domain: qfin shs

text:
Louis Aimé Fono

Christian Deffo Tassak

Kurtosis and Semi-kurtosis for Portfolios Selection with Fuzzy Returns

 quantified mean and variance of a portfolio through fuzzy probability and possibility distributions, Carlsson et al.[2]-[3] used their own definitions of mean and variance of fuzzy numbers. In particular, Huang [7] quantified portfolio return and risk by the expected value and variance based on credibility measure. Recently, Huang [7] has proposed the mean-semivariance model for portfolio selection and, Li et al.[5], Kar et al.[8] introduced mean-variance-skewness for portfolio selection with fuzzy returns.

Different from Huang [START_REF] Huang | Mean-semivariance models for fuzzy portfolio selection[END_REF] and Li et al. [START_REF] Li | Mean-variance-skewness model for portfolio selection with fuzzy returns[END_REF], after recalling the definition of mean, variance, semi-variance and skewness, this paper considers the Kurtosis and semi-Kurtosis for portfolio selection with fuzzy risk factors (i.e. returns). Several empirical studies show that portfolio returns have fat tails. Generally investors would prefer a portfolio return with smaller semi-kurtosis (or Kurtosis) which indicates the leptokurtosis (fat-tails or thin-tails) when the mean value, the variance and the asymmetry are the same. Our main objective is to contribute to a sound formal foundation of statistics and finance built upon the theory of fuzzy set.

The aim of this paper is to measure the leptokurtocity of fuzzy portfolio return by means of the two new notions of semi-kurtosis and Kurtosis. The paper is organized as follow: In Section I, we recall the notions of means, variance, semi-variance and skewness of a fuzzy variable. In Section II, we originally introduce the two notions of kurtosis and semi-kurtosis and determine some mathematical properties (which are fuzzy versions of their well-known crisp properties). We display an application in finance by establishing the mean-semivariance-skewness-semikurtosis model for a portfolio selection with fuzzy risk factors (i.e. trapezoidal risk factors).

Keywords: Fuzzy variable, Kurtosis, Semi-kurtosis, Portfolio selection.

JEL Number Classification:

1 PRELIMINARIES

Fuzzy variable and credibility

A fuzzy set A of a universe X is a mapping µ A defined from X to [0, 1]. If ∀x ∈ X, µ A (x) ∈ {0, 1}, then A is a crisp set.
A fuzzy variable ξ is a fuzzy set of R with a membership function µ. For a real number x, µ(x) represents the possibility that ξ takes value x.

There are two usual fuzzy variables:

ξ = (a, b, c, d) a trapezoidal fuzzy variable (∀x ∈ ] -∞, a] ∪ [d, +∞[, µ ξ (x) = 0; ∀x ∈ [b, c], µ ξ (x) = 1; ∀x ∈ [a, b], µ ξ (x) = 1 b-a x -a b-a ; ∀x ∈ [c, d], µ ξ (x) = 1 c-d x -d c-d ). If b = c, then ξ = (a, b, b, d) = (a, b, d) is a triangular fuzzy variable. ξ = (a, b, c, d) is symmetric if ∃t ∈ R, ∀r ∈ R, µ(t -r) = µ(t + r).
Note that for ξ taking values in B, Zadeh [START_REF] Zadeh | Fuzzy Set as basis for a theory of possibility, Fuzzy Sets and Systems[END_REF] has defined the possibility measure of B by

P os({ξ ∈ B}) = sup x∈B µ(x)
and the necessity measure of ξ by

N ec({ξ ∈ B}) = 1 -sup x∈B c µ(x).
But neither, of these measures are self-dual. That reason also justified the introduction of the credibility measure by Liu ([9]).

Liu defined the credibility measure as the average of possibility measure and necessity measure as follows:

Cr({ξ ∈ B}) = 1 2 sup x∈B µ(x) -sup x∈B c µ(x) + 1 .
It is easy to show that credibility measure is self-dual. That is,

Cr({ξ ∈ B}) + Cr({ξ ∈ B c }) = 1.
Let us end this Subsection by giving some notations useful throughout this paper: for a trapezoidal fuzzy variable ξ = (a, b, c, d) such that a = b and c = d, supp(ξ) = [a, d] its support, cor(ξ) = [b, c] its core, l s the length of supp(ξ) and l c the length of cor(ξ). We set:

α = b -a, β = d -c, l s (ξ) = d -a and l c (ξ) = c -b.
For a triangular fuzzy variable ξ = (a, b, c) such that b = a and c = a, we set:

α 1 = max{b - a, c -b} and γ = min{b -a, c -b}. ξ = (a, b, c, d) is symmetric if α = β, and ξ = (a, b, c) is symmetric if α 1 = γ.

Expected Value, Variance and Skewness of fuzzy variables

The definitions of the expected value, variance and skewness of fuzzy variables are obtained from Li et al. [START_REF] Li | Mean-variance-skewness model for portfolio selection with fuzzy returns[END_REF].

Definition 1. Let ξ be a fuzzy variable. Then:

• its expected value E[ξ] is defined as E[ξ] = e = +∞ 0 Cr{ξ ≥ r}dr - 0 -∞ Cr{ξ ≤ r} dr
provided that at least one of the above integrals is finite.

• its variance is defined as

V [ξ] = E[(ξ -e) 2 ].
• its semivariance is defined as

V S [ξ] = E[[(ξ -e) -] 2 ] = +∞ 0 Cr{[(ξ -e) -] 2 ≥ r}dr where (ξ -e) -is a fuzzy variable defined as (ξ -e) -= ξ -e if ξ ≤ e 0 if ξ > e .
• its skewness is defined as

Sk[ξ] = E[(ξ -e) 3 ].
The expected value of a trapezoidal fuzzy variable denoted ξ = (a, b, c, d) is given by E[ξ] = a+b+c+d 4

. Note that, expected value is one of the most important concepts of fuzzy variable, which gives the center of its distribution. The variance of ξ = (a, b, c, d) is

V [ξ] = -[ 1 4 (l s (ξ) + l c (ξ))] 3 ( |α -β| 3αβ ) + max( ( |α-β| 4 -1 2 l c (ξ)) 3 6α ∨ β , 0)+ |α -β| 2αβ [ 1 2 l s (ξ) - (α + β) 4 ][ 1 4 (l s (ξ) + l c (ξ))] 2 + ( |α-β| 4 + 1 2 l s (ξ)) 3 6α ∨ β - ( |α-β| 4 + 1 2 l c (ξ)) 3 6α ∧ β .
We can easily check that if ξ is symmetric (α = β), V [ξ] simply becomes

V [ξ] = 3[(c -b) + β] 2 + β 2 24 .
The semi-variance of ξ is defined by

V S [ξ] = 1 6(b -a) [( e -a 4 ) 3 + min(0, ( b -e 4 ) 3 )] + 1 6(d -c) max(0, ( e -c 4 ) 3 ). ( 1
)
The skewness of a trapezoidal fuzzy variable ξ = (a, b, c, d) is given by

Sk[ξ] = 1 8(b -a) [( b -e 4 ) 4 -( a -e 4 ) 4 ] + 1 8(c -d) [( c -e 4 ) 4 -( d -e 4 ) 4 ]. (2)
Let us recall that from these results, we can deduce those of a triangular fuzzy variable.

Remark 1. The variance of ξ is used to measure the spread of its distribution about e = E(ξ).

Note that, variance concerns not only the part "ξ" is less than e, but also the part ξ is greater than e. If we are only interested with the first part, then we should use the concept of semivariance.

2 Kurtosis and Semikurtosis

Definitions, examples and some first properties

Definition 2. Let ξ be a fuzzy variable with expected value e.

• The kurtosis of ξ, denoted K[ξ], is given by: K

[ξ] = E[(ξ -e) 4
].

• The semikurtosis, K S [ξ], is given by: K

S [ξ] = E[[(ξ -e) -] 4 ] = +∞ 0
Cr{[(ξ -e) -] 4 ≥ r}dr.

Example 1. For a trapezoidal fuzzy variable denoted ξ = (a, b, c, d) with expected value E[ξ] = e, we have the following results:

• the kurtosis is given by

K[ξ] = -[ 1 4 (l s (ξ) + l c (ξ))] 5 ( |α -β| 5αβ ) + max( ( |α-β| 4 -1 2 l c (ξ)) 5 10α ∨ β , 0) + ( |α-β| 4 + 1 2 l s (ξ)) 5 10α ∨ β |α -β| 2αβ [ 1 2 l s (ξ) - (α + β) 4 ][ 1 4 (l s (ξ) + l c (ξ))] 4 - ( |α-β| 4 + 1 2 l c (ξ)) 5 10α ∧ β
• the semikurtosis is given by

K S [ξ] = 1 10(b -a) [( e -a 4 ) 5 + min(0, ( b -e 4 ) 5 )] + 1 10(d -c) max(0, ( e - 4 ) 5 ) (3)
It is important to notice that from these results, we can deduce those of a triangular fuzzy variable.

Proposition 1. Let ξ be a fuzzy variable with finite expected value e, K S [ξ] and K[ξ] the semi-kurtosis and kurtosis of ξ respectively. Then: Cr{(ξ -e) + ≥ r}dr = 0. This equality implies that Cr{(ξ-e) + ≥ r} = 0, ∀r > 0. Since Cr is self dual, we obtain Cr{(ξ-e) + = 0} = 1.

• 0 ≤ K S [ξ] ≤ K[ξ]. • K S [ξ] = 0 if and only if Cr{ξ = e} = 1, i.e., K[ξ] = 0. • If ξ is symmetric, then K S [ξ] = K[ξ]. Proof: 1) Let us show that 0 ≤ K S [ξ] ≤ K[ξ]. Let r ∈ R.

