Laura Bourillon

MD Sylvain Demontoy

MD Alexis Lenglet

Alexandre Zampieri

Julien Fraisse

Marta Jarlier

Florence Boissière-Michot

MD Hélène Perrochia

MD Gauthier Rathat

Véronique Garambois

Florence Boissière

PhD Nathalie Bonnefoy

PhD Henri-Alexandre Michaud

PharmD Thierry Chardès

MD, PhD Diego Tosi

PhD André Pèlegrin

MD PhD David Azria

PhD Christel Larbouret

MD PhD Céline Bourgier

Celine Bourgier
email: celine.bourgier@icm.unicancer.fr

Higher Anti-Tumor Efficacy of the Dual HER3-EGFR Antibody MEHD7945a Combined with Ionizing Irradiation in Cervical Cancer Cells

INTRODUCTION

Cervical cancer represents the second most frequent malignancy in women worldwide, especially in developing countries. Radiotherapy in combination with cisplatin (as radiosensitizer) is the goldstandard therapy for the locoregional management of cervical cancer, and has significantly improved patient outcome. However, a high relapse risk still persists and leads to poor overall survival (OS) (5-year OS rate: 70%) (1-3).

Many publications have suggested the implication of the epidermal growth factor receptor (EGFR) pathway in cervical cancer via EGFR overexpression or amplification, often in association with other EGFR family members, such as HER2, 3 and 4. EGFR or HER3 overexpression in locally advanced cervical cancer (LACC) is correlated with poor prognosis [START_REF] Perez-Regadera | Negative prognostic impact of the coexpression of epidermal growth factor receptor and c-erbb-2 in locally advanced cervical cancer[END_REF][START_REF] Noordhuis | Expression of epidermal growth factor receptor (egfr) and activated egfr predict poor response to (chemo)radiation and survival in cervical cancer[END_REF][START_REF] Fuchs | The prognostic significance of human epidermal growth factor receptor correlations in squamous cell cervical carcinoma[END_REF][START_REF] Lee | Correlation between human epidermal growth factor receptor family (egfr, her2, her3, her4), phosphorylated akt (p-akt), and clinical outcomes after radiation therapy in carcinoma of the cervix[END_REF][START_REF] Ocana | Her3 overexpression and survival in solid tumors: A metaanalysis[END_REF]. Emerging evidences indicate that HER3 overexpression contributes to the acquired resistance to EGFR and HER2 targeted therapy by activating downstream pathways [START_REF] Campbell | Her3 comes of age: New insights into its functions and role in signaling, tumor biology, and cancer therapy[END_REF][START_REF] Gala | Molecular pathways: Her3 targeted therapy[END_REF], and also to carcinoma cell survival following radiation [START_REF] Contessa | Compensatory erbb3/c-src signaling enhances carcinoma cell survival to ionizing radiation[END_REF][START_REF] Li | Human epidermal growth factor receptor 3 (her3) blockade with u3-1287/amg888 enhances the efficacy of radiation therapy in lung and head and neck carcinoma[END_REF]. Preclinical studies on the association of MEHD7945A, a dual humanized IgG1 antibody targeting HER3 and EGFR, with ionizing radiation (IR) showed radiosensitization in head and neck (HN) and non-small cell lung cancer models [START_REF] Li | Antitumor effects of mehd7945a, a dual-specific antibody against egfr and her3, in combination with radiation in lung and head and neck cancers[END_REF][START_REF] Huang | Dual targeting of egfr and her3 with mehd7945a overcomes acquired resistance to egfr inhibitors and radiation[END_REF].

MEHD7945A safety, pharmacokinetics, pharmacodynamics and antitumor activity were assessed in a phase I study in which no dose limiting toxicity (DLT) was observed [START_REF] Juric | Safety and pharmacokinetics/pharmacodynamics of the firstin-class dual action her3/egfr antibody mehd7945a in locally advanced or metastatic epithelial tumors[END_REF]. When combined with chemotherapy (cisplatin/5-fluorouracil or carboplatin/paclitaxel) in patients with HN recurrent/metastatic squamous cell cancer [START_REF] Jimeno | /paclitaxel for first-line treatment of recurrent/metastatic squamous cell carcinoma of the head and neck[END_REF], DLT was recorded in four patients, and the chemotherapy dose was reduced in 19 of 24 patients.

Here, we first assessed whether EGFR and HER3 are co-expressed in cervical cancer biopsy samples, and then we used in vitro and in vivo cervical cancer models to determine whether the IR-MEHD7945A combination could be a relevant strategy for patients with cervical cancer showing EGFR-HER3 co-expression.

METHODS AND MATERIALS

Clinical and biological database

A clinical database was retrospectively generated using the data of all patients (n=75) with cervical epidermoid carcinoma referred to xxx University Hospital from October 2001 to October 2014.

Expression of EGFR and HER3 was analyzed by immunohistochemistry on all biopsies at diagnosis (n=72) and scored using the H-score. Additional details are in Supplementary Materials and Methods. The patients' and tumor characteristics are listed in Table 1. This study was approved by the Hospital Institutional Review Board.

All patients treated by radiochemotherapy (followed or not by pelvic surgery) were eligible. Most patients (87%) underwent cisplatin-based (40mg/m 2 /week) chemoradiotherapy with a total dose of 45 to 50.4 Gy to the whole pelvis, and to the entire para-aortic lymph node chain for patients with para-aortic lymph node involvement. After whole pelvis external beam radiation therapy, all patients received pulsed dose rate brachytherapy, according to the GEC-ESTRO recommendations [START_REF] Haie-Meder | Recommendations from gynaecological (gyn) gec-estro working group (i): Concepts and terms in 3d image based 3d treatment planning in cervix cancer brachytherapy with emphasis on mri assessment of gtv and ctv[END_REF], and 32 patients underwent radical hysterectomy and pelvic lymphadenectomy. Pathological complete response was defined as the absence of any residual tumor after treatment. Follow-up visits were planned every 3 months after brachytherapy or definitive surgery for the first 2 years, then every 4 months for 1 year, every 6 months for the next 2 years, and annually after 5 years (median follow-up: 3.8 years; range: 3-162 months).

