Marc Ivaldi
email: marc.ivaldi@tse-fr.eu

Jiekai Zhang
email: jiekai.zhang@hanken.1

"Simulating media platform mergers"

Keywords: Simulating media platform mergers JEL Classication: K21, L10, L40, L82, M37 Two-sided market, platform merger, advertising, TV market, competition policy

HAL is

Introduction

Recent studies on equilibrium models of advertising-nanced media platforms based on the economics of two-sided markets have contributed to the understanding of the theory of harm raised by mergers in platform industries and the measurement of its eects. Simulating the outcomes of a change in platform ownership given the estimated preferences and costs relies crucially on the precise estimation of the demand patterns of viewers and advertisers.

This paper comments on a practical challenge in the simulation of media platform mergers that arises from the multi-homing behaviour of advertisers. Since the latter decides on how many advertising minutes to buy and often combine the advertising slots of several media platforms to reach the desired amount of audience, their choice of platforms is not discrete. Therefore, we cannot rely on a discrete choice model to estimate the substitutability or complementarity between advertising platforms. As a matter of fact, this issue has been neglected in the literature. Often the demand model of advertisers has been approximated a log-linear model that relates the logarithm of advert prices of a given platform to the logarithm of its own advert quantities and audience. While it is easy to implement, this approach assumes away the substitutability and/or complementarity between the advertising slots of dierent platforms. Not only such an abstraction is not realistic, but it certainly could deteriorate the quality and the robustness of predictions based on this model, like the outcomes of merger simulation. Ivaldi and Zhang (2020, IZ2020 in what follows) proposes an alternative framework in which the advertisers' decision program consists in minimizing the total advertising costs to reach a given level of audience. This framework can be implemented by approximating the true cost function of advertisers by a Translog cost function, which allows to explicitly estimate the own-and cross-price elasticities of demand for dierent advertising platforms. The Translog cost function has two nice features: First, it does not impose any substitution patterns between advertising platforms, which is consistent with the multi-homing behaviour of advertisers. Second, it can be easily estimated even with a large number of competing platforms. Nevertheless, this approach complexies the procedure to simulate a change of policy such as a platform merger as it requires to solve sequentially two systems of equations. Indeed, a media platform, which raises revenues from selling time slots for advertisements, faces a trade-o between the preferences of viewers and the objectives of advertisers: The advertisers wish to reach a large audience while the viewers could be negatively aected by the amount of advertising. Such a media platform must internalize the network externalities between the viewers and the advertisers in order to maximize its prot. This feature should be taken into account in the merger simulation procedure. It implies in practice to nd the prot maximizing level of advertising prices as a function of the corresponding demand for advertising. A simplied model for advertisers' demand ignoring the cross-elasticities of demand on the advertising side allows one to derive directly a closed form solution for a channel's advertising price as a function of its amount of advertisement. However, a more comprehensive model such as the proposed Translog cost function requires to solve a non-linear system of equations in order to obtain the demand for advertising slots of one channel as a function of the advertising prices of all channels of the market. 1 In this paper, we assess the relevance of such a complex simulation model. Using the same data as in IZ2020, we simulate the outcomes of two hypothetical platform mergers (between two substitutable advertising sales houses (ASHs) and between two complementary ones), when we approximates the demand on the advertising side, rst by using the the Translog model proposed in IZ2020, and then by using a simple log-linear inverse demand model. Our results, comparing the simulated merger eects using the two alternative demand models for advertisers, suggest that one always over-predict the increase in total amount of advertising (accordingly, under-predict the increase in average advertising prices) when one neglects the substitutability or complementarity between the merging ASHs. In other words, ignoring the existence of substitutes or complements on the advertising side during the simulation of a platform merger would result in over-predicting the loss of the viewers' surplus and in under-predicting the gain in platforms' revenues. The magnitude of such discrepancies in the simulated merger eects is more important when it concerns a merger between two substitutable ASHs.

Data

We use data on the French digital TV market to perform the merger simulations. Our sample is the same as the one that has been used in IZ2020. The reader is referred to that paper for more details on the construction of dataset.

Dierent to IZ2020 in which we perform merger evaluation using post-merger data between 2011 and 2013, herein, we use data between 2008 and 2010 to simulate the outcome of two hypothetical mergers. Our goal is to highlight the consequence of ignoring the advertisers' cross-elasticities of demand when simulating the potential impacts of a merger, and comment on how the results dier depending on whether the merging platforms are complements or substitutes for the advertisers. We observe a signicant change in broadcasting quality of some TV channels after 2010, which could interfere with the simulated merger eects. Therefore, we prefer to restrict our simulation sample to data before Jan. 2010.

Demand primitives and merger simulation

On the audience side, we use directly the model of preferences of TV viewers estimated in IZ2020. The viewers' demand is approximated by a nested-logit model, which classies the choices of the TV viewers into g groups of TV channels and an additional group for the outside goods. The TV viewers' demand function to be estimated is given by ln s jt -ln s 0t = αA jt + σ ln s jt/g + X jt β + ξ jt , where s jt denotes the market share of channel j on audience side, s jt/g denotes the market share of channel j within its group and s 0t denotes the market shares of the outside goods. X jt is a matrix of variables including observed content characteristics, channel-xed eects, as well as month-and year-xed eects. A jt refers to the amount of advertising on channel j during period t. The estimate of parameter α, which measures the utility of advertising for viewers, is negative, suggesting that the advertisers generate negative cross-side externalities for viewers.

On the advertising side, we keep the choice model of advertisers based on the estimation of a Translog cost function presented in IZ2020. The cost share equations to be estimated is

S A jt = γ j + J i γ ij (ln p it ) + θ j (ln Y t ) + ξ A jt
, where S A jt denotes the advertising cost share of channel j in period t, p jt denotes the per minute advertising price and Y t = J j y jt is the total number of TV viewers with y jt refering to audience of channel j. The parameter γ ij measures the Allen partial elasticities of substitution between dierent input factors (i.e, the advertising slots of dierent channels).

For comparaison, we also estimate a simplied demand model for advertisers. We will refer to this later model as the inverse demand function in what follows. It ignores the cross-elasticities of demand of advertisers as it tries to explain the advertising price of a media platform j by its own amount of advertising and its own viewership, controlling for some exogeneous variables. It is specied as ln p jt = γ ln A jt + θ ln y jt + X A jt B A + ξ A jt , where the vector X A jt includes channel-, month-and year-xed eects. 2 We estimate this inverse demand function model using the two-stage least squares estimator, since both the logarithm of advert quantities ln A jt and the logarithm of audience ln y jt could be endogenous. Indeed, the error term ξ A jt is likely to include unobserved programming quality which might be correlated with both ln A jt and ln y jt .

We use the total broadcasting hours of news and entertainment of competing channels to instrument the variables ln A jt and ln y jt . The validity of this set of instruments relies on a timing assumption: Neither ASHs nor advertisers observe the error term ξ A jt until TV channels have selected the broadcasting content (news and entertainment); the selection of content does not depend on the unobserved characteristics included in ξ A jt . This assumption is met in the French digital TV market. First, a TV channel obtains the broadcasting right of a content via contract with its producer, many content producers require the TV stations to pre-purchase and to commit on the broadcasting of its content already at the production stage, via a co-nancing system. Second, national broadcast TV stations in France have the duty and some obligations to ensure the diversity of content that they oer; these channels need to agree with the sector regulator CSA on the amounts and schedules of news and entertainment content that they plan to oer at the beginning of every year. 3 The estimated value of parameter γ is equal to -0.392 and the one of parameter θ is equal to 0.909. Both are signicantly dierent from zero at the 5% level. These estimates are used to perform the merger simulations below. Note that the inverse demand function allow us to derive an analytical expression of advertising price p jt as a function of the amount of advertsing A jt and that of audience y jt , namely, p jt = (A jt ) γ (y jt ) θ exp(X A jt B A + ξ A jt ). The merger simulations are performed using the rst order condition (FOC) associated with the prot maximization program of advertising sales house (ASH) of channel j. At equilibrium, the amount of advertising is the variable which links both sides of the market: It has an impact both on the number of viewers and the advertising prices of the TV channels. An ASH internalizes the network externalities between viewers and advertisers by choosing the amount of advertising A jt which maximizes its prots. Formally, the objective of an ASH H at equilibrium can be written as: max ∂A jt ): The ASH of channel j internalizes the impact of the channel j's advertising level on the sales of advertising slots of channel k, after merging with the ASH of channel k. The simulation procedure consists of nding the vector A t = {A 1t , ..., A Jt } which solves the new equilibrium described by the FOC (1).

{A jt } j∈H j∈H p jt A t ,

Simulation with inverse demand function on the advertising side

A logit type demand model for TV viewers allows one to derive an analytical expression of audience y jt as a function of the vector of advertising levels of dierent TV channels, namely, y jt ≡ F (A t ). Substituting it in the inverse demand function for advertisers, one obtains the following relationship between channel j's advertising price p jt and the advertising levels of dierent TV channels A t = {A 1t , ..., A Jt }: p jt ≡ G (A jt , y jt (A t )) (2). One can next substitute this equation ( 2) into the FOC (1) and nd the vector A t = {A 1t , ..., A Jt } which solves the new equilibrium. The simulation procedure implied by this model is straightforward but it assumes away the direct eect of the advertising price of another channel k = j on the demand for advertising slot of channel j.

Simulation with Translog cost function on the advertising side

Alternatively, the Translog cost function proposed in IZ2020 implies a more complex relationship between the advertising price of channel j, P jt , and the advertising levels of dierent TV channels, A t :

p jt A jt j p jt A jt = γ j + J i γ ij ln p it + θ j (ln Y t ) (3)
, where Y t = J j y jt . This model takes into account the direct eect of the advertising price of channel k = j on the demand for advertising slot of channel j, as the advertisers can substitute or complement an advertising slot of channel j by the advertising slot of another channel k. The equilibrium advertising levels of dierent channels A t can be found via a xed point iteration:

A t = N (p t (A t ), Y t (A t ))
, where p t = {p 1t , ..., p Jt }.