  With the definition of (ξ -e) -, we have: [(ξ -e) -] 4 = (ξ -e) 4 if ξ ≤ e 0 if ξ > e . Thus we distinguish two cases as follows: i) If ξ(θ) ≤ e, then [(ξ(θ) -e) -] 4 = (ξ(θ) -e) 4 . And [(ξ(θ) -e) -] 4 ≥ r ⇔ (ξ(θ) -e) 4 ≥ r. ii) If ξ(θ) > e, then [(ξ(θ) -e) -] 4 = 0 and (ξ(θ) -e) 4 ≥ [(ξ(θ) -e) -] 4 . Thus the inequality [(ξ(θ) -e) -] 4 ≥ r implies (ξ(θ) -e) 4 ≥ r. We deduce that ∀θ, r, {θ/[(ξ(θ) -e) -] 4 ≥ r} ⊆ {θ/(ξ(θ)-e) 4 ≥ r}. Since Cr is monotone, we have: ∀r, Cr{[(ξ -e) -] 4 ≥ r} ≤ Cr{(ξ -e) 4 ≥ r}. Hence K[ξ] = +∞ 0 Cr{(ξ -e) 4 ≥ r}dr ≥ +∞ 0 Cr{[(ξ -e) -] 4 ≥ r}dr = K S [ξ]. 2) Let us show that K S [ξ] = 0 if and only if Cr{ξ = e} = 1, i.e., K[ξ] = 0. Assume that K[ξ] = 0. The previous result implies that K S [ξ] = 0. Assume that K S [ξ] = 0. that is, E[[(ξ -e) -] 4 ] = 0. Since E[[(ξ -e) -] 4 ] = +∞ 0 Cr{[(ξ -e) -] 4 ≥ r}dr, and the credibility measure Cr takes its value in [0; 1], then Cr{[(ξ -e) -] 4 ≥ r} = 0, ∀r > 0. By the self-duality of Cr, we have Cr{[(ξ -e) -] 4 = 0} = 1 and, we deduce that Cr{(ξ -e) -= 0} = 1. Since ξ -e = (ξ -e) -+ (ξ -e) + , then the previous equality implies ξ -e = (ξ -e) + . And E[(ξ -e)] = E[(ξ -e) + ] = +∞ 0

Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS027) p.6519

Int. Statistical Inst.: Proc. 58th World StatisticalCongress, 2011, Dublin (Session CPS027)

Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS027) p.6522

With Cr{(ξ -e) -= 0} = 1 and Cr{(ξ -e) + = 0} = 1, we deduce Cr{(ξ -e) = 0} = 1, that is, Cr{ξ = e} = 1. Assume that Cr{ξ = e} = 1. It is obvious to show that K[ξ] = 0. 3) Assume that ξ is symmetric and let us show that K

Cr{(ξ -e) 4 ≥ r}dr and K S [ξ] = +∞ 0

Cr{[(ξ -e) -] 4 ≥ r}dr, it suffices to show that: Cr{(ξ -e) 4 ≥ r} = Cr{[(ξ -e) -] 4 ≥ r}. For that we distinguish two cases: -If r < 0, then we have Cr{(ξ -e) 4

-If r ≥ 0, then (with r = r 4 ) and assume that r > 0. We have (ξ -e) 4 ≥ r ⇔ (ξ -e) ∈ ]-∞; -r ]∪[r ; +∞[, and [(ξ -e) -] 4 ≥ r ⇔ (ξ -e) -∈]-∞; -r ]∪[r ; +∞[. Therefore, we obtain Cr{(ξ-e) 4 ≥ r} = 1-Cr{-r < ξ-e < r }, Cr{[(ξ-e) -] 4 ≥ r} = 1-Cr{-r < (ξ-e) -< r }. It rests to show that Cr{-r < ξ -e < r } = Cr{-r < (ξ -e) -< r }. Let µ be the membership function of ξ -e and µ be the membership function of (ξ -e) -. Let us recall that µ = µ if ξ < e 0 otherwise .

We have:

An application in finance: review, model and determinist programm

Let ξ i be a fuzzy variable representing the return of the ith security, and let x i be the proportion of the total capital invested in security i. In general, ξ i is given as

, where p i is the closing price of the ith security at present, p i is the estimated closing price in the next year, and d i is the estimated dividends during the coming year. It is clear that p i and d i are unknown at present. If they are estimated as fuzzy variables, then ξ i is also a fuzzy variable. Thereby, the portfolios x 1 , ..., x n and the total return ξ = ξ 1 x 1 + ξ 2 x 2 + ... + ξ n x n are also fuzzy variables. When minimal expected return, minimal skewness and maximal risk are given, the investors prefer a portfolio with small semi-kurtosis. Therefore, we proposed the following mean-semivarianceskewness-semikurtosis model:

The first constraint of this first model ensures the expected return is no less than some target value s 1 , the second one assures that risk does not exceed some given level s 2 the investor can bear, the third one assures that the skewness is no less than some target value s 3 . The last two constraints imply that all the capital will be invested to n securities and short-selling is not allowed.

We now assume that (ξ i ) i∈{1,...,n} is a family of n independent triangular fuzzy variables. Thereby, based upon formulae 1, 2 and 3, we obtain the following result which display a determinist programm.

Theorem 1. Let (ξ i = (a i , b i , c i )) i=1,2,...,n be a family of n independent triangular fuzzy variables. Then the model (4) becomes the following determinist programm:

) 3 min(0, n i=1 x i (b i -e i ))] ≤ s 2 ( n i=1 x i (c i -a i )) 2 n i=1 x i (c i -2b i + a i ) ≥ 32s 3 x 1 + x 2 + ... + x n = 1 x i ≥ 0, i = 1, 2, ..., n

Concluding remarks

The next step of our research will be to design a genetic algorithm integrating fuzzy simulation for our optimization model and, to apply to real financial portfolios data.


J S Guez

J Ph Cassar

F Wartelle

P Dhulster

H Suhr

Real Time In Situ Microscopy for Animal Cell-Concentration Monitoring during High Density Culture in Bioreactor

Keywords: In Situ Microscopy, Image Analysis, Cellular Concentration, BioProcess Monitoring, Principal Component Analysis

An in situ microscope (ISM) device is utilised in this study to monitor hybridoma cells concentration in a stirred bioreactor. It generates images by using pulsed illumination of the liquid broth synchronised with the camera frame generation to avoid blur from the cell's motion. An appropriate image processing isolates the sharp objects from the blurred ones that are far from the focal plane.

As image processing involves several parameters, this paper focuses on the robustness of the results of the cells counting. This stage determines the applicability of the measuring device and has seldom been tackled in the presentations of ISM devices. Calibration is secondly performed for assessing the cell-concentration from the cell automated numeration provided by the ISM. Flow cytometry and hemacytometer chamber were used as reference analytical methods. These measures and the output of the image processing allow estimating a single calibration parameter: the reference volume per image equal to 1.08.10 -6 mL. In these conditions, the correlation coefficient between both reference and ISM data sets becomes equal to 0.99. A saturation of this system during an ultrasonic wave perfusion phase that deeply changes the culture conditions is observed and discussed. Principal component analysis (PCA) is used to undergo the robustness study and the ISM calibration step.

Introduction

The real time measurement of cell-concentration is commonly accepted as a key-point for the development of new monitoring and control strategies in biotechnological processes [Douglas 90]. This is particularly an on-going problem in animal-cell cultures [Konstantinov 94]. Indeed, an on line accurate cellular concentration estimation enables the monitoring of the specific growth rate which is a critical metabolic parameter. Among all the methods developed for determining the cell-concentration, optical techniques have been increasingly applied [Marose 99] [Ulber 03]. Direct optical technique based on computer imaging were used for offline cell counting and sizing of cell culture sample [Pons 99][Sierachi 98 ]. The automation of those systems involves circulation loops to take the sample to the microscope focus plane. Automated sampling is not only a complex technique, it is also a risk factor for contamination. In addition, the stress due to the sampling and slide preparation may modify the cells morphology.

In situ microscopes are well suited for in line measurement because they are not invasive and not destructive. They avoid all the microbiological risks relied to sampling. Their design addresses the problem of cell motion due to the broth agitation. Bittner et al. proposed a system that mechanically isolates and stops a small volume of broth [Bittner 98]. This system is used within the frame of CHO cell concentration monitoring [Joeris 02].

Suhr et al. [Suhr 91][Suhr 95] presented an ISM mounted directly in a port of a bioreactor to generate in situ images from the agitated broth using pulse illumination. The sampling volume is now a virtual volume defined by the characteristic of the microscope and the parameters of the associated image processing algorithm. A new version of this ISM has been evaluated on BALB/c mice/mice hybridoma [START_REF] Guez | Situ Microscopy for In-line Monitoring of Hybridomal Cell Culture in Agitated Bioreactor[END_REF]] and on a yeast strain [START_REF] Camisard | Effects of osmotic pressure shocks on yeast morphology: Experimental study and modelling[END_REF]], [START_REF] Camisard | Inline Characterization of Cell Concentration and Cell Volume in Agitated Bioreactors Using in Situ Microscopy: Application to Volume Variation Induced by Osmotic Stress[END_REF]]. The online signal of the cell-concentration provided by the ISM was proved to match with the results of other reference counting methods. This paper presents results on the estimation of hybridoma cell-concentration during a monoclonal antibody production process. It first gives technical specifications for the in situ microscope and details the culture conditions and the analytical methods used to evaluate cell concentration. The second part exhibits the current different stages of the image processing. The third part proposes a PCA to evaluate the robustness of the ISM cell concentration results in relation to the parameters of the image-processing algorithm. Robustness being accessed and the best parameter set being chosen, calibration is performed by using analytical results.

Material and Method

The in situ microscope

The in situ microscope [IMV Inline Microvision GmbH, Heidelberg, http\\ism.fh-mannheim.de] includes a water-immersion objective with a magnification 40 and with a numerical aperture 0.75.

The microscope tube is enclosed into an 25-mm outer tube with an optical window at the front end.

This end is submerged into the broth through the vessel wall in an 25-mm standard port. The port location should correspond to a mixing zone where the broth velocity is optimal so that the sample can be considered as representative of the broth. The location of the port should also take into account bubble of the oxygen supply which can interfere with the images acquired during the process. The submerged end supports an illumination unit with a LED as light source. This entire outer device can sustain the conditions of standard "wet-steam" sterilisation.

