Marisa Mena
email: mmena@iconcologia.net

Miren Taberna

Sara Tous

Sandra Marquez

Omar Clavero

Beatriz Quiros

Belen Lloveras

Maria Alejo

Xavier Leon

Miquel Quer

Silvia Bagué

Ricard Mesia

Julio Nogués

Montserrat Gomà

Anton Aguila

Teresa Bonfill

Carmen Blazquez

Marta Guix

Rafael Hijano

Montserrat Torres

Dana Holzinger

Michael Pawlita

Miguel Angel Pavon

Ignacio G Bravo

Silvia De Sanjosé

Francesc Xavier Bosch

Laia Alemany
email: lalemany@iconcologia.net

DOUBLE POSITIVITY FOR HPV-DNA/P16 INK4A IS THE BIOMARKER WITH STRONGEST DIAGNOSTIC ACCURACY AND PROGNOSTIC VALUE FOR HUMAN PAPILLOMAVIRUS RELATED OROPHARYNGEAL CANCER PATIENTS

Keywords: Human papillomavirus, Oropharyngeal cancer, Prognosis markers, Diagnostic accuracy, Survival

Background:The etiologic role of human papillomaviruses (HPV) in oropharyngeal cancer (OPC) is well established. Nevertheless, information on survival differences by anatomic sub-site or treatment remains scarce, and it is still unclear the HPVrelatedness definition with best diagnostic accuracy and prognostic value. Methods:We conducted a retrospective cohort study of all patients diagnosed with a primary OPCin four Catalonianhospitals from 1990 to 2013. Formalin-fixed, paraffinembedded cancer tissues were subjected to histopathological evaluation, DNA quality control, HPV-DNA detection, and p16 INK4a /pRb/p53/Cyclin-D1 immunohistochemistry. HPV-DNA positive and a random sample of HPV-DNA negative cases were subjected toHPV-E6*I mRNA detection. Demographic, tobacco/alcohol use, clinical and follow-up data werecollected. Multivariate models were used to evaluate factors associated with HPV positivity as defined by four different HPV-relatedness definitions. Proportionalhazards models were used to compare the risk of death and recurrence among HPVrelatedand non-related OPC. Results:788 patients yielded a valid HPV-DNA result. The percentage of positive cases was 10.9%, 10.2%, 8.5% and 7.4% for p16 INK4a , HPV-DNA, HPV-DNA/HPV-E6*ImRNA, and HPV-DNA/p16 INK4a , respectively. Being non-smoker or non-drinker wasconsistently associated across HPV-relatedness definitionswith HPV positivity. A suggestion of survival differences between anatomic sub-sites and treatments was observed. Double positivity for HPV-DNA/p16 INK4a showedstrongest diagnostic accuracy and prognostic value.

Conclusions:Double positivity for HPV-DNA/p16 INK4a , a test that can be easily implemented in the clinical practice, has optimal diagnostic accuracy and prognostic value.Our results have strong clinical implications for patients' classification and handling and also suggest that not all the HPV-related OPC behave similarly.

RESEARCH HIGHLIGHTS

 Six biomarkers of HPV-relatedness were assessed in788 oropharyngeal cancers  A low HPV attributable fraction in oropharyngeal cancer was observed  Double positivity for HPV-DNA/p16 INK4a showed strongest prognostic value INTRODUCTION About a decade ago the International Agency for Research on Cancer (IARC) established high-risk Human papillomavirus 16(HPV16)as a cause of oropharyngeal carcinoma (OPC) [START_REF] Iarc | IARC Monographs on the Evaluation of Carcinogenic Risks to Humans[END_REF]. Since then,increasing amount of information on the role of HPVs in OPC has been generated. The IARC estimates that approximately 29,000 new HPVrelated OPC cases occur every year, corresponding to 31% of the worldwide number of the overall incident OPCcases [START_REF] Plummer | Global burden of cancers attributable to infections in 2012: a synthetic analysis[END_REF]. These estimates, as well as previous meta-analyses assessing the quantitative contribution of HPV, found high geographic heterogeneity in HPV-attributable fractions (AFs) of OPC, ranging from less than 20% in some world regions, 24% in Southern Europe to more than 60% in North America [START_REF] Ndiaye | HPV DNA, E6/E7 mRNA, and p16INK4a detection in head and neck cancers: A systematic review and meta-analysis[END_REF][START_REF] Kreimer | Human papillomavirus types in head and neck squamous cell carcinomas worldwide: a systematic review[END_REF]. This low HPV-AF for OPC in Southern Europe has been recently confirmed in tworecent studies conducted by our group [START_REF] Castellsagué | HPV Involvement in Head and Neck Cancers: Comprehensive Assessment of Biomarkers in 3680 Patients[END_REF][START_REF] Martel | The role of HPV on the risk of second primary neoplasia in patients with oropharyngeal carcinoma[END_REF].

HPV-related OPC differs at clinical, epidemiological and molecular level to OPC caused by classic risk factors (i.e. tobacco and alcohol) [START_REF] Taberna | Human papillomavirus-related oropharyngeal cancer[END_REF]. The consistent observation of improved survival and better response to treatment of HPV-related OPC has stirred up the state-of-the-art of their management. Indeed,several clinical trialsof deescalation treatmentsare under evaluation, aimingto achievebetter results with less treatment-associated comorbidities [START_REF] Mesia | HPV-related oropharyngeal carcinoma de-escalation protocols[END_REF].However, the biological rationaleunderlyingthese strategies remains poorly understood,and most of schemes are extrapolated from HPV-negative OPC trials. Importantly, around 20% ofHPV-related patients still fail to treatment despite its good prognosis [START_REF] Taberna | Human papillomavirus-related oropharyngeal cancer[END_REF].

Diagnosis algorithms for HPV-related OPC are still under development. HPV-DNA detection alone is not sufficient to classify an OPC as HPV-driven since the presence of HPV-DNA could reflect a transient or non-related infection rather than a genuine HPV-driven oncogenic process [START_REF] Halec | Biological activity of probable/possible high-risk human papillomavirus types in cervical cancer[END_REF][START_REF] Jung | Biological and clinical relevance of transcriptionally active human papillomavirus (HPV) infection in oropharynx squamous cell carcinoma[END_REF][START_REF] Holzinger | Viral RNA patterns and high viral load reliably define oropharynx carcinomas with active HPV16 involvement[END_REF]. Additionally, the detection of high cellular p16 INK4a expressionby immunohistochemistry (IHC)is the most widely implemented technique in the clinical setting, but is not specific for HPV activity in these tumours [START_REF] Smeets | A novel algorithm for reliable detection of human papillomavirus in paraffin embedded head and neck cancer specimen[END_REF][START_REF] Pannone | Evaluation of a combined triple method to detect causative HPV in oral and oropharyngeal squamous cell carcinomas: p16 Immunohistochemistry, Consensus PCR HPV-DNA, and In Situ Hybridization[END_REF]. Indeed, it has been demonstrated that patients with p16 INK4a high expressionbut HPV-DNA-negative OPC show a significantly less favourable survival than patients with p16 INK4a high expression and HPV-DNA-positive tumours [START_REF] Rietbergen | Human papillomavirus detection and comorbidity: Critical issues in selection of patients with oropharyngeal cancer for treatment De-escalation trials[END_REF][START_REF] Prigge | Diagnostic accuracy of p16INK4a immunohistochemistry in oropharyngeal squamous cell carcinomas: A systematic review and meta-analysis[END_REF], indicating that p16 INK4a high expression alone may not accurately classify HPV-related OPC patients.The combination of HPV-DNA detection and p16 INK4a IHC is starting to be recommended to diagnose HPV-related OPCs [START_REF] Prigge | Diagnostic accuracy of p16INK4a immunohistochemistry in oropharyngeal squamous cell carcinomas: A systematic review and meta-analysis[END_REF]. Nevertheless, there is still limited information about the accuracy and prognostic value of this combination of biomarkers.

It is imperative to identify the best HPV-relatedness definition for HPV causality and prognosis in OPC. This is a prerequisite to provide a sound approach to study differences in survival of HPV-related OPCby factors such as anatomical subsite [START_REF] Garnaes | Double positivity for HPV DNA/p16 in tonsillar and base of tongue cancer improves prognostication: Insights from a large population-based study[END_REF][START_REF] Marklund | Prevalence of human papillomavirus and survival in oropharyngeal cancer other than tonsil or base of tongue cancer[END_REF]and bytreatment [START_REF] Mirghani | Oropharyngeal cancer: Relationship between epidermal groth factor receptor alterations and human papillomavirus status[END_REF].

In an attempt to elucidate these gaps, weconducted astudy in OPC to assess the association of different HPV-relatedness definitions with patients' overall survival (OS)and progression-free survival (PFS), stratified by anatomical sub-site or treatment.

METHODS

Study design and population

We designeda retrospective cohort study of all patients diagnosed with a primary OPC in four hospitals of Catalonia from 1990 to 2013 (Catalan Institute of Oncology-ICO-Hospital Universitari de Bellvitge, Hospital de Sant Pau, Hospital del Mar and Hospital ParcTaulí).Protocols were approved by the ethics committee of each participating hospitals.

Cancer cases were identified from medical records/pathology reports of the centres of origin. We included cases that fulfilled the following criteria: to be diagnosed with primary invasive cancer of the oropharynx (any histology; codes from the International Classification of Diseases for Oncology version 3:

C01.9,C02.4,C05.1,C05.2,C09,C10,C14.2),and to have access to medical records on demographic and clinical information.

From all eligible cases, we reviewed medical records of the patients and accessed information on demographics, smoking and alcohol consumption, clinical and follow-up data; andformalin-fixed paraffin embedded (FFPE) tumour samples from the diagnosis previous to treatment when available.

In order to assess potential carryover HPV contamination at the local level, we additionally includeda set of control samples selected by local investigators (5% of the number of cases evaluated, corresponding to tissue samples of patients with diagnoses non-related with HPVprocessed in the same laboratory).

FFPE Blocks Processing and Histopathological Evaluation

All specimens processing was centralized at ICO. FFPE blocks were re-embedded whenever necessary. First and last sections were used for histopathological evaluationafter hematoxylin and eosin (H&E) staining. Two in-between sections were used for HPV-DNA testing, genotypingand E6*ImRNA detection; four additional slides were obtained to assess expression of cellular proteins by IHC. A block was classified as "adequate" for HPV testing if invasive cancer was observed in the two H&E stained sections of the specimen. Pathology review was performed blind with respect to the original local diagnosis and followed a pre-established algorithm for diagnostic consensus involving three pathologists, as reported elsewhere [START_REF] Castellsagué | HPV Involvement in Head and Neck Cancers: Comprehensive Assessment of Biomarkers in 3680 Patients[END_REF]. Pathological classification was based on the World Health Organization pathological criteria for head and neck cancer [START_REF] Barnes | World Health Organization Classification of Tumours[END_REF].