In practice, nding a vector A t which solves the FOC of the prot maximizing problem of an ASH, and satises the demand function of advertisers given by their Translog cost function, requires to solve sequentially the two systems of equations given by ( 1) and ( 3). This gives one more step of complexication compares to the case where the demand of advertising is modelled according to an inverse demand function.

We compare now the merger simulation outcomes given by the two alternative models of advertisers demand to assess the relevance of a more complex simulation procedure considering explicitly the cross-elasticities of demand of advertisers.

Results

We simulate separately the eects of a merger between two substitutable advertising sales houses (ASHs) and between two complementary ones. The simulated merger eects are presented in Table 1 andTable 2.

Table 1 shows the simulated impact of mergers on the equilibrium level of advertising quantities and prices. We rst note that the merger between two (either complementary or substitutable) ASHs increases the market equilibrium amount of advertising, which is due to the substitutional eects on the viewers' side. Indeed, the fact that advertisers generate negative cross-side externalities to viewers implies that a merged ASH has incentivize to increase its advertising level, as it internalizes the negative externalities of advertising. The other ASHs increase their amount of advertising as well as a stratergic response to the choice of the merged ones. Now, without accounting for the advertisers' cross-elasticities of demand (i.e., using the inverse demand function), we predict a decrease in the advertising prices of the merging ASHs, even for the case in which the two merged ASHs are substitutes for advertisers. In fact, the log-linear inverse demand function implies that the advertising prices of a channel j, p jt , decreases in response to the increase in its amount of advertising A jt . However, this result is counterintuitive and contradictory with the basis theory of harm of merger analysis. We obtain more intuitive results with the Translog cost specication which considers explicitly the advertisers' cross-elasticities of demand. A merged ASH internalizes not only the substitutional eects on the viewers side but also the substitutional and/or complementary eect on the advertisers' side. Merging two ASHs, which are substitutable for advertisers, intuitively increases the advertising prices, as the merged entity internalizes the pricing externalities. Merging two ASHs, which are complementary for advertisers, has much less signicant impact at equilibrium. This is because the complementarity between the merging rms eliminate their pricing externalities. If the merged ASH increases the advertising price of one of its channel j, the marginal advertisers would not switch to another channel under its management which is complementary to channel j, but to the other channels that are substitutes for these channels. The prices for the complementary advertising slots are lower under joint ownership rather than independent ownership. Accordingly, while ignoring the advertisers' cross-elasticities of demand in the merger simulation, we over-predict the increase in the market equilibrium level of advertising quantities. For the case in which the merged ASHs are substitutes for advertisers, we furthermore miss-predict the impact of merger on the advertising prices without considering the cross-substitutional eects on the advertising side. Hence, using a simplied model which ignores the substitutability or the complementarity of media platform for advertisers when one simulates the eect of mergers between two ASHs is inappropriate, as such a simplied model assumes away an important feature of the two-sided market, namely, the platform internalizes the cross-externalities between the two groups of consumers (viewers and advertisers). Knowing that too much advertising reduces a media platform's audience (because advertisers generate negative externalities to viewers), which will in turn decrease the willingness to pay of advertisers (as viewers generate positive externalities to advertisers), a platform merger should not increase signciantly the market equilibirum amount of advertising. Especially, for the case in which the merged platforms are substitutes for advertisers, the merged rm has even more incentive to not loss audience with more advertising, since it could charge higher prices to the advertisers. Overall, when it comes to the magnitude of discrepancy between the predicted merger eects using the two alternative models for advertisers' demand, we note that, ignoring the advertisers' cross-elasticities of demand is more problematic when the simulated merger concerns two substitutable ASHs for advertisers than when it deals with two complementary ones.

Table 2 shows the simulated welfare impacts of the mergers. While ignoring the cross-elasticities of demand of advertisers, we over-predict the loss of viewers' surplus due to the over-predicted increase in advertising quantities. In regard to the total advertising costs, we under-predict the loss of advertisers due to the under-predicted increase in advertising prices. Ignoring the advertisers' cross-elasticities of demand impact the magnitude of change in the prots of ASHs as well. Indeed, we would conclude that the total prot of ASHs is lower in a more concentrated advertising market (following a merger between two ASHs). We obtain more intuitive results once taken into account the advertisers' cross-elasticities of demand in the merger simulation, in which case we exhibit an increase in the total prot of ASHs.

Conclusion

In this paper, we highlight the consequences of ignoring the existence of substitutes or complementes in one side of the market when evaluating the platform mergers. More specically, we assess the relevance of taking into account the advertisers' cross-elasticities of demand in the simulation of merger between ASHs. We compare the simulation results using two alternative models of demand of advertisers. We show that ignoring substitutability or complementarity between dierent ASHs results in over-predicting the loss in the viewers' surplus, and in under-predicting

  Y t y 1t (A t ), ..., y Jt (A t ) -c jt A jt . The FOCs associated with this prot maximization problem are: (p jt -c jt ) +

		k∈H	A kt ( ∂p kt ∂A jt + ∂p kt ∂y jt i,∀i	∂y it ∂A jt ) = 0 (1), where
	c jt denotes the estimated marginal cost of selling one minute of advertising, H is the advertising
	sales house of channel k.
	∂p kt	∂y it
	∂y jt i,∀i

A merger between channel j and channel k implies a change in the term k∈H A kt ( ∂p kt ∂A jt +

Table 1 :

 1 Simulated merger eects on advertising quantities and prices

		% changes in advert quantity	% changes in advert price
		of merging ASHs of non-merging ASHs of merging ASHs of non-merging ASHs
	Merging Two Substitutable ASHs
	Translog cost function	1.14	5.08	40.06	21.99
	Inverse demand function	17.31	9.00	-13.99	0.32
	Merging Two Complementary ASHs
	Translog cost function	1.92	2.00	-0.42	1.46
	Inverse demand function	5.40	1.21	-1.69	0.39

Table 2 :

 2 Simulated welfare eects of merger

		% changes in	% changes in	% changes in
		viewers surplus total advert cost total prot of ASHs
	Merging Two Substitutable ASHs
	Translog cost function	-0.13	30.65	67.02
	Inverse demand function	-3.13	2.85	-21.64
	Merging Two Complementary ASHs
	Translog cost function	-0.11	0.70	1.21
	Inverse demand function	-0.25	0.41	-1.16

The procedure to simulate a platform merger is detailed in IZ2020.

This model has been used in[START_REF] Rysman | Competition between networks: A study of the market for yellow pages[END_REF] and[START_REF] Fan | Ownership consolidation and product characteristics: A study of the US daily newspaper market[END_REF] to evaluate the impact of platform entry and of merger.

See for instance, the annual contract between dierent TV broadcasters and the CSA: https://www.csa.fr/Reguler/Espace-juridique/Les-relations-du-CSA-avec-les-editeurs/Convention-desediteurs/Les-chaines-de-television-privees-hertziennes.

the gains in the total prots of ASHs. The magnitude of discrepancies is more important when simulating a merger between two substitutable ASHs. We believe our results are useful for policy makers seeking to regulate the concentration of platform markets.


MD, PhD Joe-Elie Salem
email: joe-elie.salem@aphp.fr

; Lee

MD, PhD S Nguyen

MD Javid J Moslehi

MD Stéphane Ederhy

MD Bénédicte Lebrun-Vignes

MD Dan M Roden

MD, PhD Christian Funck-Brentano

MD Paul Gougis

Anticancer drug-induced life-threatening ventricular arrhythmias: a WHO pharmacovigilance study

Keywords: Disproportionality analysis, anticancer drugs, long QT, ventricular arrhythmias, pharmacovigilance, torsade de pointes Graphical

7 (3 figures and 4 tables in the main manuscript) + 8 (4 additional tables and 4 additional figures)

Introduction.

The development of cancer therapeutics has resulted in a better prognosis and long-term survival for patients with many malignancies. [START_REF] Arnold | Progress in cancer survival, mortality, and incidence in seven high-income countries 1995-2014 (ICBP SURVMARK-2): a population-based study[END_REF] Anticancer drugs may also lead to severe cardiovascular adverse drug reactions (ADR) carrying a high morbidity burden, and can be fatal. [START_REF] Moslehi | Cardiovascular Toxic Effects of Targeted Cancer Therapies[END_REF] This interplay between cancer and heart conditions is the subject of the booming field of cardio-oncology. Cardiac ADR of cytotoxic anticancer drugs have been identified for decades, such as anthracycline-induced heart failure or acute myocardial infarction with anti-metabolites. [START_REF] Moslehi | Cardiovascular Toxic Effects of Targeted Cancer Therapies[END_REF][START_REF] Levis | Cardiotoxic effects of anthracycline-based therapy: what is the evidence and what are the potential harms?[END_REF] With the exponential development of new classes of anticancer drugs (including immunotherapy and kinase inhibitors, KI), other heart-related ADR have emerged and represent an important concern for regulatory agencies, companies, and patients care providers. A striking example is the increased reporting of myocarditis (fatality rate ~30-50%) occasionally induced by immune checkpoint inhibitors, which are breakthrough therapeutics approved in a wide variety of cancers. [START_REF] Geraud | Clinical Pharmacology and Interplay of Immune Checkpoint Agents: A Yin-Yang Balance[END_REF][START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF] Cardiac arrhythmias are another emerging and poorly characterized concern of anticancer drugs with an increasing number of targeted therapies such as KI and anti-hormonal agents prolonging QT interval, a well-recognized marker of increased risk for cardiac arrhythmias and sudden death (SD). [START_REF] Alexandre | Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms[END_REF][START_REF] Salem | Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes[END_REF] The QT interval on the electrocardiogram (ECG), corrected for heart rate (QTc), is a measure of the duration of ventricular repolarization and is a widely used proxy of the drug-induced ventricular arrhythmia risk. [START_REF] Roden | A current understanding of drug-induced QT prolongation and its implications for anticancer therapy[END_REF] It remains the recommended standard surrogate used in human studies despite its wellrecognized limitations. [START_REF] Vicente | Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the "CiPA" Initiative and Design of a Prospective Clinical Validation Study[END_REF] Many drugs slightly prolong the QT interval, but in some patients, this prolongation can be exaggerated and provoke the morphologically distinctive polymorphic ventricular arrhythmia (VA) torsade de pointes (TdP). Symptoms associated with TdP include syncope and SD if the arrhythmia is prolonged or degenerates into ventricular fibrillation. More recently, it has been reported that some anticancer drugs, such as ibrutinib (a Bruton KI) can also lead to fatal VA without prolonging QT. [START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF] Using VigiBase, the World Health Organization's (WHO) global pharmacovigilance database, we aimed to better define and risk stratify these severe cardiac arrhythmia ADR to improve patient safety and facilitate monitoring guidelines after administration of anticancer drugs. We also sought to identify new drugs with signals for VA, long QT and TdP which were not previously identified during clinical trials.