Flash illumination with light pulses of about 300 ns in duration avoids blur from cell motion. The pulses are synchronized with a standard CCD camera frame.

The CCD camera (KP-Mi Hitachi 2/3 « CCIR s/x ») has squared 9 m x 9 m pixels. Because of the objective magnification 40, each pixel covers an object area of 0,225 m x 0,225 m. Hence, an image of a spherical cell that is 10 m in diameter consists of about 1540 pixels.

The images are acquired by a standard framegrabber synchronized with the image processing software (WIT, logical vision/Coreco Inc, Canada). Averaging cell numbers over sets of 50 images taken every two hours allows reducing the variability of the calculated cell number per image.

Indeed cell number follows a Poisson's law of parameter m (m being the expected cell number per image). Standard deviation of the mean number of cells per image is thus kept below m 50 .

Real-time image processing is then carried out for each image through appropriate algorithms detailed below.

Culture Conditions

The hybridoma cell line tested throughout this study is a BalB/c murine lymphoïd cell fused with P3-X63-Ag8.653 murine myeloma. Morphologically, this hybridom can be considered as an almost perfect spherical object. The robustness of our cell recognition algorithm based on the iterative search of circles in the image will be tested through the recognition of these nearly spherical objects. The average diameter of the studied hybridoma is 13 m and confirms the values found in literature [Hong 98] [Harada 99]. We notice that in case of heterohybridoma as human/mice, the cells are less regular spherical objects because of the mixed origin of the cellular membrane.

The strain is grown at 37°C in a stirred-tank bioreactor (Discovery 100) with a working volume of 5 litres. Two pitched blades ensure the agitation of the broth. The culture medium is the Dulbecco's Modified Eagle's Media added with 3% heat-inactivated foetal calf serum. Stirring is kept at 40 RPM and pH is controlled at 7.2 by carbon dioxide addition. To prevent contamination and foam formation, overpressure in the vessel is ensured with a surface airflow kept at 10 L/h. Dissolved oxygen is controlled at 25% v/v by injection of pure oxygen. Data acquisition is performed on a personal computer equipped with FIX software. The inoculation rate represents 5 % of the final medium volume and the initial cellular concentration is about 4.10 5 cell/mL. A 48 hours batch phase is followed by a 288 hours feed phase. The perfusion rate is manually adapted from 0.2 to 0.4 d -1 during the feed phase in order to limit lactic acid and ammonia accumulation in the broth and thus the cell viability decrease. High frequency ultrasonic waves are then used so that the cellular concentration can reach high values within bioreactor. The perfusion rate is set at 0.8 d -1 during this 48 hours phase. The steam-sterilisable resonance chamber (Biosep ADI1015, ApplisSens) is mounted on the top of the reactor on a standard 12-mm port (Figure 1). An acoustic resonance field causes an active cell aggregation in the separation chamber of the resonator. Two peristaltic pumps are required for harvesting the clarified culture and recirculating the cell suspension. Aggregates of cells sediment to the bioreactor and are suspended again by stirring. To maintain stable thermal conditions in the separation chamber, cooling is ensured with insufflating air at 10-L/min. This system was already applied successfully on hybridoma cells [Trampler 94] [Phylis 95] [Bierau 98].

Cell concentration.

Two different methods of assessing cell concentration are used. First, a routine procedure is carried out using the Trypan blue exclusion principle. 100 μL of the sample is diluted with 100 μL of Trypan blue and cells are numerated on a Malassez counting chamber after a 5 minutes incubation period at 20°C. Second, a fluorescence-based method is used. (Live/Dead® Viability/Cytotoxicity assay kit, Molecular Probes). The kit provides a two-color fluorescence assay based on the simultaneous determination of live and dead cells so that the total number of cells can be measured.

Two probes, calcein AM and ethidium homodimer are used to estimate respectively: intracellular esterase activity and plasma membrane integrity [START_REF] Hayes | Principles and Methods of Toxycology[END_REF]]. Prior to the assay, samples are washed by centrifugation at 700 g for 15 minutes. Washing is performed with sterile tissue culturegrade D-PBS (KCl (0.2 g/L), KH2 PO4 (0.2 g/L), NaCl (8 g/L) and Na2HPO4 (1,15 g/L)) to remove serum because serum esterases can cause some increase in extracellular fluorescence by hydrolyzing calcein AM. Cell staining is carried out with optimised dye concentration determined with a conventional fluorescence microscope (Optiphot, Nikon). We determined an optimal concentration of 0.1 μM for calcein AM and 8 μM for ethidium homodimer. A recommended range of 1,0.10 6 to1,5 10 6 single-cells is incubated in 1 mL D-PBS and 100 µL of each reagents for 10 minutes at room temperature. Samples are injected in a Facscalibur flow cytometer (Becton Dickinson) working at a high flow rate (60 μL/min). Cell Quest software performs data processing and classical quadrant statistics. A defined number of events are counted upon time in order to calculate the cellular concentration.

Image Processing

The frame-grabber delivers 8 bits grey valued 659x494 frames (Figure 2). Most of the objects contained in images are far from the focus plane. Cells located between the illumination diode and the focus plane can act as lenses leading to bright spots with smooth edges while the other ones are dark and blured. Grey scaled image pre-processing into binary image must distinguish these objects from the sharp ones that belong to the focal plane. It is usually based on the setting of an appropriate threshold in the grey levels of the image. Automatic calculation of such a threshold that bears on the existence of at least two peaks in the histogram of the grey levels [Sierachi 98][Vicente 96] can not be used as the grey-level histogram in ISM-Images shows only one dominant peak. Indeed, grey levels do not contain pertinent information in our specific case. Therefore, an edge enhancement is applied to the original image through a spatial highpass filter before thresholding.

Threshold is set in a very flat zone of the resulting histogram and thus robustness of the result is ensured.

The cell recognition stage is provided with a binary image. The cell membranes are matched with circles of specific sizes. Our cell recognition algorithm uses the circular-Hough transformation for an iterative search of circles in the image. The circular-Hough transform provides, for each point considered as a circle center, a value indicating which proportion of the perimeter of a given radius circle coincides with white image-pixels. Thresholding this information allows locating the candidates for cell recognition. The level of the threshold indicates the level of completeness that must be achieved before a cell is selected. The iterative search runs from large to small diameters objects.

A cell selected for a given radius contributes to the count rate of the size class corresponding to this radius. Then the search is performed again for the next radius. Figure 3 shows the result of the recognition process on the image presented in Figure 2. The size range of the search is set from 10 to 34 pixels corresponding to particles from 4.5 to 15.3 m of diameter.

Robustness study

The study aim is exhibiting the influence of the set of parameters involved in the image-processing algorithm on the cell concentration results. Indeed, it has to be proved before calibrating the ISM that these parameters only modify the scale of the concentration curves but not their shape.

The result, for a given parameter set j, is a curve that gives at each time i t the mean number of cells in an image, called ji y (t ) . If the parameters do not influence the shape of the ji y (t ) -curves each curve can be deduced from a single time function f(ti) by using a factor "aj": j i j i y (t ) a f (t )  . In this case, the different values of the j a coefficients directly express how the parameters value change the reference volume for the cell counting. However, if the parameters do influence the shape of the time functions, each time function can be expressed as a specific linear combination of a set of K reference time functions ki f (t ) . Gathering the ji y (t ) values in a single vector i (t ) y allows writing:

K i k i k k1 (t ) f (t ) ya    (1)
The normalised vectors k a are linearly independent. K is the dimension of the space of vectors i (t ) y i.e. the number of functions ki f (t ) that are needed to reconstruct all curves ji y (t ) . The non- influence of parameters with respect to the curve shapes is equivalent to K = 1.

Principal component analysis (ref) provides a very powerful tool to answer the problem of finding the dimension K and of estimating the corresponding fk -functions.

Let the information be gathered in a single matrix

1 2 n (t ) (t ) (t ) ' Y y y y
   . From (1) and the properties of the vectors k a it can be deduced that:

  k k 1 k 2 k n k f (t ) f (t ) f (t ) '  Ya f (2)
This expression is the estimate of the time function whose values are the entries of the vector k f .

PCA methods [Ramsay 97] provide the most significant functions from eigenvectors of the '

YYmatrix

The K-dimension is obtained from the number of eigenvalues that are considered as different from zero. The k f -vectors associated with these greatest eigenvalues are called principal components Let the F-matrix gather the principal components and the A-Matrix the corresponding eigenvectors.

A filtered estimation Ŷ can be be substituted to Y and is computed from the principal components F=YA.

ˆ'  

Y F A Y A A (3)
The columns of the A'-matrix thus indicate how the principal components have to be combined in order to retrieve a filtered version of the original curves. Analysing of how this combination is performed for every curve allows coming at a conclusion about the similarity of their shapes.

ISM calibration

This part aims at finding the conversion coefficient that allows estimating the cell concentration from the number of cells per image provided by the ISM. The difficulty is that the detected objects belong to a virtual probe volume. The optical device and the CCD sensor size define the lateral borders of the probe volume, while its depth depends on the parameters of the pre-treatment process. This means that the algorithm discards the objects from outside of the sharpness depth.

This can be expressed as c(t) = a y(t) where c(t) is the cell concentration (cell/ml) and y(t) the ISM measurement (cell/image). The coefficient "a" can be interpreted as the inverse of the reference volume expressed in ml/image and is the ISM calibration parameter.

Finding the conversion coefficient needs alternate measurements on cell concentrations to be performed. Cell concentration analyses are carried out during the cell culture. They provide at each time a vector that gives the references of the cell concentration. These vectors are gathered in the matrix [ ]

1 j m (t ) (t ) (t ) ' = X x x x KK .
The calibration procedure aims at finding the global calibration factor "a" that takes into account the both analysis results. Therefore, the calibration procedure is divided into three main stages:

-data processing in order to handle missing analysis data and to estimate the ISM responses at times j t because the times i t and j t do not correspond exactly;

-estimation of the first principal components from PCA to reduce influence of discrepancies between the analytical results;

-computation of the regression factor with respect to the correlation between ISM results and filtered analysis.

