62 research outputs found

    Is the breast-conserving treatment with radiotherapy appropriate in mutation carriers? Long-term results and review of the literature

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    International audienceAs tumours in mutation carriers might be more sensitive to radiation, we investigated after long-term follow-up whether mutation status influenced the rate of ipsilateral and contralateral breast cancers after breast-conserving treatment (BCT). and genes were screened for germline mutations in 131 patients with a family history of breast and/or ovarian cancer who had undergone BCT and radiotherapy. Patients were matched to 261 controls with sporadic breast cancer according to age at diagnosis and year of treatment. Controls were followed up for at least as long as the interval between diagnosis and genetic screening in familial cases. Rates of ipsilateral and contralateral cancer between groups were compared by the log-rank test. The mutations occurred in 20.6% of tested patients. Tumours in mutation carriers were more likely to be grade III ( < 10) and oestrogen receptor negative ( = 0.005) than in non-carriers and controls. Overall median follow-up was 161 months. There was no significant difference in ipsilateral tumours between mutation carriers, non-carriers and controls ( = 0.13). On multivariate analysis, age was the most significant predictor for ipsilateral recurrence ( < 10). The rate of contralateral cancer was significantly higher in familial cases: 40.7% (mutation carriers), 20% (non-carriers), and 11% (controls) ( < 10). After 13.4 years of follow-up, the rate of ipsilateral tumours was no higher in mutation carriers than in non-carriers or controls. As tumours in mutation carriers might be more sensitive to radiation, BCT is a possible treatment option

    Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival

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    Biologia computacional i bioinformàtica; Oncologia; Marcadors predictiusBiología computacional y bioinformática; Oncología; Marcadores predictivosComputational biology and bioinformatics; Oncology; Predictive markersThe expanding targeted therapy landscape requires combinatorial biomarkers for patient stratification and treatment selection. This requires simultaneous exploration of multiple genes of relevant networks to account for the complexity of mechanisms that govern drug sensitivity and predict clinical outcomes. We present the algorithm, Digital Display Precision Predictor (DDPP), aiming to identify transcriptomic predictors of treatment outcome. For example, 17 and 13 key genes were derived from the literature by their association with MTOR and angiogenesis pathways, respectively, and their expression in tumor versus normal tissues was associated with the progression-free survival (PFS) of patients treated with everolimus or axitinib (respectively) using DDPP. A specific eight-gene set best correlated with PFS in six patients treated with everolimus: AKT2, TSC1, FKB-12, TSC2, RPTOR, RHEB, PIK3CA, and PIK3CB (r = 0.99, p = 5.67E−05). A two-gene set best correlated with PFS in five patients treated with axitinib: KIT and KITLG (r = 0.99, p = 4.68E−04). Leave-one-out experiments demonstrated significant concordance between observed and DDPP-predicted PFS (r = 0.9, p = 0.015) for patients treated with everolimus. Notwithstanding the small cohort and pending further prospective validation, the prototype of DDPP offers the potential to transform patients’ treatment selection with a tumor- and treatment-agnostic predictor of outcomes (duration of PFS)

    A multicenter randomized phase II study of sequential epirubicin/cyclophosphamide followed by docetaxel with or without celecoxib or trastuzumab according to HER2 status, as primary chemotherapy for localized invasive breast cancer patients