Cell lines and reagents

The A431 (squamous cell carcinoma) and CaSki (metastatic cervical cancer) human cell lines were from ATCC (Rockville, MD, USA) and cultured following ATCC recommendations. The MEHD7945A antibody was provided by Genentech, cisplatin was purchased from the hospital (xxx).

Ionizing radiation

A linear particle accelerator was used (Varian Medical Systems; 6MV-photons; dose rates of 200UM/min and 400UM/min for in vitro and in vivo experiments, respectively) at the xxx Radiation Oncology Department. For combotherapy, MEHD7945A was added in vitro 4h before, and in vivo 24h before IR.

Clonogenic survival assay

The ability of MEHD7945A to radiosensitize A431 and CaSki cells was assessed using a standard colony-forming assay and growth curve analysis. Cells were trypsinized, washed, and seeded in triplicate in 6-well plates one day before IR. After exposure to IR (from 0 to 8Gy) and/or MEHD7945A (1μg/ml and 100μg/ml), cells were fixed with acetic acid: methanol (1:3) at different time-points (from 9 to 12 days post-treatment) and stained with Giemsa (Sigma Chemical Co., St Louis, MO, USA). MEHD7945A concentration was chosen on the basis of the results of the cell proliferation assay (see below). The survival fraction was calculated by counting the number of colonies containing at least 50 cells relative to the number of seeded cells, and multiplied by plating efficacy (number of counted cells relative to plating cells in untreated condition).

Human tumor xenografts

All in vivo experiments (minimun of 7 animals per group)were performed in compliance with the xxx regulations and ethical guidelines for experimental animal studies in an accredited establishment (Agreement No. C34-172-27). A431 cells (1x10 6 ) were injected subcutaneously in the right flank of 6-week-old female athymic nude Hsd mice (Envigo, Huntingdon, UK). Tumorbearing mice were treated with 2.5 or 10 mg/kg of MEHD7945 by intraperitoneally injection with or without IR (2 or 4 Gy), twice per week for 4 weeks. Tumor volumes were calculated with the formula: D1 x D2 x D3 /2.

Additional details about cleaved caspase 3 and Ki67 expression analysis in xenografts are in Supplementary Materials.

Cell death analysis

The Annexin A5 FITC/7-AAD Apoptosis Detection Kit (Beckman coulter, IM3614) was used according to the manufacturer's instructions to quantify apoptosis at 24h and 48h post-incubation with MEHD7945 (10 g/ml), IR (one single dose of 2, 5 or 10Gy), or IR and MEHD7945. Data were acquired on a Gallios Flow Cytometer and analyzed with the Kaluza software (Beckman Coulter, Brea, USA).

Calreticulin membrane exposure was measured at 24h and 48h post-treatment (IR, MEHD1945A or combotherapy) using an anti-calreticulin antibody (Abcam, AlexaFluor® 647/ab196159, 1:200) and a Cytoflex cytometer (Beckman) with FlowJo 10 (Tristar). Additional details are in Supplementary Materials.

Supernatants were collected 24h, 48h and 72h after treatment (IR, MEHD1945A or combotherapy), centrifuged and pellets frozen. ATP release was detected using the ATPlite Luminescence Assay System (PerkinElmer®), according to the manufacturer's recommendations.

Assessment of H2AX by immunofluorescence

Briefly, fixed cells were incubated with an anti-phosphorylated H2A.X (H2AX; Ser139) antibody (1:200 in PBS-BSA, Millipore, 05-636). H2AX foci were visualized using an epifluorescence Zeiss Imager 2 (Zeiss, Germany). The number of foci was counted in at least 100 cells. Additional details are in Supplementary Materials.

Western blotting

A431 and CaSki cells were lysed and expression of EGFR, HER3, ERK1/2, AKT, phosphorylated EGFR, HER3, ERK1/2 and AKT (antibodies from Cell Signaling Technology, Beverly, MA) assessed, as previously described [START_REF] Thomas | Her3 as biomarker and therapeutic target in pancreatic cancer: New insights in pertuzumab therapy in preclinical models[END_REF]. Equal loading was assessed with an antibody against GAPDH (Cell Signaling Technology). Band intensity was analyzed with a PXi imager (Syngene, Cambridge, UK).

Cell proliferation assay and treatments' interactions

The effect of the combination of IR, MEHD7945 and cisplatin on cell proliferation was evaluated using a sulforhodamine B (SRB) colorimetric assay, as already described [START_REF] Ogier | Targeting the nrg1/her3 pathway in tumor cells and cancerassociated fibroblasts with an anti-neuregulin 1 antibody inhibits tumor growth in pre-clinical models of pancreatic cancer[END_REF]. Growth inhibition was calculated based on the percentage of proliferating cells in treated samples relative to untreated cultures. Treatments' interactions were analyzed using dose-response matrices, according to the Bliss equation [START_REF] Greco | The search for synergy: A critical review from a response surface perspective[END_REF]. Additional details are in Supplementary Materials.

Statistical analysis

Analyses of the correlation of EGFR/HER3 expression with survival included only patients with available data (N=72 patients). For clinical results, continuous variables were described using medians and ranges, and categorical variables with frequencies and percentages. The Fisher's exact test was used to compare the distribution of categorical variables. All tests were two-sided. OS and disease-free survival (DFS) were estimated using the Kaplan-Meier method, and median follow-up durations using the reverse Kaplan-Meier method. Survival curves were generated with their logrank tests. Variables, including prognostic factors, were selected for univariable analysis. Hazard ratios (HR) were presented with 95% confidence intervals (CI).