Methods.

Study design and data sources

This observational, retrospective, pharmacovigilance study is a disproportionality analysis based on ADR reported in VigiBase, the WHO deduplicated database of individual case safety reports (i.e., reports thereafter). [START_REF] Lindquist | the WHO Global ICSR Database System: Basic Facts[END_REF] VigiBase is managed by the Uppsala Monitoring Centre (UMC, Uppsala, Sweden) and contains approximatively 19 million reports (through January 2019) submitted by national pharmacovigilance centers since 1967. The use of confidential, electronically processed patient data was approved by the Vanderbilt University Medical Center institutional review board (#181337, USA).

Procedures

This study included all drug-induced long QT (diLQT), TdP or VA classified by group queries according to the Medical Dictionary for Regulatory Activities (MedDRA, Supplementary Table 1), between inception on November 14, 1967, and January 1, 2019. DiLQT, TdP or VA specifically assessed in the analysis were those reported as suspected to be caused by a drug (versus concomitant use). Each report contains general administrative information (country of origin, date of reporting, and reporter qualification), patient characteristics (sex, age), drugs (indication, start and end dates of administration, dosage regimen, and route of administration), and reactions or events (reported terms, onset and end date, seriousness, and final outcome). A severe ADR was defined as causing death; being life-threatening; requiring hospital stay (initial or prolonged); or leading to persistent or clinically significant disability, congenital anomaly, birth defect, or any other medically important conditions.

Statistical analysis

VigiBase allows disproportionality analysis (also known as case-non-case analysis), which we used to assess whether suspected diLQT, TdP and VA were differentially reported with each drug (663 individual molecules pertaining to the anticancer drugs) vs. the full database of 20,222 drugs (Supplementary Figure 1 for the flow-chart). Disproportionality analyses compare the proportion of a selected specific ADR reported for a single drug with the proportion of the same ADR for a control group of drugs (i.e., full database with all drugs). The denominator in these analyses is the total number of ADR reported for each group of drugs. If the proportion of cases associated with a specific drug is greater than in patients without this ADR (non-cases), there is a disproportional association (signal identification) between the ADR and the drug. [START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF] We calculated a Bayesian disproportionality estimate suitable when taking the full database as comparator, i.e. the information component (IC). IC compares observed and expected number of reports for drug-ADR pairs. The IC025 is the lower-end of the 95% credibility interval for the IC so a positive value of the IC025 is deemed significant. More information concerning calculation of the IC/IC025 is provided in Supplementary Methods and these methods have been recently used in similar settings and detailed elsewhere. [START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF][START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF][START_REF] Bihan | Uses of pharmacovigilance databases: An overview[END_REF][START_REF] Bate | A Bayesian neural network method for adverse drug reaction signal generation[END_REF] Since this work focused on identifying culprit anticancer drugs, we further performed a sensitivity analysis and estimated the frequentist disproportionality association (reporting odds ratio, ROR) with diLQT, TdP, VA and SD for each anticancer drug already flagged with positive IC025, restricting the background database to reports associated with at least one anticancer drugs (defined as drugs pertaining to the anatomical therapeutic classification L: Antineoplastic and immunomodulating agents). ROR was calculated by Chi 2 test, and the 95% confidence interval (CI95%) was estimated, as previously described. [START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF][START_REF] Grouthier | Increased long QT and torsade de pointes reporting on tamoxifen compared with aromatase inhibitors[END_REF] A lower end of the ROR CI95% ≥1 is considered significant. Concordance (agreement) between the data describing liability of anticancer drugs to induce cardiac arrhythmias according to VigiBase vs. US Food and Drug Administration (FDA) labels (accessible at https://www.accessdata.fda.gov/scripts/cder/daf/) and CredibleMeds® (accessible at www.crediblemeds.org) was computed using the Cohen kappa coefficient.

Characteristics of reports in

Results.

Trends in anticancer drug-associated cardiac arrhythmia reporting over decades

The study included 42,462 reports of diLQT, TdP or VA from VigiBase inception, through January 1st, 2019. The number increased from 580 in the period 1967-1983 to 15,070 for 2014-2018 (Supplementary Figure 2). The corresponding proportion related to anticancer drugs increased from 0•9% (5/580) to 14•0% (2115/15,070) (p<0.00001). Anticancer drugs were divided into five subgroups: cytotoxic treatments (CT, including antimetabolites and anthracyclines), hormone therapies (HT), immunotherapies (IT, including immune-related cell therapies), KI (including any drug interacting directly with a kinase protein or its ligands) and other therapies (miscellaneous, Misc). The majority of this increase in reporting over years was in the KI group (graphical abstract) representing 51•6%, (1091/2115) of these cardiac arrhythmia reports associated with anticancer drugs within the 2014-2018 period versus 14•7% (311/2115) with CT, 5•9% (124/2115) with HT, 2•5% (52/2115) with IT, and 12•5% (265/2115) with a combination of any of these anticancer classes (Combo; i.e one drug or more pertaining to at least two of these classes: CT, HT, IT, KI, Misc) (p<0•00001).

Anticancer drugs associated with long QT, VA including TdP and SD

Forty anticancer drugs were significantly associated with diLQT (including 27 also associated with VA or SD), and 9 with VA without diLQT when taking as background either the full database (n=18,441,659; IC025>0) or when restricting the database to cases involving at least one anticancer drug used (n=4,197,602; ROR CI95% ≥1) (Table 1). Most (41%, 20/49) were KI, 24% (12/49) were CT, 8% (4/49) were HT, 6% (3/49) were IT, and 20% (10/49) were Misc. Details regarding the magnitude of the association by drug and per subtype of arrythmia (diLQT, VA and TdP) and signals for SD are shown in Table 1. Details concerning the year for which these anticancer drugs were first significantly associated with any of these cardiac arrhythmias are shown in Supplementary Figure 2. Details concerning number of reports per year of these cardiac arrhythmias are shown in Figure 1. To further evaluate the seriousness of these cardiac events (diLQT, VA including TdP), we stratified the 49 drugs of interest as a function of the presence or not of a significant association with drug-induced SD (Table 2). Half of these anticancer drugs (46•9%, 23/49) were associated with SD. We generated a three-level SD risk stratification (Figure 2) constituted of drugs associated with only isolated diLQT without VA nor SD (low-risk, n=13), drugs associated with VA without SD (moderate-risk, n=13) and drugs associated with VA and SD (high-risk, n=23). Among anticancer drugs with moderate and high-risk for SD, most were also associated with diLQT (75%, 27/36) but not all (25%, 9/36). The top three drugs with the highest disproportional association (Table 1, Supplementary Figure 3, using IC025) with diLQT were vandetanib (KI, n=97, IC025=5.8, year of FDA approval 2011), arsenic trioxide (Misc, n=115, IC025=5.5, year of FDA approval 2000) and ribociclib (KI, n=105, IC025=5.3, year of FDA approval 2017). This was concordant with arsenic trioxide (n=14, IC025=2.7), vandetanib (n=10, IC025=2.5) and vorinostat (Misc, n=6, IC025=1.2, year of FDA approval 2006) carrying the highest association with TdP. The top three drugs associated with VA were amsacrine (CT, n=14, IC025=3.1), arsenic trioxide (n=25, IC025=2.4) and daunorubicin (CT, n=52, IC025=1.8, year of FDA approval 1979). The top drugs in terms of absolute number of reports were respectively nilotinib for diLQT (KI, n=369, IC025=0.4, year of FDA approval 2007) and TdP (n=18, IC025=0.4), and capecitabine for VA (CT, n=161, IC025=0.8, year of FDA approval 1998) (Table 1, Figure 1). The major mechanisms of action of these drugs are detailed in Table 2.

Of note, we further validated this disproportionality method using positive and negative controls in terms of drugs at known risk of diLQT and TdP (dofetilide, sotalol, ibutilide with IC025 values among the highest (4.9 to 5•82)) vs. protective for diLQT and TdP (progesterone, levonorgestrel and testosterone carrying among the lowest IC025 values). [START_REF] Salem | Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes[END_REF][START_REF] Vicente | Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the "CiPA" Initiative and Design of a Prospective Clinical Validation Study[END_REF][START_REF] Salem | Association of Oral Contraceptives With Drug-Induced QT Interval Prolongation in Healthy Nonmenopausal Women[END_REF] These data are shown in Supplementary Figure 3 and the top 25 highest and lowest IC025 values for diLQT among all drugs available in VigiBase are shown in Supplementary Table 2 and Supplementary Table 3.

