Apport des méthodes de survie nette dans le pronostic des lymphomes malins non hodgkiniens en population générale

The net survival of cancer patients in population studies is the most relevant indicator to assess the overall efficiency of the healthcare system of a country. Net survival is defined as the survival that would be observed if the sole cause of death were cancer. This concept is crucial in comparative studies (between geographical areas and/or periods of diagnosis) that estimate specific variations of cancer-related deaths. Net survival takes into account potential differences in mortality patterns between groups. Currently, two methods provide unbiased estimations of net survival: the non-parametric estimator of Pohar-Perme and the parametric model adjusted on specific covariates (mainly, the age at diagnosis). Moreover, new improved parametric tools, such as flexible models, can model the complex covariate effects on mortality. In this work, we modeled the excess mortality rate after a non Hodgkin lymphoma diagnosis, with a model developed by Remontet et al. In addition, we used an appropriate model-building-strategy to model jointly the complex effects of some covariates (such as the time elapsed since diagnosis, the year of diagnosis, and age) on the excess mortality. Finally, this approach allowed for the covariate effects on the net survival and on the excess mortality rate. We applied this method to two different collaborative databases: first on the French database FRANCIM (1995 to 2010) to study the excess mortality after diagnosis of follicular lymphoma, then on the European data of EUROCARE-5 (1996 to 2004) to study the excess mortality after diagnosis of follicular lymphoma and diffuse large B-cell lymphoma. According to the results, the dynamics of the excess mortality rate varies over the time elapsed since diagnosis according to the lymphoma subtype, the age, and the geographical area. The trends of these dynamics over the years of diagnosis are different tooL'étude de la survie nette des patients atteints de cancer en population générale permet d'apprécier l'efficience globale du système de soin d'un pays. La survie nette se définit comme la survie qui serait observée si la seule cause de décès possible était le cancer. Ce concept est fondamental dans les comparaisons entre zones géographiques et/ou périodes de diagnostic dont l'intérêt est d'estimer les variations spécifiques de la mortalité due au cancer. Le concept de survie nette permet de prendre en compte les éventuelles différences de mortalité naturelle entre les groupes comparés. Actuellement, seuls deux outils estiment la survie nette sans biais : l'estimateur non paramétrique de Pohar-Perme et la modélisation paramétrique ajustée sur certaines covariables (essentiellement l'âge). Par ailleurs, les outils paramétriques s'étant perfectionnés, de nouveaux modèles flexibles permettent de modéliser les effets complexes des variables sur la mortalité. Ce travail repose sur la modélisation du taux de mortalité en excès à la suite d'un lymphome malin non hodgkinien, en se basant sur le modèle proposé par Remontet et al. et sur la nécessité de modéliser conjointement les effets complexes des covariables (telles que le temps de suivi, l'année de diagnostic et l'âge) sur la mortalité à l'aide d'une stratégie de modélisation adaptée. L'effet des variables est restitué sur la survie nette mais aussi sur le taux de mortalité en excès ce qui représente un élément nouveau dans les études de survie. Deux applications ont été menées sur des bases de données collaboratives de population : d'une part sur les données françaises du réseau FRANCIM à la suite d'un diagnostic de lymphome folliculaire entre 1995 et 2010 et, d'autre part, sur les données européennes d'EUROCARE-5 après un lymphome folliculaire ou un lymphome B diffus à grandes cellules diagnostiqué entre 1996 et 2004. Les résultats montrent que la dynamique du taux de mortalité en excès au cours du temps de suivi varie en fonction du sous-type de lymphome, de l'âge et de la zone géographique. Les tendances de cette dynamique en fonction de l'année de diagnostic sont également différente

Contribution of net survival methods to the prognosis of Non-Hodgkin lymphoma in population studies