For preclinical results, data were presented as the mean ± standard error of the mean (SEM) of triplicate experiments, if not mentioned otherwise. Groups were compared using the non-parametric Kruskal-Wallis test. Clonogenic survival was analyzed using a linear mixed model to explore the relationship between the surviving fraction and IR doses. IR doses, treatment group and their interaction were the variables included in the fixed part of the model. Random intercepts were considered to take into account the replicate effect inside the IR dose groups.

A p-value <0.05 was considered statistically significant; multiplicity of testing was not considered due to the exploratory nature of this study. Statistical analyses were performed using STATA 13.0 (StataCorp, College Station, TX, USA).

RESULTS

EGFR and HER3 as biological targets in cervical cancer

We first analyzed EGFR and HER3 expression in cervical cancer biopsies at diagnosis (n=72; one for each patient). EGFR was overexpressed (H-scores higher than 150) in 46 samples (63.0%):

4.1% of FIGO stage IA2/IB1, 42.5% of FIGO stage IB2-IVA, and 16.4% of FIGO stage IVB tumors (p=0.018). HER3 was overexpressed (H-scores higher than 50) in 18 samples (25.0%): 5.5% of FIGO stage IA2/IB1, 4.2% of FIGO stage IB2-IVA, and 15.3% of FIGO stage IVB tumors (p<0.001). EGFR and HER3 were concomitantly overexpressed only in seven of the 22 FIGO IVB cancer specimens (32%) (Table 1). These seven patients had pelvic and para-aortic node involvement (p<0.001; Table 1). We did not find any correlation between tumor response or relapse and EGFR, HER3, or EGFR/HER3 co-expression.

Univariable analysis of common clinical prognostic factors showed that lymph node involvement, metastatic disease, and FIGO stage were significantly correlated with the patients' outcome (DFS and OS, Supplementary Table 1). As expected, due to the fact that all patients with EGFR/HER3 co-expression had a FIGO IV cancer, this co-expression was also significantly correlated with the 3-year OS (HR=4.27; 95%CI=1.38 -13.19; p=0.006. Fig. 1; Supplementary Table 1) and DFS (HR=4.23; 95%CI=1.37 -13.00; p=0.006) (Supplementary Table 1). However, in multivariable analysis, only para-aortic lymph node involvement remained significantly correlated with outcome (DFS and OS, Supplementary Table 2). Nevertheless, in the FIGO IVB subgroup, the median OS was 14.6 and 23.1 months (p=0.317, Log-rank test) and the median DFS was 9.5 and 11.3 months (p=0.662, Log-rank test) in patients with and without EGFR/HER3 co-expression, respectively.

MEHD7945 increases the response to radiation in A431 cell tumor xenografts

Then, to investigate whether the IR-MEHD7945A combination could be a relevant strategy, particularly for cervical cancer showing EGFR-HER3 co-expression, we used A431 and CaSki cells. Immunocytochemistry and cytometry (Supplementary figure 1) showed that HER3 was mildly expressed in both cells lines, whereas EGFR expression was stronger in A431 than in CaSki cells.

In mice xenografted with A431 cells (at least 7 mice/group), tumor volume was significantly reduced and the median survival increased after combotherapy (IR=4Gy; MEHD7945=10 mg/kg) compared with IR alone (63 versus 34 days; p=0.079) (Fig. 2A and supplementary figure 2A). Due to the high rate of radiation-induced toxicity (weight loss), we performed a second experiment using lower IR dose and antibody concentration (IR=2 Gy; MEHD7945A=2.5 mg/kg). Survival was significantly higher after combotherapy than IR alone (42 vs 28 days; p=0.0408) (Fig. 2B and supplementary figure 2B). Moreover, intra-tumor apoptosis (percentage of cleaved caspase 3positive cells) was higher in the combotherapy group compared with IR alone and untreated controls (Fig. 2C). Conversely, the fraction of KI67-positive cells was not significantly different (supplementary figure 2C).

Effect of combotherapy on cell survival, cell proliferation, DNA damage and EGFR/HER3 signaling

We then evaluated in vitro the combotherapy effects by measuring the clonogenic survival in A431 and CaSki cells after 10 days of exposure to MEHD7945A and/or IR (Fig. 3A). Compared with IR, the combination of MEHD7945A (10 g/ml) with IR significantly reduced the surviving fraction (p= 0.004 for A431; p<0.001 for CaSki) while 1g/ml of MEHD7945A with IR did not have any effect (p=0.334 for A431; p=0.854 for CaSki cells). In addition, the MEHD7945A (1g/ml) and IR (10Gy) combination had the strongest inhibitory effect on A431 and CaSki cell proliferation (cell number after 72h of treatment) (Fig. 3B).

Analysis of DNA damage (H2AX foci) (Fig. 3C) indicated that the number of H2AX foci per cell was significantly increased at 1h after combotherapy compared with IR alone (p=0.0015 for A431; p=0.0001 for CaSki) or MEHD7945A alone (p<0.0001).

Finally, analysis of EGFR and HER3 signaling by western blotting (Fig. 3D and supplementary figure 3) showed that in untreated cultures, EGFR and HER3 phosphorylation levels were low in A431 cells and high in CaSki cells. In A431 cells, IR alone increased EGFR and HER3 phosphorylation starting at 15min and until 240min post-IR. Conversely, after co-exposure to IR and MEHD7945A, EGFR phosphorylation level was lower than in untreated cells, and HER3 phosphorylation was completely inhibited as well as MAPK and AKT phosphorylation. In CaSki cells, IR did not modulate EGFR and HER3 phosphorylation compared with untreated cells.

Conversely, co-exposure to IR and MEHD7945A inhibited HER3, AKT and MAPK phosphorylation, but had no effect on EGFR phosphorylation. Finally, differently from the IR-MEHD7945A combination that inhibited both the EGFR and HER3 pathways, the association of cetuximab and IR had only a slight effect on HER3 and AKT phosphorylation (supplementary figure 4).