Clinical features of cardiac arrhythmias associated with anticancer drugs in VigiBase

Clinical characteristics derived from the 2301 reports (diLQT without TdP, n=1406; TdP, n=196, and VA without TdP, n=699) associated with the 49 anticancer drugs of interest are displayed in 3. Most reports 1381/1602 (86%) had no concomitant drugs at known risk of TdP on top of the culprit anticancer drug in the diLQT and/or TdP patients. Among these 1602 reports, the most reported drug classes with molecules concomitantly used at known risk of TdP were proton pomp inhibitors (8.6%; n=137), antiemetics (7.4%, n=119), anti-infectious agents (7.1%, n=114) and antidepressants (6%, n= 96). Reports of concurrent conditions favoring diLQT and VA were frequent with 12% (275/2301) of infection, or cardiac conditions including 10% (239/2301) of heart failure, and 8% (183/2301) of cardiac ischemia (Table 3).

Median time to onset (in days) was not significantly different between patients with diLQT without TdP, TdP and non-TdP VA (21 [IQR 7-91] 4.

Concordance of cardiac arrhythmia risk evaluations between VigiBase, CredibleMeds and FDA

A total of 663 anticancer drugs were referenced in VigiBase (through July 1 st 2019), of which 199 were FDA approved at least once and 195 currently approved (through July 1 st 2019). Concordance between VigiBase results and data concerning diLQT, TdP and/or VA available in CredibleMeds database (which aggregates all known drugs prolonging QT) or US FDA labels were moderate (κ=0•47 [0.34-0.6], p<0•0001 and 0•40 [0.27-0.54]], p<0•0001, respectively, Supplementary Figure 4).

Corresponding concordance between CredibleMeds and VigiBase was high (κ=0•74 [0.62-0.85], p<0•0001). Twenty-three drugs (16 for diLQT or TdP, and 14 for VA) were not described in CredibleMeds and/or FDA databases. In contrast, CredibleMeds and/or FDA databases described 24 drugs associated with these ADR which yielded no significant association in VigiBase. Details concerning the concordance per drug and specific type of cardiac arrhythmia (diLQT, TdP, VA) between these databases are presented in Table 4. Analyses of concordance restricted to the 199 FDA approved drugs showed similar results (Supplementary Figure 4). The most relevant new signals were those carrying a very high proportion of single suspect culprit drug (SSCD) in the reports (not confounded by the concurrent intake of other liable anticancer drugs) (Supplementray Table 4). Within FDA approved drugs, these latter were carfilzomib (n=19, SSCD=100%, proteasome inhibitor), imatinib (n=64, SSCD=73%, KI), alectinib (n=5, SSCD=100%, KI), axicabtagene-ciloleucel (n=4, SSCD=100%, CAR-T anti-CD19), mogamulizumab (n=5, SSCD=100%, C-C chemokine receptor type 4 inhibitor), and bicalutamide (n=30, SSCD=93%, androgen receptor antagonist). Interestingly, four drugs were flagged before any FDA approval (amsacrine [CT], combretastatin a4 [CT], chidamide [histone deacetylase inhibitor] and enzastaurin [KI]).

Discussion.

In this worldwide pharmacovigilance study which included almost 19 million reports, disproportionality analyses yielded significant association between 49 anticancer drugs and cardiac arrhythmias including diLQT, TdP, and VA. This detailed report summarizes all available data addressing drug-induced cardiac arrhythmias extracted from US FDA labels, CredibleMeds and VigiBase. We believe our data can serve as a compendium for all clinicians using anticancer drugs and considering their potential arrhythmic risk (Table 4). FDA labels mainly summarize data systematically gathered and analyzed during drug development (thorough and concentration QT studies, clinical trials), and in some cases updates arising from post-marketing evaluation. Consensus achieved by experts from academia of these available data is found in the widely-recognized CredibleMeds website for TdP risk.

VigiBase is a complementary source which assembles data from real-life surveillance with spontaneous post-marketing reporting mainly arising from healthcare professionals. VigiBase has previously been utilized to describe other cardiovascular sequelae from anticancer therapies and has allowed a better appreciation of the magnitude of these toxicities. [START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF][START_REF] Salem | Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes[END_REF][START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF] Interestingly, in our work, 23 drugs represented new signals, while 24 flagged by Crediblemeds or FDA had no signal in VigiBase. These findings may guide clinicians and regulatory institutions to conduct further research to reevaluate cardiac monitoring requirements focusing on these specific drugs. Moreover, information generally contained in FDA labels focus on magnitude of QT prolongation identified in QT studies, but does not provide information concerning VA and TdP risk as such, because these events are often too rare and not adjudicated in cancer-focused clinical trials. CredibleMeds website only assess TdP risk in context of QTc prolongation.

Herein, we were able to identify three levels of SD risk profile with anticancer drugs only associated with isolated long QT (low-risk), associated with VA without SD (moderate-risk) and VA with SD (highrisk). This SD risk stratification may prove useful to clinicians when confronted to difficulties in the risk/benefit assessment of pursuing a liable anticancer drug with a possible overall benefit for the patient.

Importantly, we have identified a novel group that has not been particularly well flagged previously: drugs associated with potentially fatal VA but not mediated by QTc prolongation. This group is important to recognize in clinical situations where arrhythmias are suspected with a normal QTc on ECG. Lastly, this work provides a quantitative magnitude of disproportional association of 663 anticancer drugs with diLQT, TdP and VA. These data may prove useful for translational cardio-oncology researches seeking at identifying new pathways involved in arrhythmias, as we recently showed with ibrutinib and identification of kinase-dependent off-target inhibition leading to atrial fibrillation. [START_REF] Xiao | Ibrutinib-Mediated Atrial Fibrillation Attributable to Inhibition of C-Terminal Src Kinase[END_REF] To date, this study is the most extensive, analyzing over 42,000 suspected drug-induced cardiac arrhythmia events internationally reported from healthcare professionals. The evolution of reporting in VigiBase has been marked in the last decade by the introduction of new drug classes, KI, and IT; they currently represent the majority of reported drug-induced cardiac arrhythmias. As expected, CT have been associated with these ADR for far longer. In VigiBase, the first treatment to yield a significant association with cardiac arrhythmias was an anthracycline, idarubicin, in 1995. Of note, an average QTc >500 ms (normal <450 ms for men and <460 ms for women) or a >60 ms QTc change from baseline is considered as of particular concern (grade 3) according to the Common Terminology Criteria for Adverse Events, the grading mostly used in oncology trials. Anthracycline-related increase in QTc >60 ms vs. baseline has been reported with an incidence up to 14% with doxorubicin, and relates to their propensity to induce cardiomyocyte injury via overproduction of free radicals and alteration of cardiac ion currents, notably via IKs channel blockade and intra-cellular calcium dysregulation. [START_REF] Levis | Cardiotoxic effects of anthracycline-based therapy: what is the evidence and what are the potential harms?[END_REF][START_REF] Alexandre | Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms[END_REF][START_REF] Buza | Cancer Treatment-Induced Arrhythmias: Focus on Chemotherapy and Targeted Therapies[END_REF] Our study also supports multiple observations previously reported in the literature, highlighting the robustness of the methodology with positive controls (e.g. arsenic trioxide, nilotinib, vandetanib, vorinostat, ribociclib). [START_REF] Alexandre | Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms[END_REF][START_REF] Buza | Cancer Treatment-Induced Arrhythmias: Focus on Chemotherapy and Targeted Therapies[END_REF][START_REF] Barbey | Effect of arsenic trioxide on QT interval in patients with advanced malignancies[END_REF][START_REF] Porta-Sánchez | Incidence, Diagnosis, and Management of QT Prolongation Induced by Cancer Therapies: A Systematic Review[END_REF] The anticancer drug most reported with long QT in VigiBase was arsenic trioxide, a drug used against some leukemia, and significantly associated with long QT/TdP since 2002 in VigiBase. The most comprehensive QT study included 99 patients with advanced malignancies who received 170 courses of arsenic trioxide. Of them, 35/99 (35.4%) developed increase in QTc >60 ms vs. baseline, and one developed asymptomatic TdP. [START_REF] Barbey | Effect of arsenic trioxide on QT interval in patients with advanced malignancies[END_REF] Nilotinib, a second-generation BCR-ABL inhibitor, has been previously linked to moderate increase in QTc (average QTc prolongation of 5 to 15 ms). [START_REF] Alexandre | Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms[END_REF] In studies, 2.5% to 4% of patients exhibited QTc prolongation >60 ms on nilotinib, and in one study, 1.2% of patients showed QTc >500 ms. [START_REF] Alexandre | Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms[END_REF][START_REF] Buza | Cancer Treatment-Induced Arrhythmias: Focus on Chemotherapy and Targeted Therapies[END_REF][START_REF] Kantarjian | Nilotinib in imatinib-resistant CML and Philadelphia chromosome-positive ALL[END_REF] Similarly, vandetanib, a vascular endothelial growth factor receptor inhibitor, has been associated with long QT in a meta-analysis including nine phase II-III trials, which found a significant risk of QTc prolongation (all-grade according to the National Cancer Institute Common Toxicity Criteria v.2.0 or 3.0), 123/2552 (4.82%) in treated patients vs. 6/2204 (0.27%) in control groups (relative-risk 7.90, 95%CI [4.03-15.50]). [START_REF] Liu | Meta-analysis of the risks of hypertension and QTc prolongation in patients with advanced non-small cell lung cancer who were receiving vandetanib[END_REF] Vorinostat, a histone deacetylase inhibitor used in the treatment of cutaneous T-cell lymphoma, was associated with QTc prolongation (>470 ms or delta >60 ms from baseline) in 5/116 (4.3%) patients in a retrospective review including phase I-II trials. [START_REF] Lynch | QT interval prolongation and torsades de pointes in a patient undergoing treatment with vorinostat: a case report and review of the literature[END_REF] Ribociclib, a CDK 4/6 inhibitor used in breast cancer, was associated with QTc prolongation (>480 ms) in 11/334 (3.3%) of ribociclib-treated patients vs. 1/334 (0.3%) in the placebo arm, in its landmark randomized controlled trial. [START_REF] Hortobagyi | Ribociclib as First-Line Therapy for HR-Positive, Advanced Breast Cancer[END_REF] Distinct from drugs prolonging QT, several drugs were associated with VA without long QT. They included ibrutinib, and CAR-T. Indeed, although ibrutinib has not been associated with long QT (studies reported concentration-dependent QTc shortening), [START_REF] De Jong | Ibrutinib does not prolong the corrected QT interval in healthy subjects: results from a thorough QT study[END_REF] it has been associated with atrial and ventricular arrhythmias and SD. [START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF][START_REF] Lampson | Ventricular arrhythmias and sudden death in patients taking ibrutinib[END_REF] As described previously, it may correspond to a short-coupled variant of polymorphic ventricular tachycardia, which is thought to involve alteration in cardiac sarcoplasmic reticulum Ca 2+ homeostasis associated with cardiac ryanodine receptor-calmodulin-dependent protein kinase pathways. [START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF][START_REF] Lampson | Ventricular arrhythmias and sudden death in patients taking ibrutinib[END_REF] In our study, which found a few cases of CAR-T (axicabtagene-ciloleucel) related VA, there was also a signal towards association with VA but not long QT. In a retrospective study, 54% of tested patients who received CAR-T showed myocardial injury with troponin elevation and 12% developed a cardiac ADR (including heart failure, arrhythmias, and cardiac deaths). [START_REF] Alvi | Cardiovascular Events Among Adults Treated With Chimeric Antigen Receptor T-Cells (CAR-T)[END_REF] Our study also yielded new signals requiring further investigations to confirm the causality of the association, its magnitude and mechanisms at play. Alectinib, an ALK inhibitor, was not previously associated with VA nor QT modification, but it was associated with mild sinus bradycardia. [START_REF] Morcos | Effect of alectinib on cardiac electrophysiology: results from intensive electrocardiogram monitoring from the pivotal phase II NP28761 and NP28673 studies[END_REF] Carfilzomib, a proteasome inhibitor approved for the treatment of multiple myeloma, was known for its risk of cardiac failure, but not long QT and SD. [START_REF] Zamorano | ESC Position Paper on cancer treatments and cardiovascular toxicity developed under the auspices of the ESC Committee for Practice Guidelines: The Task Force for cancer treatments and cardiovascular toxicity of the European Society of Cardiology (ESC)[END_REF] We also found imatinib associated with long QT, while imatinib was considered so far as relatively safe from a cardiovascular standpoint, as compared to other BCR-ABL inhibitors including nilotinib, ponatinib and dasatinib. [START_REF] Alexandre | Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms[END_REF][START_REF] Buza | Cancer Treatment-Induced Arrhythmias: Focus on Chemotherapy and Targeted Therapies[END_REF][START_REF] Manouchehri | Tyrosine Kinase Inhibitors in Leukemia and Cardiovascular Events: From Mechanism to Patient Care[END_REF] In our study, a key element strengthening the association between these drugs and cardiac arrhythmias is the fact that in most reports alectinib (100%), carfilzomib (100%) and imatinib (73.4%) was the only anticancer drug suspect involved in the appearance of these cardiac ADR. Notably, we also observed hormone therapies blocking testosterone association with long QT and TdP, such as bicalutamide, an androgen receptor antagonist used in prostate cancer. In a translational study combining pharmacoepidemiologic and mechanistic studies using iPSC cardiomyocytes, we recently confirmed the causal association between androgen deprivation and long QT and TdP. [START_REF] Salem | Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes[END_REF] Interestingly, our analysis showed that in four instances, signals of association between an incriminated drug under development and long QT, VA and TdP, in VigiBase, appeared prior to FDA approval of those drugs (namely, amsacrine, chidamide, combrestatin-a4 and enzastaurin). In the specific field of drug-induced QT prolongation and cardiac arrhythmias, the agreement between the FDA labels, CredibleMeds and VigiBase remains modest at best, and emphasizes the complementarity of a multimodal approach to apprehend the toxicity of a specific drug.