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    International audienceTo assess anti-tumor activity of sequential epirubicin/cyclophosphamide followed by docetaxel with the randomized addition of celecoxib in HER2 negative patients or trastuzumab in HER2 positive patients. From May 2004 till October 2007, 340 patients with stage II and III breast adenocarcinoma, ineligible for breast conserving surgery, received eight sequential three weekly cycles of EC-D [epirubicin (75 mg/m)–cyclophosphamide (750 mg/m) for four cycles followed by docetaxel (100 mg/m) for four cycles]. HER2-negative patients ( = 220) were randomized to receive concomitantly with docetaxel celecoxib 800 mg/day during cycles 5–8 or no additional treatment, while HER2-positive patients confirmed by FISH ( = 120) were randomized to trastuzumab concomitant to docetaxel (8 mg/kg then 6 mg/kg IV every 3 weeks) or no additional preoperative treatment. In the HER2 negative group, pCR (grade 1 and 2 of Chevallier's classification) was observed in 11.5 and 13% of patients treated without and with neoadjuvant Celecoxib, respectively. In the HER2 positive group, pCR rate reached 26% in those who received neoadjuvant trastuzumab versus 19% in the others. There was no unexpected toxicity, no cardiac toxicity, and no toxic death. Triple negative breast cancers experience the highest pCR rate of 30%. Celecoxib is not likely to improve pCR rates in addition to EC-D in patients with HER2-negative tumor. In HER2-positive tumor patients, trastuzumab added to ECD leads to increased pCR rates. It was the only combination to deserve further study according to the two-stage Fleming's design used in this trial

    Estimation et interprétation de l'effet pronostique d'une grossesse survenant aprÚs le traitement d'un cancer du sein : approche méthodologique par des simulations de données de survie évaluant l'impact d'un événement survenant au cours du temps et en lien avec le statut pronostique

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    Almost fifty thousand women were treated for breast cancer in France in 2012. Two percents occur in women under 35 years old. As more women are postponingchildbearing until later life, physicians are more often faced with questions regarding their subsequent pregnancy. Reproductive history, hormonal influences, and breastcarcinoma may be interrelated. However no study has reported a pejorative effect of the pregnancy on the breast cancer recurrences; many studies have found that pregnancy has no adverse effects on clinical outcome in women diagnosed previously with breast cancer, and may even have a long-term protective effect in some. This phenomenon may be due to the « healthy mother bias »: only women who feel well will pursue a subsequent pregnancy. So the pregnancy is an event which occurrence is linked to the prognostic status of the patient; and its appearance might interact with the hormonal sensitivity of the breast cancer. To try to take into account this confusion bias in the estimation and interpretation of the pregnancy effect, we explore two approaches. The pregnancy is considered as an exposure. In a first way, we apply the illness-death model to analyze cohort data: the exposition is the intermediate state which value depends on time; the estimation of the factors related to the transition to the exposure allows a qualitative evaluation of the confusion. The effect of the exposure is studied through the comparison between the transitions to the event of interest: those which underwent the intermediate state and the others. This effect is estimated by taking into account the interaction between the exposure and the prognostic factors characterizing the gravity of the disease as reflecting the health status. In the second approach, pairs composed of an exposed and a non-exposed subject are created