Combotherapy increases tumor cell death

Analysis of early and late apoptosis in A431 cells exposed to IR (2, 5, or 10 Gy) and/or MEHD7945A indicated that at 24h post-exposure, apoptosis was higher in cells incubated with MEHD7945A (mean=19.5%) than in untreated cells (p= 0.0209) and irradiated cells, regardless of the IR dose (from 6.5 to 10.5%) (Fig. 4A). Conversely, at 48h, the apoptosis rates induced by 10Gy and MEHD7945A were comparable (27%). Compared to IR, combotherapy increased both early and late apoptosis/necrosis rates, especially at 24h and 48h (Fig. 4A). We obtained similar results in CaSki cells (supplementary figure 5A). This effect was associated with an increase of cleaved caspase 3 expression in IR (4 Gy) and MEHD7945A (10g/mL)-treated cells (Fig. 4B).

Then, to determine the immunogenic cell death role, we measured calreticulin exposure on the cell surface and ATP release. At 48h, calreticulin translocation was increased in A431 cells incubated with MEHD7945A alone (1g/mL), but not with IR alone (different doses) (Fig. 4C). The MEHD7945A and IR combination significantly increased calreticulin exposure, but only at high IR dose (10Gy). We obtained similar results in CaSki cells (supplementary figure 5B).

Compared with untreated cells, IR alone (but not MEHD7945A alone) increased ATP release in a dose-dependent manner (Fig. 4D). Compared with alone, ATP release was increased at 24h post-combotherapy, but not at later time points. These results suggest that IR and the combotherapy could induce immunogenic cell death.

Association of irradiation, MEHD7945A and cisplatin

As cisplatin-based chemotherapy combined with radiotherapy is the gold standard for cervical cancer, we assessed whether the combination of IR, MEHD7945A and cisplatin had a synergic or additive effect. After incubation of A431 and CaSki cells with increasing doses of IR, MEHD7945A and cisplatin for 5 days (Fig. 5), their combination had mainly an additive effect.

DISCUSSION

Our study shows that EGFR and HER3 were co-expressed only in aggressive and advanced cervical cancer (FIGO stage IVB tumors). The too small number of EGFR-and HER-positive tumor samples did not allow determining whether their co-expression is a candidate prognostic marker, and in our series only para-aortic nodal involvement was an independent prognostic factor. In a recent large cohort of patients with cervical cancer (n=401) and mainly treated by surgery alone (74%), HER2 amplification (tissue micro-array analysis) was associated with poor patients outcome, but only tumor size, vascular space invasion and histologic type were independent prognostic factors [START_REF] Halle | Clinicopathologic and molecular markers in cervical carcinoma: A prospective cohort study[END_REF]. In the study by Fuchs and coworkers in which 50% of patients were treated by surgery alone and 33% by radiotherapy alone (n=78) ( 6), 63% of tumor samples overexpressed EGFR and 74.4% HER3 (HER1-4 co-expression was not studied). EGFR overexpression was associated with higher survival, and HER3 overexpression with poor survival. Conversely, a recent translational study within a phase II clinical trial found that tumor EGFR expression was not correlated with the treatment response or patient outcome [START_REF] De La Rochefordiere | Pik3ca pathway mutations predictive of poor response following standard radiochemotherapy +/-cetuximab in cervical cancer patients[END_REF]. In our cohort, EGFR/HER3 coexpression was only observed in FIGO IVB cancer samples that are known to have a poor outcome.

Yet, in the FIGO IVB subgroup, outcome seemed to be worse in patients with EGFR/HER3 coexpressing tumors. A larger cohort is required to confirm this finding and to limit the possible selection bias.

Besides the balance between HER1-4 receptors (6), patient outcome is influenced also by the presence of Human Papillomavirus (HPV) that regulates HER3 expression [START_REF] Paolini | Human 16 e2 interacts with neuregulin receptor degradation protein 1 affecting erbb-3 expression in vitro and clinical samples cervical lesions[END_REF][START_REF] Brand | Human papillomavirus regulates her3 expression in head and neck cancer: Implications for targeted her3 therapy in hpv(+) patients. Clinical cancer research : an official journal of the[END_REF]. Fahkry and colleagues recently showed that persistent oral HPV DNA detection is associated with poor outcome in patients with HN cancer [START_REF] Fakhry | Association of oral human papillomavirus DNA persistence with cancer progression after primary treatment for oral cavity and oropharyngeal squamous cell carcinoma[END_REF]. The lack of data on HPV status is another limitation of our study in addition to the very small number of patients with tumors showing HER3 and EGFR co-expression.

Our preclinical results on the ability of MEHD7945A to radiosensitize EGFR/HER3-co-expressing cervical cancer cells lines are consistent with those previously reported in lung and HN cancers [START_REF] Li | Antitumor effects of mehd7945a, a dual-specific antibody against egfr and her3, in combination with radiation in lung and head and neck cancers[END_REF][START_REF] Huang | Dual targeting of egfr and her3 with mehd7945a overcomes acquired resistance to egfr inhibitors and radiation[END_REF]. Compared with single treatments, MEHD7945A with IR induced efficiently calreticulin exposure and ATP release, suggesting that this combotherapy might trigger immunogenic cell death. Similarly, inhibition of EGFR interaction with cetuximab (alone or in combination with chemotherapy) induces calreticulin exposure [START_REF] Pozzi | The egfr-specific antibody cetuximab combined with chemotherapy triggers immunogenic cell death[END_REF], and anti-EGFR antibodies induce immunogenic apoptosis of tumor cells [START_REF] Garrido | Induction of immunogenic apoptosis by blockade of epidermal growth factor receptor activation with a specific antibody[END_REF]. In cervical cancer, radiochemotherapy has a significant role in immune response activation through immune cell activation within the local tumor microenvironment [START_REF] Dorta-Estremera | Kinetics of intratumoral immune cell activation during chemoradiation for cervical cancer[END_REF], and cisplatin also induces apoptotic tumor cell death in a JNK-dependent [START_REF] Van Der Sluis | Vaccine-induced tumor necrosis factorproducing t cells synergize with cisplatin to promote tumor cell death[END_REF]. As our results showed a synergic effect of the combination of small dose of MEHD7945A, cisplatin and IR, their association could improve patients' outcome by increasing tumor control and by activating the immune response.

