Vibrational Analysis of Lung Tumor Cell Lines: Implementation of an

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Vibrational analysis of lung tumor cell lines: implementation of an invasiveness scale based on the cell infrared signatures Vincent Daniel Gaydou, Myriam Polette, Cyril Gobinet, Claire Kileztky, JeanFrançois Angiboust, Michel Manfait, Philippe Birembaut, and Olivier Piot Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b00590 • Publication Date (Web): 02 Aug 2016 Downloaded from http://pubs.acs.org on August 5, 2016

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Analytical Chemistry

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Vibrational analysis of lung tumor cell lines: implementation of an invasiveness scale

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based on the cell infrared signatures

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Short title: Infrared-based invasiveness scale

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Vincent Gaydou1,2, Myriam Polette3,4,5, Cyril Gobinet1,2,5, Claire Kileztky3,4, Jean-François

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Angiboust1,2, Michel Manfait1,2, Philippe Birembaut3,4, Olivier Piot1,2,5*

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1

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Champagne-Ardenne, UFR de Pharmacie, 51 rue Cognacq-Jay, 51096 Reims, France.

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3

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Cognacq-Jay, 51092 Reims, France.

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Jay, 51092 Reims, France.

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Ardenne, 51 rue Cognacq-Jay, 51096 Reims, France.

Equipe MéDIAN - Biophotonique et Technologies pour la Santé Université de Reims

CNRS UMR7369MEDyC, SFR Cap-Santé, 51 rue Cognacq-Jay, 51096 Reims, France. INSERM UMR-S 903, SFR CAP-Santé, University of Reims-Champagne-Ardenne, 45, rue

Biopathology Laboratory, Centre Hospitalier et Universitaire de Reims, 45 Rue Cognacq-

Platform of Cellular and Tissular Imaging (PICT), Université de Reims Champagne-

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*

[email protected]

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1 Abstract

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Assessing the tumor invasiveness is a paramount diagnostic step in order to improve the

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patients care. Infrared spectroscopy access the chemical composition of samples; and in

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combination with statistical multivariate processing, presents the capacity to highlight subtle

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molecular alterations associated to malignancy development. Our investigation demonstrated

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that infrared signatures of cell lines presenting various invasiveness phenotypes contain

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discriminant spectral features, which are useful informative signals to implement an objective

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invasiveness scale. This last development reflects the interest of vibrational approach as a

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candidate biophotonic label-free technique, usable in routine clinics, to characterize

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quantitatively tumor aggressiveness. In addition, the methodology can reveal the

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heterogeneity of cancer cells, opening the way to further researches in cancer science.

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2 Introduction

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Metastatic progression is a multistep process involving basement membrane disruption, tumor

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cell dispersion from the primarytumor cluster, stromal connective tissue invasion by tumor

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cells,neo-angiogenesis, intravasation and extravasation and ﬁnally colonization of a target

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organ. The estimation of the invasive properties of tumor cells is of paramount interest for a

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more precise diagnosis, opening the way to a personalized therapy.The implementation of a

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methodology dedicated to the assessment of the tumor invasivenesswould help for the

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prognosis and then may guide the management of patients.In addition, in a retrospective

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approach,invasiveness scoring on biopsy or surgical specimens could help to classify tissues

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in order to further investigate the biochemical mechanisms implicated.

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This need is particularly relevant in bronchial cancers and more precisely for non-small cells

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lung carcinomas (NSCLC). Indeed, the histological examination of bronchial carcinomas

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distinguishes two main types. The first type, representing only 15 % of the casescorresponds

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to small cell carcinoma. This tumor has a worse prognosisand requires rarely a surgical

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treatment.1 NSCLC are also very aggressive lesions frequently discovered at advanced stages.

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Their treatment, according to the degree of extension, includes surgery, radiotherapy and/or

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chemotherapy. In the present study, our investigation concerned these aggressive bronchial

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NSCLC,particularly the squamous cell carcinoma developed from bronchial epithelial cells.2

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Presently, imagingdiagnostic techniques such as X-ray, MRI (Magnetic Resonance Imaging),

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thoracic tomodensitometry or PET (Positon Emission Tomography) do not give access to

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information on the tumor invasiveness.3 Presently, recovering such information requires

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complex biological test such as genetic analysis to highlight the associated mutations or

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immune-histochemical studies, performed on cancer tissues.4 These analyses allow



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improvingthe diagnostic reliability by classifying the cancer type as well as its staging and

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level of invasiveness.Nevertheless, they requiresharp protocols with the use of chemicals.

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Their sensitivity and reproducibility rely also on the expertise of the operator and the

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pathologist interpreting the data.5

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In this context, vibrational spectroscopy appears as a candidate label-free technique to classify

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tumor samples according to their invasiveness level. The combination of infrared(IR)

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absorption spectroscopy and microscopy for tissue analysis, also called Spectral

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HistoPathology (SHP) proved well suited for differentiating various histological structures

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and for identifying pathology.6-9 Presented as a twin technique of IR modality, Raman

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spectroscopy also proved as an effective tool to diagnostic purposes.10,11 Since SHP is a

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computer-based digital technique, the procedure of tissue evaluation can be automated in an

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independent manner of the operator subjectivity.12 The large set of spectroscopic data is

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treated by means of chemometric and statistical methods which can be standardizedto deliver

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reproducibleresults, objectivelyinterpretable.13-16

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In addition, some quite recent studies report that tumor samples of different degrees of