L'étude de la survie nette des patients atteints de cancer en population générale permet d'apprécier l'efficience globale du système de soin d'un pays. La survie nette se définit comme la survie qui serait observée si la seule cause de décès possible était le cancer. Ce concept est fondamental dans les comparaisons entre zones géographiques et/ou périodes de diagnostic dont l'intérêt est d'estimer les variations spécifiques de la mortalité due au cancer. Le concept de survie nette permet de prendre en compte les éventuelles différences de mortalité naturelle entre les groupes comparés. Actuellement, seuls deux outils estiment la survie nette sans biais : l'estimateur non paramétrique de Pohar-Perme et la modélisation paramétrique ajustée sur certaines covariables (essentiellement l'âge). Par ailleurs, les outils paramétriques s'étant perfectionnés, de nouveaux modèles flexibles permettent de modéliser les effets complexes des variables sur la mortalité. Ce travail repose sur la modélisation du taux de mortalité en excès à la suite d'un lymphome malin non hodgkinien, en se basant sur le modèle proposé par Remontet et al. et sur la nécessité de modéliser conjointement les effets complexes des covariables (telles que le temps de suivi, l'année de diagnostic et l'âge) sur la mortalité à l'aide d'une stratégie de modélisation adaptée. L'effet des variables est restitué sur la survie nette mais aussi sur le taux de mortalité en excès ce qui représente un élément nouveau dans les études de survie. Deux applications ont été menées sur des bases de données collaboratives de population : d'une part sur les données françaises du réseau FRANCIM à la suite d'un diagnostic de lymphome folliculaire entre 1995 et 2010 et, d'autre part, sur les données européennes d'EUROCARE-5 après un lymphome folliculaire ou un lymphome B diffus à grandes cellules diagnostiqué entre 1996 et 2004. Les résultats montrent que la dynamique du taux de mortalité en excès au cours du temps de suivi varie en fonction du sous-type de lymphome, de l'âge et de la zone géographique. Les tendances de cette dynamique en fonction de l'année de diagnostic sont également différentesThe net survival of cancer patients in population studies is the most relevant indicator to assess the overall efficiency of the healthcare system of a country. Net survival is defined as the survival that would be observed if the sole cause of death were cancer. This concept is crucial in comparative studies (between geographical areas and/or periods of diagnosis) that estimate specific variations of cancer-related deaths. Net survival takes into account potential differences in mortality patterns between groups. Currently, two methods provide unbiased estimations of net survival: the non-parametric estimator of Pohar-Perme and the parametric model adjusted on specific covariates (mainly, the age at diagnosis). Moreover, new improved parametric tools, such as flexible models, can model the complex covariate effects on mortality. In this work, we modeled the excess mortality rate after a non Hodgkin lymphoma diagnosis, with a model developed by Remontet et al. In addition, we used an appropriate model-building-strategy to model jointly the complex effects of some covariates (such as the time elapsed since diagnosis, the year of diagnosis, and age) on the excess mortality. Finally, this approach allowed for the covariate effects on the net survival and on the excess mortality rate. We applied this method to two different collaborative databases: first on the French database FRANCIM (1995 to 2010) to study the excess mortality after diagnosis of follicular lymphoma, then on the European data of EUROCARE-5 (1996 to 2004) to study the excess mortality after diagnosis of follicular lymphoma and diffuse large B-cell lymphoma. According to the results, the dynamics of the excess mortality rate varies over the time elapsed since diagnosis according to the lymphoma subtype, the age, and the geographical area. The trends of these dynamics over the years of diagnosis are different to

Trends in excess mortality in follicular lymphoma at a population level.

BACKGROUND: Since the 1990s and since the development of humanised monoclonal antibodies in 1998, the treatment of non-Hodgkin lymphoma has undergone profound changes. Follicular lymphoma (FL) was the first to benefit from this treatment, and several clinical trials have shown a significant improvement in overall survival, but little information is available at a population level. OBJECTIVE: Our objective was to estimate changes in FL-specific mortality at a population level, with an appropriate methodology. METHODS: Two French retrospective population-based studies on FL were conducted, one from 1995 to 2004, in 1477 patients, and one from 1995 to 2010, in 451 patients. Trends in excess mortality rates (EMRs) according to age, sex, Ann Arbor stage and year of diagnosis were evaluated using the flexible model of Remontet et al. RESULTS: Trends in the EMR differed according to age at diagnosis and was higher in advanced stage (III, IV) in patients older than 65 yr. The EMR decreased linearly from 1995 to 2010. This decrease was more marked for advanced stages. CONCLUSION: FL-specific mortality decreased over the years of diagnosis, and the difference according to the lymphoma stage diminished in more recent years. However, progress in the management of FL was not able to erase age-related differences

A partial review of cure models with an application to French cancer registries data to improve patients' access to insurance and credit