Although in the MEGHAN trial, the outcome of patients with recurrent/metastatic HN cancer who progressed after at least one line of cisplatin-based chemotherapy was not improved by MEHD7945A compared with cetuximab [START_REF] Fayette | Randomized phase ii study of duligotuzumab (mehd7945a) vs. Cetuximab in squamous cell carcinoma of the head and neck (mehgan study)[END_REF], McKnight and colleagues [START_REF] Mcknight | Imaging egfr and her3 through (89)zr-labeled mehd7945a (duligotuzumab)[END_REF] suggested that EGFR and HER3 inhibition should be considered in patients resistant to EGFR treatment. As our results showed a synergic effect of the combination of MEHD7945A, cisplatin and radiotherapy, we propose to assess MEHD7945A as a radiosensitizer. Moreover, our data indicate that MEHD7945A could be effective at low dose. This might reduce the occurrence of adverse events when combined with radiochemotherapy, compared with the MEGHAN trial. Furthermore, it is well described that blocking one HER receptor can be compensated by activation of another HER family member, particularly HER3 that has been identified as a key contributor to resistance to anti-EGFR or -HER2 therapy [START_REF] Gaborit | Emerging anti-cancer antibodies and combination therapies targeting her3/erbb3[END_REF]. MEHD7945A capacity to overcome this type of resistance has been already reported for non-small lung carcinoma [START_REF] Huang | Dual targeting of egfr and her3 with mehd7945a overcomes acquired resistance to egfr inhibitors and radiation[END_REF]. We found that MEHD7945A combined with IR can inhibit HER3 signaling, particularly in CaSki cells that are derived from HPV-positive cervical cancer and in which basal HER3 phosphorylation is very high, probably due to a PI3KCA mutation that strongly induces HER3 and AKT activity. Conversely, EGFR phosphorylation was inhibited only in A431 cells, but not in CaSki cells that show high basal EGFR phosphorylation and harbor super-enhancers in the proto-oncogene EGFR [START_REF] Chen | E6 protein expressed by high-risk hpv activates super-enhancers of the egfr and c-met oncogenes by destabilizing the histone demethylase kdm5c[END_REF]. Despite this difference, the MEHD7945A-IR combination had a strong effect on cell viability and proliferation in both cell lines, confirming the importance of HER3 in MEHD7945A anti-proliferative effect.

The MEHD7945A-IR combination also increased cell apoptosis (both in vitro and in vivo) and H2AX expression in A431 and CaSki cells. These results indicate that MEHD7945A the radiation response by modulating DNA damage-induced cell death.

Finally, in HPV-positive HN cancer, HER3 overexpression is associated with worse prognosis, and HER3 targeting inhibits growth of HPV-positive cell lines [START_REF] Brand | Human papillomavirus regulates her3 expression in head and neck cancer: Implications for targeted her3 therapy in hpv(+) patients. Clinical cancer research : an official journal of the[END_REF]. Furthermore, as the development of cervical malignant squamous cell lesions is commonly related to HPV infection, our preclinical and clinical results the possible use of the MEHD7945A-IR combination in cervical cancer.

These preliminary results should be confirmed in a larger sample before transfer to clinical development. A431 and CaSki cells were exposed to the association at the indicated concentrations for five days and then cell proliferation was measured using the SRB colorimetric assay (n=3). Results are presented as the percentage relative to untreated cells. The interactions between the tested drugs were addressed using dose-response matrices.

Figure 1 :

 1 Figure 1: EGFR and HER3 are relevant targets in cervical cancer.

Figure 2 :

 2 Figure 2: The association of MEHD7945A with irradiation survival of mice

Figure 3 :

 3 Figure 3: Effect of the MEHD7945A and irradiation combination on the cell surviving

Figure 4 :

 4 Figure 4: The association of MEHD7945A with irradiation induces apoptosis and

Figure 5 :

 5 Figure 5: Synergy of the association of irradiation, cisplatin and MEHD7945A.

Table 1 . Patients' characteristics

 1

	No EGFR/HER3 co-(n=65) expression	EGFR/HER3 (n=7) co-expression	p-value

This work was supported by grants from the SIRIC Montpellier Cancer « INCa-DGOS-INSERM 6045 »; a Material Transfer Agreement was made between the Institut Régional du Cancer de Montpellier ("Institution") and Genentech Inc., South San Francisco,


Hmamouche Youssef

Prévot Laurent

Ochs Magalie

Chaminade Thierry

Identifying Causal Relationships Between Behavior and Local Brain Activity During Natural Conversation

Keywords: multimodal signals processing, natural conversation, machine learning, human-human and human-machine interactions, Functional MRI

Characterizing precisely neurophysiological activity involved in natural conversations remains a major challenge. We explore in this paper the relationship between multimodal conversational behavior and brain activity during natural conversations. This is challenging due to Functional Magnetic Resonance Imaging (fMRI) time resolution and to the diversity of the recorded multimodal signals. We use a unique corpus including localized brain activity and behavior recorded during a fMRI experiment when several participants had natural conversations alternatively with a human and a conversational robot. The corpus includes fMRI responses as well as conversational signals that consist of synchronized raw audio and their transcripts, video and eye-tracking recordings. The proposed approach includes a first step to extract discrete neurophysiological time-series from functionally well defined brain areas, as well as behavioral time-series describing specific behaviors. Then, machine learning models are applied to predict neurophysiological timeseries based on the extracted behavioral features. The results show promising prediction scores, and specific causal relationships are found between behaviors and the activity in functional brain areas for both conditions, i.e., human-human and humanrobot conversations.