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invasiveness present specific IR markers. Baker et al.employed IR spectroscopy to investigate

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prostate cancer epithelial cell lines and to discriminate cell lines in regard to their

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invasiveness phenotype.17 Also, Sabbatini et al. presented the potential of IR spectroscopy to

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discriminate tissuesof oral squamous cell carcinoma, corresponding to well, moderately and

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poorly differentiated tumor cells.18 More recently, Diem et al. demonstrated the state of

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advancement of SHPin a studydedicated to malignant and benign tumors of the lung

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carcinoma.19 Actually, major of human carcinoma SHP studies show the discriminant power

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and the limitations of infrared spectroscopy. The efficiency of this SHP approach relies on the

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constitution of the sample set that has to reflect the tissue diversity. Also, in these studies, the


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discriminant potential of the IR approach was highlighted, but without exploring its ability to

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quantify the tumor invasiveness. So, the objective of our original work was to develop a

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methodology based on IR spectroscopy permitting to score the invasiveness phenotype of

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tumor cells.

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More precisely, our research concerned broncho-epithelial squamous cell carcinoma as we

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argued above. Indeed, our aim was to establish an invasiveness scale based on IR signatures

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of the cell lines (16HBE, BEAS-2B, BZR and BZR-T33) included for presenting different

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invasive phenotypes. To achieve this objective, specific chemometric algorithms were

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developed specifically for the IR data collected on these cellular specimens. Firstly, a spectral

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preprocessing protocol, based on EMSC (Extended Multiplicative Signal Correction), was

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optimized to producehomogeneous IR data bank bycorrecting mathematically the spectral

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interferences originating from the paraffin used as embedding material, and by normalizingthe

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cell signal independentlyof the cyto-block preparation. Secondly, PLS-DA (Partial Least

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Square - Discriminant Analysis) and PLS (Partial Least Square) were run to establish

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qualitative (i.e. discriminative) and quantitative models respectively. The PLS approach

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aimed at determining a multivariate regression curve to associate an “invasiveness score” to

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each bronco-epithelial cell lines provided.These models were developed and optimized by an

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image-basedcross validation, in which all pixels/spectra of one image constituted a unique

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independent sample.

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3 Materials and Methods

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3.1 Cell culture

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First, the prediction IR model was constructed from 4 different cell lines. Human lung 16HBE

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14o-, BEAS-2B and BZR cell lines were obtained from the American Type Culture Collection

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(Rockville, MD, USA). BZR-T33 lung cells were provided by Curtis C. Harris (National

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Cancer Institute, Bethesda, MD, USA). In a second step of external validation, 2 additional

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cell lines, CALU3 and NCIH, were included.

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The 6 cell lines were cultured in DMEM (Gibco, Invitrogen, Carlsbad, CA, USA) containing

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10% fetal calf serum (FCS) (Gibco). The invasive phenotype of these lung cancercells,

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assessed by a modified Boyden chamber assay, has been extensivelycharacterized

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previously.20 Considering the number of invading cells, 16HBE and CALU3 wereconsidered

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as non-invasive cell lines, BEAS-2B cells display moderate invasive capacities, BZR and

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BZR-T33 present highly invasive properties. NCIH cells invasiveness is intermediary

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between BEAS-2B and BZR. For each of these cell lines, several samples were cultured

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independently. For each of the calibration cell lines (16HBE, BEAS-2B, BZR and BZRt33), 4

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passages were cultured. For external validation (CALU3 and NCIH1299), 2 passages were

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cultured. Cell cultures fixed in formalin were centrifuged then embedded in paraffin and 8

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µm-thick tissue sections were deposited on CaF2 windows suitable for IR measurements.

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All the cell lines used in these experiments are largely used in the literature and most of them

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are provided by ATCC. Thus, there is no ethical limitation for their use.

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3.2 IR imaging

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The IR acquisitions were performed with aHyperion imager (Bruker, Germany) equipped

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with a Focal Plane Array (FPA) detector allowing to image large tissue areas. The FPA used

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present a matrix of 64x64pixels, 2.7 x 2.7 µm² of size. Each pixel can be considered as an

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individual IR detector.This device allows collecting a consequent number of spectra, in a

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limited time,which is required for the statistical processing of the data.

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Spectra were collected with a 2 cm-1 spectral resolution and a number of accumulations of 32

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for the cell spectra and 240 for the background signal. The background is recorded on a very

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clean CaF2 area. Paraffin signal is also collected for each sample on an area surrounding the

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cell spots. Cell samples are analyzed on area of approximatively 0.15 mm² (around 15000

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spectra).

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3.3 Preprocessing of infrared data

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To eachcellular sample, it was corresponding 2 spectral images: one image of paraffin and

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one image of the cell culture. A precise protocol of preprocessing was developed as described

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in figure 1.This methodology aimsat building homogeneous imaging data bank according to

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several steps.

