Impact of organised programs on colorectal cancer screening

<p>Abstract</p> <p>Purpose</p> <p>Colorectal cancer (CRC) screening has been shown to decrease CRC mortality. Organised mass screening programs are being implemented in France. Its perception in the general population and by general practitioners is not well known.</p> <p>Methods</p> <p>Two nationwide observational telephone surveys were conducted in early 2005. First among a representative sample of subjects living in France and aged between 50 and 74 years that covered both geographical departments with and without implemented screening services. Second among General Practionners (Gps). Descriptive and multiple logistic regression was carried out.</p> <p>Results</p> <p>Twenty-five percent of the persons(N = 1509) reported having undergone at least one CRC screening, 18% of the 600 interviewed GPs reported recommending a screening test for CRC systematically to their patients aged 50–74 years. The odds ratio (OR) of having undergone a screening test using FOBT was 3.91 (95% CI: 2.49–6.16) for those living in organised departments (referent group living in departments without organised screening), almost twice as high as impact educational level (OR = 2.03; 95% CI: 1.19–3.47).</p> <p>Conclusion</p> <p>CRC screening is improved in geographical departments where it is organised by health authorities. In France, an organised screening programs decrease inequalities for CRC screening.</p

Minimal information for studies of extracellular vesicles 2018 (MISEV2018):a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines

The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points

Abstract 1145: Therapeutic drug monitoring of Sunitinib in real-world patients

International audienceAbstract Therapeutic Drug Monitoring (TDM) with model-based adaptive dosing is expected to quickly ensure that exposure levels of anticancer agents are in the adequate range of plasma concentrations. Here, we have compared such TDM + in silico approach with empirical changes in dosing performed upon clinical signs in patients with metastatic renal cell carcinoma treated with Sunitinib. A total of 31 patients were monitored in our institute. All patients were treated with a standard starting dose of 50 mg QD following the 4/2 schedule. Sunitinib and n-desethyl sunitinib trough levels were assayed at steady state by a fully validated LC-MS/MS analysis. A PK-pop model with Bayesian estimate implemented on Monolix helped to identify individual PK parameters from a single time-point. Two different target exposures were considered (i.e., trough levels comprised between 50 and 100 ng/ml or 0-24h AUC comprised between 1250 and 2150 ng/ml.h). The PK model enabled to simulate both trough levels and 0-24h AUC from a single sample, regardless of the sampling time. Overall, dosing was modified empirically in about 54% of the patients after treatment started, mostly (46%) for reducing the dosing after that toxicities showed. Clinical benefit (i.e., stable disease + partial response + complete reponse) was achieved in 54% of patients but treatment discontinuation was observed in 58% of the patients eventually, mostly because of severe side-effects. A relationship between baseline exposure levels and early onset treatment-related toxicities was found. Because of the various changes in dosing, no such relationship was found between baseline exposure levels and efficacy evaluated at 3 months. TDM showed that only 45% of the patients were in the right window for trough levels (i.e., 50-100 ng/ml) and only 26% when considering AUC as the target exposure (i.e., 1200-2150 ng/ml.h). Consequently, the PK/PD model would have suggested to rapidly customize dosing in a much larger part of the patients (i.e., 84% with respect to the target AUCs). Importantly, Sunitinib dosing was empirically reduced only in 41% patients who displayed early-onset severe toxicities, whereas modelling would have immediately proposed to cut the dosing in more than 80% of those patients, thus suggesting that the safety of Sunitinib could have been improved when using our model. Although performed on a limited number of patients, this real-world study supports the hypothesis that TDM associated with PK modelling could help the prescriber to identify the best Sunitinib dosing in a quicker way than empirical change in dosing. Citation Format: Laurent Ferrer, Jonathan Chauvin, Dr Jean-Laurent Deville, Joseph Ciccolini. Therapeutic drug monitoring of Sunitinib in real-world patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1145