The first stage is achieved by linear interpolation procedures. In the second stage X is filtered from PCA (by keeping a reduced number of principal components) to give the better estimate of the cell concentration from analytical results. In the third stage, factor 'a' of the regression model is obtained from the least square estimation procedure between the filtered X-values and the mean cell numbers calculated with parameter k.

Results and Discussion

The algorithm used for the cell counting involves three parameters. The first one is the size of the high pass filter that enforces the shape detection. The second one is the value of the threshold used after the Hough transformation to decide the matching with a circle. The third one is the number of circles examined, beginning from 15,3 m diameter circles and decreasing iteratively 0.9 m by 0.9 m until the last circle diameter is reached.

Table 1 gives the values of these parameters and the corresponding number of parameter sets.

Figure 4 presents the curves of the mean cell numbers that are obtained with the chosen parameter sets.

Figure 5 shows the projections of the yj(ti) curves in the first principal plane. The co-ordinates in this plane are the columns of the matrix A' restricted to dimension 2. For each point labelled by a number j, they give the combination of the two first principal components that are needed to recompose a filtered version of the original curve yj(ti) associated with the parameter set j. A projection close to the unit circle indicates that two principal components are sufficient to well regain the curve and thus the dimension K is equal to 2. It can be noticed that the projections of the parameters sets from 1 to 5 corresponding to 5x5 filter size are farther from the unit circle indicating the need of a third principal component. This is why these parameter sets are excluded for the rest of the study. The other parameter sets projections are close to the unit circle.

The closer the projections are, the more similar the original shapes are. Taking this into account, we can analyse the group of parameter sets exhibited in figure 5 as follows: the filter size has almost no influence on the shape of the curve. The parameter sets that are only distinguished by the value of this parameter (by example 6-15, 7-16) belong to the same group. The two groups 11-20-12-21 and 8-17-10-19 correspond to a strategy that compensates a more demanding threshold by an increase of the number of circles used in the Hough transformation. However, all these influences are weak and can be considered as negligible. At this stage, we can conclude about the influence of the parameter sets that:

-The filter size exerts no influence if its value is limited between 10 and 20.

-Threshold and number of circles have a weak influence in the ranges that have been explored and lead to variations in the curves shape that are insignificant regarding the dispersion of the acquired data. Now let us consider the calibration stage. Figure 6 gives the cell concentration obtained from both kinds of analysis. As we have only two kind of analysis only one principal component as to be kept and then it is the mean value of the curves. In figure 5, this principal component is projected (bold circle) onto the same principal plane than the ISM data. It can be deduced that the shape corresponding to the reference analyses is very close to the shape corresponding to the curves given by parameter sets 9 -18 and 10-17. For the parameter set 9 (k=9) chosen among these points, figure 7 exhibits the regression expressed in between analysis data estimated as the first principal component and corresponding ISM data. It shows that the last data point at maximum concentration is far from the linear regression model. Hence, the linear model may not be the best calibration model if the maximum concentration is included. Excluding this last analysis, the projection onto the first principal plane of ISM results becomes closer to the parameter sets 7 and 16 (diamond in Figure 5).

With this restricted set of analyses, results of the regression with the parameter sets corresponding to a filter size of 10x10 leads to the results shown in Table 2 (it could have been indifferently a filter size of 20x20 as it was showed previously). The term "Slope" indicates the regression coefficient 'a', from which the reference volume is inferred. The obtained standard deviations do not differ significantly, so that the robustness of the algorithm according to changes in the set of parameters is confirmed. However, it can be seen that the reference volume increases if the threshold decreases.

This behaviour is to be expected because lower thresholds allow the detection of less sharp cell images.

The regression model calculated for parameter set k=7 gives a value for 'a' equal to 0.99.

Concentrations values calculated using this model and the corresponding ISM data are given figure

8. An interval of confidence, associated with a 5% risk, is computed for each estimate from the error variance estimation. The comparison with the original analysis values shows the quality of the calibration for concentration below 4.5 10 6 cells/ml.

Let us now focus on the observed saturation. The deviation of the last analysis point is clearly related to the perfusion phase. The resonance field both causes an increase in the cell concentration and an active cell aggregation phenomenon that is displayed in Figure 9. As the image-processing algorithm is not well suited to undergo the counting of the cells belonging to flocs of cells, the cellular concentration may thus be under-estimated. Moreover, the evolution of the optical characteristics of the broth was noticed: images become darker and the cells got more blurred than at the beginning of the process. That may also contribute to the underestimation of the number of cells.

Conclusion

A very carefull procedure has been proposed to establish how the image-processing algorithm is robust and efficient when faced with spherical or nearly spherical objects. For concentrations below 4.5 10 6 cells/ml, the ISM gives a good on-line estimate of the cell-concentration and is robust faced to image processing parameter changes. Indeed, the selected set of parameters allows the ISM cellconcentration curve to fit almost perfectly the reference method results and the correlation coefficient between both reference and ISM data sets is then equal to 0.99. The calibration method applied for this set of parameters corresponds to a 1.08.10 -6 mL reference volume that can be considered as a reference volume for further ISM utilisation. Thus, the presented system could be used in an extensive way for enhancing process control in the frame of agitated broth cell cultivation.

Figure 1 :

 1 Figure 1: Diagram of the In Situ Microscope mounted in a bioreactor growing cells suspended in a

Figure 2 :

 2 Figure 2: Original ISM image of the stirred culture broth. Hybridomal cells occur as individual cells

Figure 3 :

 3 Figure 3: Objects recognised as cells after the pre-processing phase and the cell recognition phase.

Figure 4 :

 4 Figure 4: Mean cell number curves (cells/image) during the time of the culture. Each curve

Figure 6 :

 6 Figure 6: Evolution of the hybridoma concentration results (cell/ml) during the culture (h).

Figure 7 :

 7 Figure 7: Regression model between ISM cell-concentration results (cells/image) based on

Figure 8 :

 8 Figure 8: Final ISM cell-concentration results () and the 5% confidence interval (---) considering

Figure 9 :

 9 Figure 9: 8 bit gray scaled image of cell aggregates due to the acoustic resonance field during the

  Figure 4:

  Figure 9:

Table 1 )

 1 .Figure5: Coordinates in the first principal plane of cell-number per images curves. Projections are numered according to parameter sets in Table1. and are the projection of the first principal component of the analytical results respectively for 20 and 19 values.

Table 1 :

 1 Parameter sets used to specify filter-size, binarisation-threshold and number of circles in the image processing algorithm.

Table 2 :

 2 Regression results for filter size 10x10. The data-point at maximum concentration is excluded from this analysis in order to avoid saturation.

Table 1

 1

				Filter size
			5x5	10x10	20x20
	Number
		of	10 11 12 10 11 12 10 11 12
	circles
	Threshold	200 1 2 3 6 7 8 15 16 17 180 4 5 / 9 10 11 18 19 20
		160 / / / 12 13 14 21 22 23

Acknowledgements

This work was supported by: DIAGAST SA, Parc Eura Santé, 251, avenue Eugène Avinée -B.P. 9, 59374 Loos Cedex Pr. Joël Mazurier for flow cytometry analysis, UMR N°8576 CNRS/USTL Lille


Frederic Delom
email: frederic.delom@yahoo.fr

Inaki Begiristain

Thomas Grenier

Hugues Begueret

Fabienne Soulet

Geraldine Siegfried

Abdel-Majid Khatib

Jacques Robert

Delphine Fessart
email: delphine.fessart@inserm.fr

Patients Lung Derived Tumoroids (PLDTs) to model therapeutic response

Keywords: Tumoroids, Three-dimensional culture, Radiation, Chemotherapeutic drugs, Lung cancer

Preclinical lung cancer models are essential for a basic understanding of lung cancer biology and its translation into efficient treatment options for affected patients. Lung cancer cell lines and xenografts derived directly from human lung tumors have proven highly valuable in fundamental oncology research and anticancer drug discovery. Both models inherently comprise advantages and caveats that have to be accounted for. Recently, we have enabled reliable in vitro culture techniques from lung cancer biopsies as Patients Lung Derived Tumoroids (PLDTs). This breakthrough provides the possibility of high-throughput drug screening covering the spectrum of lung cancer phenotypes seen clinically. We have adapted and optimized our in vitro three-dimensional model as a preclinical lung cancer model to recapitulate the tumor microenvironment (TME) using matrix reconstitution.

Hence, we developed directly PLDTs to screen for chemotherapeutics and radiation treatment. This original model will enable precision medicine to become a reality, allowing a given patient sample to be screened for effective ex vivo therapeutics, aiming at tailoring of treatments specific to that individual. Hence, this tool can enhance clinical outcomes and avoid morbidity due to ineffective therapies.

Introduction

Lung cancer is the most common cause of cancer-related mortality worldwide. Of lung cancer diagnosed, more than 80% are non small cell lung cancer (NSCLC), which includes adenocarcinoma, squamous cell carcinoma and large cell carcinoma [START_REF] Yokota | Molecular footprints of human lung cancer progression[END_REF]. The most common histological subtype of lung cancer is adenocarcinoma, accounting for almost half of all cancers [START_REF] Travis | Paradigm shifts in lung cancer as defined in the new IASLC/ATS/ERS lung adenocarcinoma classification[END_REF][START_REF] Travis | Pathology of lung cancer[END_REF]. The majority of NSCLC patients are diagnosed during the later stages of lung cancer and dies without successful treatment [START_REF] Yokota | Molecular footprints of human lung cancer progression[END_REF][START_REF] Sekido | Molecular genetics of lung cancer[END_REF][START_REF] Dutt | Mouse models of lung cancer, Clinical cancer research : an official journal of the[END_REF].