International audienceBackgroundSurvival cure models are widely used in public health researches to analyze time-to-event data in which some subjects would never experience the event of interest; these subjects are said to be statistically cured. There are two types of cure models, the mixture cure modeland the non-mixture cure model which were first formulated respectively by Boag(1949)[1] and Yakovlev et al. (1993) [2]. These models have been intensively developed [3,4 among others] and have also been extended to the net survival framework [5-7 for instance].In cancersurvival analysis,net survival is a measure of survival in the hypothetical world wherecancer would bethe only possible cause of death [8,9].proportion of the subjects who are no longer at risk to die from their cancer i.e. the subjects without additional risk of death due to cancer (curedsubjects) ii) the time from which subjects can be supposed to be cured(thus the timeto surtax-free insurance).Methods : The principles Underlying the formulation of both the mixture and the non-mixture cure models were recalled and abrief review of the two types of models was provided. The extension of cure models to the net survival framework was exposed and the flexible non -mixture cure model based on net survival and developed by Andersson et al. (2011) was described. The later model was fitted to melanoma, colorectal and liver cancers data from the French cancer registries network. The data included all patients diagnosed between 1989 and 2010, aged between 15 and 74 at diagnosis and followed -up on June 31, 2013 for vital status. Cure time T was defined as the time when 90% of deaths due to cancer had occurred. T corresponded to the time at which the net survival reached a plateau at a non-zero value defined as the cure proportion P.T was referred to as the time from diagnosis to surtax-free insurance.Results : For melanoma, netsurvival reached a plateau at a cure proportion P of 88% for women and 82% for men. Cure times T were respectively 11.5 and 8.0 years after diagnosis. For colorectal cancer P was 57% for women and 51% for men,corresponding T were 7.5 and 8.4 years. T varied according to age, ranging from 7.3 years to 7.8 years forwomen and 8.2 to 8.6 years for men. For liver cancer, P varied according to age from 6 to 21% for women and 6 to 11% for men. T ranged from 3.4 to 5.1 years for women and 4.2 to 5.0 years for men. Conclusions : Cure models are useful tools to improve access to insurance and credit by allowing time to surtax free to rest on statistical evidence, and to be adjusted according to cure time.Cure time varied with cancer site, age and sex. It was lower than 10 years in various cases. Time to surtax free insurance should be reassessed for each site according to newly estimated time to cure.Howeverthe cure time as defined and estimated when using cure models is not entirely satisfactory and is subject to criticism. Further works on cure models are then needed to improve the estimation of the cure time

Different profile and distribution of antigen specific T cells induced by intranasal and intrarectal immunization with rotavirus 2/6-VLP with and without LT-R192G

International audienceIn this study, we compared both the profile and distribution of antigen specific primed T cells after intrarectal (IR) and intranasal (IN) immunization with rotavirus (RV) 2/6-VLP, alone or in the presence of LT-R192G, in order to highlight the differences between the two routes and the impact of the adjuvant. Adult BALB/c mice were immunized once with 2/6-VLP with or without adjuvant and the T cell response was analyzed in lymphoid tissues after in vitro restimulation with the antigen. IN, but not IR, immunization of mice with 2/6-VLP alone induced antigen-specific IL-10 and IL-17 secreting T cells. IL-10-, in contrast to IL-17-, secreting T cells did not migrate to the mesenteric lymph nodes (MLN) whereas they were detected in cervical lymph nodes (CLN) and spleen. With the IN route, the adjuvant allowed to complete this profile with the secretion of IL-2 and IL-4, increased IL-17 secretion and induced antigen specific CD4+CD25+Foxp3+ and Foxp3- T cells in all studied organs (CLN, spleen and MLN) but did not impact on IL-10 secreting T cells. With the IR route, the adjuvant induced IL-2 and IL-17 secretion but, in contrast to the IN route, did not allow IL-4 production. These results show that, for a same antigen, T cell priming not only depends on the presence of adjuvant but also on the mucosal route of administration. Moreover, they show a different dissemination of IL-10 secreting T cells compared to other subtypes. (C) 2013 Elsevier Ltd. All rights reserved

Flexible Modeling of Net Survival and Cure by AML Subtype and Age: A French Population-Based Study from FRANCIM

With improvements in acute myeloid leukemia (AML) diagnosis and treatment, more patients are surviving for longer periods. A French population of 9453 AML patients aged ≥15 years diagnosed from 1995 to 2015 was studied to quantify the proportion cured (P), time to cure (TTC) and median survival of patients who are not cured (MedS). Net survival (NS) was estimated using a flexible model adjusted for age and sex in sixteen AML subtypes. When cure assumption was acceptable, the flexible cure model was used to estimate P, TTC and MedS for the uncured patients. The 5-year NS varied from 68% to 9% in men and from 77% to 11% in women in acute promyelocytic leukemia (AML-APL) and in therapy-related AML (t-AML), respectively. Major age-differenced survival was observed for patients with a diagnosis of AML with recurrent cytogenetic abnormalities. A poorer survival in younger patients was found in t-AML and AML with minimal differentiation. An atypical survival profile was found for acute myelomonocytic leukemia and AML without maturation in both sexes and for AML not otherwise specified (only for men) according to age, with a better prognosis for middle-aged compared to younger patients. Sex disparity regarding survival was observed in younger patients with t-AML diagnosed at 25 years of age (+28% at 5 years in men compared to women) and in AML with minimal differentiation (+23% at 5 years in women compared to men). All AML subtypes included an age group for which the assumption of cure was acceptable, although P varied from 90% in younger women with AML-APL to 3% in older men with acute monoblastic and monocytic leukemia. Increased P was associated with shorter TTC. A sizeable proportion of AML patients do not achieve cure, and MedS for these did not exceed 23 months. We identify AML subsets where cure assumption is negative, thus pointing to priority areas for future research efforts