FFPE blocks were processed under strict conditions of pre/post polymerase chain reaction (physical separation), and blank paraffin blocks were systematically tested in parallel to serve as sentinels for contamination as previously published [START_REF] De Sanjose | Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study[END_REF].

HPV-DNA Detection and Genotyping

The detailed methods used for HPV-DNA detection and genotyping have been reported elsewhere [START_REF] Alemany | Human papillomavirus DNA prevalence and type distribution in anal carcinomas worldwide[END_REF].Briefly, we used a PCR with the consensus primers SPF 10 PCR and a DNA enzyme immunoassay (DEIA) to test for the presence of HPV-DNA. Virus genotyping was performed using reverse hybridization line probe assay (LiPA25_v1) on all samples testing positive for viral DNA, targeting 25 HPV types with different oncogenic risk (Laboratory Biomedical Products Rijswijk, The Netherlands). DNA quality was evaluated in all HPV-DNA negative samples by testing for thetubulin-β gene [START_REF] Alemany | Human papillomavirus DNA prevalence and type distribution in anal carcinomas worldwide[END_REF]. All DEIA and LiPA25_v1 assays were performed at ICO.

HPV-E6*I mRNA Detection

All HPV-DNApositive samples underwent RNA extraction and HPV-E6*I mRNA detection at DKFZ, Heidelberg, Germany [START_REF] Halec | Biological evidence for a causal role of HPV16 in a small fraction of laryngeal squamous cell carcinoma[END_REF]. Briefly, the assays target a total of 20

Immunohistochemistry

Protein expression patterns were evaluated for p16 INK4a , pRb, p53, and Cyclin-D1 in all samples, independently of HPV results. All IHC assays were all performed at Hospital General de L'Hospitalet, L'Hospitalet de Llobregat, Spain, under the manufacturer's standards: Roche mtm Laboratories AG (Heidelberg, Germany) for p16 INK4a , Vision Biosystems Novocastra (Newcastle, USA) for pRb, and Dako (Denmark) for p53 and Cyclin-D1. We used the predefined algorithm developed by Halec and colleagues [START_REF] Alemany | Human papillomavirus DNA prevalence and type distribution in anal carcinomas worldwide[END_REF]to determine the cutoff values for high vslow expression of pRb, p53, and Cyclin-D1. For p16 INK4a , the intensityof nuclear and cytoplasmic staining within the tumourswas scored and those with a strong staining of>70% were considered p16 INK4a high [START_REF] Westra | Detection of human papillomavirus (HPV) in clinical samples: Evolving methods and strategies for the accurate determination of HPV status of head and neck carcinomas[END_REF].The expected pattern for HPV-relatedcancers was high expression of p16 INK4a and low expressionofthe other three cellular markers.

Statistical Analyses

Cancer samples having tested negative for both viral and human DNA were excluded from the analyses. In line with work from several authors [START_REF] Halec | Biological evidence for a causal role of HPV16 in a small fraction of laryngeal squamous cell carcinoma[END_REF], we established that in order to explore algorithms to classify an OPC as HPV-relatedwe needed to consider biomarkers of HPV infection (HPV-DNA detection), biomarkers of transcriptional activity of HPV oncogenes (HPV-E6*I mRNA), and surrogate biomarkers of HPV-related cellular transformation (p16 INK4a , pRb, p53, andCyclin-D1). We used HPV-mRNA positivity as the gold standardforviral activity. We assumed that 90% of HPV-DNA (3), and all of them were primary tumors. The tonsil was the most common anatomical sub-site (40.0%). After adjusting for significant co-variates, HPV-related patients were significantly more likely to be non-smokers and non-drinkers andto have a SCC of the tonsil, consistently across the four HPV-relatedness definitions analyzed. Association of HPV-positivity and female gender was observed in all univariate but none multivariate analyses.

As described in table S3a, double positivity for HPV-DNA/p16 INK4a was the biomarker combination that showed the highest AUC. Among surrogate biomarkers of HPVrelated cellular transformation alone, p16 INK4a high expression was the one that showed best accuracy for diagnosis. Best accuracy parameters were observed in tonsillar cancers (table S3b).

We Hazard ratios (HR)for death and for recurrence by HPV status according to the four proportional hazards model for death for all anatomical sites. This interaction was not consistent across the four HPV-relatedness definitions and did not substantially improve the model. Thus, it was not further considered in the model. However, we explored the interaction further by creating a combined variable of HPV-status (as defined by double positivityfor HPV-DNA/p16 INK4a ) and tobacco use and examining the OS of each combination (figure S6), as well as stratifying the analyses by HPV status (tables S5a and S5b). Age was a prognostic factor for death in both HPV-positive and HPV-negative patients, consistently for all HPV-relatedness definitions. However, tobacco use was only a prognostic factor for death in HPV-positive (for all HPVrelatedness definitions with the exception of double positivity for HPV-DNA/p16 INK4a ), but not in HPV-negative cases. On the other hand, stage and treatment scheme were prognostic factors in HPV-negative but not HPV-positive cases (with the exception of high expression of p16 INK4a for treatment). Adjusted HRs for death were also examined for all cellularprotein biomarkers and their combinations (table S4).A better OS was observed for positivity to all markers, either individually or combined, except for low pRband/or p53 expression.Again HPV-DNA/p16 INK4a showed the strongest association with survival.

DISCUSSION

Mounting evidence supports the etiologic role of oncogenic HPVs in certain OPCs and the potential implications in the management of HPV-related patients. Our knowledge remains however incomplete regarding differences in prognosis by anatomic sub-site or treatment, or about the differential performance in terms of diagnostic accuracy and prognostic values between HPV-related biomarkers that can be easily implemented in the clinical setting.

To the best of our knowledge, this study represents the first attempt to address jointly all these issues in a large retrospective series of unselected patients.In an era of deescalation clinical trials,this information is crucial in order to unequivocally identify patients who can really benefit from de-escalate protocolsand to avoidworsening their outcomes.

The epidemiology of HPV-related OPC in our cohort differed in some aspects from what is observed in other high-income countries.HPV-AFs were slightly higherin women than in men, as has already been observed in other series [START_REF] Castellsagué | HPV Involvement in Head and Neck Cancers: Comprehensive Assessment of Biomarkers in 3680 Patients[END_REF], in contrast with what is observed in the United Statesin cohorts from the same time periods [START_REF] Gillison | Epidemiology of Human Papillomavirus-Positive Head and Neck Squamous Cell Carcinoma[END_REF],. This discrepancy may reflect distinct temporal, geographical,and sociodemographic trendsin population exposure toboth tobaccouse and/or oral HPV infection, leading to arapid shift in the epidemiology of HPV-relatedOPC.

We examined the HPV-diagnostic accuracy of several biomarkers with a previously validated robust and comprehensive methodology [START_REF] Castellsagué | HPV Involvement in Head and Neck Cancers: Comprehensive Assessment of Biomarkers in 3680 Patients[END_REF]. In line with our previous results [START_REF] Castellsagué | HPV Involvement in Head and Neck Cancers: Comprehensive Assessment of Biomarkers in 3680 Patients[END_REF] and a recent meta-analysis [START_REF] Prigge | Diagnostic accuracy of p16INK4a immunohistochemistry in oropharyngeal squamous cell carcinomas: A systematic review and meta-analysis[END_REF], doublepositivity for HPV-DNA/p16 INK4a showed higher AUCsthan any other combinations of biomarkers. Importantly, the double testing for HPV-DNA/p16 INK4a can be easily implemented in the clinical setting.

We examined the prognostic value of HPV-related biomarkers in OPC as defined by four different HPV-relatedness definitions. We found that HPV-positivity had stronger prognostic value than stage (7 th edition TNM), consistently for all tests, since HPVrelatedlocally advanced OPC patients had better OS and PFS thanstage I-II HPV-non-relatedones. However, double positivity for HPV-DNA/p16 INK4a was the only biomarker showing the best prognostic value for HPV-relatedpatientsas also reported in a recent meta-analysis [START_REF] Coordes | Meta-analysis of survival in patients with HNSCC discriminates risk depending on combined HPV and p16 status[END_REF].

When examining the prognostic value of double positivity for HPV-DNA/p16 INK4a in locally advanced OPC patients by their standard treatments, we found that HPV-related OPCs showedimproved OS for all treatment schemes with the exception of those who underwent bioradiotherapy. A recent study also suggested better outcomes in locally advanced HNSCC patients receiving concurrentcisplatin over cetuximab(anti-EGFR therapy)regardless of HPV/p16 INK4a status [START_REF] Ou | Concurrent chemoradiotherapy with cisplatin or cetuximab for locally advanced head and neck squamous cell carcinomas: Does human papilloma virus play a role?[END_REF].Thesefindings have strong clinical implications becausecetuximabis being exploredas an alternative to cisplatin when given concurrently with radiotherapy as one main de-escalation strategies for HPV-relatedOPC patientsaiming to reduce toxicities [START_REF] Mesia | HPV-related oropharyngeal carcinoma de-escalation protocols[END_REF].However, our results should be interpreted with caution since the number of HPV-positive patients treated with bioradiotherapy was very small and thus underpowered to draw firm conclusions.

Noteworthy, anti-EGFR therapies are not currently recommended for treatment of anogenital HPV-related cancer [START_REF] Nogueira-Rodrigues | Phase 2 trial of erlotinib combined with cisplatin and radiotherapy in patients with locally advanced cervical cancer[END_REF][START_REF] Schilder | A phase II trial of erlotinib in recurrent squamous cell carcinoma of the cervix: a Gynecologic Oncology Group Study[END_REF].Todate, the available evidencesupporting the use of anti-EGFR therapies in HPV-related OPC is therefore not conclusive; and we must wait for results of ongoing de-escalation clinical trials.

We also wanted to elucidate the differences in OS and PFS according to HPV-status by anatomical sub-sites within the oropharynx. For all four HPV-relatedness definitions herein evaluated, HPV hadsignificant prognostic value only in tonsillarcarcinoma, and double positivity for HPV-DNA/p16 INK4a was the biomarker with best prognostic value.