Introduction

Identifying dependencies between behavior and brain activity during conversations is an essential step towards understanding the brain bases of conversational speech. We study here those dependencies in two conditions: human-human vs. humanrobot interaction. Besides exploring differences in neurophysiological processes of an subject conversing with a human or with a robot, it also enables us to scrutinize brain activities related to conversation in the case of impoverished social context. A strength of our approach is the use of fMRI that provides invaluable data to localize brain activity and with a methodology that allows a relatively fine grained sampling in the time domain as well. The result is a unique data set of natural langugage conversations recorded in fMRI, providing synchronized neurophysiological and behavioral signals [START_REF] Birgit | Brain activity during reciprocal social interaction investigated using conversational robots as control condition[END_REF].

This dataset is unique in that participants' behaviour is unconstrained and therefore different from fMRI dataset generally acquired in highly controlled conditions. The classical approach of contrasting two or more well controlled experimental conditions therefore cannot be used. Existing works (cf. Section 2) have a major drawback: They use one or a small number of behavioral signals that are derived from very controlled tasks. Our contribution consists in handling complex raw multimodal behavioral signals acquired during relatively unconstrained conversations, and deriving from them high-level features as predictive variables to predict fMRI responses in well-defined functional regions of interest (ROI).

In this paper, we present a framework for (i) predicting fMRI responses based on behavioral signals in localized brain areas recorded during conversations, and (ii) identifying causal relationships between conversational behavior and brain activity. The proposed approach consists of two main steps:

1. Feature extraction: high level (verbal and non-verbal) features are extracted from raw behavioral data. They are grounded on speech produced by the two interlocutors, as well as eye-tracking signals of the participant and the video of the human or artificial interlocutor.

2. Applying feature selection and prediction based on the extracted features and the fMRI time series.

Evaluations are performed on 24 participants (cf. Section 3). The best prediction result allows us to identify the most relevant features for each brain area, yielding on the way a discussion comparing the cases where the interlocutor is a human or a robot.

After presenting related work in the next section, we describe the fMRI experiment and data sets acquisition in section 3. Then, we present our approach in section 4 and our results in section 5.

Related Work

Several approaches have been proposed in the literature to predict brain activity based on behavior. In [START_REF] Okada | An fMRI Study of Audiovisual Speech Perception Reveals Multisensory Interactions in Auditory Cortex[END_REF], the authors investigate the effect of adding the visual speech to auditory speech signals in increasing the activity of auditory cortex areas. The results show significant increase in the activation of the studied regions of interests (ROIs) based on ANOVA analysis. In [START_REF] Mitchell | Predicting Human Brain Activity Associated with the Meanings of Nouns[END_REF], the fMRI neural activation associated to meanings is predicted based on a large text data. The brain regions studied are in the sensory-motor cortex. The model used consists of transforming the text into semantic features, then building a regression model that expresses the fMRI brain activity as a linear combination of semantic features. The authors show a prediction accuracy of 0.62 or higher, but on each participant independently. This issue has also been addressed with multi-subject approach, i.e., by concatenating data from multiple subjects. For example, in [START_REF] Huth | Natural speech reveals the semantic maps that tile human cerebral cortex[END_REF], the goal was to predict voxels activity from cortical areas, measured via the Blood-Oxygen-Level Dependent (BOLD) signal based on the speech signal. The data used has been collected from an fMRI experiment on 7 subjects. The methodology adopted is based, first, on constructing semantic features from natural language, then, a dimension reduction using PCA (Principal Component Analysis) is applied to reduce the number of the predictive variables, and a model is learned based on multiple linear regression with regularization in order to predict the BOLD signal. Finally, the obtained prediction results and the principal components of the predictive variables are both combined to classify brain areas according to the semantic features categories. Other types of behavioral signals have been investigated by evaluating the effect of a single feature on the brain activity. For example, the speech reaction time has been used to predict activity in specific brain regions [START_REF] Yarkoni | BOLD Correlates of Trial-by-Trial Reaction Time Variability in Gray and White Matter: A Multi-Study fMRI Analysis[END_REF]. In [START_REF] Suzuki | Effects of echoic mimicry using hummed sounds on human-computer interaction[END_REF], the prosodic mimicry generated by computers has been used to study its effect on interpersonal relations in human-computer interaction. In [START_REF] Chen | A Gaussian mixture regression approach toward modeling the affective dynamics between acoustically-derived vocal arousal score (vc-as) and internal brain fMRI bold signal response[END_REF], the acoustically-derived vocal arousal score [START_REF] Bone | Robust Unsupervised Arousal Rating: A Rule-Based Framework with Knowledge-Inspired Vocal Features[END_REF] is used to predict the BOLD signal using the Gaussian mixture regression model. In [START_REF] Knops | Recruitment of an Area Involved in Eye Movements During Mental Arithmetic[END_REF], the authors predict the BOLD signal in the posterior parietal cortex based on eye movement data using a multivariate regression model. More general approaches try to predict the brain activity of various areas using different types of signals at the same time. For example, in [START_REF] Desouza | Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis[END_REF], correlations are analyzed using linear regression between the BOLD signal and behavioral features computed from observed facial expressions, speech reaction time, and eye-tracking data.

These works analyse dependencies between behavior and specific functional brain areas. However, one or few modalities are included to describe the behavior used to predict the brain activity. In addition, the methods used are generally based on correlation analysis or multiple regression.

However, machine learning methods can be particularly relevant for this kind of questions, such as prediction models based on decision trees and artificial neural networks, aside with feature selection techniques. In our case, we propose a framework that consists in extracting high-level features from raw data, then applying feature selection and prediction with different classifiers to predict discretized neuro-physiological signals from multimodal behavioral signals composed of audios, videos and eye-tracking recording.

Datasets acquisition and processing

The data is collected from an fMRI experiment described in [START_REF] Birgit | Brain activity during reciprocal social interaction investigated using conversational robots as control condition[END_REF], and illustrated in Figure 1. It involves 24 participants, and consists of four sessions, each containing six conversations of 60 seconds, three with a human and three with a conversational robot alternatively. An "advertising campaign" provides a cover story: participants are informed that they should guess what is the message carried by images in which fruits appear either as 'superheroes' or 'rotten'. Each conversation between the participant and either a confederate of the experimenter or a FURHAT conversational robot [START_REF] Al Moubayed | Furhat: A back-projected human-like robot head for multiparty human-machine interaction[END_REF] (controlled by the confederate in a Wizard-of-Oz mode, unbeknown to the participant), is about one single image of the purported "advertising campaign".