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The first stepcorresponded to a smoothing using the Savitzky-Golay method on a window of 6

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points withapolynom degree of 1.21

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Then the spectralimages were processed by EMSC (Extended Multiplicative Signal

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Correction). This correction was first time proposed by Martens and Stark in 1991. A major

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advantage of EMSC is that prior chemical or physical knowledge can be integrated into the

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preprocessing model.22 The multivariatesignals were normalized with respect to a reference



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signal by means of decomposition of original spectrum in signalsaccording to the following

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equation (1):

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= ̂ + + +

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with a (scalar), b and c (both vectors) regression coefﬁcients, ̂ an estimate of the data set (also

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call “target”), I the matrix of known interferencespectra (interference matrix), P a polynomial

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modeling background and offsets dueto scatter effects and finallye the model error(also call

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“residual”). If thereis not a good estimate of ̂ available, one couldchoose the average of the

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whole data set for ̂ . In our case, ̂ was selected following specific rules as explained later. The

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, andcparameters were estimated by minimizing the weighted sum-of-squares of the

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residual . Thus,

corresponded to EMSC corrected spectrumand was calculated according

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to equation (2):

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= + ̂

(1)

(2)

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In this study, the principal parasite signal was associated to paraffinin the form of an

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interference matrix as it has already ever been done for IR analysis of paraffin-embedded

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samples.23 Theinterference matrix was established by considering the first main components

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of the PCA (Principal Component Analysis) computed on non-centered paraffin

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spectracollected around cells.

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In our investigation, a double EMSC was carried out: the first one for the correction of each

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spectral image independently of the other ones, and the second to make these images

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comparable to the one to the other as illustrated in figure 1. Indeed, first every sample was

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treated independently in the aim to get high quality spectra with neutralized paraffin

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interferences. The cell culture spectra computed and qualified were gathered in non-

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homogeneous multiple imaging data matrix. For each cell image, the paraffin signal was Page 8 sur 32 ACS Paragon Plus Environment

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corrected by EMSC pretreatment but the baseline correction and the proportion between

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paraffin and cell signals were not the same for each cell images.

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So, in order to transform this non-homogeneous multiple imaging data matrix into a

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homogeneous one, a second EMSC was used. The interference matrix was constructed with

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PCA componentscomputed from the set of paraffin spectra of all samples. To perform these

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EMSC pre-processing, a consequent number of parameters have to be monitored. The set of

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these parameters are indicated in table 1. Their optimization was obtained from the results of

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the PLS-DA, described below. One of the main parameters is the “target” that has to be

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defined as the model spectrum the most representative of the biological sample. The

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computing of this target will be explained in the result section.

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3.4 Chemometric algorithms

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PLS

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PLS-based algorithms were used to construct a prediction model allowing data quantification

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with the advantage also to highlight the discriminant spectral features.24

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The PLS algorithm is based on a multivariate regression principle. Particularly, it allows to

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maximize the covariance between 2 matrix by means of multidimensional and orthonormal

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regression vectors. The 2 following matrix equations describe the PLS principle, the aim is to

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solve T with X and Y as known variables.

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X = T P + R

(3)

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Y = T q + f

(4)

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With X the data matrix (n × m) (spectral data);

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Y the quantitative references values (n × l) (invasiveness score);

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T the score matrix (n × k);

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P the loadings (or regression vectors) matrix (k × m) associates to X prediction fromT ; Page 9 sur 32 ACS Paragon Plus Environment


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R the residual associate to X prediction (n × m);

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q the loading associates to Y(k × l);

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f the residual associated to Y prediction (n × l);

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k the number of computed latent variables (corresponding to the PLS model dimension).

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Where l, m and n are respectively the number of quantitative reference value to predict for

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each sample (invasiveness value), the number of experimental values for each sample (depend

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of spectral resolution) and the number of sample (number of spectra).

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The vectorial space established by the regression vectors allows to link spectra (here X)to

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values, scores orreference quantitative variables (here Y). Thus, the PLS regression permits to

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quantify data on the basis of quantitative features. It is particularly adapted to wide and

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covariant data bank. Also this processing can easily thwart by under or over fitting.To prevent

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this bias, the number of latent variables used for modelmust be well mastered.25

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The accuracy of the PLS models was assessed by computing the RMSE (Root Mean Square

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Error) on both calibration and validation sets of data, according the following formula:

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=

∑& !'( ! – #! $ )*+

%

(5)

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With ,- : reference value of spectra i, ,.- model predicted value of ithspectra and n is the

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number of used spectra.

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PLS-DA

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PLS-DA (Partial Least Square - Discriminant Analysis) algorithm was employed to

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implement qualitative classification models.For every qualitative variable, a binary code (0 or

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1) is associated. A multivariate regression PLS is then realized between thespectral data

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matrix and the binary matrix. Then, the PLS-DA model predict multivariate valueslinked to

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spectra present in data set. The multivariate values obtained are then correlated to Page 10 sur 32 ACS Paragon Plus Environment

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corresponding qualitative groups. This classification method is widely used for the

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exploitation of the vibrational data.26

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The performance of the PLS-DA models was evaluated from the PGP (percentage of good

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prediction), on both calibration and validation data sets, calculated as follows:

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/ = ) ∗ 100

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With g the number of well predicted spectra.

0

(6)

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Cross validation

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In our study, the estimation of the performances of the prediction models was based on a cross

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validation method, on the 4 cells lines of the calibration set. More precisely, it was carried out

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at the level of the spectral images rather that at the pixel scale to avoid overfitting. While the

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total number of pixels is high, the number of cytoblock sections is quite limited justifying this

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approach, which we qualify of “partial cross validation”. The principle of cross validation (or

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also internal prediction) is based on the prediction of a sample (an image in our case)

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beforehand removed it from the step of modeling. This process is repeated for all the samples.