This has also been reported for OS in a recent study of a large cohort of Danish patients [START_REF] Garnaes | Double positivity for HPV DNA/p16 in tonsillar and base of tongue cancer improves prognostication: Insights from a large population-based study[END_REF]. However, this Danish study found equivalent results forBOT carcinoma, while in our case, although HPV-relatedBOT carcinoma displayed higher OS with lowest mortality observed for double positivity for HPV-DNA/p16 INK4a , the results were not significantly different. This could be partially explained by the lower HPV prevalence in BOT carcinoma in our Spanish cohort (5.8%)as compared to the Danish one (46%).On the other hand, our results on other locations than tonsil or BOT were in line with previous results from Sweden [START_REF] Marklund | Prevalence of human papillomavirus and survival in oropharyngeal cancer other than tonsil or base of tongue cancer[END_REF], where HPV-DNA and p16 INK4a status had no impact on clinical outcome in OPCs other than tonsil or BOT. However, the HRs of around 0.5 in these locations were in the same direction as those for tonsillar cancers, as it was observed for BOT cancers, despite their wide confidence intervals. Again, these results should be interpreted with caution due to small number of cases.

When we examined adjusted HRs for death stratified by HPV status, we found differences between HPV-positive and negative OPC patients. The lack of prognostic advantage of non-smokers among HPV-negative patients could be partially explained by the limitation of self-reported data and warrant further research with biomarkers of tobacco use. On the other hand, the fact that stage was not a prognostic factor in HPVpositive patients evidences the limitation of the 7 th edition of TNM to accurately classify HPV-positive OPCs.

Finally, when we evaluated the prognostic value of cellular biomarkers of protein expression alone or combined, none of them showed better HR than double positivity for HPV-DNA/p16 INK4a , but we found better OS for p16 INK4a overexpression alone than previous publications [START_REF] Holzinger | Identification of oropharyngeal squamous cell carcinomas with active HPV16 involvement by immunohistochemical analysis of the retinoblastoma protein pathway[END_REF]. The discrepancy may be due to the differences in the difficulties for comparing cut-off points for p16 INK4a expression between studies.

Our study has several limitations. The retrospective nature of our cohort may have hampered the thorough characterization of the patients according to risk factors such as tobacco-alcohol use, since this kind of information could only be partially obtained from medical records. Also, paraffinblocks were not available at diagnosisfor an important number of cases, notably BOT carcinoma,alocation particularlymore difficult to biopsy, as well as forcases from older periods. For HPV-diagnostic accuracy analyses, we assumed that the 90% of HPV-DNA negative cases not tested for HPV-E6*I mRNA were mRNA negative. Our classification of other sub-sites than tonsil or BOT comprised many different locations, including oropharynx specified or overlapping lesions that could include also tonsil and BOT. In addition, we have a low rate of HPV-related OPC patients included in the analysis (i.e. Kaplan-Meir analysis by treatment), because HPV-related OPC AFs in our country is still low in comparison with other geographic regions like United States or Northern Europe.

CONCLUSION

Our findings from a large cohort of unselected OPC Spanish patients provide robust evidence that double positivity for HPV-DNA/p16 INK4a has optimal diagnostic accuracy and prognostic value as compared with a broad battery of HPV-related biomarkers.Noteworthy, this is a test that can be easily implemented and used in the clinical practice. Moreover, our results suggest that one of the main de-escalation treatment strategies for HPV-relatedOPC being currently evaluated in clinical trials (anti-EGFR/radiotherapy)may not be appropriate for HPV-related patients. Our results also suggest that there may be differences between OPC sub-sites regarding diagnostic accuracy and prognostic value of HPV-related biomarkers and thus, the need to address the management of the patients accordingly. Finally, our results have strong clinical implications as they contribute to a better classification of the patients to provide them with the best personalized treatment. Panel "b" showed Kaplan-Meier curve for patients who underwent induction chemotherapy followed by chemo-radiotherapy or bioradiotherapy. Panel "c"

CONFLICT OF INTEREST

showed Kaplan-Meier curve for patients who underwent cisplatin-radiotherapy.

Panel "d" showed Kaplan-Meier curve for patients who underwent cetuximabradiotherapy. Improved OS was not observed on panel "d".

RT: radiotherapy; CT: chemotherapy; iCT: induction chemotherapy; bio-RT: bioradiotherapy (radiotherapy-cetuximab)

Ref.

Sub-site

Tonsil BOT Tonsil & BOT Others 315 (

  examined the crudeOS and PFS of OPC patients based on Kaplan-Meier curves stratified by HPV positivity according to the four different HPV-relatedness definitions(figure 1 and figure S2, respectively). Double positivity for HPV-DNA/p16 INK4a showedthe best prognostic value. Moreover, it classified better HPVrelated casesand showed improved five years OS and PFS irrespective of having an early or locally advanced OPC stage (figures S3 and S4).However, when examiningcrude OS of locally advanced OPC patients based on Kaplan-Meier curves stratified by standard treatments, better OS were not observed for patients'doublepositive for HPV-DNA/p16 INK4a treated with bioradiotherapy (anti-EGFR concomitant with radiotherapy), as it was observed for other treatments (figure 2).Improved PFSwereobserved in patients' double positive for HPV-DNA/p16 INK4a for all treatment schemes herein evaluated (figureS5), although those were not statistically significant. We also analyzed crude OS of OPC patients according to the four possible combinations of HPV-DNA detection and p16 INK4a expression results. Pairwise analyses showed that only patients double positive for HPV-DNA/p16 INK4a had a statistically better OS compared to any other combination of those biomarkers(figure3). Importantly, HPV-DNA-negative/p16 INK4a positive patientsdisplayed OS similar to HPV-DNAnegative/p16 INK4a -negativeor HPV-DNA-positive/p16 INK4a -negative ones.

Figure 1 :

 1 Figure 1: 5 years Overall Survival by HPV status according to four different HPV-

Figure 2 :

 2 Figure 2: 5 years Overall Survival by standard treatment for locally advanced OPC

Figure 3 :

 3 Figure 3: 5 years Overall Survival by HPV-DNA detection and p16 INK4a high

Figure 1 .

 1 Figure 1. 5 years Overall Survival by HPV status according to four different HPV-relatedness definitions

Figure 2 .Figure 3 .

 23 Figure 2. 5 years Overall Survival by standard treatment for locally advanced OPSCC patients (stages III, IVa and IVb) and HPV status according to HPV-DNA positivity and p16 INK4a overexpression

Figure 1 :

 1 Figure 1: 5 years Overall Survival by HPV status according to four different HPV-relatedness definitions. Legend: Data on 5 years Overall Survival by HPV status according to four different HPV-relatedness definitions. Panel "a" showed Kaplan-Meier curve for HPV/DNA detection. Panel "b" showed Kaplan-Meier curve for HPV/DNA and HPV mRNA detection. Panel "c" showed Kaplan-Meier curve for p16 INK4a detection. Panel "d" showed Kaplan-Meier curve for double positivity for HPV-DNA/p16 INK4a . Panel "d", double positivity for HPV-DNA/p16 INK4a showed the best prognostic value, since it classified better HPV-related cases and showed improved 5 years OS.

Figure 2 :

 2 Figure 2: 5 years Overall Survival by standard treatment for locally advanced OPC patients (stages III, IVa and IVb) and HPV status according to double positivity for HPV-DNA/p16 INK4a . Legend: Data on 5 years Overall Survival by standard treatment for locally advanced OPC patients (stages III, IVa and IVb) and HPV status double positivity for HPV-DNA/p16 INK4a . Panel "a" showed Kaplan-Meier curve for patients who underwent surgery with/without adjuvant chemo-radiotherapy.

Figure 3 :

 3 Figure 3: 5 years Overall Survival by HPV-DNA detection and p16 INK4a high expression. Legend: Pairwise analyses showed that only patients double positive for HPV-DNA/p16 INK4a had a statistically better OS compared to any other combination of those biomarkers.

  carcinoma; SD: Standard deviation; H: Hospital; SCC: Squamous cell carcinoma; BOT: Base of the tongue; RT: Radiotherapy; CT: Chemotherapy; iCT: Induction chemotherapy. a Column percentage. b Row Percentage. c Adjusted by gender, sub-site, tobacco and alcohol consumption and treatment. d Includes symptomatic treatment (n=60). e Includes SCC sarcomatoid, undifferentiated carcinoma and neuroendocrine carcinoma. *Not considered in the multivariate model as explained in Materials and Methods.

FIVE

a

  Adjusted by age, gender, tobacco consumption, stage, sub-site and treatment.

Table 1 .

 1 Hazardratios for deathandrecurrence for OPC patients, all sitesandstratifiedbyanatomicalsub-site (stageVIcpatientsareexcluded).

			STATEMENT
	RM has received personal feesand non-financialsupportfrom Merck, and personal
	feesfromAstraZenecaand MSD. MT has received non-financialsupportfrom Merck
	andArchimedes,	and	personal	feesfrom	Sanofi	Pasteur	and	Merck.
	CancerEpidemiologyResearchProgram (LA MM SdS FXB SM ST BQ OC MT MTo MP)
	has receivedsponsorship for grantsfrom Merck andco.Therest of authorshavedeclared
	no conflicts of interest.

STUDY GROUP

Catalan

Institute of Oncology/Hospital Universitari de Bellvitge: Laia Alemany, Xavier Bosch, Vanesa Camon, Omar Clavero, Ana Esteban, Yolanda Florencia, Montserrat Gomà, Alicia Lozano, Manel Maños, Antonio Marí, Marisa Mena, Ricard Mesía, Julio Nogués, Miquel Ángel Pavón, Beatriz Quirós, Silvia de Sanjosé, Miren Taberna, Montserrat Torres, Sara Tous, GriseldaVenturas, MarlenyVergara; Hospital general de L'Hospitalet: María Alejo, Ana Mulero; Hospital de la Santa Creu i Sant Pau: Silvia Bagué, Jacinto García, Xavier León, Miquel Quer; Hospital del Mar: Marta Guix, Rafael Hijano, Belén Lloveras; Hospital Universitari Parc Taulí: Antón Aguilà, María Rosa Bella, Carmen Blazquez, Teresa Bonfill. DKFZ: Dana Holzinger, Michael Pawlita. (SLT002/16/00404), Asociación Española Contra el Cáncer (personal grant to LA), Rio Hortega-SEOM (ISCIII-SpanishSociety of Medical Oncology) (personal grant to MT),

33 (0.18-0.63) Ref. 0.32 (0.16-0.62)

	259 / 87	Ref.
	49 / 6	0

.26 (0.12-0.60) Ref. 0.18 (0.07-0.45)

								151 / 37	Ref.	-	262 / 63	Ref.	-
								14 / 2	0.50 (0.12-2.1)		16 / 2	0.37 (0.09-1.5)
	DNA / mRNA
	Other	704 / 197	Ref.	Ref.	263 / 89	Ref.	Ref.	152 / 37	Ref.	-	270 / 64	Ref.	-
	+ / +	66 / 7	0.27 (0.13-0.58)	0.26 (0.12-0.57)	45 / 4	0.18 (0.07-0.50)	0.12 (0.04-0.36)	13 / 2	0.55 (0.13-2.3)		8 / 1	0.36 (0.05-2.6)
	p16
	Low	685 / 193	Ref.	Ref.	252 / 87	Ref.	Ref.	152 / 38	Ref.	-	263 / 62	Ref.	-
	High	83 / 11	0.36 (0.20-0.67)	0.35 (0.19-0.67)	55 / 6	0.24 (0.10-0.54)	0.16 (0.06-0.40)	12 / 1	0.29 (0.04-2.1)		15 / 3	0.69 (0.22-2.2)
	DNA / p16
	Other	712 / 200	Ref.	Ref.	267 / 91	Ref.	Ref.	155 / 38	Ref.	-	271 / 64	Ref.	-
	+ / high	58 / 4	0.17 (0.06-0.46)	0.16 (0.06-0.44)	41 / 2	0.10 (0.02-0.39)	0.06 (0.01-0.26)	10 / 1	0.32 (0.04-2.4)		7 / 1	0.41 (0.06-3.0)

a Adjustedbyage, tobaccoconsumption, stageandtreatment. b Adjustedbytobaccoconsumption, stageandtreatment.