from the cohort data. In the literature, the matching is realized by creating pairs a posteriori, as if the pregnancy was known at the cancer diagnosis date. We propose in this particular case where the event characterizing the member of the pair is an event occurring over time, a newmatching method. Moreover, we studied some known models of analysis dedicated to the censored correlated data: the stratified Cox model of Holt and Prentice, the more used, and the Cox model of Lee, Wei and Amato. All the work is based on a large simulation study in order to estimate and interpret the prognostic effect of an event occurring over time and related to the prognostic status. The simulations conclusions were applied to the analysis of real data of young women treated for a breast cancer, in order to evaluate the possible prognostic effect of a subsequent pregnancy.Le cancer du sein a touchĂ© prĂšs de 49000 femmes en France en 2012. Deux pour cents surviennent chez des femmes de moins de 35 ans. Comme beaucoup de femmes reportent leur maternitĂ© Ă  plus tard, les mĂ©decins font de plus en plus face aux questions relatives Ă  une grossesse post-traitement. L’histoire reproductive, les influences hormonales et le cancer du sein semblent liĂ©s. Pourtant aucune Ă©tude n’a rapportĂ© d’impact pĂ©joratif de la grossesse sur l’évolution du cancer ; par contre beaucoup d’études ont annoncĂ© que la grossesse n’avait aucun impact, et pourrait mĂȘme avoir un effet de protection Ă  long terme. Toutefois l’interprĂ©tation des rĂ©sultats est souvent limitĂ©e par un biais de confusion connu, le « healthy mother bias » : seules les femmes se sentant en bonne santĂ© vont mener une grossesse. La grossesse est donc un Ă©vĂ©nement dont la survenue est liĂ©e au statut pronostique de la patiente et qui pourrait interagir avec l’hormonosensibilitĂ© du cancer du sein. Pour tenter de prendre en compte ce biais de confusion dans l’estimation et l’interprĂ©tation de l’effet d’une grossesse qui est un Ă©vĂ©nement survenant au cours du temps, nous avons explorĂ© deux approches. La grossesse est assimilĂ©e Ă  une exposition survenant au cours du temps. Dans un premier temps nous avons appliquĂ© le modĂšle « illness-death » sur des donnĂ©es de cohorte : l’exposition est l’état intermĂ©diaire dont la valeur dĂ©pend du temps ; l’estimation des facteurs influençant le passage vers l’exposition permet une Ă©valuation qualitative de la confusion. L’effet de l’exposition est Ă©tudiĂ© Ă  partir de la comparaison des transitions vers l’évĂ©nement d’intĂ©rĂȘt : celles ayant eu l’évĂ©nement intermĂ©diaire versus les autres. Cet effet est estimĂ© en prenant en compte l’interaction entre les facteurs pronostiques caractĂ©risant le niveau de gravitĂ© de la maladie et l’exposition. Dans la seconde approche, nous avons constituĂ©, Ă  partir de la cohorte, des paires composĂ©es d’un sujet exposĂ© et d’un sujet non exposĂ©. Dans la littĂ©rature, l’appariement est rĂ©alisĂ© en crĂ©ant les paires a posteriori comme si la grossesse Ă©tait connue lors du diagnostic du cancer. Nous proposons, dans ce cas particulier oĂč l’évĂ©nement caractĂ©risant les sujets d’une paire survient au cours du temps, une nouvelle mĂ©thode d’appariement. De plus, nous Ă©tudions des modĂšles d’analyse connus et dĂ©diĂ©s aux donnĂ©es censurĂ©es corrĂ©lĂ©es : le modĂšle de Cox stratifiĂ© de Holt et Prentice, le plus utilisĂ©, et le modĂšle de Cox de Lee Wei et Amato. Tout le travail est basĂ© sur une large Ă©tude de simulation afin d’estimer et interprĂ©ter l’effet pronostique d’un Ă©vĂ©nement survenant au cours du temps et en lien avec le statut pronostique. Les conclusions de ces simulations ont Ă©tĂ© appliquĂ©es Ă  l’analyse d’une cohorte rĂ©elle de patientes jeunes traitĂ©es pour un cancer du sein, afin d’évaluer l’effet d’une Ă©ventuelle grossesse post-traitement sur l’évolution de la maladie