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An averaged precision of prediction is then obtained. This average value gives the optimal

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hope expected from the model.27

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All the computing steps were processed on Matlab R2013a (32 bit) (Mathwork, USA), the

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PLS algorithm originates from “saisir” toolbox developed by Bertrand and Cordella, INRA,

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France.28

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3 Results

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Development and optimization of preprocessing protocols were summarized in figure 1 and

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table 1. The way to build an objective and homogenous spectral data matrix is complex

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because it exists a strong variability between each paraffin embedded cell culture. First,cell

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culture thickness may be variable within each samplesection. This variability is due to

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material and mechanical property associated to sample preparation. To avoid these variances,

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the EMSC pretreatment was employed following two steps as indicated in the Material &

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Methods section.

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During the first EMSC performed at the level of each spectral image, we can notice the

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influence of the “target” selected as reference as reference spectrum. If the mean spectrumis

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chosen as target (usual choice), a markedvariability between the targets of the different

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spectral image is observed. This target variability mainly originates from the numberof

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pixelsby images who effectivelypresent acellular signal. To avoid this problematic, it was

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decided to compute targets in function of cell signal for each image. The integrated intensity

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under the amide I and II bands (1450 –1750cm-1) was computed and sorted (top to down). The

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spectra thatcorresponded to the second quartile of this ordering were then used to compute the

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target. For each spectral image, the target was determined by this quartile method, which

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permitted also to provide quite similar targets representative of the cellular signal within the

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set of spectral images. First, third and fourth quartiles were also tested but with worst results.

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In addition, the selection of the target strongly influences the EMSC quality test. Computing

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the target on the second quartile ensures to have a representative cell signal and leads that

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near 80% of pixels were rejected after the first EMSC. The rejected spectra were deleted due


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to the poor signal or/and with a high paraffin contribution. This quality test depends of a and

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b parameters as indicated in table 1. After the second EMSC, for all spectra of each images,

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the baseline was homogeneous and the paraffin contribution identical. Figure 2 displays the

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mean and the minimum-maximum values of all raw spectra of the set of the images, after the

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first and the second EMSC steps. The proportion between cellular material and paraffin was

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so variable from one sample to the other that the second EMSC cannot correct entirely this

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variance. Consequently, to avoid to take into account this signal variability originating from

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the sample preparation, the spectral ranges were reduced to 920 to 1350 cm-1 and 1500 to

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1850 cm-1 and a min-max normalization was computed on the 1670cm-1wavenumber

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corresponding to the amide I band associated to vibrations of the peptide bonds of the protein

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content.

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Figure 3 presents the results of PLS-DA, performed on the 4 cell lines of the calibration

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sample set. For this classification model, 3 reference groups were chosen, 16HBE as normal

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cells, BEAS-2B as moderate invasive cells and BZR and BZR-T33 invasive cells. The BZR

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and BZR-T33 were gathered together because of the phenotypic likelihood between both cell

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types.

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Figure 3.a shows the exploration of the number of latent variablesbased on thepartial cross-

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validation method used. It displays the evolution of PGP as function of the number of latent

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variables (until 30) for both calibration and validation steps. The calibration PGP tends to

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100% when the number of latent variablesincreases, reflecting a convergence of the PLS-DA

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processing. Examining the evolution of the validation PGP, a number of 10 latent variables

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was determined as being optimal based on the fact that the PGP maximum, around 90%, is

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reached for this number; and by preventing under or over fitting. Table 2 indicates the

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prediction results for the 3 groups of cellular specimens (16HBE, BEAS-2B, and a common



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group for BZR and BZR-T33) corresponding to various invasiveness phenotypes. The PGP

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values are over 90% for the extreme groups of invasiveness while the BEAS-2B cells present

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a value near to 77% reflecting the intermediary phenotype of this cell line.In order to visualize

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the discriminant potential of our approach a 3D representation is given (figure 3.b). The latent

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variables chosen to draw this representation were the first, the 4th and the 7th ones, since these

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latent variables gave a good visual aspect of the data projection.While the borders of the

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groups were not totally frank with some spectra superimposed, a distinction between these 3

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levels of invasiveness can be outlined.

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The results of PLS-DA processing show the ability to discriminate bronchial SCC of different

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invasiveness phenotypes from their infrared spectral signatures. Based on these encouraging

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results, our objective was then to investigate the possibility to order cells quantitatively by

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constructing a spectral scale of the cell invasiveness. In this purpose, PLS models were then

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developed.

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While PLS-DA gives qualitative memberships toa restricted number of classes (3 in our

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study), PLS is a more moderated processing offering the possibility to consider continuous

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and quantitative scores.29 Thus, PLS was run with the 4 levels of cell invasiveness as

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reference inputs, by distinguishing the 2 highest invasiveness levels, i.e. BZR and BRZ T33

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samples.First, various PLS models were tested by changing simultaneously the reference scale

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of invasiveness and the number of latent variables. The best results were obtained with a scale

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ranging from 1 to 2.3 as indicated in table 3, and an optimal number of latent variables to 10.

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Indeed, the number of latent variables was determined by plotting the RMSE for a varying

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number of variables, similarly to the first step of PLS-DA previously explained (figure 4.a).

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The calibration and validation RMSE tend to6%and 15% with the increasing of the latent


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variablesnumber, respectively. The optimal number, corresponding to the first minimum, is

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the most appropriate to avoid under and over fitting for the PLS models.