Table 1 .

 1 Hazard ratios for death and recurrence for OPC patients, all sites and stratified by anatomical sub-site (stage IVc patients are excluded).

	FIVE-YEARS OVERALL SURVIVAL
			ALL SITES			TONSIL			BASE OF TONGUE			OTHERS
	HPV BIOMARKER	cases /	HR crude	HR adjusted a	cases /	HR crude	HR adjusted a	cases /	HR crude	HR adjusted	cases /	HR crude	HR adjusted
		deaths	(95%CI)	(95%CI)	deaths	(95%CI)	(95%CI)	deaths	(95%CI)	(95%CI)	deaths	(95%CI)	(95%CI)
	DNA -+	691 / 426 79 / 23	Ref. 0.37 (0.24-0.56)	Ref. 0.37 (0.24-0.58)	259 / 165 49 / 11	Ref. 0.27 (0.15-0.50)	Ref. 0.24 (0.12-0.48)	14 / 6 151 / 98	0.53 (0.23-1.2) Ref.	-	16 / 6 262 / 149	0.51 (0.23-1.2) Ref.	-
	DNA / mRNA -or + / -+ / +	704 / 434 66 / 15	Ref. 0.27 (0.16-0.46)	Ref. 0.26 (0.15-0.45)	263 / 168 45 / 8	Ref. 0.20 (0.10-0.41)	Ref. 0.18 (0.08-0.39)	13 / 5 152 / 99	0.47 (0.19-1.2) Ref.	-	8 / 2 270 / 153	0.30 (0.07-1.2) Ref.	-
	p16 Low High	685 / 422 83 / 26	Ref. 0.41 (0.27-0.61)	Ref. 0.32 (0.21-0.50)	252 / 159 55 / 16	Ref. 0.36 (0.22-0.61)	Ref. 0.26 (0.14-0.46)	152 / 99 12 / 5	Ref. 0.59 (0.24-1.5)	-	15 / 5 263 / 150	0.45 (0.19-1.1) Ref.	-
	DNA / p16 -/ low or high + / high	712 / 439 58 / 10	Ref. 0.20 (0.11-0.38)	Ref. 0.21 (0.11-0.40)	267 / 171 41 / 5	Ref. 0.13 (0.05-0.33)	Ref. 0.11 (0.04-0.29)	10 / 3 155 / 101	0.38 (0.12-1.2) Ref.	-	7 / 2 271 / 153	0.35 (0.09-1.4) Ref.	-
	FIVE-YEARS DISEASE-FREE SURVIVAL
		cases /	HR crude	HR adjusted b	cases /	HR crude	HR adjusted b	cases /	HR crude	HR adjusted	cases /	HR crude	HR adjusted
		recurrences	(95%CI)	(95%CI)	recurrences	(95%CI)	(95%CI)	recurrences	(95%CI)	(95%CI)	recurrences	(95%CI)	(95%CI)
	DNA -+	691 / 194 79 / 10	Ref. 0.33 (0.18-0.63)	Ref. 0.32 (0.16-0.62)	259 / 87 49 / 6	Ref. 0.26 (0.12-0.60)	Ref. 0.18 (0.07-0.45)	14 / 2 151 / 37	0.50 (0.12-2.1) Ref.	-	16 / 2 262 / 63	0.37 (0.09-1.5) Ref.	-
	DNA / mRNA
	-or + / -	704 / 197	Ref.
	+ / +	66 / 7	0

.27 (0.13-0.58) Ref. 0.26 (0.12-0.57)

	263 / 89	Ref.
	45 / 4	0

.18 (0.07-0.50) Ref. 0.12 (0.04-0.36)

			152 / 37	Ref.	-	270 / 64	Ref.	-
			13 / 2	0.55 (0.13-2.3)		8 / 1	0.36 (0.05-2.6)
	p16
	Low	685 / 193	Ref.
	High	83 / 11	0

.36 (0.20-0.67) Ref. 0.35 (0.19-0.67)

	252 / 87	Ref.
	55 / 6	0

.24 (0.10-0.54) Ref. 0.16 (0.06-0.40)

  Adjusted by age, tobacco consumption, stage and treatment. b Adjusted by tobacco consumption, stage and treatment.

								152 / 38	Ref.	-	263 / 62	Ref.	-
								12 / 1	0.29 (0.04-2.1)		15 / 3	0.69 (0.22-2.2)
	DNA / p16 -/ low or high + / high	712 / 200 58 / 4	Ref. 0.17 (0.06-0.46)	Ref. 0.16 (0.06-0.44)	267 / 91 41 / 2	Ref. 0.10 (0.02-0.39)	Ref. 0.06 (0.01-0.26)	10 / 1 155 / 38	0.32 (0.04-2.4) Ref.	-	7 / 1 271 / 64	0.41 (0.06-3.0) Ref.	-
	a

Table S1 .

 S1 Association of demographics and clinical characteristics of OPC patients included in the study and HPV positivity as defined by double positivity for HPV DNA/p16

				INK4a
		OPC	HPV-DNA detection AND p16 INK4a high expression
	Characteristics	samples (n = 788) No. (%) a	Positive (n = 58) No. (%) b	OR crude (95%CI)	OR adjusted c (95%CI)
	Age at diagnosis
	Mean (SD)	60.5 (10.5)	59.7 (14.3)	0.99 (0.97-1.0)	-
	Gender
	Male	702 (89.2)	40 (5.7)	Ref.	Ref.
	Female	85 (10.8)	18 (21.2)	4.5 (2.4-8.2)	1.7 (0.76-3.7)
	Center
	H Sant Pau	363 (46.1)	29 (8.0)	1.4 (0.55-3.4)
	H ICO-Bellvitge	241 (30.6)	18 (7.5)	1.3 (0.49-3.3)	-
	H Mar	100 (12.7)	6 (6.0)	Ref.
	H Parc Taulí	84 (10.7)	5 (6.0)	0.99 (0.29-3.4)
	Period of diagnosis
	1991-1994	87 (11.0)	4 (4.6)	Ref.
	1995-1999	111 (14.1)	4 (3.6)	0.78 (0.19-3.2)
	2000-2004	155 (19.7)	8 (5.2)	1.1 (0.33-3.9)	-
	2005-2009	276 (35.0)	25 (9.1)	2.1 (0.70-6.1)
	2010-2013	159 (20.2)	17 (10.7)	2.5 (0.81-7.6)
	Tobacco use
	Non smoker	82 (11.1)	29 (35.4)	18.5 (9.4-36.2)	8.1 (3.5-18.7)
	< 20 cigarettes/day	98 (13.3)	13 (13.3)	5.2 (2.4-11.1)	3.2 (1.4-7.6)
	≥ 20 cigarettes/day	557 (75.6)	16 (2.9)	Ref.	Ref.
	Alcohol consumption
	Non drinker	137 (18.5)	32 (23.
	< 100 grams/day	220 (29.7)
	≥ 100 grams/day	383 (51.8)

Table S2 .

 S2 Association of demographics and clinical characteristics of OPC patients included in the study and HPV positivity according to three different HPV-relatedness definitions