Quantitative cell-free DNA markers for prediction of early progression in patients undergoing immunotherapy

International audienceEarly prediction of resistance to immunotherapy is a major challenge in oncology. The ongoing SChISM (Size Cell-fre DNA (cfDNA) Immunotherapies Signature Monitoring) clinical study proposes an innovative approach based on patented cfDNA quantification methods, providing concentration and size profile fluctuations of plasmatic circulating DNA for early therapeutic management of immune checkpoint inhibitors treated patients. The main interest is that such measures can be performed much earlier than the first imaging evaluation, thanks to liquid biopsies. Five cancer types were investigated: melanoma, head and neck, renal, bladder and lung cancers, with a total of 260 patients at the end of the study, described by their clinical and classical biological data, and cfDNA features, as concentration, first and second peak of the cfDNA size distribution, and specific size ranges of cfDNA fragments. The principal purpose is to early predict response to immunotherapy by searching for a longitudinal signature in the concentration and sizes in cfDNA. The study aims to develop mechanistic model of cfDNA joint kinetics with other longitudinal markers and tumor size imaging. Such models embedded within a statistical framework will then be calibrated on our population data. Machine learning models will be used to predict some outcomes as radiologically confirmed progression at the first imaging evaluation, progression-free survival or the overall survival, thanks to these dynamic parameters and other variables available at baseline and in order to predict response to immunotherapy. Thus, typical classification models as logistic regression, or survival models as proportional hazard Cox regression model will be tested to analyze feature at baseline, and models consisting of a dynamic system of differential equations will help us to describe the evolution of the quantitative profile of cfDNA over time

Quantitative cell-free DNA markers for prediction of early progression in patients undergoing immunotherapy

International audienceEarly prediction of resistance to immunotherapy is a major challenge in oncology. The ongoing SChISM (Size Cell-fre DNA (cfDNA) Immunotherapies Signature Monitoring) clinical study proposes an innovative approach based on patented cfDNA quantification methods, providing concentration and size profile fluctuations of plasmatic circulating DNA for early therapeutic management of immune checkpoint inhibitors treated patients. The main interest is that such measures can be performed much earlier than the first imaging evaluation, thanks to liquid biopsies. Five cancer types were investigated: melanoma, head and neck, renal, bladder and lung cancers, with a total of 260 patients at the end of the study, described by their clinical and classical biological data, and cfDNA features, as concentration, first and second peak of the cfDNA size distribution, and specific size ranges of cfDNA fragments. The principal purpose is to early predict response to immunotherapy by searching for a longitudinal signature in the concentration and sizes in cfDNA. The study aims to develop mechanistic model of cfDNA joint kinetics with other longitudinal markers and tumor size imaging. Such models embedded within a statistical framework will then be calibrated on our population data. Machine learning models will be used to predict some outcomes as radiologically confirmed progression at the first imaging evaluation, progression-free survival or the overall survival, thanks to these dynamic parameters and other variables available at baseline and in order to predict response to immunotherapy. Thus, typical classification models as logistic regression, or survival models as proportional hazard Cox regression model will be tested to analyze feature at baseline, and models consisting of a dynamic system of differential equations will help us to describe the evolution of the quantitative profile of cfDNA over time

Quantitative cell-free DNA markers for prediction of early progression in patients undergoing immunotherapy