One of the most difficulties in lung oncology is to develop a reliable model to assess the potential effects of candidate drugs. Preclinical research on anticancer drugs has the lowest success rate of all therapeutic areas, with 5% of candidate compounds having completed Phase III clinical safety and efficacy trials [START_REF] Hutchinson | High drug attrition rates--where are we going wrong?[END_REF]. The most simplistic lung cancer models are bronchial cells derived from human and animal lung tumors grown as a flat monolayer in cell culture media. There is no doubt that immortalized lung tumor lines grown in 2D have contributed to a great deal of knowledge about the mechanisms leading to cancer development. However, in this monolayer model, lung cells adhere to an artificial substrate and are in contact with other cells only at the periphery. Thus lung cells cultured in flat layers on plastic surfaces do not reflect the multicellular microenvironment found in the bronchial tree. Animal models have been used as live incubators in which human lung tumors have been cultured, thus interacting with an entire organism, though often in an immunedepressed environment. Lung cancers have been established in rodent models by surgically implanting pulmonary tumor cells or by creating genetically modified animals that spontaneously develop human lung tumors in response to experimental modification of the expression of a specific gene. At first glance, this seems an inherently desirable system aiming at mimicking human lung cancer. Despite their intensive use for drug screening, these expensive animal models have not resulted in therapies translating into better outcomes of human cancer [START_REF] Hait | Anticancer drug development: the grand challenges[END_REF][START_REF] Van Der Worp | Can animal models of disease reliably inform human studies?[END_REF]. Overall, these models have not generated therapies with a high return on investment.

For their fascinating potential as tools to probe human biology and disease, the journal Nature Methods has chosen organoids as "method of the year" in 2018 [START_REF] Rios | Imaging organoids: a bright future ahead[END_REF]. One of the most powerful aspects of organoids as research tools is how they have moved forward our understanding of human cancer biology. We have previously developed a 3D organoids/tumoroids tissue culture model from normal and malignant human lung epithelial cells [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF]. Indeed, we have enabled a reliable in vitro model from lung cancer biopsies as Patient-Lung Derived Tumoroids (PLDTs) [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF] to establish novel personalized lung cancer therapies. These therapies could define specific treatments for each patient on an individual basis. Using this method, we have characterized J o u r n a l P r e -p r o o f organoids/tumoroids and showed that epithelial organization and many physiological cell-cell and cell-ECM contacts, cellular polarity, and secretory functions are preserved in epithelial organoids [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF][START_REF] Fessart | Secretion of protein disulphide isomerase AGR2 confers tumorigenic properties[END_REF]. Therefore, the tumoroids, in which cellular heterogeneity of tumor cells is preserved, have emerged as a promising tool. Recently, numerous studies highlighted the application of patientderived tumoroids in personalized cancer medicine in terms of gene -drug association treatments, the identification of new therapies and the prediction of patient response. Thus using PLDT with similar characteristics to the original lung tumors may result in more accurate predictions of drug responses in patients. PLDTs have several advantages over pre-existing models, including conserving the molecular and cellular composition of the original tumor [START_REF] Kim | Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening[END_REF]. These advantages highlight the tremendous potential of PLDTs in personalized lung cancer therapy, particularly preclinical drug screening and predicting patient responses to selected treatment regimens.

In the present study, we have adapted and optimized our in vitro 3D model [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF] as a potential personalized lung cancer medicine model. Personalized cancer medicine is an approach to tailoring effective therapeutics strategies for each patient according to a tumor genomic characterization. We have investigated the utility of our PLDT model in testing the activity of chemotherapeutic drugs alone or in combination with irradiation by comparing 2D vs 3D cultures of the same lung cancer cells obtained from biopsies of lung cancer patients. Herein, we show that PLDTs are more resistant to chemotherapeutic drugs and irradiation that cells in 2D culture. Thus, the PLDT model could enable precision medicine to become a reality, allowing patients samples to be screened for effective therapy, with the tailoring of treatments specific to each individual. Hence, this tool can enhance clinical outcomes by developing and delivering the right therapeutics, for the right cancer in the right patients. This would mean that outcome, morbidity, and ultimately survival should be improved for all patients with advanced lung cancer.

J o u r n a l P r e -p r o o f

Materials and methods

Cell isolation and cell lines culture

Cancer lung epithelial cells obtained from tumor biopsies of patients were cultured in two different culture media: medium A containing 5 ng/ml epidermal growth factor (EGF), 50 μg/ml bovine pituitary extract (BPE) and 1% antibiotics mixt (100 IU/ml penicillin and 100 μg/ml streptomycin); and medium B supplemented with 15 μg/ml BPE, 10 ng/ml EGF, 0.5 μg/ml epinephrine, 5 μg/ml insulin, 10 μg/ml transferrin, 10 ng/ml triiodo-L-thyronine, 0.5 μg/ml hydrocortisone, 1.5 μg/ml albumin, 0.1 ng/ml retinoic acid, 1% Fetal Calf Serum (FCS) and 1% antibiotics mixt (100 IU/ml penicillin and 100 μg/ml streptomycin).

Samples of human lung cancer tissues were obtained from the Haut-Levêque University Hospital (Bordeaux, France) and reviewed by an expert pathologist in the field (H. Begueret) as previously published in [START_REF] Fessart | Secretion of protein disulphide isomerase AGR2 confers tumorigenic properties[END_REF]. These procedures were approved by the Institutional Review Board at Haut-Levêque University Hospital (NFS96900 Certification) in accordance with The Code of Ethics of the World Medical Association. Immediately after surgical resection, tumor biopsies were rinsed in Hank's Balanced Salt Solution (PAN Biotech) and cut in 2-3 mm pieces. For the explant culture, these pieces are placed in a 6-well plate, previously coated with a solution containing 30 μg/ml collagen, 10 μg/ml fibronectin and 10 μg/ml BSA. The explant was covered with a drop of culture medium (medium A or B) and incubated at 37°C, 5% CO 2 . Then 2 ml of culture medium A or B was added to the explant. The culture medium was changed every 2 days. When the cells were confluent, they were detached from the plate with trypsin and seeded in 12-well plates.

For the enzymatic digestion, tissues from biopsies were rinsed in HBSS and cut into 2-3 mm pieces before being minced and incubated for 1 to 2 hours at 37°C in DMEM alone containing 50 to 1500 μl of collagenase I (Worthington). After digestion, the supernatant was recovered gently and filtered through a 40 μm filter. The samples were then centrifuged for 7 min at 4°C at 250 g. The pellet was taken up twice in DMEM alone and was centrifuged for 5 min at 4°C at 250 g. Finally, cells were taken up in culture medium A and seeded in a 6-well plate. Once the cells adhered to the support, the medium was changed every 2 days.

Two human cell lines were used: human fetal lung fibroblasts (IMR-90) and human embryonic kidney cells (HEK293). HEK293 were cultured in DMEM medium supplemented with 10% FBS and 1% antibiotics (100 IU/ml penicillin and 100 μg/ml streptomycin). The IMR-90 cells were cultured in Eagle MEM medium supplemented with 10% FBS, 1% L-glutamine and 1% antibiotics (100 IU/ml J o u r n a l P r e -p r o o f penicillin and 100 μg/ml streptomycin). All cultures were kept in a humid incubator at 37°C and 5% CO 2 . Sub-culturing was performed when the cells were 80% confluent.

Chemotherapeutic assay in 2D monolayers

Cells were seeded in triplicate at 5000 cells/mL in a 96-well plate and incubated at 37°C and 5% CO 2 .

Four days after seeding, cells were treated with concentrations of 0; 0.01; 0.1; 1; 10 and 100 μM of Docetaxel, Paclitaxel, Pemetrexed and Gemcitabine. To perform this treatment, 50% of the medium was replaced by complete DMEM medium supplemented with the chemotherapeutic agent. In parallel, untreated cellular control was performed. Three and five days after starting treatment, the cytotoxic effect of the four chemotherapeutic agents was measured by a cell viability assay (AlamarBlue). Briefly, AlamarBlue® solution was added to each well, 10% of the final volume, and the plate was incubated at 37°C, in the dark for 2 hours. After incubation, the absorbance was read at 570 and 600 nm using a spectrophotometer. The reduction percentage of AlamarBlue, corresponding to cell viability, was determined according to manufacturer instructions.

Measuring response to Gemcitabine in soft-agar colony formation

Colony formation efficiency was evaluated as previously described [START_REF] Fessart | Secretion of protein disulphide isomerase AGR2 confers tumorigenic properties[END_REF]. Briefly, cells were plated in medium supplemented with 3.3% FBS containing 0.35% soft agar in 6-wells plates over a layer of solidified medium containing 0.7% soft agar. Four days after seeding, cells were treated with concentrations of 0; 0.01; 0.1; 1; 10 and 100 μM of Gemcitabine. The medium was replaced with fresh Gemcitabine twice a week to maintain humidity. After growth, colonies were stained with Crystal Violet (0.05%) for 10 min and counted using a phase-contrast microscope (Leica) at objective 5×. The acquisition of images and their analysis were performed with the Leica software of the microscope (LAS X Software, Leica).

Measuring response to Gemcitabine in 3D

Cells were grown in 3D tumoroids according to our protocol [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF][START_REF] Fessart | Secretion of protein disulphide isomerase AGR2 confers tumorigenic properties[END_REF]. Briefly, the cells were seeded in a Matrigel matrix at the density of 2000 cells/cm 2 in a 96-well plate and incubated at 37°C and 5% CO 2 . Treatment with chemotherapeutic agents began 4 days after seeding and was repeated at 7, 9 days. For each treatment, tumoroids were treated with concentrations of 0; 0.01; 0.1; 1; 10 and 100 μM Gemcitabine. For this purpose, 50% of the medium was replaced by a complete DMEM medium supplemented with the chemotherapeutic agent. In parallel, cellular control was performed with the vehicle. To calculate the number of tumoroids, a representative field in each condition was taken using a phase contrast-microscope (Leica) at objective 5×. The acquisition of a Z-stack (15 images) of each well and its analysis was performed with the Leica software of the microscope (LAS Software, J o u r n a l P r e -p r o o f Leica). Briefly, for each multifocal image obtained with a Z-stack, we have applied a step-by-step analysis workflow containing 11 steps to obtain the organoid count and a size-wise classification. First, we selected the multifocal image, next we applied filters with gray processing followed by an enhancement of black objects set up to 14. The threshold was then adjusted to reduce the background noise. The image was then converted to binary processing for tumoroid detection. Once the new binary image was obtained, we have controlled that each tumoroid was recognized. We have then processed to the measurement of each tumoroid, and defined each tumoroid as an object, to automatically calculate the diameter and the number of each object. Afterward, we have classified the objects by size using 10 classes from the smaller tumoroid to the biggest one. Finally, we have obtained a histogram with the number of tumoroid per field and the diameter of tumoroids.