Int J Epidemiol

BACKGROUND: In descriptive epidemiology, there are strong similarities between incidence and survival analyses. Because of the success of multidimensional penalized splines (MPSs) in incidence analysis, we propose in this pedagogical paper to show that MPSs are also very suitable for survival or net survival studies. METHODS: The use of MPSs is illustrated in cancer epidemiology in the context of survival trends studies that require specific statistical modelling. We focus on two examples (cervical and colon cancers) using survival data from the French cancer registries (cases 1990-2015). The dynamic of the excess mortality hazard according to time since diagnosis was modelled using an MPS of time since diagnosis, age at diagnosis and year of diagnosis. Multidimensional splines bring the flexibility necessary to capture any trend patterns while penalization ensures selecting only the complexities necessary to describe the data. RESULTS: For cervical cancer, the dynamic of the excess mortality hazard changed with the year of diagnosis in opposite ways according to age: this led to a net survival that improved in young women and worsened in older women. For colon cancer, regardless of age, excess mortality decreases with the year of diagnosis but this only concerns mortality at the start of follow-up. CONCLUSIONS: MPSs make it possible to describe the dynamic of the mortality hazard and how this dynamic changes with the year of diagnosis, or more generally with any covariates of interest: this gives essential epidemiological insights for interpreting results. We use the R package survPen to do this type of analysis

Increased Risk of Acute Myeloid Leukemias and Myelodysplastic Syndromes in Patients Who Received Thiopurine Treatment for Inflammatory Bowel Disease.

International audienceBACKGROUND: & Aims: Treatment with immunosuppressive thiopurines such as azathioprine is associated with an increased risk of leukemogenesis. We assessed the risk of myeloid disorders, such as acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS), in a large cohort of patients with inflammatory bowel disease (IBD) in France. METHODS: We performed a prospective observational study of 19,486 patients with IBD enrolled in the Cancers Et Surrisque Associé aux Maladies inflammatoires intestinales En France study from May 2004 through June 2005; patients were followed through December 31, 2007. The incidence of myeloid disorders in the general population, used for reference, was determined from the French Network of Cancer Registries. RESULTS: During 49,736 patient-years follow-up, 5 patients were diagnosed with incident myeloid disorders (2 with AML and 3 with MDS). Four of these patients had been exposed to thiopurines (1 with ongoing treatment and 3 with past exposure). The risk of myeloid disorders was not increased among the overall IBD population, compared with the general population; the standardized incidence ratio (SIR) was 1.80 (95% confidence interval [CI], 0.58-4.20). The risk of myeloid disorders was not increased among patients with IBD and ongoing thiopurine treatment (SIR = 1.54; 95 % CI, 0.05-8.54), but patients with past exposures to thiopurines had an increased risk of myeloid disorders (SIR = 6.98; 95% CI, 1.44-20.36). CONCLUSIONS: Past exposure to thiopurines increases the risk of myeloid disorders 7-fold among patients with IBD. This finding should be considered when initiating thiopurine therapy, so risks and benefits can be calculated

A new approach to estimate time-to-cure from cancer registries data

IF 2.343International audienceBackgroundCure models have been adapted to net survival context to provide important indicators from population-based cancer data, such as the cure fraction and the time-to-cure. However existing methods for computing time-to-cure suffer from some limitations.MethodsCure models in net survival framework were briefly overviewed and a new definition of time-to-cure was introduced as the time TTC at which P(t), the estimated covariate-specific probability of being cured at a given time t after diagnosis, reaches 0.95. We applied flexible parametric cure models to data of four cancer sites provided by the French network of cancer registries (FRANCIM). Then estimates of the time-to-cure by TTC and by two existing methods were derived and compared. Cure fractions and probabilities P(t) were also computed.ResultsDepending on the age group, TTC ranged from to 8 to 10 years for colorectal and pancreatic cancer and was nearly 12 years for breast cancer. In thyroid cancer patients under 55 years at diagnosis, TTC was strikingly 0: the probability of being cured was >0.95 just after diagnosis. This is an interesting result regarding the health insurance premiums of these patients. The estimated values of time-to-cure from the three approaches were close for colorectal cancer only.ConclusionsWe propose a new approach, based on estimated covariate-specific probability of being cured, to estimate time-to-cure. Compared to two existing methods, the new approach seems to be more intuitive and natural and less sensitive to the survival time distribution