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The regression results, calculated with these conditions, are given in table 3 with

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corresponding regression curve depicted in figure 4.b. The average validation RMSE was

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computed to 16.6%; with 11 and 23.8% as minimal and maximal prediction errors obtained

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for BZR and BZR-T33 respectively. A significant linear regression was highlighted between

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the reference and the predicted invasiveness scales, with a correlation coefficient R² equal to

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0.76 (R² > rα,withrα=0.01 = 0.6226 for n-2=14 as degrees of freedom).30 Interestingly, the

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reference scale highlightsinvasiveness levels which are not regularly distributed between 1 for

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16HBE and 2.3 for BZR-T33 cells. Indeed, B2B and BZR cells were positioned at 1.3 and 2.1

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respectively, reflecting a slight proximity of B2B with 16HBE and a very close invasiveness

337

phenotype of BZR and BZR-T33.

338

In a further step to demonstrate the validity of our approach, 2 additional cell samples

339

considered as an external validation set were projected on the PLS model, previously

340

calibrated with the first 4 cell lines. The prediction results of CALU3 and NCIH1299 cells

341

appear coherent with their invasiveness phenotype, as visible in Table 4.

342



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

343

4 Discussion

344 345

Main applications of FT-IR spectrometry in cancer science consist in demonstrating the

346

diagnostic potential of this label-free biophotonic technique. In our study, we tried to exploit

347

further the wealth of the vibrational approach by highlighting and quantifying cell

348

invasiveness phenotype by using distinct human lung cell lines.

349 350

In this work, a double EMSC preprocessing proved its capacity for ensuring quality test and

351

homogenizing the set of data in an automated manner. In paraffin-embedded tissue sections, a

352

one-step EMSC is sufficient,31 on cells samples it was necessary to implement a more

353

sophisticated pre-processing using a specific way to compute the target spectrum and a drastic

354

selection of retained spectra; near 80% of the raw spectra were rejected. Here, the numerous

355

pre-processing parameters were optimized in regards to the classification results, since pre-

356

processing affects strongly the later statistical processing of the data.

357

The advantage of our approach is to give the possibility to work with several spectral images

358

recorded from paraffin embedded cell culture samples. As it is observed on figure 2, the great

359

variability of raw spectra forbids data interpretability. The double EMSC can correct a high

360

part of parasiticvariability due to the paraffin embedding but also to sample preparation.

361

Nevertheless, a rest of variability is still present. This concerns particularly the high

362

wavenumber range with the impossibility to distinguish CH3 and CH2IR absorption signal

363

between paraffin and cell material. To skirt this problem, we were obliged to addin the

364

preprocessing protocol, stepssuch as wavenumber range selection andnormalization on the

365

amide I band. Other advantage of EMSC, is the possibility to perform a quality test to

366

eliminate outlier spectra and retain exploitable cell spectra.

367


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368

A possibility to perform the statistical processing would have been to use cross validation at

369

the image pixel level. In our study, the number of spectra per images was very high, near to

370

ten thousand. This method was not relevant considering the risk of high overfitting.32 To

371

avoid such a methodological bias, we opted for cross-validation at the image level although

372

the number of images was limited. Indeed, a total of 16 samples corresponding to 4 invasive

373

distinct phenotypes was included to develop our approach. In addition, the PLS model of

374

invasiveness scale was further validated by projecting 2 additional human lung cell lines

375

(CALU3 and NCIH1299).

376 377

The retained PLSmodel,lead toa RMSEP error prediction less than 17%, required 10latent

378

variables,translating the fact that the useful spectral information, associated to the tumor

379

invasiveness, is quite subtle and not comprised only in the first latent variables that carry the

380

main covariance between the infrared signals and the reference invasiveness scale.33 The

381

visualization of these latent variables permits to reveal the infrared molecular vibrations

382

implicated in the discrimination process. Figure 5, presenting the 10PLS latent variables,

383

highlights the discriminant wavenumbers. Particularly due to their strong loading coefficients,

384

the following vibrations appear to be involved into the invasiveness scoring.

385

_ 1015 and 1117 cm-1 assigned to C-O stretching (νC-O) vibrations in carbohydrate

386

molecules, _ 1585 cm-1 of the amide II band specific of the protein content,

387

_ 1618 cm-1 attributed to νC=O bonds probably due to β-turn proteins secondary structure,

388

_ 1740 cm-1 attributed to νC=O bonds in lipid compounds (triglycerides or phospholipids).

389 390

The regression PLS model was built by determining the optimal invasiveness scale. We

391

decided to fix the origin at 1, firstly for algorithmic reasons since predicting a null or negative

392

value can generate a strong computing bias and secondly because 16HBE cells present slight



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

393

invasiveness characteristics. Then, many arbitral values were tested to quantify cell lines

394

invasiveness.

395

Based on this reference scale, the lowest invasiveness level was over-estimated contrary to the

396

highest level that was underestimated. This particularity emphases the difficulty to precisely

397

predict the samples corresponding to the extreme invasiveness degrees. The reference

398

invasiveness were optimized to limit this effect.

399

As indicated inTable 3, the invasiveness value of the cell lines were not regularly spaced out,

400

nevertheless the corresponding regression curve is linear (Figure 4.b). This observation is

401

closer to the linear nature of the PLS principle and also to the Beer Lambert law lying on a

402

linear relationships between the molecular concentration and the infrared absorbance.

403 404

A marked dispersion of the data was observed. Indeed, as shown on figure 4.b, the repartition

405

of predicted invasiveness for spectral data of a same invasiveness reference score is

406

important. This is also illustrated by the value of the standard deviation in the PLS model

407

(Tables3 and 4), thatclearly illustrates the spectral variability within each cell culture. In our

408

methodological approach, each infrared image was correspondingto a separate culture of cell

409

line. Indeed, the analyzed cell samples obtained at different passages of cell culturemay be a

410

supplementary source of variability. Furthermore, for each culture the cells probed canmay be

411

at various stages of invasivenesswhich mayinfluencethe spectral signal. Thus, our approach

412

presents the capability to assess such variable biological features and could find valuable

413

application in the investigation of tumor heterogeneity.