Cell death assay

Cells were seeded into a 96-well plate and grown for 4 days in 2D and 3D. After 4 days, cells were treated with different doses of Gemcitabine for 3 days. Cells were then stained for 30 min with NucBlue Live ReadyProbes Reagent (1 drop/ml media) and NucGreen Dead 488 ReadyProbes Reagent (1 drop/ml media) (ThermoFisher). The 96-well plate was imaged immediately. High-content images were acquired with the Cytation 3 automated microscope (Biotek) at 4× magnification, and analysis was performed using the Analysis software (Biotek). Cells were imaged simultaneously in the fluorescein isothiocyanate (FITC) (NucGreen) and 4', 6-diamidino-2-phenylindole (DAPI) (NucBlue) channels. A cell was defined as undergoing cell death when 50% or more of the nucleus, as defined by NucBlue-positive pixels, was overlapped by NucGreen-positive pixels.

Chemoradiation in vitro studies

Cells were seeded in 96-well format using a Perkin-Elmer Janus Mini liquid handling platform. Plates were incubated for the indicated times, and the medium was changed every two days. Tumoroids were plated in 96-well plates and allowed to grow for 1 day. Images were taken on a Leica DMi8 inverted microscope using a 4× objective before chemoradiation treatment. After imaging of monolayer or tumoroids, the medium was replaced with a feeding medium containing Gemcitabine at the desired concentrations, and tumoroids were subsequently exposed to ionizing radiation using an Xtrail XENX XSTRAHL Irradiator delivering a dose rate of approximately 0.75 Gy/minute. After 3 days, the culture medium was replaced with a standard medium. Before reading, cells in monolayers were incubated 30 minutes with Readyprobes blue (Invitrogen) for nuclei staining for live-cell imaging. High-content images were acquired with the Cytation 3 automated microscope (Biotek) at 4× magnification, and analysis was performed using the Analysis software (Biotek). All experiments were completed at least in triplicate.

J o u r n a l P r e -p r o o f 2.7 Immunofluorescence

Cells were seeded on slides and incubated for 48 h at 37°C, 5% CO 2 . After two PBS washes, cells were fixed for 15 min with 4% paraformaldehyde, followed by PBS wash before permeabilization with PBS 1×-0.1% NP40 for 15 min. Cells were then incubated with the appropriate primary antibody diluted in 1% BSA-0.5% Tween 20 for 1 h and then with the secondary antibody and with DAPI (4', 6'diamidino-2-phenylindole). Finally, two washes were performed before mounting the slides in the mounting medium (Dako Fluorescent Mounting Medium). The different antibodies used were mouse anti-cytokeratin 18 (CK-18) monoclonal antibody (sc-32722, Santa Cruz), rabbit anti-Protein C (SP-C) rabbit polyclonal antibody (Ab 40879, Abcam) and mouse smooth muscle anti-actin (alpha-SMA) monoclonal antibody (A5228, Sigma). The secondary antibodies coupled were Alexa-fluor 488 goat anti-mouse (Life Technologies), the Alexa-fluor 546 anti-rabbit monkey (Life Technologies) and Alexafluor 546 goat anti-mouse (Life Technologies). After mounting the slides, slides were visualized with a Nikon Eclipse 2000-C1 confocal microscope and filters corresponding to the characteristics of the fluorochrome used, and the 20× and 60× immersion objectives. The images were then converted to Tiff format and analyzed using the Nikon EZ-C1 microscope software.

Statistics

Results are presented with a standard deviation of the mean in all studies. Statistical differences between each subtype of cancer were analyzed using a Student's t-test or ANOVA, as indicated, with GraphPad Prism 4 software. A p-value of 0.05 or less is considered significant. All experiments are compared to a control condition (cells only treated with the vehicle) to allow statistical comparisons.

J o u r n a l P r e -p r o o f

Results

Patient-Lung Derived Tumoroid (PLDT) lines characteristics

Recently, we have shown that PLDTs can be readily established from the majority of clinical tumor-containing NSCLC specimens tested [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF]. Starting from January 2015 to February 2016, we collected a biopsy sample from 12 different lung cancer patients. The clinical characteristics of these patients are presented in Table 1. Known oncogenic mutations were sought in the genes EGFR, KRAS, ERBB2, ALK, ROS1, and BRAF, and mutations were detected for seven of the twelve patients (58.3%) (Table 1). Among these 7 samples, two had an EGFR mutation (CLC3 and CLC7) and five a KRAS mutation (CLC1, 6, 8, 9, 10). The histology included eleven adenocarcinoma and one squamous cell carcinoma (Table 1).

Human lung tumor cells isolation from biopsies by explant cell-culture.

The primary explant technique is particularly useful for disaggregation of small quantities of tissues. The tissue in basal salt solution was finely chopped (Figure 1A, a), and washed by settlings.

The basal salt solution was then removed. The tissue pieces were spread into 6-well plates and supplemented with two different types of mediums. The explants were attached to the bottom of the plate (Figure 1A,b) and observed periodically. After the addition of the appropriate medium, incubation was carried out for 3-8 days. When the outgrowth of tumor cells was observed (Figure 1A, c), cells were transferred into a fresh new dish for subculture (Figure 1B). This method requires serial sub-culturing to obtain primary lung tumor cell lines (Figure 1B). Explants of human bronchial tumor biopsies have been cultured in two different mediums: Medium A and Medium B (see Materials and methods) (Figure 1C-E). We have assessed the success rate of the establishment of patient-derived cell lines obtained with both mediums (Figure 1C). We then compared and evaluated the yield and the presence of epithelial cells for each medium. We observed a better yield with medium B (Figure 1D). The outgrowth of epithelial cells was observed within 6 to 8 days (Figure 1E). Taken together, these results show that human lung tumor cells grown from our explant cell culture method from patients' biopsies in medium B is suitable for developing primary lung tumor cell lines.

J o u r n a l P r e -p r o o f

Journal Pre-proof each cancer lung cell line (CLC). D, The bar graph shows the yield in percentage of cells (mean ± s.e.m) obtained in Medium A as compared to Medium B from three independent biological replicates. *p<0.05 (Student's t-test). The yield in percentage of cells obtained with the explant was than calculated for each condition. E, Time in days to obtain primary lung cancer epithelial cells in their respective medium A vs B (mean ± s.e.m); from three independent biological replicates, n.s., non-significant. F, Schematic representation of the different steps to dissociate human lung tissue from biopsy with collagenase. G, Table recapitulating the conditions assessed, two variables were tested: the concentration of collagenase and the duration of collagenase treatment.( -) no cells were growing, ( + ) some cells were growing, ( ++ ) few cells were growing to further expand, and ( +++ ) a sufficient amount of cells were growing to further expand. H, The chronology illustrates the different steps required to expand cells either with the collagenase method (Pink). I, Yield in percentage of cells obtained with the explant as compared to collagenase (mean ± s.e.m from three independent biological replicates). *p<0.05 (Student's t-test).

J o u r n a l P r e -p r o o f

Journal Pre-proof

Human lung tumor cells isolation from biopsies by enzymatic digestion.

Enzymatic dissociation is a commonly used practice to digest minced tissue into a single-cell suspension due to proper digestion of tissue and preservation of cell viability and integrity. Enzymatic disaggregation can be carried out by using trypsin, collagenase, or some other enzymes. Since numerous studies have shown that type I collagenase is more effective than other enzymes for dissociation of lung tissue [START_REF] Quatromoni | An optimized disaggregation method for human lung tumors that preserves the phenotype and function of the immune cells[END_REF], we have used type I collagenase for biopsy dissociation. The procedure to isolate human tumor bronchial cells from biopsies with type I collagenase is outlined in Figure 1F. We have assessed several concentrations and incubation times of type I collagenase (Figure 1G). Our results showed that the best efficiency was obtained with a concentration of 1,500 U of type I collagenase and a time of incubation of 2 hours. Once the cells are attached to the 12-well plate and start to divide to reach about 90% of confluence, cells are sub-cultured sequentially into 6well plates and finally in flasks. Although these two methods, presented in Figure 1B and 1H, yielded human lung epithelial tumor cells, the explants of human bronchial tumor biopsies appeared to give the greatest number of human lung epithelial tumor cells within the shortest time (Figure 1I).

J o u r n a l P r e -p r o o f

Morphological characteristics of purified human lung tumor cells.

Structural characterization of human lung tumor cells showed the characteristic morphology of epithelial cells, such as an adherent monolayer, a cobblestone-like arrangement and a tightly packed pattern (Figure 1B). These primary human lung tumor epithelial cells obtained from biopsies have been maintained for 5 passages, and no obvious morphological change has been observed.

Each cell appeared polygonal and flattened, with a large, round nucleus, typical features of normal epithelial cells (Figure 1B). To further characterize these human lung tumor epithelial cells, the expression of cytokeratin 18 (CK18) (an epithelial cell marker) [START_REF] Makino | Cytokeratins 18 and 8 are poor prognostic markers in patients with squamous cell carcinoma of the oesophagus[END_REF][START_REF] Barak | Clinical utility of cytokeratins as tumor markers[END_REF] (Figure 2A) and surfactant protein-C (SP-C) (a specific lung cell marker) [START_REF] Kim | Identification of bronchioalveolar stem cells in normal lung and lung cancer[END_REF] (Figure 2B) were analyzed at the 2nd passage for the phenotype of Cancer Lung Cells (CLC) from 4 different biopsies (CLC6, CLC1, CLC8, CLC7). The expression of the epithelial cell marker CK18 was examined by immunofluorescence, indicating the epithelial character of these cells (Figure 2A). Moreover, immunofluorescence staining showed that SP-C was expressed by all cells, for each patient, indicating the lung origin of these cells (Figure 2B).