International audienceEarly prediction of resistance to immunotherapy is a major challenge in oncology. The ongoing SChISM (Size Cell-fre DNA (cfDNA) Immunotherapies Signature Monitoring) clinical study proposes an innovative approach based on patented cfDNA quantification methods, providing concentration and size profile fluctuations of plasmatic circulating DNA for early therapeutic management of immune checkpoint inhibitors treated patients. The main interest is that such measures can be performed much earlier than the first imaging evaluation, thanks to liquid biopsies. Five cancer types were investigated: melanoma, head and neck, renal, bladder and lung cancers, with a total of 260 patients at the end of the study, described by their clinical and classical biological data, and cfDNA features, as concentration, first and second peak of the cfDNA size distribution, and specific size ranges of cfDNA fragments. The principal purpose is to early predict response to immunotherapy by searching for a longitudinal signature in the concentration and sizes in cfDNA. The study aims to develop mechanistic model of cfDNA joint kinetics with other longitudinal markers and tumor size imaging. Such models embedded within a statistical framework will then be calibrated on our population data. Machine learning models will be used to predict some outcomes as radiologically confirmed progression at the first imaging evaluation, progression-free survival or the overall survival, thanks to these dynamic parameters and other variables available at baseline and in order to predict response to immunotherapy. Thus, typical classification models as logistic regression, or survival models as proportional hazard Cox regression model will be tested to analyze feature at baseline, and models consisting of a dynamic system of differential equations will help us to describe the evolution of the quantitative profile of cfDNA over time

Quantitative cell-free DNA markers for prediction of early progression in patients undergoing immunotherapy

International audienceEarly prediction of resistance to immunotherapy is a major challenge in oncology. The ongoing SChISM (Size Cell-fre DNA (cfDNA) Immunotherapies Signature Monitoring) clinical study proposes an innovative approach based on patented cfDNA quantification methods, providing concentration and size profile fluctuations of plasmatic circulating DNA for early therapeutic management of immune checkpoint inhibitors treated patients. The main interest is that such measures can be performed much earlier than the first imaging evaluation, thanks to liquid biopsies. Five cancer types were investigated: melanoma, head and neck, renal, bladder and lung cancers, with a total of 260 patients at the end of the study, described by their clinical and classical biological data, and cfDNA features, as concentration, first and second peak of the cfDNA size distribution, and specific size ranges of cfDNA fragments. The principal purpose is to early predict response to immunotherapy by searching for a longitudinal signature in the concentration and sizes in cfDNA. The study aims to develop mechanistic model of cfDNA joint kinetics with other longitudinal markers and tumor size imaging. Such models embedded within a statistical framework will then be calibrated on our population data. Machine learning models will be used to predict some outcomes as radiologically confirmed progression at the first imaging evaluation, progression-free survival or the overall survival, thanks to these dynamic parameters and other variables available at baseline and in order to predict response to immunotherapy. Thus, typical classification models as logistic regression, or survival models as proportional hazard Cox regression model will be tested to analyze feature at baseline, and models consisting of a dynamic system of differential equations will help us to describe the evolution of the quantitative profile of cfDNA over time

Mechanistic modeling of the longitudinal tumor and biological markers combined with quantitative cell-free DNA

International audienceEarly prediction of resistance to immunotherapy is a major challenge in oncology. The ongoing SChISM (Size Cell-fre DNA (cfDNA) Immunotherapies Signature Monitoring) clinical study proposes an innovative approach based on patented cfDNA quantification methods, providing concentration and size profile fluctuations of plasmatic circulating DNA for early therapeutic management of immune checkpoint inhibitors treated patients. The main interest is that such measures can be performed in a less invasive, less expansive way, and especially much earlier than the first imaging evaluation, thanks to liquid biopsies. Five cancer types are investigated: melanoma, head and neck, renal, bladder and lung cancers, with a total of 260 patients at the end of the study, described by their clinical and classical biological data, and cfDNA features, such as concentration, first and second peak of the cfDNA size distribution, and specific size ranges of cfDNA fragments. We developed a mechanistic model of cfDNA joint kinetics with other longitudinal markers and tumor size imaging to help describe and understand the time dynamics of the quantitative profiles of cfDNA over time. The model consists of a dynamical system of differential equations that estimates specifically the component corresponding to cfDNA production by tumor lesions. Subsequently, the model is embedded within a nonlinear mixed-effects statistical framework in order to quantify inter-patient variability, and calibrated on the data. Future perspective will use machine learning models to predict early progression, progression-free survival or overall survival, combining these dynamic parameters and other variables available at baselin