9 research outputs found

    Therapeutic drug monitoring of neoadjuvant mFOLFIRINOX in resected pancreatic ductal adenocarcinoma

    Get PDF
    Background: Despite a potentially curative treatment, the prognosis after upfront surgery and adjuvant chemotherapy for patients with resectable pancreatic ductal adenocarcinoma (PDAC) is poor. Modified FOLFIRINOX (mFOLFIRINOX) is a cornerstone in the systemic treatment of PDAC, including the neoadjuvant setting. Pharmacokinetic-guided (PKG) dosing has demonstrated beneficial effects in other tumors, but scarce data is available in pancreatic cancer. Methods: Forty-six patients with resected PDAC after mFOLFIRINOX neoadjuvant approach and included in an institutional protocol for anticancer drug monitoring were retrospectively analyzed. 5-Fluorouracil (5-FU) dosage was adjusted throughout neoadjuvant treatment according to pharmacokinetic parameters and Irinotecan (CPT-11) pharmacokinetic variables were retrospectively estimated. Results: By exploratory univariate analyses, a significantly longer progression-free survival was observed for patients with either 5-FU area under the curve (AUC) above 28 mcgh/mLorCPT11AUCvaluesbelow10mcgh/mL or CPT-11 AUC values below 10 mcgh/mL. In the multivariate analyses adjusted by age, gender, performance status and resectability after stratification according to both pharmacokinetic parameters, the risk of progression was significantly reduced in patients with 5-FU AUC 28 mcgh/mL[HR¼0.251,95andCPT11AUC<10mcgh/mL [HR ¼ 0.251, 95% CI 0.096e0.656; p ¼ 0.005] and CPT-11 AUC <10 mcgh/mL [HR ¼ 0.189, 95% CI 0.073e0.486, p ¼ 0.001]. Conclusions: Pharmacokinetically-guided dose adjustment of standard chemotherapy treatments might improve survival outcomes in patients with pancreatic ductal adenocarcinoma

    Omics approaches in pancreatic adenocarcinoma

    Get PDF
    Pancreatic ductal adenocarcinoma, which represents 80% of pancreatic cancers, is mainly diagnosed when treatment with curative intent is not possible. Consequently, the overall five-year survival rate is extremely dismal—around 5% to 7%. In addition, pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030. Therefore, advances in screening, prevention and treatment are urgently needed. Fortunately, a wide range of approaches could help shed light in this area. Beyond the use of cytological or histological samples focusing in diagnosis, a plethora of new approaches are currently being used for a deeper characterization of pancreatic ductal adenocarcinoma, including genetic, epigenetic, and/or proteo-transcriptomic techniques. Accordingly, the development of new analytical technologies using body fluids (blood, bile, urine, etc.) to analyze tumor derived molecules has become a priority in pancreatic ductal adenocarcinoma due to the hard accessibility to tumor samples. These types of technologies will lead us to improve the outcome of pancreatic ductal adenocarcinoma patients

    Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

    Get PDF
    Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56-0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations

    Aplicación de Machine Learning y análisis PK/PD a programas de tratamiento neoadyuvante en pacientes con adenocarcinoma de páncreas localmente avanzado