Taken together, these immunofluorescence results show that cells are positive both for epithelial and lung markers (Figure 2 A and B). Since one of the Cancer Lung cell line (CLC5) was expending very fast, we decided to further exploit this cell line. First, we validated by immunofluorescence these markers (CK18 and SP-C). CLC5 cells showed uniform positive immunofluorescence signals for CK18 (Figure 2C) and SP-C (Figure 2D). In contrast, immunofluorescence with a specific antibody against fibroblasts cell type did not show any indication of contamination of cultures by fibroblasts (α-SMA) (Figure 2E), and CLC5 are also negative for E-cadherin (Figure 2F). In NSCLC, several studies have reported a reduction of E-cadherin during malignant transformation [START_REF] Myong | Reduced expression of E-cadherin in human non-small cell lung carcinoma, Cancer research and treatment[END_REF]. Taken together, these results demonstrate that we have isolated human lung tumor cells from patient biopsies using the explant cell culture method.

J o u r n a l P r e -p r o o f

J o u r n a l P r e -p r o o f

Journal Pre-proof cells stained positive for cytokeratin 18 (C) and SP-C (D). Cultured lung cells did not stain for α-SMA (E), indicating no contamination of the primary cultured lung epithelial cells with mesenchymal cells and stained negative for E-cadherin/Alexa Fluor 546 (F). Positive control for α-SMA-cy3 is shown on fibroblasts (E). Positive control for E-cadherin is shown on HBEC epithelial cells (F). Scale Bar=100 μm. Images were acquired with the confocal microscope Nikon Eclipse 220-C1.

J o u r n a l P r e -p r o o f

PLDT establishment from patient-derived cell lines

Previously, we have described the growth of cancer human bronchial epithelial cells in 3D basement membrane culture with Matrigel [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF][START_REF] Fessart | Secretion of protein disulphide isomerase AGR2 confers tumorigenic properties[END_REF]. By comparison between 3D architecture in normal and malignant cell lines, we were able to distinguish normal and malignant human bronchial cells [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF][START_REF] Fessart | Secretion of protein disulphide isomerase AGR2 confers tumorigenic properties[END_REF]. Our next objective was to compare the drug response in our two different culture systems (2D vs 3D) using the same 2D and 3D methods developed and characterized by our group [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF]. Thus, we first established and monitored the 3D cell culture growth of CLC5 tumoroids and evaluated the size of tumoroids over time (Figure 3A). From day 1 to day 6, tumoroids were in an exponential growth phase and then reached a plateau at day 7 (Figure 3B). We then measured the size (diameter) and the number of tumoroids (Figure 3C-E) using Leica LasX software (see Materials and methods). We set up 10 classes of tumoroids of diameters ranging from 45 µm to 245 µm (Figure 3D).

J o u r n a l P r e -p r o o f

Figure 3: Tumoroids analysis setup. A, Representative field of a tumoroid grown in 3D culture acquired by phase-contrast microscopy (Leica) at objective 5×. B, Analysis of the diameter of tumoroids followed every day until day 12. C-E, A binary treatment was applied to select the tumoroids to be analyzed (i-iv). The accounting software of the total tumoroids by classes (D) previously predefined (E) to measure the tumoroids according to their diameter.

J o u r n a l P r e -p r o o f

Drug sensitivity of 2D cultures as compared to 3D tumoroids (PLDTs).

Next, we examine the drug sensitivity of 2D cultures from human lung tumor cells relative to their respective 3D PLDTs cultures. Figure 4A describes the strategy used to investigate differences between monolayer and PLDTs cultured tumor lines, obtained from lung cancer patient biopsies. To determine differences in drug sensitivity between cells cultured in 2D and cultured in a 3D matrix (PLDTs), we assessed the impact of chemotherapeutic drug treatments on cell viability. CLC5 cells were cultured in 2D monolayer and treated for 3 or 5 days with a dose ranging from 0 to 100 µM with Pemetrexed, Paclitaxel, Docetaxel, or Gemcitabine. Cell viability was then assessed upon exposure to different concentrations of the chemotherapeutic drugs for 3 and 5 days. As shown in Figure 4, the cell viability of CLC5 cells was reduced for each drug tested (Figure 4B-F) in a dosedependent manner, with a remarkably strong effect for Gemcitabine (Figure 4F). Since Gemcitabine is the drug which has the best efficacy on the 2D cell viability (Figure 4F), we performed a soft agar colony formation assay to compare the effects of this agent on the ability of treated cells to divide and form colonies (Figure 4G). Cells treated with Gemcitabine had significantly less transformation than control cells (P<0.0001, Fig. 4H). These results demonstrated that Gemcitabine treatment effectively inhibited cell transformation at a dose similar to the one observed in the 2D cell viability assay.

J o u r n a l P r e -p r o o f

J o u r n a l P r e -p r o o f

Our objective was to compare the drug sensitivity of 2D cultures from human lung tumor cells relative to their respective 3D PLDTs cultures. Our data showed that the anti-cancer Gemcitabine drug had the best efficacy on the 2D cell viability (reduction viability by 50% (Figure 4F)) and a very strongest effect on colony growth assay on soft agar (inhibition cellular transformation by 50% (Figure 4H)) on the CLC5 cells, thus, we have investigated the effect of this anti-cancer drug in our 3D model (PLDTs) to compare 2D vs 3D drug responses. Thereby, the impact of Gemcitabine in 3D tumoroid culture was evaluated and compared to the 2D culture (Figure 5). PLDTs from CLC5 were cultured with a dose ranging from 0 to 100 µM of Gemcitabine and the number and diameter of PLDTs were measured at 3, 5, and 7 days following treatment (Figure 5A-B). The number of PLDTs was significantly reduced in a dose-dependent manner (Figure 5B). In contrast, the diameter of PLDTs was not changed whatever the concentration of gemcitabine (data not shown). The cell viability rate of the CLC5 cell line in the presence of Gemcitabine was measured on day 3 and day 5 in 3D (Figure 5C) and 2D (Figure 5D) and the ratio of cell viability rates at day 5 was calculated (Figure 5 E). The EC 50 for the CLC5 cells following 3 days of treatment was 6.8×10 -7 M for 2D cultures and 1×10 -5 M for 3D cultures. We then tested the Gemcitabine effect on cell death in 2D as compared to 3D culture with drug (Gem) treatment. As shown in Supplemental Figure 1, cells were most susceptible to cell death when compared with control cells. The dose-dependent response of gemcitabine treatments was more effective in 2D than in 3D (Figure S1). We observed clearly different levels of sensitivity to Gemcitabine, translating into lower rates of survival when challenged with different drug amounts in 2D-culture (Figure 5E). Overall, cells grown in 3D tumoroids showed higher viability after treatment with increasing doses of gemcitabine compared with cells in 2D monolayer culture. The most straightforward explanation for drug resistance after Gemcitabine treatment in the tumoroids is that the cells inside the tumoroid are protected from drug accumulation. Taken together, our findings show that PLDT culture (3D) rather than monolayer culture (2D) better recapitulate the in vivo response. Chemotherapy and radiation are standard treatments for patients with locally advanced lung cancer. Thus, we sought to analyze the differential response of 2D monolayers as compared to PLDTs treated with chemotherapy and radiation. 2D cell monolayer and tumoroids were grown in feeding media for 96 hours prior to treatment. Baseline brightfield images were acquired prior to treatment with Gemcitabine (0, 1, and 10 μM) and radiation (0, 2, or 5 Gy). Cells were exposed to Gemcitabine for 72 h hours and radiation once. Brightfield imaging was performed again after 3 and 5 days (Figure 6A,D,G,J). Change in diameter of individual tumoroids was measured and compared across samples and treatment conditions as a marker of PLDTs growth response (Figure 6B,E,H,K). A significant variation in the cell viability response to Gemcitabine and radiation was observed across samples for both radiation and the gemcitabine dose tested (Figure 6C,F,I,L). CLC5 were responsive to both Gemcitabine and radiation with increasing concentrations of Gemcitabine and increased radiation dose as evidenced by a significant reduction in means tumoroids size (PLDT Mean Growth, Figure 6 B,E,H,K), and in cell viability both in 2D and in 3D (Figure 6 C,F,J,L). These results indicate a high effect on cellular viability after treatment with Gemcitabine combined with 2-Gy and 5-Gy radiation.

J o u r n a l P r e -p r o o f

The highest effect was after 5-Gy radiation and subsequent Gemcitabine treatments.

J o u r n a l P r e -p r o o f Human cell-based models are required for determining relevant pathways involved in human lung cancer mechanisms and for developing cell-based in vitro screening of new drugs, leading to early evaluation and better success rates. Personalized lung cancer medicine is devoted to tailoring the most appropriate drug to be prescribed to individual patients. To achieve this goal, genomicbased drug response prediction has enabled better decision making in oncology. However, evaluating the functionality of these predictions is difficult because of the lack of representative patient lung tumor models that can recapitulate all the key features of original tumors.

Here, we showed that PLDTs have great potential for predicting clinical responses to drugs and for selecting treatments. This type of culture has been previously shown to maintain the features of cancers from which they derived, including genetic and metabolic alterations, and also drug responses [START_REF] Kim | Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening[END_REF]. In this study, we presented detailed methods for culturing and expand primary human lung tumor epithelial cells from biopsy. We obtained lung cancer explants, freshly collected by biopsy and confirmed by immunofluorescence for the presence of specific human lung tumor epithelial cells, and tried to establish lung cancer cell lines from twelve patients' tumors (CLC1 to CLC12). The efficiency of PLDT lines establishment from biopsies was 75% (9 lines from 12 ''attempts'' from cancer biopsies) and could presumably be improved by further optimization of growth conditions. We demonstrated how explants of bronchial tumor tissue, cultured in media that promote epithelial cell growth, can provide a continuous source of human lung tumor epithelial cells.