    Estimation et interprétation de l'effet pronostique d'une grossesse survenant aprÚs le traitement d'un cancer du sein : approche méthodologique par des simulations de données de survie évaluant l'impact d'un événement survenant au cours du temps et en lien avec le statut pronostique

    No full text
    Almost fifty thousand women were treated for breast cancer in France in 2012. Two percents occur in women under 35 years old. As more women are postponingchildbearing until later life, physicians are more often faced with questions regarding their subsequent pregnancy. Reproductive history, hormonal influences, and breastcarcinoma may be interrelated. However no study has reported a pejorative effect of the pregnancy on the breast cancer recurrences; many studies have found that pregnancy has no adverse effects on clinical outcome in women diagnosed previously with breast cancer, and may even have a long-term protective effect in some. This phenomenon may be due to the « healthy mother bias »: only women who feel well will pursue a subsequent pregnancy. So the pregnancy is an event which occurrence is linked to the prognostic status of the patient; and its appearance might interact with the hormonal sensitivity of the breast cancer. To try to take into account this confusion bias in the estimation and interpretation of the pregnancy effect, we explore two approaches. The pregnancy is considered as an exposure. In a first way, we apply the illness-death model to analyze cohort data: the exposition is the intermediate state which value depends on time; the estimation of the factors related to the transition to the exposure allows a qualitative evaluation of the confusion. The effect of the exposure is studied through the comparison between the transitions to the event of interest: those which underwent the intermediate state and the others. This effect is estimated by taking into account the interaction between the exposure and the prognostic factors characterizing the gravity of the disease as reflecting the health status. In the second approach, pairs composed of an exposed and a non-exposed subject are created from the cohort data. In the literature, the matching is realized by creating pairs a posteriori, as if the pregnancy was known at the cancer diagnosis date. We propose in this particular case where the event characterizing the member of the pair is an event occurring over time, a newmatching method. Moreover, we studied some known models of analysis dedicated to the censored correlated data: the stratified Cox model of Holt and Prentice, the more used, and the Cox model of Lee, Wei and Amato. All the work is based on a large simulation study in order to estimate and interpret the prognostic effect of an event occurring over time and related to the prognostic status. The simulations conclusions were applied to the analysis of real data of young women treated for a breast cancer, in order to evaluate the possible prognostic effect of a subsequent pregnancy.Le cancer du sein a touchĂ© prĂšs de 49000 femmes en France en 2012. Deux pour cents surviennent chez des femmes de moins de 35 ans. Comme beaucoup de femmes reportent leur maternitĂ© Ă  plus tard, les mĂ©decins font de plus en plus face aux questions relatives Ă  une grossesse post-traitement. L’histoire reproductive, les influences hormonales et le cancer du sein semblent liĂ©s. Pourtant aucune Ă©tude n’a rapportĂ© d’impact pĂ©joratif de la grossesse sur l’évolution du cancer ; par contre beaucoup d’études ont annoncĂ© que la grossesse n’avait aucun impact, et pourrait mĂȘme avoir un effet de protection Ă  long terme. Toutefois l’interprĂ©tation des rĂ©sultats est souvent limitĂ©e par un biais de confusion connu, le « healthy mother bias » : seules les femmes se sentant en bonne santĂ© vont mener une grossesse. La grossesse est donc un Ă©vĂ©nement dont la survenue est liĂ©e au statut pronostique de la patiente et qui pourrait interagir avec l’hormonosensibilitĂ© du cancer du sein. Pour tenter de prendre en compte ce biais de confusion dans l’estimation et l’interprĂ©tation de l’effet d’une grossesse qui est un Ă©vĂ©nement survenant au cours du temps, nous avons explorĂ© deux approches. La grossesse est assimilĂ©e Ă  une exposition survenant au cours du temps. Dans un premier temps nous avons appliquĂ© le modĂšle « illness-death » sur des donnĂ©es de cohorte : l’exposition est l’état intermĂ©diaire dont la valeur dĂ©pend du temps ; l’estimation des facteurs influençant le passage vers l’exposition permet une Ă©valuation qualitative de la confusion. L’effet de l’exposition est Ă©tudiĂ© Ă  partir de la comparaison des transitions vers l’évĂ©nement d’intĂ©rĂȘt : celles ayant eu l’évĂ©nement intermĂ©diaire versus les autres. Cet effet est estimĂ© en prenant en compte l’interaction entre les facteurs pronostiques caractĂ©risant le niveau de gravitĂ© de la maladie et l’exposition. Dans la seconde approche, nous avons constituĂ©, Ă  partir de la cohorte, des paires composĂ©es d’un sujet exposĂ© et d’un sujet non exposĂ©. Dans la littĂ©rature, l’appariement est rĂ©alisĂ© en crĂ©ant les paires a posteriori comme si la grossesse Ă©tait connue lors du diagnostic du cancer. Nous proposons, dans ce cas particulier oĂč l’évĂ©nement caractĂ©risant les sujets d’une paire survient au cours du temps, une nouvelle mĂ©thode d’appariement. De plus, nous Ă©tudions des modĂšles d’analyse connus et dĂ©diĂ©s aux donnĂ©es censurĂ©es corrĂ©lĂ©es : le modĂšle de Cox stratifiĂ© de Holt et Prentice, le plus utilisĂ©, et le modĂšle de Cox de Lee Wei et Amato. Tout le travail est basĂ© sur une large Ă©tude de simulation afin d’estimer et interprĂ©ter l’effet pronostique d’un Ă©vĂ©nement survenant au cours du temps et en lien avec le statut pronostique. Les conclusions de ces simulations ont Ă©tĂ© appliquĂ©es Ă  l’analyse d’une cohorte rĂ©elle de patientes jeunes traitĂ©es pour un cancer du sein, afin d’évaluer l’effet d’une Ă©ventuelle grossesse post-traitement sur l’évolution de la maladie