		OPC		HPV-DNA detection	HPV-DNA AND E6*I mRNA detection		p16 INK4a high expression
	Characteristics	samples (n = 788) a	Positive (n = 80) No. (%) b	OR crude (95%CI)	OR adjusted c (95%CI)	Positive (n = 86) No. (%) b	OR crude (95%CI)	OR adjusted c (95%CI)	Positive (n = 86) No. (%) b	OR crude (95%CI)	OR adjusted c (95%CI)
	Age at diagnosis
	Mean (SD)	60.5 (10.5)	60.3 (13.7)	1.0 (0.98-1.0)	-	61.1	1.0 (0.98-1.0)	-	61.1 (13.7)	1.0 (0.99-1.0)	-
						(13.7)
	Gender
	Male	702 (89.2)	57 (8.1)	Ref.	Ref.	62 (8.9)	Ref.	Ref.	62 (8.9)	Ref.	Ref.
	Female	85 (10.8)	23 (27.1)	4.2 (2.4-7.3)	1.80 (0.91,3.56)	24 (28.2)	4.2 (2.4-7.3)	1.7 (0.80-3.6)	24 (28.2)	4.1 (2.4-6.9)	1.9 (0.96-3.6)
	Center
	H Sant Pau	363 (46.1)	44 (12.1)	2.2 (0.89-5.2)		43 (11.8)	2.2 (0.89-5.2)		43 (11.8)	1.3 (0.63-2.9)
	H ICO-Bellvitge	241 (30.6)	22 (9.1)	1.6 (0.62-4.0)	-	24 (10.0)	1.6 (0.62-4.0)	-	24 (10.0)	1.1 (0.50-2.5)	-
	H Mar	100 (12.7)	6 (6.0)	Ref.		9 (9.1)	Ref.		9 (9.1)	Ref.
	H Parc Taulí	84 (10.7)	8 (9.5)	1.7 (0.55-5.0)		10 (11.9)	1.7 (0.55-5.0)		10 (11.9)	1.4 (0.52-3.5)
	Period of diagnosis
	1991-1994	87 (11.0)	8 (9.2)	Ref.		8 (9.2)	Ref.		8 (9.2)	Ref.
	1995-1999	111 (14.1)	5 (4.5)	0.47 (0.15-1.5)		12 (10.8)	0.47 (0.15-1.5)		12 (10.8)	1.0 (0.47-3.1)
	2000-2004	155 (19.7)	11 (7.1)	0.75 (0.29-2.0)	-	14 (9.0)	0.75 (0.29-2.0)	-	14 (9.0)	0.98 (0.39-2.4)	-
	2005-2009	276 (35.0)	33 (12.0)	1.3 (0.59-3.0)		30 (10.9)	1.3 (0.59-3.0)		30 (10.9)	1.2 (0.53-2.7)
	2010-2013	159 (20.2)	23 (14.5)	1.7 (0.71-3.9)		22 (14.0)	1.7 (0.71-3.9)		22 (14.0)	1.6 (0.68-3.8)
	Tobacco use
	Non smoker	82 (11.1)	33 (40.2)	11.1 (6.3-19.5)	4.81 (2.37-9.77)	31 (38.8)	11.1 (6.3-19.5)	6.4 (3.0-13.9)	31 (38.8)	8.6 (5.0-15.1)	4.3 (2.0-8.8)
	< 20 cigarettes/day	98 (13.3)	15 (15.3)	3.0 (1.5-5.7)	2.03 (0.99-4.16)	16 (16.3)	3.0 (1.5-5.7)	2.8 (1.2-6.1)	16 (16.3)	2.7 (1.4-5.0)	1.8 (0.92-3.7)
	≥ 20 cigarettes/day	557 (75.6)	32 (5.7)	Ref.	Ref.	38 (6.8)	Ref.	Ref.	38 (6.8)	Ref.	Ref.
	Alcohol consumption
	Non drinker	137 (18.5)	38 (27.7)	11.9 (6.0-23.6)	4.69 (2.06-10.71)	38 (27.9)	11.9 (6.0-23.6)	8.1 (2.9-22.4)	38 (27.9)	8.0 (4.5-15.4)	3.6 (1.7-7.7)
	< 100 grams/day	220 (29.7)	30 (13.6)	4.9 (2.4-9.8)	4.17 (2.02-8.61)	30 (13.7)	4.9 (2.4-9.8)	6.2 (2.4-16.1)	30 (13.7)	3.4 (1.8-6.4)	3.1 (1.6-5.9)
	≥ 100 grams/day	383 (51.8)	12 (3.1)	Ref.	Ref.	17 (4.4)	Ref.	Ref.	17 (4.4)	Ref.	Ref.
	Subsite
	Tonsil	315 (40.0)	49 (15.6)	3.1 (1.7-5.5)	2.54 (1.33-4.85)	56 (17.8)	3.1 (1.7-5.5)	5.1 (2.2-11.9)	56 (17.8)	3.4 (1.9-6.0)	3.0 (1.6-5.6)
	BOT	171 (21.7)	15 (8.8)	1.6 (0.77-3.3)	1.21 (0.54-2.73)	12 (7.1)	1.6 (0.77-3.3)	2.5 (0.92-6.6)	12 (7.1)	1.2 (0.55-2.6)	0.83 (0.36-1.9)
	Tonsil & BOT	19 (2.4)	0 (0.0)	-	-	1 (5.3)	-	-	1 (5.3)	0.87 (0.11-6.9)	0.83 (0.10-7.1)
	Others	283 (35.9)	16 (5.7)	Ref.	Ref.	17 (6.0)	Ref.	Ref.	17 (6.0)	Ref.	Ref.
	Stage (7 th edition TNM)
	I	60 (7.6)	1 (1.7)	Ref.
	II	100 (12.7)	6 (6.0)	3.8 (0.44-32.1)
	III	174 (22.2)	22 (12.6)	8
	IVa	358 (45.6)	47 (13.1)
	IVb	78 (9.9)	3 (3.8)
	IVc	15 (1.9)	1 (6.7)

.5 (1.1-64.8) 8.9 (1.2-65.9)

		Ref.	4 (6.7)	Ref.
		2.62 (0.28-24.43)	5 (5.0)	3.8 (0.44-32.1)
		7.74 (0.95-63.47)	21 (12.1)	8
		8.88 (1.11-71.02)	48 (13.5)
	2.4 (0.24-23.3)	2.53 (0.23-27.63)	5 (6.4)
	4.2 (0.25-71.6)	5.67 (0.27-120.77)	3 (20.0)

.5 (1.1-64.8) 8.9 (1.2-65.9)

								Ref.	4 (6.7)	Ref.	Ref.
								1.9 (0.19-19.5)	5 (5.0)	0.74 (0.19-2.9)	0.32 (0.07-1.5)
								5.7 (0.65-49.8)	21 (12.1)	1.9 (0.63-5.8)	1.5 (0.45-5.1)
								7.4 (0.88-62.9)	48 (13.5)	2.2 (0.76-6.3)	2.0 (0.62-6.4)
							2.4 (0.24-23.3)	2.5 (0.21-28.9)	5 (6.4)	0.96 (0.25-3.7)	0.95 (0.21-4.2)
							4.2 (0.25-71.6)	6.1 (0.25-71.6)	3 (20.0)	3.5 (0.69-17.7)	6.7 (1.1-42.2)
	Treatment
	RT	119 (15.5)	10 (8.4)	Ref.		12 (10.1)	Ref.		12 (10.1)	Ref.
	Surgery +/-CT/RT	185 (24.1)	19 (10.3)	1.3 (0.56-2.8)		18 (9.7)	1.3 (0.56-2.8)		18 (9.7)	0.96 (0.45-2.1)
	CT-RT (cisplatin)	119 (15.5)	21 (17.6)	2.3 (1.1-5.2)	-	20 (16.9)	2.3 (1.1-5.2)	-	20 (16.9)	1.8 (0.85-3.9)	-
	Bio-RT (cetuximab)	44 (5.7)	6 (13.6)	1.7 (0.59-5.1)		4 (9.1)	1.7 (0.59-5.1)		4 (9.1)	0.89 (0.27-2.9)
	iCT + CT-RT/Bio-RT Palliative treatment d	211 (27.4) 91 (11.8)	20 (9.5) 2 (2.2)	1.1 (0.52-2.5) 0.2 (0.05-1.2)		20 (9.5) 11 (12.1)	1.1 (0.52-2.5) 0.2 (0.05-1.2)		20 (9.5) 11 (12.1)	0.94 (0.44-2.0) 1.2 (0.51-2.9)
	Histology*
	SCC Conventional non keratinizing	205 (26.0)	28 (13.7)	2.6 (1.5-4.5)		23 (11.2)	2.81 (1.5-5.2)		27 (13.2)	2.3 (1.3-3.9)
	SCC Conventional keratinizing	511 (64.9)	29 (5.7)	Ref.		22 (4.3)	Ref.		32 (6.3)	Ref.	-
	SCC Basaloid, papillary, exophitic Others e	62 (7.9) 10 (1.3)	22 (35.5) 1 (10.0)	9.1 (4.8-17.4) 1.9 (0.23-15.1)		21 (33.9) 1 (1.00)	11.4 (5.8-22.4) 2.5 (0.30-20.4)		25 (40.3) 2 (20.0)	10.1 (5.4-18.8) 4.3 (0.85-21.4)
	Tumor differentiation*
	Grade 1	1 (0.1)	1 (100.0)	-		0 (0.0)	-		0 (0.0)	-
	Grade 2	317 (40.2)	19 (6.0)	Ref.		17 (5.4)	Ref.		17 (5.4)	Ref.	-
	Grade 3	470 (59.7)	60 (12.8)	2.3 (1.3-3.9)		69 (14.7)	2.3 (1.3-3.9)		69 (14.7)	3.1 (1.8-5.3)

OPC: Oropharyngeal carcinoma; SD: Standard deviation; H: Hospital; SCC: Squamous cell carcinoma; BOT: Base of the tongue; RT: Radiotherapy; CT: Chemotherapy; iCT: Induction chemotherapy. a Column percentage. b Row Percentage. c Adjusted by gender, sub-site, tobacco and alcohol consumption and stage. d Includes symptomatic treatment (n=60). e Includes SCC sarcomatoid, undifferentiated carcinoma and neuroendocrine carcinoma. *Not considered in the multivariate model as explained in Materials and Methods.

Table S5a .

 S5a Hazard ratios for death in HPV-positive OPC patients, according to four different HPV relatedness definitions (stage IVc patients are excluded)

	FIVE-YEARS OVERALL SURVIVAL IN HPV-POSITIVE OPC PATIENTS
			DNA+ (n=79)			p16+ (n=83)			p16+ / DNA+ (n=58)		DNA+ / mRNA+ (n=66)
	RISK FACTORS	cases /	HR crude	HR adjusted a	cases /	HR crude	HR adjusted a	cases /	HR crude	HR adjusted a	cases /	HR crude	HR adjusted a
		deaths	(95%CI)	(95%CI)	deaths	(95%CI)	(95%CI)	deaths	(95%CI)	(95%CI)	deaths	(95%CI)	(95%CI)
	Age Mean (SD)	60.5 (13.7)	1.0 (1.0-1.1)	1.1 (1.0-1.1)	61.3 (13.9)	1.0 (1.0-1.1)	1.1 (1.0-1.1)	59.7 (14.3)	1.0 (1.0-1.1)	1.1 (0.99-1.1)	60.2 (13.8)	1.0 (1.0-1.1)	1.1 (1.0-1.1)
	Gender
	Male	18/56	Ref.	Ref.	22/60	Ref.	Ref.	8/40	Ref.	Ref.	11/45	Ref.	Ref.
	Female	5/23	0.63 (0.24-1.7)	0.59 (0.15-2.3)	4/23	0.40 (0.14-1.2)	0.68 (0.17-2.3)	2/18	0.55 (0.12-2.6)	0.38 (0.04-3.5)	4/21	0.74 (0.24-2.3)	0.43 (0.08-2.4)
	Tobacco use
	Non smoker	5/33	Ref.	Ref.	4/31	Ref.
	< 20 cigarettes/day	5/15	2.9 (0.85-10.2)	6.1 (1.4-26.6)	6/15	4.4
	≥ 20 cigarettes/day	13/31	3.8 (1.3-10.6)	4.1 (1.1-15.4)	15/36

(1.2-15.5) 4.2 (1.4-12.6)