The variety of anticancer drug classes associated with long QT highlights the heterogeneity of the mechanisms which may underly cardiac arrhythmias related to these agents. The generally accepted common mechanism whereby drugs prolong QT is block of a key cardiac repolarizing potassium current, IKr. [START_REF] Roden | A current understanding of drug-induced QT prolongation and its implications for anticancer therapy[END_REF] While some anticancer drugs associated with diLQT and TdP have been shown to inhibit IKr, recent works focusing on anticancer drugs prolonging QT identified new pathways. [START_REF] Roden | A current understanding of drug-induced QT prolongation and its implications for anticancer therapy[END_REF] The in-vitro effects of some KI to prolong cardiac action potentials (the cellular correlate of QT) can be rescued by intracellular phosphatidylinositol 3,4,5-trisphosphate, the downstream effector of phosphoinositide-3-kinase. This finding supports a role for inhibition of this enzyme, either directly or by inhibition of upstream kinases, to prolong QTc through mechanisms that are being investigated, but include enhanced inward 'late' sodium current (INaL) activation during the plateau of the action potential. [START_REF] Roden | A current understanding of drug-induced QT prolongation and its implications for anticancer therapy[END_REF] These observations emphasize the need to better explore the kinome in general, and in particular, the effects of kinases located up-and downstream to that of phosphoinositide-3-kinase, to better understand their influence on cardiac electrophysiology. [START_REF] Fleuren | The kinome 'at large' in cancer[END_REF] We acknowledge several inherent limitations to our results related to pharmacovigilance studies. [START_REF] Bihan | Uses of pharmacovigilance databases: An overview[END_REF] First, the exact denominator of patients exposed to anticancer drugs cannot be evaluated, hence the true incidence of the events cannot be computed, and all values are expressed as relative to each other, with the basis that VigiBase aggregates millions of reports, hence may allow for a generalization of the findings. The second bias stems from the observational and declarative nature of the reports with variable degree of exhaustivity. Third, the number of reports for a particular medicinal product may be influenced by the extent of use of the product, publicity, the nature of the reactions, and other bias. [START_REF] Bihan | Uses of pharmacovigilance databases: An overview[END_REF] Fourth, some drugs specifically annotated in Supplementary Table 4 (low SSCD) were systematically used in association, which require cautious interpretation of the results when one of these were known to be associated with cardiac arrhythmias. Finally, in the specific context of anticancer drugs, the added risk due to these drugs is difficult to assess, as end-stage cancers may be associated with cardiac arrhythmias; however, this have been partially mitigated by our sensitivity disproportionality analysis restricted to patients on anticancer drugs. While these limitations are numerous, the added value of pharmacovigilance studies have already been demonstrated in various settings. [START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF][START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF][START_REF] Grouthier | Increased long QT and torsade de pointes reporting on tamoxifen compared with aromatase inhibitors[END_REF][START_REF] Salem | Association of Oral Contraceptives With Drug-Induced QT Interval Prolongation in Healthy Nonmenopausal Women[END_REF] Nevertheless, they are only to be taken as signal-generating studies and all hypotheses generated require validation by translational mechanistic or prospective studies. [START_REF] Salem | Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes[END_REF][START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF] Indeed, while they are mandatory to establish efficacy, their power to detect ADR may be lower, due to the rarer incidence in these events and the fact that "real-world population" may differ from included patients in the said clinical trial. Translational experimental studies specifically designed to answer a question of cardiotoxicity in oncology remain the most comprehensive design available, yet. [START_REF] Salem | Androgenic Effects on Ventricular Repolarization: A Translational Study From the International Pharmacovigilance Database to iPSC-Cardiomyocytes[END_REF] Contributors. J-ES and PG were involved in study design. PG did the literature search. PG and J-ES made the figures. PG, J-ES, BL-V were involved in data collection. PG and J-ES analyzed the data. PG, LSN and J-ES were involved in data interpretation. PG, LSN, J-ES, JJM, CF-B, SE, AC, DR, and BL-V were involved in the writing of the manuscript. All authors edited the manuscript. Declaration of interests. JES had paied lectures from AstraZeneca and BMS, unrelated to this work and have patents pending and issued related to methods for detecting risk of Torsade-de-Pointes. JJM had consultancy fees from BMS, AstraZeneca, Deciphera, Janssen, Takeda, Cytokinetics, Audentes, Boston Biomedical, and Myovant unrelated to this work. All other authors have nothing to disclose.

Tables and Figures.

Table 1.