Processing fMRI signals

Standard fMRI acquisition procedures were used, described in details in [START_REF] Birgit | Brain activity during reciprocal social interaction investigated using conversational robots as control condition[END_REF]. BOLD signal 3-dimensional images are recorded in the whole brain every 1.205 seconds. Standard SPM12 preprocessing procedures are used [START_REF] Penny | Statistical Parametric Mapping: The Analysis of Functional Brain Images[END_REF], including correction for time delays in slice acquisition ("slice timing"), image realignment, magnetic field inhomogeneities correction, normalization to the standard MNI space using the DARTEL [START_REF] Ashburner | A fast diffeomorphic image registration algorithm[END_REF] procedure for coregistration of individual participants' anatomy, and fi- nally spatial smoothing with a 5-mm full-width half-maximum 3-dimensional Gaussian kernel. Extraction of the BOLD signal in regions of interest is performed using the conn toolbox [START_REF] Whitfield-Gabrieli | Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks[END_REF], and includes several denoising procedures, firstly a linear detrending using a high-pass filter with a threshold of 128 seconds, secondly using realignment parameters to calculate nuisance regressors related to participants' movement during scanning, thirdly taking heartbeat and breathing recordings to remove physiological artifacts with the PhysIO toolbox [START_REF] Kasper | The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data[END_REF], and finally extracting BOLD signal in the white matter and cerebrospinal fluid and using the 5 first eigen variate of the time-series as nuisance representing signal fluctuations in noncortical brain tissues. A 275-area parcellation based on functional and anatomical connectivity patterns [START_REF] Fan | The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture[END_REF] defines ROIs for the whole brain, and specific regions are chosen based on their anatomical location. Continuous time-series (385 time points) are extracted for each ROI and each session and participant representing the mean activity after denoising.

The Proposed Approach

We propose a framework for (i) predicting local brain activity based on multimodal behavioral signals during bidirectional conversations, and (ii) identifying dependencies between them. The idea is to mimic how brain areas activate in a bidirectional conversation depending on what the brain receives and produces. A schema of this framework is shown in Figure 2. It consists of two main modules. A predictors extractor module which takes as input raw multimodal signals, then extracts high level features using methods and pre-trained models. Next, it resamples them to have time series with the same number of observations. Finally, the time series are represented as sequences The prediction module applies feature selection and learns supervised classifiers from the processed features and the BOLD signals. In the rest of this section, we detail the two parts of the proposed framework.

Extracting features from multimodal signals

This step involves processing 3 types of signals. Facial features are directly extracted from video of the interlocutor, speech features are extracted from manual transcripts, and eye-tracking features are extracted from participant's data recorded inside the fMRI scanner. In total, more than 40 features are extracted. For the sake of space, we just present the name of these features per modality in Table 1. For more details, all features are available online 1 , and described in [START_REF] Rauchbauer | Multimodal corpus of bidirectional conversation of human-human and human-robot interaction during fMRI scanning[END_REF].

Raw signals Extracted Features

Audios

Speech activity, interpersonal speech items (particles items and discourse markers [START_REF] Schiffrin | Discourse markers[END_REF]), Overlap, Reaction Time, Filled-breaks, Feedbacks, Laughters, Lexical Richness, Sentiment analysis (polarity and subjectivity).

Videos

Facial Action Units, Head pose coordinates, Gaze coordinates, smiles, emotions.

Eyetracking data

Coordinates of the gaze movement, and binary variables categorizing resp. the presence of saccades, and where the participant is looking at interlocutor's face, eyes of mouth. Table 1: Summary of the extracted behavioral features.

The extracted time series have two types (discrete and continuous), and have different frequencies, while the used machine learning models require variables with the same number of observations. We adopted two resampling methods. For continuous features, we resample them by considering their relative average between each two successive time points. For discrete features, we consider the number of occurrences.

The prediction module

Before starting the prediction formulation, let us describe the dynamic link BOLD signal and the behavior events from a neuroscience point of view. The fMRI response to a behavioral event is characterized by the Hemodynamic Response Function (HRF), which peaks around 5 seconds after a trigger event [START_REF] Gössl | Bayesian modeling of the hemodynamic response function in bold fMRI[END_REF]. We focus in this work on predicting if a brain area is active or not. Therefore, we discretize first the BOLD signal in each ROI.

The BOLD time series of each participant are normalized, then discretized using thresholding. The thresholds used are chosen after cross-validating the prediction module with different values close to the mean activation of each ROI, then selecting the appropriate value in terms of the prediction scores.

Let Y (t) be the discretized BOLD signal of a given brain area, and X(t) = {X1(t), X2(t), . . . , X k (t)} be kdimensional time series representing the behavioral variables. One could express each value of the BOLD signal at time t as a function of the predictive features at time t -5s. Considering the fact that the delay varies around 5s depending on behaviors, brains areas, and subjects, our approach consists in considering more points near to this delay in order to cover its variability. We express the BOLD signal at time t as a function of the lagged variables of each feature between times t -7s and t -4s. This model can be written as follows:

Y (t) = f (X t-τ 1 :t-τ 2 1 , . . . , X t-τ 1 :t-τ 2 k ) + U (t), ( 1
)
where f is the function that we aim at determining, X t-τ 1 :t-τ 2 i are the lagged variables of the i th behavioral feature Xi, τ1 = 7s, τ2 = 4s, and U (t) represents the error of the model.