    Estimation et interprétation de l'effet pronostique d'une grossesse survenant aprÚs le traitement d'un cancer du sein : approche méthodologique par des simulations de données de survie évaluant l'impact d'un événement survenant au cours du temps et en lien avec le statut pronostique

    No full text
    Le cancer du sein a touchĂ© prĂšs de 49000 femmes en France en 2012. Deux pour cents surviennent chez des femmes de moins de 35 ans. Comme beaucoup de femmes reportent leur maternitĂ© Ă  plus tard, les mĂ©decins font de plus en plus face aux questions relatives Ă  une grossesse post-traitement. L’histoire reproductive, les influences hormonales et le cancer du sein semblent liĂ©s. Pourtant aucune Ă©tude n’a rapportĂ© d’impact pĂ©joratif de la grossesse sur l’évolution du cancer ; par contre beaucoup d’études ont annoncĂ© que la grossesse n’avait aucun impact, et pourrait mĂȘme avoir un effet de protection Ă  long terme. Toutefois l’interprĂ©tation des rĂ©sultats est souvent limitĂ©e par un biais de confusion connu, le « healthy mother bias » : seules les femmes se sentant en bonne santĂ© vont mener une grossesse. La grossesse est donc un Ă©vĂ©nement dont la survenue est liĂ©e au statut pronostique de la patiente et qui pourrait interagir avec l’hormonosensibilitĂ© du cancer du sein. Pour tenter de prendre en compte ce biais de confusion dans l’estimation et l’interprĂ©tation de l’effet d’une grossesse qui est un Ă©vĂ©nement survenant au cours du temps, nous avons explorĂ© deux approches. La grossesse est assimilĂ©e Ă  une exposition survenant au cours du temps. Dans un premier temps nous avons appliquĂ© le modĂšle « illness-death » sur des donnĂ©es de cohorte : l’exposition est l’état intermĂ©diaire dont la valeur dĂ©pend du temps ; l’estimation des facteurs influençant le passage vers l’exposition permet une Ă©valuation qualitative de la confusion. L’effet de l’exposition est Ă©tudiĂ© Ă  partir de la comparaison des transitions vers l’évĂ©nement d’intĂ©rĂȘt : celles ayant eu l’évĂ©nement intermĂ©diaire versus les autres. Cet effet est estimĂ© en prenant en compte l’interaction entre les facteurs pronostiques caractĂ©risant le niveau de gravitĂ© de la maladie et l’exposition. Dans la seconde approche, nous avons constituĂ©, Ă  partir de la cohorte, des paires composĂ©es d’un sujet exposĂ© et d’un sujet non exposĂ©. Dans la littĂ©rature, l’appariement est rĂ©alisĂ© en crĂ©ant les paires a posteriori comme si la grossesse Ă©tait connue lors du diagnostic du cancer. Nous proposons, dans ce cas particulier oĂč l’évĂ©nement caractĂ©risant les sujets d’une paire survient au cours du temps, une nouvelle mĂ©thode d’appariement. De plus, nous Ă©tudions des modĂšles d’analyse connus et dĂ©diĂ©s aux donnĂ©es censurĂ©es corrĂ©lĂ©es : le modĂšle de Cox stratifiĂ© de Holt et Prentice, le plus utilisĂ©, et le modĂšle de Cox de Lee Wei et Amato. Tout le travail est basĂ© sur une large Ă©tude de simulation afin d’estimer et interprĂ©ter l’effet pronostique d’un Ă©vĂ©nement survenant au cours du temps et en lien avec le statut pronostique. Les conclusions de ces simulations ont Ă©tĂ© appliquĂ©es Ă  l’analyse d’une cohorte rĂ©elle de patientes jeunes traitĂ©es pour un cancer du sein, afin d’évaluer l’effet d’une Ă©ventuelle grossesse post-traitement sur l’évolution de la maladie.Almost fifty thousand women were treated for breast cancer in France in 2012. Two percents occur in women under 35 years old. As more women are postponingchildbearing until later life, physicians are more often faced with questions regarding their subsequent pregnancy. Reproductive history, hormonal influences, and breastcarcinoma may be interrelated. However no study has reported a pejorative effect of the pregnancy on the breast cancer recurrences; many studies have found that pregnancy has no adverse effects on clinical outcome in women diagnosed previously with breast cancer, and may even have a long-term protective effect in some. This phenomenon may be due to the « healthy mother bias »: only women who feel well will pursue a subsequent pregnancy. So the pregnancy is an event which occurrence is linked to the prognostic status of the patient; and its appearance might interact with the hormonal sensitivity of the breast cancer. To try to take into account this confusion bias in the estimation and interpretation of the pregnancy effect, we explore two approaches. The pregnancy is considered as an exposure. In a first way, we apply the illness-death model to analyze cohort data: the exposition is the intermediate state which value depends on time; the estimation of the factors related to the transition to the exposure allows a qualitative evaluation of the confusion. The effect of the exposure is studied through the comparison between the transitions to the event of interest: those which underwent the intermediate state and the others. This effect is estimated by taking into account the interaction between the exposure and the prognostic factors characterizing the gravity of the disease as reflecting the health status. In the second approach, pairs composed of an exposed and a non-exposed subject are created from the cohort data. In the literature, the matching is realized by creating pairs a posteriori, as if the pregnancy was known at the cancer diagnosis date. We propose in this particular case where the event characterizing the member of the pair is an event occurring over time, a newmatching method. Moreover, we studied some known models of analysis dedicated to the censored correlated data: the stratified Cox model of Holt and Prentice, the more used, and the Cox model of Lee, Wei and Amato. All the work is based on a large simulation study in order to estimate and interpret the prognostic effect of an event occurring over time and related to the prognostic status. The simulations conclusions were applied to the analysis of real data of young women treated for a breast cancer, in order to evaluate the possible prognostic effect of a subsequent pregnancy