414

The vibrational signals associated with different invasive properties of the cells may be

415

further applied on human tissue sections of lung cancers, particularly in pre-invasive lesions.

416

Indeed, in these conditions, classical histology cannot detect the potential aggressiveness of

417

such heterogeneous lesions. Thus, this original infrared spectral approach which can be


Page 18 of 32

Page 19 of 32

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418

directly used on formalin fixed paraffin embedded tissue sections, may bring new information

419

on the behavior of these tumor cells.

420



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

421

5 Conclusion and perspectives

422 423

This study, led on cell samples of various invasiveness phenotypes, demonstrated the

424

possibility to establish an invasiveness scale on the basis of the cell infrared signature. This

425

quantitative and label-free assessment of the invasiveness relies exclusively on the intrinsic

426

molecular composition of the samples, without requiring any particular preparation and while

427

preserving the cellular integrity. In addition, within a same invasiveness level, a spectral

428

heterogeneity can be highlighted among the cells population. This demonstrates the interest of

429

the vibrational spectroscopic approach to reveal and analyze the tumor heterogeneity for

430

future investigations in cancer science.

431

Further developments will concern the incorporation of feature extraction methods in the data

432

statistical processing. Indeed, algorithms such Sparse PLS or combining a processing by

433

Genetic Algorithm appear as an innovative way to improve the prediction model, with the

434

advantage to identify simultaneously the molecular vibrations involved in the discriminant

435

process.34-36

436

Also, the methodology implemented and the results obtained on cellular samples will be

437

transferred at the tissue scale, in order to take into account the influence of the complex

438

ecosystem on the functionality of the tumor cells.

439 440


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441


6Acknowledgements

442 443

With financial support from ITMO Cancer AVIESAN (National Alliance for Life Sciences &

444

Health) within the framework of the Cancer Plan.CNRS UMR7369 MEDyC (Matrice

445

Extracellulaire et Dynamique Cellulaire) laboratory and the technological platform PICT

446

“Imagerie Cellulaire et Tissulaire”are gratefully acknowledged for instrumental support.

447

INSERM (SFR CAP-Santé, University of Reims-Champagne-Ardenne) and Pol Bouin

448

laboratory (CHU of Reims) are also gratefully acknowledgedfor sample supply and scientific

449

recommend.

450



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

451

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Wissler, M.-P. Bilan de l’analyse du statut mutationnel EGFR de 1000 patients atteints d’adenocarcinomes pulmonaires pris en charge par la plateforme d’oncologie moleculaire du Chu-Cav de Nancy, Thesis, Université de Lorraine, Faculté de Médecine de Nancy, 2012.

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Bonastre, E.; Brambilla E.; Sanchez-CespedesM.; J. Pathol.,2016, 238, 606-616.

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Sato, M.; Sakurada, A.; Sagawa, M.; Minowa, M.; Takahashi, H.; Oyaizu, T.; Okada, Y.; Matsumura, Y.; Tanita, T.; Kondo, T. Lung Cancer, 2001, 32, 247-253.

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Travis, W.D.; Brambilla, E.; Noguchi, M.; Nicholson, A. G.; Geisinger, K.; Yatabe, Y.; Powell, C. A.; Beer, D.; Riely, G.; Garg, K.; Austin, J. H. M.; Rusch, V. W.; Hirsch, F. R.; Jett, J.; Yang, P.-C.; Gould, M. J. Thorac. Oncol.,2011, 6, 244-85.

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Diem, M.; Miljkovic, M.; Bird, B.; Chernenko, T.; Schubert, J.; Marcsisin, E.; Mazur, A.; Kingston, E.; Zuser, E.; Papamarkakis, K.; Laver, N. Spectroscopy,2012, 27, 463496.

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Kumar, S.; Shabi, T. S.; Goormaghtigh, E. Plos one,2014, 9, 1-8.

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(12) Lasch, P.; Haensch, W.; Naumannc, D.; Diem, M. BBA, Mol. Basis Dis.,2004, 1688, 176-186.

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(13) Yano, K.; Ohoshimab, S.; Gotouc, Y.; Kumaidod, K.; Moriguchia, T.; Katayama, H. Anal. Biochem.,2000, 287, 218-225.

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(14) Petibois, C.; Drogat, B.; Bikfalvi, A. ; Déléris, G.; Moenner, M. FEBS Lett.2007, 581, 5469-5474.

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(15) Nallala, J.; Diebold, M.-D.; Gobinet, C.; Bouché, O.; Sockalingum, G. D.; Piot, O.; Manfait, M. Analyst, 2014, 139, 4005-4015.

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(16) Mu, X.; Kon, M.; Ergin, A.;Remiszewski, S.,Akalin, A.;Thompson, C. M.; Diem, M.Analyst, 2015, 140, 2449-2464.

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(17) Baker, M. J.; Clarke, C.; Démoulin, D.; Nicholson, J. M.; Lyng, F. M.; Byrne, H. J.; Hart, C. A.; Brown, M. D.; Clarke N. W.; Gardner, P. Analyst,2010, 135, 887-894. Page 22 sur 32 ACS Paragon Plus Environment

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(18) Sabbatini, S.; Conti, C.; Rubini, C.; Librando, V.; Tosi, G.; Giorgini, E. Vib. Spectrosc.,2013, 68, 196–203.