Anticancer drugs associated with at least one of the following adverse drug reactions: drug-induced long QT syndrome (diLQT), torsade de pointes (TdP), and ventricular arrhythmias (VA) based on disproportionality analysis in VigiBase (through 01/01/2019). Associations were deemed significant when the lower end of the 95% credibility interval was positive (IC025 >0) for analysis vs. full database (n=18,441,659); or when the lower end of the 95% confidence interval of the reporting odds ratio was >1 (rOR025 >1) for analysis restricted to reports with anticancer drugs as background (n= 4,197,602). Non-significant associations are not represented. Results with sudden death (SD) are also represented for these latter drugs. diLQT: drug-induced long QT syndrome; VA: ventricular arrhythmia; SD: sudden death; TdP: torsade de pointes Table 4.

capecitabine $-€ CT 49174 161 0•8 3•8 [3•2-4•4] 319 0•3 2•1 [1•9-2•3] clofarabine $-€ CT 2216 5 3•1 [1•3-7•5] 2 5 [1•2-20] 11 0•5 5•6 [3•1-10] 57 2•0 8•3 [6•4-10•9] combretastatin a4 CT 25 3 0•7 189 [56-630] 1 230 [31-1703] 1 47 [6•3-345] cytarabine $-€ CT 26300 52 0•6 2•8 [2•1-3•6] 14 3 [1•8-5] 89 0•8 3•9 [3•1-4•8] 358 1•4 4•4 [4-4•9] daunorubicin $-€ CT 6655 28 1•4 5•9 [4•1-8•5] 9 0•4 7•5 [3•9-15] 52 1•8 8•9 [6•8-12] 149 2•0 7•3 [6•2-8•6] decitabine $-€ CT 2894 10 0•6 4•8 [2•6-8•9] 22 2•4 [1•6-3•7] fluorouracil $-€ CT 65547 138 0•2 2•4 [2-2•9] 383 0•2 1•9 [1•7-2•1] idarubicin $-€ CT 3539 12 0•7 4•7 [2•7-8•3] 5 7•8 [3•3-19] 18 0•9 5•7 [3•6-9•1] 42 0•9 3•8 [2•8-5•1] mitoxantrone $-€ CT 4847 12 0•3 3•4 [1•9-6•1] 9 0•8 10 [5•4-20] 28 1•2 6•5 [4•5-9•5] 60 1•0 4 [3•1-5•1] nelarabine $-€ CT 370 4 0•3 12 [4•6-33] pegaspargase $-€ CT 4481 22 0•9 5•5 [3•6-8•4] 146 2•5 10•7 [9•1-12•6] bicalutamide $-€ HT 4802 13 0•5 3•8 [2•2-6•5] 10 1•0 12 [6•2-22] 23 0•9 5•4 [3•6-8•2] 24 1•6 [1•1-2•4] letrozole $-€ HT 15564 25 0•1 2•2 [1•5-3•3] tamoxifen $-€ HT 18567 28 0•0 2•1 [1•4-3] 8 2•4 [1•2-4•8] toremifene $-
Comparison of VigiBase signals (IC025 >0 vs. full database; or rOR025 >1 vs. anticancer drug background) for drug-induced long QT (diLQT), torsade de pointes (TdP), and ventricular arrhythmias (VA) with information retrieved in CredibleMeds ® website and US FDA labels (through July 1st, 2019). Among the 663 anticancer drugs screened (full list in Supplementary Table 3), only those with evidence of association with diLQT, TdP, VA mentioned in one of these three reference sources are shown. For these latter drugs, information concerning sudden death (SD) is also represented.

Signal in

Figure 1. Evolution of the absolute number of long QT syndrome and/or ventricular arrhythmias including torsade de pointe reports over time for each of the 49 culprit anticancer drugs identified using VigiBase (see Table 1). For each drug, the year of FDA approval was added when available (otherwise, NA stands for not available). Supplementary methods.

Disproportionality analysis compares the proportion of selected specific adverse drug reaction (ADR) reported for a single drug (e.g., each anticancer drugs) vs. the proportion of the same ADR for a control group of drugs (e.g., full database). The denominator in these analyses is the total number of ADR reported for each group of drugs. If the proportion of ADR is greater in cases exposed to a specific drug (cases) than in cases not exposed to this drug (non-cases), then an association can be made between the specific drug and the reaction and is deemed a potential safety concern. Disproportionality can be calculated by the information component (IC, Bayesian approach) or the reporting odds ratio (ROR, frequentist approach). [START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF]33 Calculation of the IC using a Bayesian confidence propagation neural network was developed and validated by the Uppsala Monitoring Center as a flexible, automated indicator value for disproportionate reporting that compares observed and expected drug-ADR associations to find new drug-ADR signals with identification of probability difference from the background data (full database). Probabilistic reasoning in intelligent systems (information theory) has proved to be effective for the management of large datasets, is robust in handling incomplete data, and can be used with complex variables. The information theory tool is ideal for finding drug-ADR combinations that are highly associated compared with the generality of the stored data. Several examples of validation with the IC exist, showing the power of the technique to find signals soon after drug approval by a regulatory agency (e.g., an association between immune-checkpoint inhibitors and myocarditis), [START_REF] Salem | Cardiovascular Toxicities Associated With Ibrutinib[END_REF]33 and to avoid false positives, whereby an association between a common drug and a common ADR occurs in the database only because the drug is widely used and the ADR is frequently reported (e.g., between bevacizumab and acne, and between bevacizumab and rash). The statistical formula is as follows: -Ntotal being the total number of reports in the database. IC025 is the lower end of a 95% credibility interval for the IC. A positive IC025 value (>0) is the traditional threshold used in statistical signal detection at the UMC. IC025 values have only been validated for comparison of drug-specific ADR vs. the full database and cannot be used to compare disproportionate reporting with a subgroup background. Since the focus of this work concerned identification of culprit anticancer drugs, we further performed a sensitivity analysis and estimated the frequentist disproportionality association (ROR) with LQT, TdP, VA and SD for anticancer drugs already flagged as signal with IC025 filtering, restricting the background database to reports associated with at least one anticancer drugs (defined as drugs pertaining to the anatomical therapeutic classification L: Antineoplastic and immunomodulating agents). ROR was calculated by Chi 2 -test, and the 95% confidence interval (CI95%) was estimated, as recently described elsewhere [Grouthier V et al. , Heart 2018]. A lower end of the ROR CI95%≥1 was deemed significant.

- - 0 - - 1 1•3 -2•5 2 1•6 -1 asciminib no 14 0 - - 0 - - 0 - - 0 - - asparaginase Yes 7976 2 -1•7 -4•3 1 -1•4 -5•2 5 -1•3 -2•8 51 0•5 0 atacicept no 4 0 - - 0 - - 0 - - 0 - - atezolizumab Yes 2071 1 -0•8 -4•6 0 - - 3 -0•1 -2•2 9 -0•1 -1•2 atrasentan no 22 0 - - 0 - - 0 - - 2 2•1 -0•5 avapritinib no 5 0 - - 0 - - 0 - - 0 - - avelumab Yes 324 0 - - 0 - - 0 - - 2 0•3 -2•3 aviscumine no 1 0 - - 0 - - 0 - - 0 - - axitinib Yes 5802 6 0•1 -1•3 2 -0•3 -2•9 7 -0•4 -1•6 15 -0•8 -1•6 azacitidine Yes 9256 14 0•6 -0•3 2 -0•9 -3•5 16 0•1 -0•7 57 0•4 0 bafetinib no 5 0 - - 0 - - 0 - - 0 - - balixafortide no 3 0 - - 0 - - 0 - - 0 - - barasertib no 6 0 - - 0 - - 0 - - 0 - - bavituximab no 3 0 - - 0 - - 0 - - 0

  VigiBase were described in terms of means ± standard-deviation or medians and interquartile range [IQR] for quantitative variables, and in terms of numbers and proportion for qualitative ones. Comparisons were performed by Chi2 test, Wilcoxon test with Dunn's post-tests, as appropriate. P<0.05 was deemed significant.

3 .

 3 vs. 23 [IQR 5•5-139] vs. 24 [IQR 4-120] days, respectively; p=0•93) (Figure 3). When comparing different drug classes, median time to onset were variable ranging from 9 days [IQR 3-23] for IT, 9 [IQR 3-34] for CT, 12 [IQR 3-38] for Misc treatments, 31 [IQR 11-140] for KI, to 142 [IQR 25-409] for HT (p<0.0001). The differences between anticancer drug classes are displayed in Figure The differences between anticancer drug molecules are displayed in Supplementary Table

Figure 2 .

 2 Figure 2. Three-level sudden death (SD) risk stratification for the 49 liable anticancer drugs associated with isolated diLQT (low-risk), ventricular arrhythmias (VA) without SD (moderate-risk) and VA with SD (highrisk) identified in VigiBase (through Jan 1, 2019). Absolute number of VA (NVA) including torsade de pointes (TdP) associated (NLQT+NVA) or not with diLQT reports by drug and SD risk level is displayed in panels A and B, respectively. The corresponding proportion of such cases (NVA or NLQT+NVA) over the total number of overall adverse drug reactions per drug (Ndrug) are displayed in panels C and D, respectively.

Figure 3 .

 3 Figure 3. Overlap and overall fatality rate of drug-induced long QT syndrome (diLQT), torsade de pointe (TdP), and ventricular arrhythmia (VA) reports associated with the 49 drugs identified in VigiBase (A) with their respective time to onset (B). Overlap in VigiBase for reports of diLQT and VA including TdP for these 49 drugs as a function of the underlying drug classes (C) with their corresponding time to onset (D). Differences in median time to onset by groups were compared by Wilcoxon tests and Dunn's post-test. * and ** stands for p≤0.05 and ≤0.0001, respectively.Abbreviations: combo: reports containing at least one culprit anticancer drugs from at last 2 different anticancer drug classes; CT: cytotoxic therapy; HT: hormone therapy; IT: immunotherapy; KI: kinase inhibitor; Misc: Miscellaneous

Figure 1 .

 1 Figure 1.

Figure 2 .

 2 Figure 2.

  IC = log2 ((Nobserved+0•5)/(Nexpected+0•5) with Nexpected being the number of reports expected for the drug-ADR combination and Nexpected = (Ndrug*Neffect) / Ntotal -Nobserved (Nobs) being the actual number of reports observed for the drug-ADR combination -Ndrug being the number of reports for the drug, regardless of ADR -Neffect being the number of reports for the ADR, regardless of the drug.