    Evaluation du risque infectieux lié aux cathéters veineux centraux en oncologie (méthode des risques compétitifs)

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    PARIS5-BU MĂ©d.Cochin (751142101) / SudocPARIS-BIUM (751062103) / SudocCentre Technique Livre Ens. Sup. (774682301) / SudocSudocFranceF

    Addressing the issue of bias in observational studies: Using instrumental variables and a quasi-randomization trial in an ESME research project

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    International audiencePurpose: Observational studies using routinely collected data are faced with a number of potential shortcomings that can bias their results. Many methods rely on controlling for measured and unmeasured confounders. In this work, we investigate the use of instrumental variables (IV) and quasi-trial analysis to control for unmeasured confounders in the context of a study based on the retrospective Epidemiological Strategy and Medical Economics (ESME) database, which compared overall survival (OS) with paclitaxel plus bevacizumab or paclitaxel alone as first-line treatment in patients with HER2-negative metastatic breast cancer (MBC).Patients and methods Causal interpretations and estimates can be made from observation data using IV and quasi-trial analysis. Quasi-trial analysis has the same conceptual basis as IV, however, instead of using IV in the analysis, a “superficial” or “pseudo” randomized trial is used in a Cox model. For instance, in a multicenter trial, instead of using the treatment variable, quasi-trial analysis can consider the treatment preference in each center, which can be informative, and then comparisons of results between centers or clinicians can be informative.Results In the original analysis, the OS adjusted for major factors was significantly longer with paclitaxel and bevacizumab than with paclitaxel alone. Using the center-treatment preference as an instrument yielded to concordant results. For the quasi-trial analysis, a Cox model was used, adjusted on all factors initially used. The results consolidate those obtained with a conventional multivariate Cox model. Conclusion: Unmeasured confounding is a major concern in observational studies, and IV or quasi-trial analysis can be helpful to complement analysis of studies of this nature
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