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(20) Nawrocki-Raby, B.; Polette, M.; Gilles, C.; Clavel, C.; Strumane, K.; Matos, M.; Zahm, J. M.; Van Roy, F. Bonnet, N.; Birembaut, P. Int. J. Cancer,2001, 93, 644-652.

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(21) Lasch, P. Chemom. Intell. Lab. Syst.,2012, 117, 100-114.

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(22) Kohler, A.; Böcker, U.; Warringer, J.; Blomberg, A.; Omholt, S. W.; Stark, E.; Martens, H. Appl. Spectrosc.,2009, 63, 296-305.

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(23) Wolthuis, R.; Travo, A.; Nicolet, C.; Neuville, A.; Gaub, M.-P.; Guenot, D.; Ly, E.; Manfait, M.; Jeannesson P.; Piot, O. Anal. Chem.,2008, 80, 8461-8469.

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(24) Walker, H.; Land, Jr.;William, F.;Park J.-W.;Mathur, R.;Hotchkiss, N.;Heine, J.;Eschrich, S.;Qiao, X.;Yeatman, T. Procedia Comput.Sci., 2011, 6, 273-278.

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(25) Bastien, P.; Bastiena, P.; Vinzib, V. E.; Tenenhaus, M. Comput. Stat. Data Anal., 2005, 48, 17-46.

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(26) Preisner, O.; Lopesb J. A.; Menezesa, J. C. Chemom. Intell. Lab. Syst.,2008, 94, 33-42.

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(27) Arlot, S.; Celisse, A. Stat. Surv.,2010, 4, 40-79.

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(28) Cordella, C.B.Y.; Bertrand, D. Trends Anal. Chem., 2014, 54, 75-82.

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(29) Roggo, Y.; Duponchel, L.; Ruckebusch C.; Huvenne, J.-P. J. Mol. Struct.,2003, 654, 253-262.

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(30) Fisher, R. A.; Yates, F. Statistical Tables for Biological, Agricultural and Medical Research. 6th Ed. Oliver & Boyd, Edinburgh and London, 1963.

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(31) D’inca, H.; Namur, J.; Ghegediban, S. H.; Wassef, M.; Pascale, F.; Laurent, A.; Manfait, M. Am. J. Pathol.,2015, 185, 1877-1888.

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(32) Celisse A.; Robin, S. Comput. Stat. Data Anal., 2008, 52, 2350-2368.

511 512

(33) Gaydou, V.; Lecellier, A.; Toubas, D.; Mounier, J.; Castrec, L.; Barbier, G.; Ablain, W.; Manfait M.; Sockalingum, G. D.Anal. Methods,2015, 7, 766-778.

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(34) Hyonho, C.; Sündüz, K. J. R. Statist. Soc.,2010, 7, 3-25.

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(35) Karaman, I.; Qannari, E.M.; Martens, H.; Hedemann, M.S.; Bach Knudsen, K.E.; Kohler, A. Chemom. Intell. Lab. Syst.,2013, 122, 65-77.

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518

8 Tables and figures

519

Table 1 : Glossary of tested parameters Step

Parameter category Pretreatment 01

Paraffin 01

First step : individual paraffin normalization (sample by sample)

EMSC 01

Target 01

Second step : global paraffin normalization (all samples in one Data matrix)

Paraffin 02

EMSC 02 Target 02

Third step : final Chemometrics process (modelization)

Pretreatment 02

PLS_DA/PLS

520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537

Description

Page 24 of 32

Optimal parameters

Parameters tested by Cross Validation

*

CO2 wavelength erased, smoothing (Stavinsky Golay)

[5,10,15,20]

5

[0.5,1,1.5,2,2.5,3,3.5,4]

2,5

[1,2,3,4,5,6,7]

5

[1,1.1,1.2,1.3,1.4,1.5,1.6,1.8,2,2.5,3]

1.3

[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]

0.9

[1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2]

1.1

*

mean spectrum of second quartile computed on amide signal areas (1450-1750 cm-1)

[5,10,15,20]

5

[0.5,1,1.5,2,2.5,3,3.5,4]

2,5

[1,2,3,4,5,6,7]

5

*

mean spectrum

Severals possibility (baseline corection, normalisation, derivating, SNV…)

*

reduction spectral range (920-1 1870cm ) erase paraffin signal (1350-1 1500cm ) normalization (min-max) at 1670 cm-1 (amide I signal)

Number of computed latent variable vector

[1 to 30] (one by one)

10**

Several possibilities (baseline corection, normalization, derivating, SNV…) Number of principal PC during PCA on paraffin spectra maximum error fitting between spectra and mean spectra of paraffin spectral image polynom order choice for baseline estimation maximum residual after EMSC decomposition (ai parameter) minimum bi parameter accepted after EMSC maximum bi parameter accepted after EMSC

taget spectrum for EMSC

Number of PC computed during PCA on paraffin spectra maximum error fitting between spectra and mean spectra of paraffin spectral image polynom order choice for baseline estimation taget spectrum for EMSC

* : various non algebraic parameterspossibility Parameters were ordered by their chronological use.The fourth column describes (when is possible) all tested parameters. The fifth column designs the variability range of tested parameters during computing models. The last column presents the selected values as optimal for modeling. Paraffin spectra were excluded when the computed Euclidean distances between spectra and the mean spectrum exceed the value “max_paraffin_error”. The number of PCA components used is ordered by the “number_Paraffin_PC” parameter. The baseline derivation was modeled by means of a polynomial function with polynomial order defined by “mscOrder” parameter (“mscOrder_2” for the second EMSC). Aquality test was applied on spectral image by the way of weighted coefficient matrix and by using “mscMaxResidue”, “mscMinCoef” and “mscMaxCoef” parameters. The “target 01” was the mean of specific part of spectral image described in result section. For second EMSC, the computed “target02” was the average of non-homogeneous multiple imaging data matrix.“max_paraffin_error_2” permits to realise a quality test on the second interference matrix constitute by “number_Paraffin_PC_2” first main component of PCA of multiple imaging paraffin matrix.


