVM classification accuracy basedon the true and predicted data class labels. The spec-tral data was split independently at a patient levelinto 1/3 blind and 2/3 training where all spectra fromone patient was either in the train set or blind set.Sensitivities and specificities were calculated for eachSVM model and for each separate disease group.

Three different test and blind data test sets at thepatient level, so no patient spectrum was in the testset that was in the blind set., were used to provide arange of sensitivities and specificities for whole se-rum. Pre-processing and multivariate (MVA) analy-sis was carried out on the raw spectral data in Ma-tlab� (7.11.0 [R2010b] (The MathWorks, Inc. USA)using in-house written software.

3. Results

3.1 Drying study

Figure 2 displays the typically observed ATR-FTIRspectral data from 1 mL of whole human serum over arange of 0–32 minutes during drying. The spectrahave been offset for ease of visualisation. At roomtemperature (~18 �C) 1 mL of serum has been foundto dry after 8 minutes through repeat drying experi-ments. Effective spectral collection requires intimatecontact between the serum sample and the ATR-FTIR crystal to allow interaction with the evanescentfield; this can be achieved by allowing the liquid serumsample to dry [28]. Drying allows the intensity of thebands to increase exponentially as swelling decreases,

thus reducing the distance between the reflecting in-terference (water) and the sample molecules [29].

Spectra observed after allowing the 1 mL of se-rum to dry for 8 minutes creating a dried film on theATR-FTIR crystal is consistent with serum spectrapresented by Backhaus et al. (2010) [19], Petrichet al. (2009) [20], Hands et al. (2013) [22], Gajjaret al. (2013) [25] and Liu et al. (2002) [30].

3.2 Variance study

Figure 3 shows ATR-FTIR raw and pre-processedspectra in the region of 3900–900 cm�1 (Figure 3a–b)and in the fingerprint region of 1800–900 cm�1 (c–d). The four spectra display an average spectrum sur-rounded by a standard deviation (STD) error margin.The largest variance between the raw (unprocessed)spectral data was at 1637.27 cm�1 (STD: 0.4209) inboth analysed wavenumber regions. The smallest var-iance in the raw data was at 3735.44 cm�1 (STD:0.0038) between 3900–900 cm�1 and at 1792.51 cm�1

(STD: 0.0138) in the fingerprint region. Noise reduc-tion (30 principal components) and vector normaliza-tion pre-processing methods were applied to the datato reduce the baseline and to smooth the data. Thepre-processing methods significantly reduced theSTD and variance of the spectral data. The largestraw data STD at 1637.27 cm�1 was reduced from0.4209 to 0.0043 (pre-processed) a difference of

Figure 2 ATR-FTIR spectra showing 1 mL of human se-rum drying after 8 minutes.




195.9%. The smallest spectral variance STDs were re-duced from 0.0038 to 0.00123 at 3735.44 cm�1 andfrom 0.0138 to 0.0004 at 1792.51 cm�1. The averageSTD across the 3900–900 cm�1 raw and pre-pro-cessed data was 0.0137 and 0.0015 respectively. TheSTD values of the raw spectra were low initially butwere reduced further by implementing pre-proces-sing methods. The reproducibility of spectral datausing ATR-FTIR is high and exhibits minimal var-iance, especially after pre-processing.

3.3 ATR-FTIR spectral diagnostic modelresults

3.3.1 PCA and loadings

Figure 4(A) shows a three-dimensional PCA scatterplot displaying the separation between normal (non-cancer) [blue], low-grade cancer [red] and high-grade cancer [green] patient spectral data. Principalcomponent 3 (PC3) on the x-axis shows good se-

paration between low-grade cancer and normal(non-cancer) data. PC2 on the y-axis shows good se-paration between high-grade cancer from low-gradeand normal (non-cancer). Spectral data collectedfrom the three serum grades in this study have beenshown to successfully separate into their differentspectral groups using PCA. Figure 4(B, C) are theloadings of PC1 and PC3. Table 4 shows the majorspectral peaks and proposed biomolecular assign-ments responsible for PC1 loadings and Table 5 forPC3 loadings [31, 34–36]. The axes of the loadingplots have positive and negative directions. The posi-tive and negative peaks in the loading plots of PC1and PC3 correspond to the spectral peaks that thePCA scatter plot is using to discriminate the groupswithin the patient spectral dataset.

The major peaks of PC1 (Figure 4B) are –ve1649 cm�1 which is assigned as Amide I, +ve1513 cm�1 assigned as Amide II and –ve 1077 cm�1

assigned as C––O stretch (DNA/RNA). The positiveand negative peaks separate the low-grade cancer(red) and non-cancer (blue) from the high-grade(green) data points. Table 4 shows the proposed bio-molecular assignments from the loadings of PC1.