Table 3 and in Supplementary Table 4. Overlap between culprit anticancer drug classes within these reports is represented in Figure 3. Median

 3

	age was 63 years (IQR 51-71). Male predominance was found in VA
	reports excluding TdP (64•9%, 431/664), contrasting with female predominance in diLQT and TdP
	reports (51•4%, 698/1359, p<0.0001). Most reports were in the last 5 years (1434/2301, 62%) and were
	by health-care professionals (1701/1943, 88%) in America (1038/2301, 45%) or Europe (762/2301,
	33%). Most reports involved at least one culprit KI (64%, 1477/2301). A majority of reports were
	considered serious (94%, 1946/2078). All-cause fatality was 13•8% (317/2301) and 10% reported SD
	(228/2301, Table 3). The final outcome after stopping the culprit anticancer drug was available for 397
	reports, of which 326/397 (82%) resolved. Most patients (49%, 766/1555) had hematologic diseases,
	particularly chronic myeloid leukemia (23%, 363/1555) or other leukemia (17%, 272/1555). Among
	solid tumors, the most represented were colorectal, lung, breast, and kidney cancers (8•1%, 126/1555;
	7•3%, 114/1555; 6•7%, 104/1555 and 6•1%, 95/1555; respectively).
	Details concerning concurrent drugs and conditions favoring QT prolongation, TdP and VA are
	shown in Table

Table 2 .

 2 Classification of the 49 anticancer drugs as a function of the signals identified in VigiBase for drug-induced long QT syndrome (diLQT), ventricular arrhythmias (VA) including torsade de pointes (TdP), and sudden death (SD).

	drug	class	subclass	target / mechanism

CAR: chimeric antigen receptor; CT: chemotherapy; HT: hormonotherapy; IT immunotherapy; KI: kinase inhibitor; MAB: monoclonal antibody; Misc: Miscellaneous; SERM: selective estrogen receptor modulator * decitabine was significantly associated with diLQT and SD, but not with TdP nor VA

Table 3 .

 3 Characteristics of patients receiving at least one of the 49 anticancer drugs associated significantly with diLQT, TdP or VA through 01/01/2019, in VigiBase.

	Total N= 2301

  NA, not applicable; Nobs: number of reports observed for diLQT, TdP and/or VA with the drug of interest * In this analysis, the denominator is the total number of reports (Nobs of diLQT, TdP and/or VA) associated significantly (IC025>0, Table-1) with the drug of interest

	osimertinib		37		100%		70; [62-75]		26		36% (12/33)	103; [72-306]		8	81% (30/37)	27%
	panobinostat		24		96%		70.5; [59-73]		14		35% (6/17)		NA		0	88% (21/24)	8%
	pazopanib		40		100%		65; [60-72]		32		55% (17/31)	34; [16-204]		10	100 % (39/39)	8%
	pegaspargase		22		23% a		16 [10-20]		13		68% (15/22)		0		1	100% (22/22)	50%
	ribociclib		105		80%		67; [57-73]		44		0% (0/84)	14; [13-40]		21	98% (92/94)	0%
	romidepsin		13		100%		47.5; [44-51]		2		50% (2/4)		NA		0	100% (13/13)	15%
	sunitinib		71		100%		66.5; [62-69]		24		70% (23/33)	31; [21-44]		6	99% (67/68)	4%
	tamoxifen		28		100%		63.5; [50-78]		24		15% (4/27)	90; [16-158]		7	90% (19/21)	0%
	toremifene		4		75%		73; [69-76]		4		0% (0/4)			NA		0	100% (3/3)	0%
	trametinib		26		12% c		68; [60-73]		19		62% (16/26)	71; [28-186]		10	100% (25/25)	19%
	tretinoin		14		29% d		51; [34-55]		12		70% (7/10)	21; [14-28]		2	92% (12/13)	7%
	vandetanib		98		99%		60; [46-69]		74		67% (2/3) 58% (53/91)	NA 29; [15-74]		0 22	100% (4/4) 91% (86/95)	25% 6%
	belinostat vemurafenib		3 106		67% 76%		75 67; [60-74]		1 75		100% (1/1) 56% (56/100)	NA 35; [27-154]		0 31	100% (3/3) 89% (93/105)	33% 10%
	bicalutamide vorinostat		30 24		93% 83%		71; [69-76] 49.5; [49-66]		22 16		100% (29/29) 39% (7/18)	139; [3-486] 65		8 1	100 % (20/20) 92 % (22/24)	10% 12%
	bosutinib		9		89%		67.5; [54-68]		6		71% (5/7)	6.5; [5-8]		2	100 % (9/9)	0%
	capecitabine		161		95%		58; [48-66]		141		65% (98/151)	12; [7-64]		52	100% (134/134)	18%
	carfilzomib		19		100%		67; [56-72]		18		72% (13/18)	78; [22-202]		4	79% (15/19)	26%
	ceritinib		21		91%		57; [44-64]		16		32% (6/19)	13.5; [9-74]		4	62% (13/21)	10%
	chidamide		7		100%		73; [64-116]		7		57 % (4/7)	0; [0-188]		6	71% (5/7)	0%
	clofarabine		11		46% a		63.5; [38-64]		10		82% (9/11)		0; [0-0]		1	100% (11/11)	64%
	cobimetinib		20		5%		58; [54-72]		13		45% (9/20)	28; [27-43]		5	90% (18/20)	5%
	combretastatin a4		3		100%		73; [67-74]		3		0% (0/3)			NA		0	100% (3/3)	0%
	crizotinib		102		95%		59; [48-68]		75		51% (46/90)	17; [9-78]		26	93% (93/100)	9%
	cytarabine		134		22% a		55; [20-67]		95		60% (75/126)	11.5; [4-18]		26	98% (115/117)	40%
	dabrafenib		27		11%		68.5; [60-73]		18		63% (17/27)	87; [29-162]		11	100% (26/26)	19%
	dasatinib		94		85%		60; [46-67]		70		47% (37/78)	7; [4-34]		12	96% (87/91)	2%
	daunorubicin		77		18% a		45; [18-66]		55		51% (37/73)	12; [10-22]		14	98% (62/63)	44%
	decitabine		10		30%		70; [54-72]		6		50% (3/6)		NA		0	100% (10/10)	10%
	enzastaurin		3		100%			NA		0		100 % (2/2)		5; [5-5]		1	100% (3/3)	100%
	fluorouracil		138		95%		60; [53-69]		121		62% (85/137)	4; [2-30]		58	100% (95/95)	33%
	gemtuzumab ozogamicin	16		38% a		67; [54-70]		9		50% (7/14)	12; [12-28]		3	93% (14/15)	50%
	ibrutinib		99		96%		68; [60-74]		75		78% (72/92)	127; [67-286]		13	96% (92/96)	11%
	idarubicin		27		15% a		47; [38-62]		23		81% (21/26)	15; [9-35]		9	95% (19/20)	44%
	imatinib		64		74%		61; [46-72]		43		50% (27/54)	5; [0-14]		8	97% (57/59)	2%
	interferon alfacon-1		5		100%			34		1		100% (4/4)		NA		0	100% (4/4)	20%
	lenvatinib		11		100%		71.5; [64-78]		8		56% (5/9)	13; [13-13]		1	82% (9/11)	0%
	letrozole		25		40% b		67; [57-74]		15		0% (0/22)	15; [14-52]		5	100% (17/17)	4%
	midostaurin mitoxantrone bcg organisms-methanol extraction residue mogamulizumab bcg vaccine nelarabine nilotinib	no Yes	34 36 5 4 397	40 14207	56% 53% a 0 100% 0 50% a 93%	--	58.5; [49-66] 58.5; [42-67] -65.5; [56-71] -7.5; [6-8] 66; [54-74]	0 0	22 28 4 4 235	--	43% (12/28) 48% (16/33) -0 100% (5/5) -3 75% (3/4) 55% (188/342)	11; [6-24] 92; [10-120] --0.5; [0-4] -3•3 -5•3 19 53; [13-214]	0 28	15 9 4 1 93	---1•8	100% (33/33) -100% (29/29) 100% (5/5) -100% (4/4) -2•3 90% (343/383)	6% 33% 100% 50% 4%
	becatecarin	no		6	0	-		-	0		-	-	0	-	-	0		-	-
	belinostat	Yes		61	3	2•6	0•6	0		-	-	0	-	-	1		0•9	-2•9
	belotecan	no		365	0	-		-	0		-	-	0	-	-	0		-	-
	bendamustine	Yes		8747	9	0•1	-1	2		-0•8	-3•4	6	-1•1	-2•5	40		0	-0•5
	berbamine	no		25	0	-		-	0		-	-	0	-	-	0		-	-
	bevacizumab	Yes		59059	24	-1•3	-1•9	13		-1	-1•9	88	-0•1	-0•4	375		0•5	0•3
	bexarotene	Yes		638	0	-		-	0		-	-	0	-	-	4		0•4	-1•4
	bgb 324	no		3	0	-		-	0		-	-	0	-	-	0		-	-
	bi 2536	no		94	0	-		-	0		-	-	0	-	-	0		-	-
	bi 836826	no		11	0	-		-	0		-	-	0	-	-	0		-	-
	bicalutamide	Yes		4802	13	1•4	0•5	10		2	1	23	1•5	0•9	24		0•1	-0•5

a number (proportion) of reports associated with (other) antimetabolites and/or (other) anthracyclines co-suspected: clofarabine 5/11 (45%); cytarabine 84/134 (63%); daunorubicin 59/77 (77%); gemtuzumab ozogamicin 7/16 (44%); idarubicin 14/27 (52%); mitoxantrone 15/36 (42%); nelarabine 1/4 (25%); pegaspargase 17/22 (77%) b number (proportion) of ribociclib coreporting in letrozole cases: 15/25 (60%) c number (proportion) of dabrafenib coreporting in trametinib cases: 23/26 (88%) d number (proportion) of arsenic trioxide coreporting in tretinoin cases: 9/14 (64%)

Acknowledgments. The supplied data from VigiBase come from various sources. The likelihood of a causal relationship is not the same in all reports. The information does not represent the opinion of WHO.