							Ref.	3/29	Ref.	Ref.	4/32	Ref.	Ref.
							6.6 (1.5-29.1)	4/13	3.8 (0.85-17.3)	6.4 (0.97-42.5)	4/14	2.9 (0.71-11.5)	7.9 (1.3-47.5)
							6.7 (1.6-28.4)	3/16	2.1 (0.42-10.4)	2.1 (0.16-27.9)	7/20	3.5 (1.0-12.1)	5.0 (0.81-30.6)
	Sub-site
	Tonsil	11/49	0.63 (0.23-1.7)	0.69 (0.22-2.2)	16/55	0.94 (0.35-2.6)	0.64 (0.18-2.3)	5/41	0.47 (0.09-2.4)	0.24 (0.02-2.3)	8/45	0.82 (0.17-3.9)	0.28 (0.03-2.4)
	BOT	6/14	1.4 (0.44-4.2)	4.1 (0.95-18.0)	5/12	1.7 (0.48-5.8)	1.0 (0.16-5.7)	3/10	1.4 (0.24-8.7)	1.2 (0.10-14.5)	5/13	2.1 (0.41-11.0)	3.3 (0.36-30.1)
	Tonsil & BOT	-	-	-	0/1	-	-	-	-	-	-	-	-
	Others	6/16	Ref.	Ref.	5/15	Ref.	Ref.	2/7	Ref.	Ref.	2/8	Ref.	Ref.
	Stage
	I/II	3/7	Ref.	Ref.	2/9	Ref.	Ref.	0/4	-	-	2/6	Ref.	Ref.
	III	5/22	0.39 (0.09-1.6)	0.17 (0.03-0.97)	6/21	0.98 (0.20 -4.9)	0.83 (0.08-8.2)	2/15	Ref.	Ref.	2/17	0.26 (0.04-1.8)	0.06 (0.01-0.64)
	IVa/IVb	15/50	0.60 (0.17-2.1)	0.28 (0.05-1.5)	18/53	1.3 (0.30 -5.6)	1.41 (0.16-12.5)	8/39	1.7 (0.36-8.1)	8.2 (0.73-91.7)	11/43	0.64 (0.14-2.9)	0.27 (0.03-2.2)
	Treatment
	RT	3/10	Ref.	Ref.	3/12	Ref.
	Surgery +/-CT/RT	4/19	0.91 (0.20-4.1)	1.5 (0.21-10.7)	3/18	0.85 (0.17-4.2)
	CT-RT (cisplatin)	5/21	0.92 (0.22-3.9)	2.6 (0.37-18.5)	5/20	1.2 (0.29-5.2)
	Bio-RT (cetuximab)	4/6	4.2 (0.94-19.0)	5.7 (0.71-45.1)	2/4	3.4 (0.57-20.5)
	iCT + CT-RT/Bio-RT	6/20	1.3 (0.31-5.1)	2.0 (0.27-14.4)	5/20	1.3 (0.31-5.4)
	Palliative	1/2	3.2 (0.32-31.0)	2.6 (0.18-37.4)	7/8

20.3 (4.5-90.7)

	Ref.
	2.4 (0.37-15.1)
	4.7 (0.69-31.3)
	9.9 (0.96-20.5)
	2.4 (0.36-15.4)

16.1 (2.0-131.9)

  Adjusted by age, gender, tobacco consumption, sub-site, stage and treatment.

	2/8	Ref.	Ref.	2/8	Ref.	Ref.
	1/14	0.35 (0.03-3.9)	0.27 (0.01-8.8)	2/16	0.64 (0.09-4.5)	1.3 (0.12-14.0)
	3/17	0.87 (0.15-5.2)	0.43 (0.02-11.8)	4/19	1.0 (0.19-5.7)	1.4 (0.14-13.4)
	2/4	3.3 (0.46-23.5)	4.4 (0.13-156.4)	3/5	4.1 (0.69-24.9)	2.6 (0.11-58.6)
	2/14	0.68 (0.10-4.9)	0.15 (0.00-6.6)	3/16	0.90 (0.15-5.4)	0.36 (0.02-6.4)
	0/1	-	-	1/2	3.6 (0.32-41.3)	1.3 (0.06-26.6)

a

Table S5b .

 S5b Hazard ratios for death in HPV-negative OPC patients, according to four different HPV relatedness definitions (stage IVc patients are excluded)

Ayudas Merck Serono-Fundación Salud 2000 de investigación 2015 andfrom Sanofi Pasteur MSD and Merck & Co, Inc., whohad no role in the data collection, analysis or interpretation of theresults.


Javad Amirian
email: javad.amirian@inria.fr

Jean-Bernard Hayet
email: jbhayet@cimat.mx

A C Cimat

México

Julien Pettré
email: julien.pettre@inria.fr

Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs

This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene. As GANs are very susceptible to mode collapsing and dropping, we show that the recently proposed Info-GAN allows dramatic improvements in multi-modal pedestrian trajectory prediction to avoid these issues. We also left out L2-loss in training the generator, unlike some previous works, because it causes serious mode collapsing though faster convergence.

We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution. In particular, to prove this claim, we have designed a toy example dataset of trajectories that can be used to assess the performance of different methods in preserving the predictive distribution modes.

Introduction

Many end-user applications make an intensive use of data analytics about pedestrians motion: urban safety, city planning, marketing, autonomous driving, to name a few ones. Typically, this implies the recollection and the offline analysis of these data, for understanding the pedestrians behaviors and taking decisions about the environment. In some contexts, however, one needs to go further and anticipate, in an online way, what will be the next pedestrian moves and infer their short or mid-term intentions. This allows to trigger early alarms or to take preventive actions when monitoring systems with critical real-time decisiontaking processes. In the case of autonomous driving, for ex-* Figure 1. Illustration of the trajectory prediction problem. Having the observed trajectories of a pedestrian of interest, here shown with a star, and the ones of other pedestrians in the environment, the system should be able to build a predictive distribution of possible trajectories (here with two modes in dashed yellow lines).

ample, inferring the intention of the pedestrians surrounding the car is of paramount importance in avoiding collisions.

Nevertheless, this inference problem is extremely complicated to solve. First, because there are many variables which are strongly relevant for the trajectories of single pedestrians: The nature of the surrounding obstacles and their spatial distribution, the nature of the ground, the longterm goal of the pedestrian, his age, his mental state, etc. Then, to make things even more difficult, the motions of a whole set of agents sharing a common space are dependent, through a whole range of interactions that can go from avoidance to meeting intention or person following. A number of interesting studies from neuroscience and biomechanics have isolated single factors or optimization principles governing the human motion in very specific contexts (one-to-one interactions, well-stated goals. . . ). However, in more general cases, one may rapidly attain the limits of hand-tailored mathematical models. This has motivated the pursuit of more flexible, data-driven statistical approaches that can automatically select the most relevant features for explaining pedestrians walks, and that can benefit from the great efficiency of machine learning techniques.

Our work belongs to the aforementioned category of data-driven methods for predicting the motion of pedestrians in the horizon of a few seconds, given a set of observations of their own past motion and of those of the pedestrians sharing the same space, as illustrated in Fig. 1. It relies on a Generative Adversarial Network (GAN)-based trajectory sampler to propose plausible future trajectories. It naturally encompasses the uncertainty and the potential multi-modality of the pedestrian steering decision, which is of critical importance when using this predictive distribution as a belief in higher level decision-making processes.

The main contributions of this work are the following:

• An efficient, unsupervised process to train a trajectory prediction GAN architecture based on Info-GAN [3], without L2 loss, which gives better results than previous works [6,[START_REF] Sadeghian | Sophie: An attentive gan for predicting paths compliant to social and physical constraints[END_REF] in preserving the multi-modal nature of the predictive distribution.

• The definition of an attention-based pooling scheme that relies on a few hand-designed interaction features inspired from the neuroscience/bio-mechanics literature, as a form of prior; the best way to combine them to assess the interaction is learned by our system.

• The design of a synthetic dataset specifically oriented to the evaluation of the preservation of multi-modality in trajectories predictive distributions.

Our architecture is described in Fig. 2. It adopts a new strategy to produce plausible samples for an agent from the joint predictive distribution of the set of agents. Our Sampler (Fig. 2 and Section 3.2) is trained to generate plausible predictions for a single agent, given past observations of trajectories for the whole set of the agents.

Related work

Closed-form mathematical models. Many closed-form mathematical models explaining human motion have been introduced in the simulation, graphics and crowd animation areas. Computational geometry-based approaches [START_REF] Van Den Berg | Reciprocal n-body collision avoidance[END_REF] produce optimal motions typically at the limits of collision and not human-like. Optimization-based methods [START_REF] Yamaguchi | Who are you with and where are you going?[END_REF] optimize on-the-fly the parameters of an objective function hand-designed to cover relevant aspects of the motion.

In multiple-target tracking, Bayesian techniques typically require prediction processes with simple motion models (random walk or constant velocity) or with parameterized modelling of the social interactions, the goal, etc. [13].

Data-driven statistical models. Because of the complexity of pedestrians motion, hand-tailored deterministic models fail to adapt to a wide range of contexts, whereas machine-learning based techniques benefit from large human motion datasets. In [START_REF] Leal-Taixé | Learning an image-based motion context for multiple people tracking[END_REF], for tracking pedestrians from a vehicle, interaction features useful for avoidance are learned from optical flow data. In [START_REF] Keller | Will the pedestrian cross? a study on pedestrian path prediction[END_REF], pedestrian path prediction, in the same context of mobile sensing, is done in a lowdimensional latent space through Gaussian process dynamical models with augmented features extracted from the video optical flow. In [START_REF] Trautman | Robot navigation in dense human crowds: Statistical models and experimental studies of human-robot cooperation[END_REF], interacting mixtures of Gaussian Processes (GPs) are used for predicting the whereabouts of goal-driven social agents in crowds, where the parameters are learned from training data.

NN-based data-driven models. With the advent of NNbased machine learning, the sequential nature of motion has motivated the use of Recurrent Neural Networks or more efficient variants, such as LSTMs [5], for the prediction task. The Social-LSTM architecture [START_REF] Alahi | Social lstm: Human trajectory prediction in crowded spaces[END_REF] associates each agent to a LSTM network and a social pooling aggregates the hidden states of the neighboring agents, to form an interaction feature. Then, each agent interaction feature is combined with its own hidden state to generate the predicted positions for the future frames, with another LSTM network.

In [START_REF] Vemula | Social attention: Modeling attention in human crowds[END_REF], groups of agents are modeled as spatio-temporal graphs where edges (temporal and spatial) are associated to RNNs. Temporal edges capture the evolution of single humans while spatial edges capture the evolution of agentto-neighbors relationships. These hidden features are combined linearly to produce an influence score feeding the temporal network. The prediction output takes the form of a bivariate Gaussian distribution.

In [22], a Crowd Interaction Deep Neural Network uses four modules: A trajectory encoding module encodes individual trajectories using LSTMs units; A location encoding module maps the locations of the pedestrians and the influence they have on each other; An interaction module forms linear combinations of other agents trajectory encodings, weighted by their influence; Finally, the predicted trajectory is determined by sending this linear combination through a fully connected layer. The reported results look promising, however we were not able to reproduce them entirely.

In [START_REF] Pfeiffer | A data-driven model for interactionaware pedestrian motion prediction in object cluttered environments[END_REF], LSTMs capture the evolution of single trajectories, while the interaction history is handled through a LSTM fed with histograms of closest distances over an angular discretization of the surrounding, while the local obstacles are embedded in an occupancy grid.