Figure 3 Raw/unprocessed and processed spectral data for whole serum complete spectrums (3900–900 cm�1) and finger-print regions (1800–900 cm�1).


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The major peaks of PC3 (Figure 4(C)) are þve1655 cm�1 and �ve 1644 cm�1 both which are as-signed Amide I and þve 1589 cm�1 assigned asAmide II. The major þve and �ve peaks separatethe low-grade from the non-cancer PCA data pointsand �ve 1644 cm�1. Table 4 and 5 show the spectralassignments for PC1 and PC3 loadings respectively(Figure 4(B, C)). Table 5 shows the proposed biomo-lecular assignments from the loadings of PC3.

3.4 SVM results

ATR-FTIR spectra from all whole serum and serumfiltrate aliquot samples were analysed to investigatesensitivities and specificities possible on patient andspectral levels using SVM-RBF analysis. Three dif-ferent test and blind spectral datasets were used to

provide a range of sensitivities and specificities forwhole serum (Table 6). One test and blind spectraldataset was used to achieve sensitivities and specifi-cities for the filtrate aliquots. The whole serum SVMdiagnostic model achieved 93.75% sensitivity and96.53% specificity (overall average) on a patient lev-el. The whole serum dataset also achieved 92.61%sensitivity and 96.12% specificity on a spectral level.The best whole serum RBF-SVM diagnostic modelmisclassified three patients in the blind dataset; onelow-grade patient and two high-grade patients. Theblind dataset consisted of one average spectrum per1 mL of patient serum analysed resulting in threespectra per patient in the dataset. If two or morespectra from the three averaged for each patientwere classified as either high-grade, low-grade ornon-cancer, then this is regarded as the diagnosis. Incomparison to the whole serum SVM-RBF results,the 100 kDa (Table 7), 10 kDa (Table 8) and 3 kDa

Figure 4 3D PCA scatter plot and PC1 and PC3 loadings from whole patient serum.

Table 4 Major peaks and proposed biomolecular assignmens of PC1 loadings [Figure 3(B)]. Spectral assignments takenfrom [28, 32–34].

DirectionLoading onPC 1 (B)

Wavenumber(cm�1)

Proposed Biomolecular Assignment

þve 1698 Amide I of antiparallel b-sheet/aggregated strand protein structures (C––O stretch (76%),C––N stretch (14%), CCN deformation (10%))

þve 1513 Amide II of proteins (b-pleated sheet structures) d N––H (60%), n C––N (40%)þve 1445 CH2 deformation of methylene group, lipidsþve 1386 CH3 deformation, lipids�ve 1649 Amide I of a-helical protein structures (C––O stretch (76%), C––N stretch (14%),

CCN deformation (10%))�ve 1619 Amide I of proteins (b-pleated sheet structures) n C¼O (80%), n C––N (10%),

d N––H (10%)�ve 1553 Amide II of proteins (b-pleated sheet structures) d N––H (60%), n C––N (40%)�ve 1077 C––O stretch, deoxyribose/ribose, DNA, RNA




(Table 9) serum filtrate aliquots did not achieve sen-sitivities or specificities as high. The 100 kDa diag-nostic model achieved 69.05% sensitivity and85.87% specificity on a patient level and the 10 kDadiagnostic model achieved a sensitivity and specifi-city on a patient level with 79.68% and 88.75% re-spectively. The 3 kDa dataset achieved the lowestsensitivities and specificities and on a patient levelachieved 65.28% and 80.81% respectively. Table 10shows the optimal Cost and Gamma values for thewhole serum and serum filtrate SVM analysis wheretraining accuracy is the correctly predicted spectrafrom the training set and total accuracy is the cor-rectly predicted spectra from the blind set.

Whole serum contains a range of biochemicalcomponents such as cytokines and chemokineswhich are redundant proteins that are secreted with

growth, differentiation, and the activation functionthat is involved in regulating and determining thenature of an immune response, such as with the de-velopment of cancer [32]. Cytokines have previouslybeen shown to differ between serum from gliomaand non-cancer patients [22]. The whole serumSVM-RBF diagnostic model achieved the greatestpatient and spectral sensitivities and specificities as aresult of the infrared spectral dataset exhibitingbands characteristic of all serum components such ascytokines and chemokines which have varied mole-cular weights [33]. Filtrate serum aliquots producedfrom whole serum samples achieved lower sensitiv-ities and specificities as a result of filtering and re-moving serum biomolecular components above thefilter cut-off range. Petrich et al. (2009) reported thesignificant differences in infrared spectra of dried

Table 5 Major peaks and proposed biomolecular assignments of PC3 loadings [Figure 3(C)]. Spectral assignments takenfrom [28, 32–34].