We thank the custom searches team at the Uppsala Monitoring Centre (Uppsala, Sweden) research section, without whom this study would not have been possible.

no role in study design, collection, analysis or interpretation of the data, in the writing of the manuscript, and in the decision to submit it for publication.

Supplementary Data.

Spectrum of anticancer drug-induced Long QT, Torsade-de-Pointes, and ventricular arrhythmiasa WHO pharmacovigilance study Joe-Elie Salem, MD, PhD 1,2 ; Lee S Nguyen, MD, PhD [START_REF] Arnold | Progress in cancer survival, mortality, and incidence in seven high-income countries 1995-2014 (ICBP SURVMARK-2): a population-based study[END_REF][START_REF] Alexandre | Anticancer drug-induced cardiac rhythm disorders: Current knowledge and basic underlying mechanisms[END_REF] ; Javid J. Moslehi, MD [START_REF] Moslehi | Cardiovascular Toxic Effects of Targeted Cancer Therapies[END_REF] ; Stéphane Ederhy, MD [START_REF] Levis | Cardiotoxic effects of anthracycline-based therapy: what is the evidence and what are the potential harms?[END_REF] ; Bénédicte Lebrun-Vignes, MD [START_REF] Arnold | Progress in cancer survival, mortality, and incidence in seven high-income countries 1995-2014 (ICBP SURVMARK-2): a population-based study[END_REF][START_REF] Geraud | Clinical Pharmacology and Interplay of Immune Checkpoint Agents: A Yin-Yang Balance[END_REF] ; Dan M. Roden, MD [START_REF] Moslehi | Cardiovascular Toxic Effects of Targeted Cancer Therapies[END_REF][START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF] ; Christian Funck-Brentano, MD, PhD 1 ; Paul Gougis, MD [START_REF] Arnold | Progress in cancer survival, mortality, and incidence in seven high-income countries 1995-2014 (ICBP SURVMARK-2): a population-based study[END_REF] 1 INSERM, CIC-1901, Sorbonne Université, AP-HP, Pitié-Salpêtrière Hospital, Department of Pharmacology and Clinical Investigation Center, CLIP² Galilée, regional pharmacovigilance center, UNICO-GRECO Cardio-oncology program, F-75013, Paris, France. joe-elie.salem@aphp.fr, joe-elie.salem@vanderbilt.edu; Centre d' Investigation Clinique Paris-Est, Hôpital La Pitié-Salpêtrière, 47-83 Bld de l'hôpital, 75013 Paris, Secretariat: +33 1 42 17 85 31, Fax: +33 1 42 17 85 32 or Department of Medicine, Cardio-oncology program, Vanderbilt University Medical Center, Nashville, Tennessee, USA, contact: +1 (615) 322-0067.

Supplementary Figure 1. Flow chart of drugs analyzed in VigiBase. There were 663 anticancer drugs in total, of which 199 had an FDA approval (195 still active). Supplementary Figure 2. Cumulative number of individual case safety reports in VigiBase for druginduced long-QT syndrome (diLQT), or ventricular arrhythmias (VA) including Torsade-de-pointes (TdP) over years for all drugs with the subset of anticancer drugs per classes in VigiBase (A). The year for which the disproportional reporting became significant (IC025>0 vs. full database, see Supplementary methods) for each of the 49 identified liable anticancer drugs significantly associated with either diLQT, or VA including TdP is in bracket (B). Supplementary Figure 3A. Information component (IC) and its 95% lower-end credibility interval (IC025>0, see methods) for all drugs significantly associated with drug-induced long-QT syndrome (diLQT), Torsade-de-pointes (TdP) or ventricular arrhythmias (VA) in VigiBase thru Jan 1, 2019.

Anticancer drugs are flagged by their name and different colors depending on their classes: cytotoxic chemotherapy (CT); hormone therapy (HT); immunotherapy (IT); kinase inhibitor (KI) and Miscellaneous (Misc). See Supplementary-Figure -3B and Supplementary-Table-2 for more details for top associations with diLQT and TdP of non-anticancer drugs including positive controls at know risk of diLQT and TdP (ibutilide, dofetilide, sotalol), [START_REF] Grouthier | Increased long QT and torsade de pointes reporting on tamoxifen compared with aromatase inhibitors[END_REF][START_REF] Salem | Association of Oral Contraceptives With Drug-Induced QT Interval Prolongation in Healthy Nonmenopausal Women[END_REF] and negative controls (testosterone, progesterone, and levonorgestrel). [START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF][START_REF] Xiao | Ibrutinib-Mediated Atrial Fibrillation Attributable to Inhibition of C-Terminal Src Kinase[END_REF] Supplementary Figure 3B. Information Component (IC, a Bayesian disproportionality estimate) and its 95% lower end credibility interval (IC025>0 is deemed significant) for diLQT (drug-induced Long-QT syndrome) and TdP (Torsade-de-Pointes) associated with all drugs with at least one report available in VigiBase(n=330/1,498 significant for diLQT and all drugs in VigiBase of which 37/164 anticancer molecules, and n=239/1,134 significant for TdP and all drugs in VigiBase of which 10/105 anticancer drugs). Each bar represents one drug including the anticancer drugs (AntiK) which are displayed in red or pink if the association is significant or not, respectively. Drugs known to prolong QT and to induce TdP, such as sotalol, ibutilide, and dofetilide are used as positive controls (blue). [START_REF] Grouthier | Increased long QT and torsade de pointes reporting on tamoxifen compared with aromatase inhibitors[END_REF][START_REF] Salem | Association of Oral Contraceptives With Drug-Induced QT Interval Prolongation in Healthy Nonmenopausal Women[END_REF] Drugs known to protect against drug-induced QT-prolongation and eventually TdP, such as progesterone, testosterone, and levonorgestrel were used as negative controls. [START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF][START_REF] Xiao | Ibrutinib-Mediated Atrial Fibrillation Attributable to Inhibition of C-Terminal Src Kinase[END_REF] Supplementary Figure 4. Concordance (Cohen's kappa coefficient) of signals in VigiBase and information retrieved in crediblemeds [START_REF] Bihan | Uses of pharmacovigilance databases: An overview[END_REF] and US FDA (Food and Drug Administration) labels [START_REF] Bate | A Bayesian neural network method for adverse drug reaction signal generation[END_REF] for druginduced long-QT syndrome (diLQT) and/or ventricular arrhythmias (VA) including Torsade-de-pointes (TdP) for anticancer drugs (See supplementary-Table-3 for the full list of 663 screened anticancer drugs). Drugs were considered at risk of diLQT and/or VA (including TdP) cardiac pro-arrhythmias in VigiBase if there was any significant association with diLQT, VA or TdP (IC025>0; vs. full database); in CredibleMeds (queried July 1 st ,2019) if the drugs were flagged at possible or known risk for TdP (conditional risk not accounted); and in USA FDA labels (queried July 1 st ,2019) if the last available drug label mentioned a risk for diLQT, VA or TdP as a warning (boxed or not) or a clear mention in the text (Table-4). Panel A shows the results when considering the 663 VigiBase referenced anticancer drugs and panel B when restricting the analysis to FDA approved drugs (199, of which 195 with still an active approval). 1. Cardiac phenotypes grouping used in our VigiBase analysis as a function of Medical Dictionary for Regulatory Activities (MedDRA) preferred term (PT) levels Classification Version 21.1. 2. Number of reports observed (Nobs) for a given drug and an adverse drug reaction (ADR); and disproportionality association evaluated by the IC025 (lower-end of the 95% credibility interval, >0 is significant) for the top 25 drugs (highest and lowest IC025) with drug-induced Long-QT Syndrome (diLQT) in VigiBase thru Jan 1, 2019 (using full database as background).

Supplementary Table

Supplementary Table

Association with ventricular arrhythmias (VA), Torsade-de-Pointes (TdP) and sudden-death (SD) are also displayed for these latter drugs. Positive control drugs are in blue (sotalol, ibutilide, and dofetilide), [START_REF] Grouthier | Increased long QT and torsade de pointes reporting on tamoxifen compared with aromatase inhibitors[END_REF][START_REF] Salem | Association of Oral Contraceptives With Drug-Induced QT Interval Prolongation in Healthy Nonmenopausal Women[END_REF] negative control drugs are in green (progesterone, testosterone, and levonorgestrel), [START_REF] Salem | Cardiovascular toxicities associated with immune checkpoint inhibitors: an observational, retrospective, pharmacovigilance study[END_REF][START_REF] Xiao | Ibrutinib-Mediated Atrial Fibrillation Attributable to Inhibition of C-Terminal Src Kinase[END_REF] and anticancer drugs are in red. (-) stands for the absence of reports and the possibility for IC025 evaluation.

Abbreviations: Ndrug being the number of reports for the drug, regardless of the adverse drug reaction (ADR); Neffect being the number of reports for the ADR, regardless of the drug. 3. Number of reports observed (Nobs) for a given drug and the adverse drug reaction (ADR) of interest; and disproportional association evaluated by the IC025 (lower-end of the 95% credibility interval, >0 is significant) for anti-cancer drugs with drug-induced Long-QT Syndrome (diLQT), ventricular arrhythmias (VA), Torsade-de-Pointes (TdP) and sudden-death (SD) in VigiBase thru Jan 1, 2019 (using full database as background). Anticancer drugs approved by US FDA (as of 1 st of July 2019) are also shown even in the absence of any Nobs (-) for diLQT, TdP, VA and SD in VigiBase.

Supplemental Table

In total, 663 anticancer drugs were referenced in VigiBase, of which 199 had an US FDA approval (195 still active as of 1 st of July 2019).

Abbreviations: Ndrug being the number of reports for the drug, regardless of the adverse drug reaction (ADR); Neffect being the number of reports for the ADR, regardless of the drug.

Supplemental

Supplementary Table 3.