These human lung tumor cells can be utilized in drug testing systems that more closely resemble cells living in their actual physiologic environment. Cell-cell and cell-ECM interactions observed during in vivo tumor progression cannot be studied in 2D models, whereas the 3D models are capable of mimicking these conditions. Consequently, various lung cancer cell lines have been tested in the literature for their ability to grow as tumoroids in 3D culture [START_REF] Fessart | Three-dimensional culture model to distinguish normal from malignant human bronchial epithelial cells[END_REF][START_REF] Sachs | Long-term expanding human airway organoids for disease modeling[END_REF]. Thus, in this study, we have investigated and showed that 3D cultures of human lung epithelial tumor cell lines are generally more resistant to chemo-and radio-therapy than their 2D counterparts. Anticancer effects of various chemotherapeutic agents were studied and conventional 2D and 3D cell cultures were compared. Chemo-resistance in tumors is mediated by various mechanisms. The classical mechanisms are based on ATP-binding cassette (ABC) transporter proteins, such as ABCC5 (MRP5) [START_REF] Hagmann | Interdependence of gemcitabine treatment, transporter expression, and resistance in human pancreatic carcinoma cells[END_REF], which have been reported to contribute to chemo-resistance [START_REF] Domenichini | ABC transporters as cancer drivers: Potential functions in cancer development[END_REF]. High-affinity uptake of gemcitabine is basically mediated by the pyrimidine-preferring nucleoside transporter hCNT1 and also hENT1 which plays a role in drug uptake [START_REF] Garcia-Manteiga | Nucleoside transporter profiles in human pancreatic cancer cells: role of hCNT1 in 2',2'-difluorodeoxycytidineinduced cytotoxicity, Clinical cancer research[END_REF]. Thus we cannot exclude the possibility that J o u r n a l P r e -p r o o f nucleoside transporters would significantly contribute to gemcitabine cytotoxicity, especially in lung tumor types in which this drug is currently used. This remains to be elucidated. However, validated 3D in vitro lung cell models have not been developed for rapid and standardized screening of chemo-and radio-resistance. A combined measure using the change of tumoroid size and number appears to be a robust measure of response for studying chemoresistance mechanisms.

One of the goals of this work was also to gain further experience using PLDTs to predict the effectiveness of chemotherapy and radiation for patients with lung cancer. In the present study, gemcitabine and irradiation treatment were more effective in monolayers as compared to PLDT.

Combination treatment led to both a significant decrease in cell viability and PLDT growth.

We found that 3D tumoroids are substantially more resistant to radiation than monolayer cultures, with a dose modification factor of 3 for 2-Gy radiation and 25 for 5-Gy radiation. This is probably due to factors characteristic of tumoroids, such as the presence of hypoxic cells and of a high proportion of non-proliferating cells. As hypoxia is thought to be a major factor responsible for the radioresistance of tumor cells [START_REF] Sutherland | Tumor hypoxia and gene expression--implications for malignant progression and therapy[END_REF], it is possible that hypoxic regions in tumoroids might influence the effects of radiation. However, in some studies, the correlation between radioresistance and hypoxia was not evident [START_REF] Gorlach | pO2-and pH-gradients in multicellular spheroids and their relationship to cellular metabolism and radiation sensitivity of malignant human tumor cells[END_REF][START_REF] Buffa | Radiation response and cure rate of human colon adenocarcinoma spheroids of different size: the significance of hypoxia on tumor control modelling[END_REF], suggesting that other factors such as cell-cell contact and the importance of the fraction of non-proliferating cells might play a more important role in radiation outcome. This remains elusive.

In addition to its cytotoxic effect, Gemcitabine is a potent radiosensitizer [START_REF] Shewach | Gemcitabine and radiosensitization in human tumor cells[END_REF][START_REF] Lawrence | Gemcitabine-mediated radiosensitization[END_REF].

Gemcitabine is activated by nucleoside kinases into two compounds, gemcitabine diphosphate (dFdCDP) and gemcitabine triphosphate (dFdCTP) [START_REF] Risbood | Synthesis of gemcitabine triphosphate (dFdCTP) as a tris(triethylammonium) salt[END_REF]. Gemcitabine diphosphate (dFdCDP) has been shown to inhibit ribonucleotide reductase, which is responsible for catalyzing the reactions that generate the deoxynucleotides required for DNA synthesis and repair; whereas gemcitabine triphosphate competes with dCTP for incorporation into DNA during replication, which results in a termination of chain elongation [START_REF] Risbood | Synthesis of gemcitabine triphosphate (dFdCTP) as a tris(triethylammonium) salt[END_REF]. The mechanism of radio-sensitization or radio-enhancement by gemcitabine has been associated with the depletion of the dATP pools, the accumulation of cells in S phase of the cell cycle, the p53 status, the induction of apoptosis, the increase of DNA double-strand breaks, and the modification of DNA repair pathways [START_REF] Pauwels | Combined modality therapy of gemcitabine and radiation[END_REF]. Herein, we showed that gemcitabine can enhance the effects of irradiation in both 2D and 3D tumoroids, while the effect was more pronounced in 2D. Although, a critical limitation remains from this model is the lack of a cancer microenvironment including stromal cells and immune cells, because tumoroids are derived only from human lung epithelial cancer cells. Thus, to investigate the interaction between cancer cells and J o u r n a l P r e -p r o o f the microenvironment, a co-culture system with immune/stromal cells should be developed in the next future.

In conclusion, we established a PDLTs model that will be a useful platform for drug screening and new clinical trials. Additionally, considering the short length of time from the model establishment to drug testing, our model can be used for predicting patient-specific drug responses as well as broader preclinical studies. Therefore, the ability to predict outcomes in preclinical animal experiments and clinical trials can be better understood using 3D cultures, which is a potentially useful predictive tool in the clinical setting for drug screening in the future. Using PLDTs cultures from surgical patients can be expanded to various fields of drug sensitivity testing, radiosensitivity testing, and novel drug delivery approaches for personal therapeutic strategies of lung cancer patients.

J o u r n a l P r e -p r o o f

Figure 1

 1 Figure 1 Human bronchial tissue segments and freshly isolated lung cancer epithelial cells. A, Representative picture of human bronchial tissue segments -~1-2 cm long and <1 cm diameter-(a) used for primary cultures of human lung epithelial cells. (b-c) Brightfield pictures representative of the outgrowth of the explant. B, Human lung epithelial cells grown from explants on tissue culture plates. Note the ring of cells of about 1-2 cm which becomes visible to the naked eye after 3-4 weeks. Images represent the edge of explant tissue and epithelial cells as they migrate from the explant, Scale bar=100 μm. The chronology illustrates the different steps to amplify cells. C, Table recapitulating the conditions assessed, two variables were tested: medium A and/or medium B for

Figure 2 :

 2 Figure 2: Immunostaining of sub-cultured lung epithelial cells. A-D, Primary cells from patients were seeded on a coverslip and fixed with 4% paraformaldehyde. Cells were stained with cytokeratin 18 (A and C) or surfactant protein C (SP-C) (B and D) and counterstained with DAPI for nuclei. C-F, CLC5

Figure 4 :

 4 Figure 4: Comparison of the effects of four chemotherapeutic agents on cell viability. A, Timeline of the strategy used to measure the effects of the chemotherapeutic drugs in 2D and in 3D. B-F, Cells are seeded on 96-well plates to measure cell viability on 2D monolayer. Cells are treated with increasing doses ranging from 0 μM to 100 μM of Docetaxel, Paclitaxel, Gemcitabine, or Pemetrexed, respectively. Representative bright-field phenotype of the cells after drug exposure is shown in B. Scale bar=100 μm. C-F, Histograms measuring the 2D cell viability (mean ± s.e.m) following drug treatment from three independent biological replicates. The statistical analysis was performed between no treatment (0) and treated cells. * indicates p<0.05, ** indicates p<0.001 and ***indicates p<0.001 (Student's t-test). G, Soft agar colony formation in the presence of different doses of Gemcitabine. H, The graph shows the number of colonies (mean ± SEM.) after 3 weeks of three biological replicates. The p values (determined by Student's t test) are relative to control cells (0 µM). **p≤0.01, ***p≤0.001 and ****p≤0.0001.

Figure 5 :

 5 Figure 5: Gemcitabine effect on PLDTs viability after 72 h, 120 h and 168 h of treatment. A, Tumor epithelial cells were seeded in 3D culture in a 96-well plate. Cells were treated with different doses of Gemcitabine (0; 0.01; 1; 10 and 100 μM), and its effect was measured after 3, 5, and 7 days of treatment. Brightfield pictures were collected using a 10× objective. The scale bar corresponds to 100 μm. B, Analysis of gemcitabine response on 3D tumor organoids treated for 72 h with different doses of Gemcitabine or without treatment. Dose-response experiments are presented as a bar

Figure 6 :

 6 Figure 6: Responses to combined treatment of Gemcitabine and radiation using PLDTs. A,D,G,J, PLDTs were treated with increasing doses of Gemcitabine and/or radiation (0, 2, or 5 Gy) and then PLDTs were observed over 3 and 5 days. Brightfield images are for PLDTs treated with control (vehicle), 1 or 10 µM Gemcitabine and 2 or 5-Gy radiation baseline and 3 or 5 days post-treatment. Scale bar=100 µm. B, E, H, K, Mean tumoroids growth (%) was measured for the different treatment conditions. Growth is defined as a change in tumoroid diameter. C, F, I, L, Analysis of radiation and or

Acknowledgements

We gratefully acknowledge the members from the ARTiSt group for their critical remarks. This work was supported by grants from the "Région Nouvelle-Aquitaine". FD was supported by grants from the "Fondation ARC pour la Recherche sur le Cancer". DF has been supported by the "Agence Nationale de la Recherche (ANR)". TG was supported by the "SIRIC Brio". We would like to acknowledge Marie-Alix Derieppe for help in setting up the radiation experiment.

J o u r n a l P r e -p r o o f

J o u r n a l P r e -p r o o f

Journal Pre-proof