DirectionLoading onPC 3 (C)

Wavenumber(cm�1)

Proposed Biomolecular Assignment

þve 1655 Amide I of antiparallel b-sheet/aggregated strand protein structures(C––O stretch (76%), C––N stretch (14%), CCN deformation (10%))

þve 1589 Amide II (NH bend (43%), C––N stretch (29%), CO bend (15%), C––C stretch (9%),N––C stretch (8%))

þve 1462 CH2 stretch deformation of methylene group (lipids)þve 1416 CH3 asymmetric stretch (lipids)þve 1394 CH3 deformation, lipidsþve 1361 CH3 stretch (symmetric)þve 1119 C––O stretch (antisymmetric), COH bend, lipidsþve 1040 n C––O, deoxyribose/ribose DNA, RNA�ve 1644 Amide I of a-helical protein structures (C––O stretch (76%), C––N stretch (14%),

CCN deformation (10%))�ve 1513 C¼C stretch�ve 1505 C¼C stretch�ve 1220 C––C stretch, C––H bend�ve 1006 Phenylalanine (ring breathing)�ve 935 C––C residue a-helix

Table 6 Dataset 1 including range from dataset 2 and 3: Sensitivities and specificities for whole serum on patient andspectral levels for normal (non-cancer), low-grade and high-grade cancer with an overall average and overall range.

Normal(%)

NormalRange(%)

Low(%)

LowRange(%)

High(%)

HighRange(%)

OverallAverage(%)

OverallRange(%)

Patientsensitivity

100 75.00–100.00 87.5 87.50–87.50 93.75 92.86–93.75 93.75 75.00–100.00

Patientspecificity

95.83 95.45–100.00 100 95.45–100.00 93.75 87.50–93.75 96.53 87.50–100.00

Spectrasensitivity

95.83 78.26–95.83 86.36 85.00–91.67 95.65 92.86–95.65 92.61 78.26–95.83

Spectraspecificity

97.06 95.45–100.00 100 95.45–100.00 91.30 86.36–91.30 96.12 86.36–100.00


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serum before and after filtration. A Wilcoxon test re-vealed that whole serum has more spectral regionswhich differentiate between serum samples from pa-tients with acute myocardial infarction (AMI) fromserum samples from non-AMI patients compared to100 kDa and 10 kDa serum filtrates. The moleculesresponsible for the significant differences have a mo-lecular mass below 100 kDa and 10 kDa, thus wholeserum spectra has more areas of significance whendifferentiating disease states. The amide-II regionexhibited statistically significant differences betweenthe diseased and non-diseased groups [20].

The results achieved show that the combinationof multivariative support vector machine analysisand spectroscopic profiles of the molecular vibra-

tions within 1 mL dried patient serum sample filmson a ATR-FTIR crystal can be used to diagnose anddifferentiate between high-grade, low-grade andnon-cancer with high sensitivities and specificities.Once a serum sample has been collected from a pa-tient a result can be obtained within 10 minutes ofserum deposition onto the ATR-FTIR crystal.

4. Conclusion

The results show that the combination of supportvector machine analysis and ATR-FTIR spectro-scopic profiles of the molecular vibrations within

Table 7 Sensitivities and specificities for 100 kDa serum filtrate on patient and spectral levels for normal (non-cancer),low-grade and high-grade cancer with an overall average.

Normal (%) Low (%) High (%) Overall Average (%)

Patient sensitivity 50 57.14 100 69.05Patient specificity 95.45 95.45 66.7 85.87Spectra sensitivity 54.17 61.90 93.75 69.94Spectra specificity 94.12 94.12 67.44 85.39

Table 8 Sensitivities and specificities for 10 kDa serum filtrate on patient and spectral levels for normal (non-cancer),low-grade and high-grade cancer with an overall average.


Patient sensitivity 85.71 70 83.33 79.68Patient specificity 85 100 81.25 88.75Spectra sensitivity 75 66.67 78.38 73.35Spectra specificity 80.33 98.25 78.72 85.77

Table 9 Sensitivities and specificities for 3 kDa serum filtrate on patient and spectral levels for normal (non-cancer), low-grade and high-grade cancer with an overall average.


Patient sensitivity 62.5 66.67 66.67 65.28Patient specificity 73.68 100 68.75 80.81Spectra sensitivity 70.83 69.23 68.57 69.54Spectra specificity 76.36 98.04 74.47 82.96

Table 10 The optimal Cost and Gamma values for the whole serum and serum filtrate.