Page 25 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

538 539

540 541 542 543 544 545


Table 2 : Results of PLS-DA discrimination Cell culture type

Number of spectra

Referenc e group

Number of spectra predicted as group 01



Percentage of Good Prediction (%)

16HBE

3346

01

3077

195

74

92,0

BEAS-2B

2142

02

279

1643

220

76,7

BZR BZR-T33

3428 4082

03

24 8

190 56

3214 4018

93,8 98,4

96,3

Total spectra :

12998

mean :

90,2

88,3

The 3 first columns explain the cell culture name, the number of spectra or pixels taken in account in the computing and the reference group designation chosen (qualitative variable). The columns 4, 5 and 6 show the total number of spectra predicted for each reference group and for each cell cultures. The last column summarize these results by exposing the PGP for each cell cultures (a global PGP was added for both BZR and BZRT33 culture cells).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

546 547

548 549 550 551 552 553 554 555

Page 26 of 32

Table 3 : Results of PLS regression Calibration cell lines

Reference invasivity

PLS prediction invasivity

RMSE of validation (%)

Bias

Standard Deviation

16HBE BEAS-2B BZR BZR-T33

1 1,3 2,1 2,3

1,14 1,42 2,02 2,09

17,0 14,7 11,0 23,8

0,14 0,12 -0,08 -0,21

0,286 0,225 0,195 0,186

Mean :

16,6

-0,01

0,22

The first and second column presents respectively each studied cell culture and the reference invasiveness chosen. The third column presented the mean predicted invasiveness. The 4th, 5th, and 6th columns show (respectively for each cell culture) the detailed statistic results of the PLS regression. The Root Mean Square Error of Prediction illustrate the mean error proportionally to the invasiveness scale during the validation step of cross validation. The bias present the mean interval between reference and predicted invasiveness. The last column contained the standard deviation of prediction for each cell culture.


Page 27 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

556 557

558 559 560 561 562


Table 4 : Results of external validation of the PLS model Validation Cell lines

Number of IR pixels (7,3 µm²/pixel)

Mean of External Prediction

Standard Deviation

CALU3

4239

1,2

0,24

NCIH1299

3715

1,7

0,35

The first and second columnsindicated the cell lines used as external validation set and the number of qualified IR pixel used for prediction, respectively. The third column presents the invasiveness score predicted by the calibrated PLS model and the last column presents the corresponding standard deviations.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

563

564 565 566 567 568

Figure 1 : Preprocessing Organigram in three steps : Firstly thesamples collecting, secondly theoverriding of paraffin by EMSC preprocessing and thirdly thebuildingof homogeneous data bank linked to biological variables (second EMSC)


Page 28 of 32

Page 29 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

569 570 571 572 573 574 575


Figure 2 : Infrared spectra evolution of paraffined bronchial squamous cell lines during the pretreatment steps, full lines correspond to mean of spectral data matrix after recording, first and second EMSC processing steps, and colored area represents minimum to maximum space of variability values of these data matrixes,



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

576 577 578 579 580 581 582 583

Figure 3 : Discrimination with PLS-DA method, a- optimization by cross validation of PLS-DA latent variable numbers, b- 3 dimensional discrimination volume with latent variable 1, 4 and 7. In black (blue) the 16HBE, in light grey (green) the BEAS-2Band in dark grey (red) the BZR and BZR-T33 cells spectra.


Page 30 of 32

Page 31 of 32

584 585 a-

b-

PLS Cross-Validation result calibration

validation

Linear regression curve 3,5

y = 0,7371x + 0,428 R² = 0,7635

31 3 26

21

16

BEAS-2B pixels

2,5

Predicted invasivity

10 is the optimal number of latent variables RMSE (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


16HBE pixels

2

1,5 BZR-T33 pixels

1

BZR pixels

11 0,5

0

6 1

586 587 588 589 590 591 592 593

594 595 596 597 598 599

6

11 16 21 Number of Latent Variables

26

0,7

1,2

1,7 Reference invasivity

2,2

Figure 4 : Regression with PLS method, a- optimization by cross validation of PLS latent variable numbers, b- references versus predicted invasiveness : spectral reparation on invasiveness scale with linear correlation curve.With circle, square, diamond and triangle respectively linked to 16HBE, BEAS-2B, BZR and BZR-T33 cell spectra.

Figure 5 : PLS latentes variables full line is the mean of first ten selected PLS latentes variables and colored area represents minimum to maximum space of variability of these PLS latentes variables, dotted line corresponds to the mean of final spectral data matrix (the target of second EMSC)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

600

Table of Contents (TOC)/Abstract (ABS) Graphic

601 602 603


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Vibrational Analysis of Lung Tumor Cell Lines: Implementation of an

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