Evaluations and results

Prediction Procedure

In the current study, we focus on 6 ROIs: the left and right Fusiform Gyrus (FG) involved in face perception, the left and right Motor Cortex (MC) which support speech production, and the left and right Superior Temporal Sulcus (STS) involved in speech perception. Two evaluations are performed independently in two conditions: human-human and human-machine interactions. For each condition, the obtained data contain 13248 observations 1 in which the data associated to 4 participants (≈ 17%) are kept as test set. The classifiers used are from the Scikit-learn library [START_REF] Pedregosa | Scikit-learn: Machine learning in python[END_REF]: Support-Vector Machine (SVM), Random Forest (RanForest), and the Logistic Regression (Lo-gReg). Since the predictive variables are in form of sequences, we also used the Long Short Term Memory (LSTM) network from the Tensorflow library [START_REF] Abadi | Tensorflow: A system for large-scale machine learning[END_REF]. A baseline classifier is used with 3 strategies: a stratified way by generating random predictions regarding the distribution of the training data, a uniform way by generating random predictions uniformly, and the last one based on the most frequent label.

Brain activation does not follow the same distribution in all ROIs. For example some ROIs may be activated rarely during conversations depending on the situation. This may cause imbalanced data, and can affect the classification quality. To handle this issue, we applied the ADASYN algorithm on the training data [START_REF] He | Adasyn: Adaptive synthetic sampling approach for imbalanced learning[END_REF], which generates synthetic observations taking into account the distribution of the data. Then, a 10-foldcross-validation is applied on training data to find the appropriate parameters of the classifiers and avoiding over-fitting. This is conducted on all classifiers except the LSTM network, because it takes a huge amount of time. For this specific model, we applied one training-test pass directly with a fixed architecture composed of one LSTM hidden layer and a fully connected output layer containing one neuron to provide one prediction each time using the sigmoid activation function. The network is trained using the ADAM optimization algorithm [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF], and the binary cross-entropy is used as loss function.

Feature selection is performed with the help the classifiers themselves by ranking features based on their weights. To evaluate the predictions, three classification measures are considered, the weighted recall, precision and F-score. We focus here on the weighted F-score.

Results

Table 2 shows the prediction results for both human-human and human-robot data. Overall, the Random Forest provides the best predictions with F-scores between 0.64 and 0.73. The Student's t-test is applied to test the equality (null hypothesis) of the means of the F-scores between the best and the baseline classifiers obtained by the 10-fold-cross-validation. This test is one of recommended methods to compare the performance of machine learning algorithms [START_REF] Dietterich | Approximate statistical tests for comparing supervised classification learning algorithms[END_REF]. The obtained p-values (the probability of the null hypothesis) are shown in Table 3. For MC and STS, the p-values are less than 0.01. The best F-scores are significantly better than the baseline F-scores at a significance threshold less than 1%. The right FG area are the most difficult to predict, where the p-value is equal to 0.005. between F-scores of best and baseline classifiers.

Discussion

The obtained results show promising F-scores, especially for MC and STS areas. However, it is not easy to get very accurate predictions perhaps because brain activities might depends on other factors that can not be recorded during this fMRI experiment.The statistical test used to compare the best classifier and the baseline can be applied to compare the results of a classifier with two different subsets of features. We performed that by comparing the F-scores of the best classifier with two conditions: the first one using the best set of features obtained via feature selection, and the second one using non related features, which belong to the set of non-selected features that are supposed to have no effect on the ROIs. We obtained very low p-values. Finally, we discuss the obtained dependencies between behavior and the brain activity. They are the output of the combination between feature selection and prediction, by selecting, for each ROI, the smallest subset of features yielding the prediction scores. These dependencies can be divided into two categories:

(i) Existing dependencies: our results confirm existing hypothesis related to MC and FG areas. For left and right MC, only the speech activity of the participant is selected for both human-human and human-robot interactions. For left and right FG, the selected features are: variables characterizing where the participant is looking (face, eye or mouth), head movements, facial expressions of the interlocutor, and participant's saccades and eyes movements speed.

(ii) New dependencies: for left and right STS areas, in addition to existing hypothesis which include speech perception, we found three modalities involved in perception. The first one includes linguistic features of the interlocutor: speech activity, reaction time, lexical richness, sentiments (polarity and subjectivity). The second one includes facial features of the interlocutor: head movements, and facial expressions. The third modality includes a binary variable that represents the existence or not of the participant's saccades.

Conclusion

In this paper, we developed a framework for identifying causal relationships between brain activity and behavioral features for bidirectional non-controlled conversations. Evaluations are made on ROIs involved in speech perception and production, and face perception. The results show that the obtained predictions are significantly better than those obtained with the baseline model, and the obtained dependencies confirm hypothesis about the relationship between behaviors and the functional brain areas. Importantly, new dependencies are found for the STS area.

Figure 1 :

 1 Figure 1: The experimental design.

Figure 2 :

 2 Figure 2: A schema of the used multimodal prediction process.

Table 2 :

 2 Prediction results on test data. The F-scores of classifiers are provided for both human-human and human-robot interactions.

	ROIs		F-scores (Human-human)			F-scores (Human-machine)
		LogReg	LSTM	RanForrest SVM	baseline	LofReg	LSTM	RanForrest SVM	baseline
	Left FG	0.59	0.65	0.66	0.57	0.54	0.63	0.69	0.69	0.62	0.55
	Left MC	0.70	0.67	0.72	0.71	0.54	0.68	0.64	0.72	0.72	0.55
	Left STS	0.70	0.70	0.72	0.71	0.53	0.65	0.65	0.66	0.66	0.51
	Right FG	0.64	0.61	0.64	0.59	0.53	0.65	0.65	0.65	0.60	0.54
	Right MC	0.71	0.65	0.73	0.73	0.53	0.68	0.64	0.73	0.73	0.53
	Right STS 0.68	0.68	0.69	0.65	0.53	0.66	0.60	0.68	0.68	0.54

Table 3 :

 3 The obtained p-values of the statistical test (T-test)

The processed datasets, the implementations, and the detailed results are available in https://github.com/Hmamouche/ NeuroTSConvers, last accessed on

28/07/2020.