WholeSerum

WholeSerum (2)

WholeSerum (3)

100 kDaData

10 kDaData

3 kDaData

Optimal Cost (C) 22.63 32 22.63 2048 2048 2048Optimal Gamma (g) 4 5.66 8 0.85 16 16Training Accuracy 85.86 % 87.96% 86.46% 72.58 % 90.80 % 78.53%SVM Total Accuracy 96.875 % 86.46% 91.58% 79.57 % 79.76 % 75.64 %




1 mL dried patient serum sample films on an ATR-FTIR crystal can be used to diagnose and differenti-ate between high-grade, low-grade and non-cancerwith high sensitivities and specificities. Once a serumsample has been collected from a patient a resultcan be obtained within 10 minutes of serum deposi-tion onto the ATR-FTIR crystal.

This work has shown that a 1 mL spot of serumon an ATR-FTIR crystal can dry to a state whereexcellent spectra can be collected, consistent withserum spectra presented in other research studies[20–22, 28–29]. The raw spectra collected from 1 mLserum spots after 8 minutes of drying have a lowstandard deviation which is further reduced withnoise reduction and vector normalisation pre-proces-sing methods. The variance between the spectraldata after pre-processing is as little as 0.0015 forspectra collected from 3900–900 cm�1. The reprodu-cibility of spectral data using ATR-FTIR is high andexhibits minimal variance especially after pre-pro-cessing. We have demonstrated that the combinationof mid-infrared spectral data collected from wholeserum samples from high-grade, low-grade and nor-mal (non-cancer) patients and multivariative supportvector machine (SVM) statistical analysis has theability to be implemented within the clinical environ-ment as a rapid, reagent-free and cost-effective diag-nostic regime. Serum filtrate aliquots in this study(100, 10 and 3 kDa) have been shown to not achieveas high sensitivities and specificities as whole serum;filtration removes key biomolecules which exhibitspectral bands that are involved in the MVA diag-nostic process. The loadings from PC1 and PC3 (Fig-ure 4(A, B)) show the peaks which are responsiblefor the separation between high-grade, low-gradeand non-cancer spectral datasets. The major peaksfrom PC1 shows that Amide I, Amide II and C––Ostretch (DNA/RNA) bands are responsible for theseparation between non-cancer and low-grade can-cer from high-grade cancer PCA data points. Themajor peaks from PC2 shows that Amide I andAmide II are responsible for the separation of low-grade cancer from non-cancer PCA data points.

We have presented a method which has the abil-ity to diagnose gliomas from whole patient serumATR-FTIR spectral data to sensitivities and specifi-cities as high as 100.00% and 95.83% respectively.The described spectroscopic methodology has thepotential to be used in monitoring the therapeuticresponse of the cancer treatments given to brain tu-mour patients. A blood sample collected from a pa-tient being treated for brain tumour could be col-lected and analysed using ATR-FTIR. The spectraldata can then be used to determine whether the pa-tient is responding well to the treatment or whetherthe patient would benefit from an alternative treat-ment plan. We plan to collect ATR-FTIR spectraldata from sera obtained from patients diagnosed

with metastatic tumours of the brain from variousprimary sites and meningioma. Gajjar et al. (2009)has shown the ability to differentiate between pa-tients diagnosed with either ovarian or endometrialcancer from non-cancer controls (including patientswith known benign gynaecological conditions) usingblood and serum samples applied with ATR-FTIRspectroscopy. The ability to detect a disease stateusing patient sera and ATR-FTIR has many advan-tages such as early diagnosis, thus applying thismethodology to a larger sample set including meta-static sera may enable the site of origin to be distin-guished [25]. The ability to not only diagnose a me-tastatic brain tumour, but also to be able todetermine the primary site from a serum sampleusing ATR-FTIR spectroscopy would have manybenefits for both patients and clinicians. The devel-opment of an ATR-FTIR spectroscopic prognosticindex would assist clinicians when using the pro-posed regime in the clinical environment where aprognosis is required.

The diagnostic regime proposed over the courseof this paper describes a rapid diagnostic methodol-ogy which would greatly reduce current diagnosistimes and mortality rates, thus revolutionising theclinical environment.

Acknowledgements The authors would like to acknowl-edge the support of Brain Tumour North West, The Syd-ney Driscoll Neuroscience Foundation, The Centre forMaterials Science at the University of Central Lancashireand Dr Alex Henderson, from the University of Manches-ter, for providing the necessary statistical codes requiredto analyse the ATR-FTIR spectral data.

Author biographies Please see Supporting Informationonline.

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