Handling the multimodal nature of predictions with generative NNs. In many situations, the predictive distribution of a pedestrian motion is inherently multi-modal, e.g., at a crossroads. Without a proper modeling of this multi-modality, RNN-based methods, given observed trajectories with multiple possible outcomes, may simply be condemned to average all the possible outputs. The DE-SIRE architecture [START_REF] Lee | Desire: Distant future prediction in dynamic scenes with interacting agents[END_REF] handles this multi-modality. A Sample Generation Module based on variational auto-encoders generates samples of potential outcome trajectories and the Ranking and Refinement Module evaluates a learned longterm score associated to the sampled trajectories and refines these trajectories, in an inverse optimal control scheme.

In [START_REF] Su | Forecast the plausible paths in crowd scenes[END_REF], a social-aware LSTM, similar to [START_REF] Alahi | Social lstm: Human trajectory prediction in crowded spaces[END_REF], embeds the prior from the training data as hidden feature. Motion variability is taken into account by using layered Gaussian processes acting on the hidden features of the LSTMs.

Finally, following the success of Generative Adversarial Networks (GAN) [START_REF] Goodfellow | Generative adversarial nets[END_REF] in other areas to learn data distributions and produce new samples [START_REF] Berthelot | Began: Boundary equilibrium generative adversarial networks[END_REF], Gupta et al. have proposed a trajectory sampler that handles the interactions between all the observed pedestrians by pooling the GAN input random vector with a vector combining the hidden representations of the other pedestrians trajectories [6].

Problem statement and system overview

Notations and problem formulation

In the following, we use indices i, j ∈ {1, ..., N } to refer to pedestrians, where N is the total number of pedestrians; a single observation of pedestrian i in the scene at time t is denoted by the 4 × 1 vector x i t , which itself contains the position p i t and velocity v i t of the pedestrian:

x i t = ∆ ((p i t ) T , (v i t ) T ) T .
We assume that we have access to τ + 1 consecutive observed samples x i -τ :0 of the pedestrians trajectory for each i ∈ {1, ..., N }. We also handle the set of observed samples of all pedestrians except i with

X ¬i -τ :0 = ∆ {x j -τ :0 |j ∈ {1, ..., N }, j = i}.
The problem is then to predict the trajectories of each pedestrian for the next T time steps, i.e. x i 1:T . The rationale behind our approach is the following: When deciding his steering actions, a pedestrian anticipates likely scenarios about the evolution of his surrounding in the near future. Now, this anticipation may not be always very easy, because of the uncertainties in the neighbors future motion and intentions. In most recent NN-based motion prediction systems [START_REF] Vemula | Social attention: Modeling attention in human crowds[END_REF]22,[START_REF] Pfeiffer | A data-driven model for interactionaware pedestrian motion prediction in object cluttered environments[END_REF], the input is taken as the set of most recent observations of the surrounding pedestrians. Hence, the mappings from observations to predicted trajectories built through the networks do not consider explicitly the uncertain and multimodal nature of the neighbors future trajectories, and, in a way, the network is expected to learn it too, which may be too much to expect.

GAN-based Individual Trajectory Sampler

Our Social Ways GAN generates independent random trajectory samples that mimic the distribution of trajectories among our training data, conditioned on observed initial tracklets of duration τ for all the agents in the scene. This system is depicted in Fig. 2. It takes as an input the observed trajectories of N pedestrians, X -τ :0 and a random vector z sampled from a fixed distribution p z . It samples a plausible trajectory xi,k 1:T for agent i for the next T time steps, where k identifies one generated sample. The network should learn the whereabouts of an agent altogether with the impact a surrounding crowd has on its trajectory.

A GAN contains two components that act in opposition to each other during the training phase [START_REF] Goodfellow | Generative adversarial nets[END_REF]. The Discriminator D is trained to detect fake samples from real ones, while the Generator G should produce new samples that fool the Discriminator and confuse its predictions. In a conditional version, both the Generator and the Discriminator are conditioned on some given data. Here, our GAN is conditioned on recent observations x i -τ :0 , for agent i, and X ¬i -τ :0 , for the other agents, and the Generator uses a noise vector z to complete x i -τ :0 into a full trajectory G(z|x i -τ :0 , X ¬i -τ :0 ).

Description of the Generator network

Our system shares a number of characteristics with existing trajectory generation systems [6,[START_REF] Sadeghian | Sophie: An attentive gan for predicting paths compliant to social and physical constraints[END_REF] but it also includes critical novelties. The Generator network uses one LSTM layer (denoted as LSTM-E) to learn the temporal features along trajectories. The encoding of past trajectories x i -τ :0

for an agent is similar to [6]. The LSTM-E cell encodes the history of the agent i through the recursive application of:

h i t = λ e (h i t-1 , µ(x i t ; W µ ); W λ e ) (1)
with t ∈ [-τ, 0], µ a linear embedding of the agent state and λ e the cell of LSTM-E. h i t is the hidden state vector in LSTM-E at time t. It is depicted at the left part of Fig. 2.

For the decoding process and the generation of samples, we apply a similar process through another LSTM layer (denoted as LSTM-D) with hidden state k i t

k i t = λ d (k i t-1 , o i t-1 ; W λ d ) (2)
with t ∈ [1, T ] and λ d the decoding LSTM-D layer. The input vector is:

o i t = [(h i t ) T , ( j =i a ij h j t ) T , (z) T ] T (3)
It stacks information from the encoded history of observations of agent i up to t, h i t , from the noise vector z, and from the impact of future trajectories of the neighboring agents j, j =i a ij h j t . The construction of this term is described hereafter.

Social Ways: Attention pooling

The influence of the other agents on agent i is evaluated by encoding the vector X ¬i 1:T , through LSTM-E, and by applying an attention weighting process that produces weights a i = ∆ [a i1 , .., a ij , ..., a iN ] T for agent i. They are defined as in [START_REF] Sadeghian | Sophie: An attentive gan for predicting paths compliant to social and physical constraints[END_REF], for j = i, based on pre-defined geometric features δ ij ∈ R 3 stacking (1) the Euclidean distance between agents i and j, (2) the bearing angle of agent j from agent i (i.e. the angle between the velocity vector of agent i and the vector joining agents i and j), and (3) the distance of closest approach (i.e. the smallest distance two agents would reach in the future if both maintain their current velocity) [8].

An interaction feature vector between agents i and j is defined as an embedding in R dσ of the social features δ ij , through a FC layer f ij = φ(δ ij ; W φ ). Finally, the attention weights are obtained with the following scalar products and softmax operations between the hidden history vectors h k and the interaction feature vectors f ik

σ(f ik , h k ) = N -1 √ dσ < f ik , Wσh k >, (4)
a ij = exp(σ(f ij , h j )) k =i exp(σ(f ik , h k )) (5)
where d σ is the common number of rows of the embedded features f and of the linear mapping W σ applied on the hidden features.

Discriminator

The Discriminator is described on the right part of Fig. 2. It contains two encoding LSTM layers, one (applied τ + 1 times) for observations, and one (applied T times) for predictions, and 2 FC layers to predict the samples labels.

It takes as an input either a composite candidate trajectories for agent i, [x i -τ :0 , xi,k 1:T ], or a ground truth trajectory, [x i -τ :T ], and outputs a probability for any of them to have been taken as a sample from the data.

Training the GAN

GAN training is known to be hard, as it may not converge, exhibit vanishing gradients when there is imbalance between the Generator and the Discriminator, or may be subject to mode collapsing, i.e. sampling of synthetic data without diversity. When predicting pedestrian motion, it is critical to avoid mode collapsing, as it could result in catastrophic decisions, i.e. for an autonomous driving agent.

Here, we have introduced two major changes in the GAN training. First, we do not use, as in other stochastic prediction methods [6, 16], an L2 loss term G(z|x i -τ :0 , X ¬i -τ :0 )x i -τ :T 2 enforcing the generated samples to be close to the true data, because we have observed negative impact of this term in the diversity of the generated samples. Also, we have implemented an Info-GAN [3] architecture, which, as we will see in the experimental results section, has a very positive impact on avoiding the mode collapsing problem with respect to other versions of GANs. Info-GAN learns disentangled representations of the sources of variation among the data, and does so by introducing a new coding variable c as an input (see Fig. 2). The training is performed by adding another term to maximize a lower bound of the mutual information between the distribution of c and the distribution of the generated outputs, which requires training another sub-network Q(c|x 1:T ) (with parameters θ Q ) which serves as a surrogate to evaluate the likelihoods p(c|x 1:T ) over the generated data x 1:T . The training optimization problem is written as:

min θ G ,θ Q max θ D V (θG, θQ, θD) = E p data (x i -τ :T ) [log D(x i 1:T |x i -τ :0 ; θD)]+ E pz (z) [log(1 -D(G(z|x i -τ :0 , X ¬i -τ :0 ; θG); θD))]- λE p(c),pz (z) [log Q(c|G(z|x i -τ :0 , X ¬i -τ :0 ; θG); θQ)] (6)
where z is the noise input and c the new latent code.

Experimental results

Implementation details

We implemented our system using PyTorch framework. First, note that all the internal FC layers of both the Generator and the Discriminator are associated to LeakyReLU activation functions, with slope 0.1.

Generator: comprises a first FC linear embedding µ of size 4 × 128, over positions and velocities. The Encoder block in Generator contains one layer of 128 LSTM units (LSTM-E). Using 2 continuous latent code, noise vector with length of 62, and pooling vectors of size 64, which totally gives a 256-d vector, the Decoder LSTM (LSTM-D's) then uses 128 LSTM units in one layer and 3 FC layers with size of 64, 32, 2 to decode the predictions. Weights are shared among LSTM layers with the same function.

Discriminator: uses two LSTM blocks (LSTM-OE and LSTM-PE) with hidden layers of size 128 to process both the observed trajectories (size 4 × τ + 4) and the predicted/"future" trajectories (size 4 × T ); these outputs are processed in parallel with two 64 × 64 FC layers. Then they are concatenated in fed to two separate FC blocks: softclassifier (D) [64 × 1] and latent-code reconstructor [64 × 2] (Q). Finally, τ and T are set to 7 and 12 respectively.

In each dataset, we train the GAN network with the following hyper-parameters setting: mini-batch size 64, learning rate 0.001 for Generator and 0.0001 for Discriminator, momentum 0.9. The GAN is trained for 20000 epochs.

Datasets

For the evaluation of our approach, we use two publicly available datasets: ETH [13] and UCY [11]. These datasets consist of real-world human trajectories. They are labeled manually at a rate of 2.5 fps. The ETH dataset contains 2 experiments (coined as ETH and Hotel) and the UCY dataset contains 3 experiments (ZARA01, ZARA02 and Univ). In order to evaluate the prediction algorithm, each dataset is split into 5 subsets, where we train and validate our model on 4 sets and test on the remaining set.

Baseline Predictors and Accuracy Metrics

We consider two sets of baselines.

1. Deterministic prediction models, that generate one trajectory for each observation:

• Linear: This is a simple constant velocity predictor.

• S-Force: It uses an energy function based on Social Forces to optimize the next agent action. The function penalizes jerky movements, high minimum distance to other agents and so on. We use the version by Yamaguchi et al. [START_REF] Yamaguchi | Who are you with and where are you going?[END_REF], in which a term enforces the agent to stay close to the group it belongs to.

• S-LSTM [START_REF] Alahi | Social lstm: Human trajectory prediction in crowded spaces[END_REF]: It associates each pedestrian to one LSTM unit (the Social-LSTM) and gathers the hidden states of neighboring pedestrians with a so-called social-pooling mechanism to perform the prediction.

2. Stochastic prediction models, that generate a set of samples from a surrogate of the predictive distribution:

• Social-GAN: A GAN-based prediction [6]. We consider the variants S-GAN-P and S-GAN, with and without a pooling mechanism, respectively.

• SoPhie [START_REF] Sadeghian | Sophie: An attentive gan for predicting paths compliant to social and physical constraints[END_REF] which implements Social and Physical attention mechanism in a GAN predictor.

Similarly to previous works [6, 20], we use the following metrics to evaluate the proposed system over the prediction on one testing data x i -τ :T : 1. Average Displacement Error (ADE), averaging Euclidean distances between ground truth and predicted positions over all time steps:

ADE(x i -τ :T ) = 1 T T t=1 x i t -xi t (x i -τ,0 , X ¬i -τ,0 ) . (7)
2. Final Displacement Error (FDE), i.e. Euclidean distance between the ground truth and predicted final position:

FDE(x i -τ :T ) = x i T -xi T (x i -τ,0 ) . (8)
Then, we evaluate the expectations of these errors over all the samples in our testing datasets. We observe τ = 8 frames (2.8 seconds) and predict the next T = 12 frames (4.8 seconds).

To evaluate stochastic models (that generate a set of samples), we use the methodology proposed in [6]. We generate K samples and take the closest one to Ground truth for evaluation. Hereafter, we consider K = 20.

Evaluation of Prediction Errors

The average prediction errors for both ADE and FDE metrics are shown in Table 1. As it can be seen, the use of our approach leads to significantly lower prediction errors for the ETH and Hotel experiments, but not on the ZARA experiments. We attribute this behavior in that, in the ZARA experiments, the width of the waypath for pedestrians is significantly smaller than in the Hotel and ETH scenes. Hence, there is less variance in the trajectories. Our proposed system intrinsically tends to generate various samples that result in good performance with more complex scenes and non-linear trajectories.

Among the deterministic models, though Social-LSTM model uses a much more complex system than its counterparts, it fails to outperform the other baselines and as the authors in [6] mention it, it needs a synthetic dataset as a second source of training to improve the system accuracy. In Figure 3, we give qualitative examples of the outputs and intermediate elements in our approach. We generated 128 samples with our method and the predictive distribution are shown with magenta points. In most of the scenarios (including non-linear actions, collision avoidance and group behaviors), the distribution has a good coverage of the ground truth trajectories and also generates what seems to be plausible alternative trajectories.

Quality of the Predictive Distributions

As commented in Section 3.2, our architecture and its training process are designed to preserve the modes of the predictive trajectory distribution. However, in all the datasets that we have tested, there are very few examples of clearly multi-modal predictive trajectory distributions. Hence, we have created a toy example dataset to study the mode collapsing problem with stochastic predictors.

This toy example is depicted in Fig. 4: Given an observed sub-trajectory (blue lines), the Generator should predict the rest of the trajectory (red lines). Each of the 6 groups represents one separate condition to the system (x i -τ :0 ), and each of the 3 sub-groups represents a different mode in the conditional distribution p(x i 1:T |x i -τ :0 ). Note that the interactions between agents are not considered here.

In order to compare our approach with other GAN-based techniques, we implemented several baselines. In all of them, the prediction architecture is the one we proposed without the attention-pooling; the GAN subsystem changes.

• Vanilla-GAN: This is simplest baseline, where the Generator is just trained with the adversarial loss.

• L2-GAN In addition to adversarial loss, a L2 loss is added to the Generator optimizer.

• S-GAN-V20: The Variety loss proposed in Social-GAN method [6] is added to the adversarial loss. This L2loss only penalizes the closest prediction to ground truth among V = 20 predictions and gives more freedom to choose prediction samples.

• Unrolled10: Vanilla-GAN with the unrolling mechanism proposed in [12]. The number of unrolling steps is 10.

For each of the 6 possible observations, we generate 128 samples, which are depicted in Fig. 5. The Info-GAN together with Unrolled-GAN performs the best, with a slight advantage for Info-GAN, since almost all of the modes are preserved successfully after 90,000 iterations. At the same time, Vanilla-GAN, L2-GAN and S-GAN-V20 could not preserve the multi-modality of the predictions. One can see that using L2 loss, the model is converging faster than Vanil-laGAN and S-GAN-V20.

For a more quantitative evaluation of generative models, we have used the following two metrics to assess the set of fake trajectories versus the set of real samples [START_REF] Xu | An empirical study on evaluation metrics of generative adversarial networks[END_REF]. Given two sets of samples S r = {x i r } and S g = {x j g } with |S r | = |S g | and x i r ∼ P r and and x j g ∼ P g :

1. A 1-Nearest Neighbor classifier, used in two-sample tests to assess whether two distributions are identical. We compute the leave-one-out accuracy of a 1-NN classifier trained on S r and S g with positive labels for S r and negative labels for S g . The classification accuracy for data from an ideal GAN should be close to 50% when |S r | = |S g | is large enough. Values close to 100% mean that the generated samples are not close to real samples enough. Values close to 0% mean that the generated samples are exact copies of real samples, and that there is a lack of innovation in such system.

2. The Earth Mover's Distance (EMD) between the two distributions. It is computed as in Eq. 9:

EM D(Pr, Pg) = min

w∈R n×m n i=1 m j=1 w ij d(x i r , x j g ) s.t. ∀i, j w ij ≥ 0, m k=1 w ik = 1 n , n k=1 w kj = 1 m . (9)
where d() is called the ground distance. In our case we use the ADE of Eq. 7, between the future parts of the two trajectories.

We computed both 1-NN and EMD metrics on our toy dataset with |S r | = |S g | = 20, for each of the 6 observed trajectories. The results for different baselines are shown in Figures 6. We added evaluations for a few combinations of the aforementioned baselines (e.g., Info-GAN+unrolling steps or Unrolled+L2). The lower 1-NN accuracy of our approach using Info-GAN shows its higher performance for matching the target distribution, compared to Vanilla-GAN and other baselines. It is worth noting that the fluctuations in the accuracies are related to the small size of the set of samples. As it can be seen, Unrolled10 and Info+Unrolled5 have also better performances, while it is obvious that by adding L2 loss, the results are getting worse. The results of the EMD test also proves that both Info-GAN and Un-rolled10 offer more stable predictors with lower distances between the fake and real samples. There is no evidence that the Variety loss offers better results than a Vanilla-GAN.

Moreover, on real trajectories, we have tested our algorithm on the Stanford Drone Dataset (SDD) [15]. In fact, we have used subsets of trajectories from two scenes (Hyang-6 and Gates-2). As you see in Fig. 7, with our system (left column), separate modes of the predictions appear clearly where the intuition would set them, while the Vanilla-GAN (right column) could not produce various paths.

Conclusions and Future Works

We have presented a novel approach for the prediction of pedestrians trajectories among crowds. It uses an Info-GAN to produce samples from the predictive distribution of individual trajectories, and integrates a few hand-designed interaction features inspired from the neuroscience/bio- mechanics literature, as a form of prior over the attention pooling process. We have shown through extensive evaluations on commonly used datasets that this approach partly improves the prediction accuracy of state-of-the-art methods on the datasets where the predictive distributions have the largest variances. We have also proposed a specifically designed dataset and an evaluation benchmark to show that Info-GANs achieve the best results in preserving multimodality, compared with other variants. Finally, we are aware that is still room for improving the current generative models in pedestrian motion prediction and, above all, for exploiting these models in decision making.

Figure 2 .

 2 Figure 2. Block Diagram of the Social Ways prediction system. The yellow ellipses represent loss calculations. The dashed arrows show the backpropagation directions. The bold arrows carry ground truth data.

Figure 3 .

 3 Figure 3. In this figure, we illustrate our sample outputs (in magenta color). The observed trajectories are shown in blue and ground truth prediction and constant-velocity predictions are shown in cyan and orange lines, respectively. [Best viewed in color.]

Figure 4 .

 4 Figure 4. Toy trajectory dataset. There are six groups of trajectories, all starting from one specific point located along a circle (blue dots). When approaching the circle center, they split into 3 subgroups. Their endpoints are the green dots.

Figure 5 .

 5 Figure 5. Results of learning baselines on Toy Example, for different numbers of iterations. [Best viewed in color.]

Figure 6 .Figure 7 .

 67 Figure 6. Statistics for different GAN implementations over training iteration. Upper row: 1-NN accuracy metric (closer to %50 is better). Lower row: Earth Mover's Distance between generated and ground truth samples (the lower, the better).

Table 1 .

 1 Comparison of prediction error of our proposed method (S-Ways) vs baselines. The ADE and FDE values are separated by slash.

		Deterministic Models		Stochastic Models
	Dataset	Linear	S-Force	S-LSTM	S-GAN	S-GAN-P	SoPhie	S-Ways
	ETH	0.59 / 1.22 0.67 / 1.52 1.09 / 2.35 0.68 / 1.26 0.77 / 1.38 0.70 / 1.43	0.39 / 0.64
	Hotel	0.36 / 0.64 0.52 / 1.03 0.79 / 1.76 0.47 / 1.01 0.44 / 0.89 0.76 / 1.67	0.39 / 0.66
	Univ	0.82 / 1.68 0.74 / 1.12 0.67 / 1.40 0.56 / 1.18 0.75 / 1.50 0.54 / 1.24	0.55 / 1.31
	ZARA01 0.44 / 0.98 0.40 / 0.60 0.47 / 1.00 0.34 / 0.69 0.35 / 0.69 0.30 / 0.63	0.44 / 0.64
	ZARA02 0.43 / 0.95 0.40 / 0.68 0.56 / 1.17 0.31 / 0.64 0.36 / 0.72 0.38 / 0.78	0.51 / 0.92

The research is supported by the CrowdBot H2020 EU Project http: //crowdbot.org/ † J.B. Hayet is partially funded by the Intel Probabilistic Computing initiative.