    No full text
    INTRODUCCIÓN El cáncer de páncreas (CP) constituye la cuarta causa de muerte por cáncer en Europa y Estados Unidos. El 50% de los pacientes con CP presentan enfermedad metastásica al diagnóstico, mientras que tan solo el 10-20% se consideran resecables. El abordaje quirúrgico continúa siendo el único tratamiento potencialmente curativo en CP. Sin embargo, incluso en los pacientes operados, el pronóstico sigue siendo desalentador. La búsqueda de estrategias de mejora en este contexto oncológico se ha convertido en un foco de estudio prioritario. ANÁLISIS RETROSPECTIVO DE LA EFICACIA DEL TRATAMIENTO NEOADYUVANTE EN PACIENTES CON CP RESECABLE Entre octubre de 2005 y mayo de 2016, 40 pacientes con CP resecable fueron tratados con neoadyuvancia (poli-quimioterapia, seguido, en caso de respuesta o estabilidad de la enfermedad, de quimio-radioterapia). En relación con la poli-quimioterapia (PQT), en 14 pacientes (35%) se administró mFOLFOXIRI, en 21 pacientes (52,5%) GEMOX-Capecitabina, y en 5 pacientes (12,5%) GEMOX. Durante la PQT, todos los pacientes experimentaron algún tipo de toxicidad grado 1 2 y 16 pacientes (40%) presentaron al menos una toxicidad grado 3 - 4. Treinta y siete pacientes (92,5%) recibieron quimio-radioterapia (QRT). En el 54% de los casos la radioterapia se administró con técnica 3D y en el 46% de los casos mediante técnica IMRT. Durante la QRT, 11 pacientes (29,7%) presentaron algún tipo de toxicidad grado 3. En total, 36 pacientes fueron intervenidos (90%), y en todos ellos se consiguió una cirugía R0. Tras la cirugía, 9 pacientes (22,5%) recibieron tratamiento adyuvante. La mediana de seguimiento fue de 33,5 meses. La mediana de supervivencia libre de progresión (SLP) y de supervivencia global (SG) fue de 18 meses y 39 meses, respectivamente. ANÁLISIS IN SILICO MEDIANTE ALGORITMOS DE MACHINE LEARNING PARA GENERAR UN MODELO POBLACIONAL DE PREDICCIÓN INDIVIDUAL DE RECAÍDA A 2 AÑOS EN PACIENTES CON CP RESECADOS TRAS TRATAMIENTO NEOADYUVANTE Entre septiembre de 2005 y noviembre de 2016, 45 pacientes con CP fueron intervenidos tras neoadyuvancia. El mejor modelo poblacional de predicción de recaída a dos años fue el de Regresión Logística, con una Tasa de Acierto del 71%, una Sensibilidad del 70%, una Especificidad del 73% y una media de ABC de 0,75. Las variables que deben formar parte del modelo son: Factores estimulantes de colonias granulocíticas (G-CSF) durante la quimioterapia neoadyuvante: SI / NO Número de ganglios linfáticos resecados Invasión perineural: SI / NO Márgenes quirúrgicos: R0 / R1 Sobre el modelo de Regresión Logística se realizó una validación externa, determinando una capacidad predictiva a nivel individual del 64%. ANÁLISIS RETROSPECTIVO DE LA MONITORIZACIÓN FARMACOCINÉTICA Y FARMACODINÁMICA DEL 5-FLUOROURACILO ADMINISTRADO DENTRO DEL ESQUEMA mFOLFOXIRI EN CP Entre diciembre de 2011 y septiembre de 2016, 25 pacientes con CP fueron intervenidos tras neoadyuvancia con monitorización cinética del 5-Fluorouracilo. Veinte pacientes (80%) recibieron mFOLFOXIRI seguido de quimio-radioterapia, y 5 pacientes (20%) recibieron mFOLFOXIRI como única modalidad de tratamiento neoadyuvante. Por extrapolación de estudios en cáncer de colon y ORL, se estableció un rango diana de Área Bajo la Curva (ABC) de entre 20 y 35 mcg*h/mL. El estudio determinó que la monitorización terapéutica del 5-Fluorouracilo dentro del esquema mFOLFOXIRI en CP es factible en la práctica clínica diaria, permitiendo ajustes posológicos estrechos del mismo dentro del rango de ABC preestablecido. No se encontró una asociación estadística entre ABC, perfil de toxicidad y SG. Sin embargo, los pacientes con un ABC >/= 27 mcg*h/mL tras el segundo ciclo monitorizado presentaron una SLP de 33,5 meses frente a los 14,5 meses de aquellos pacientes con un ABC < 27 mcg*h/mL, diferencias que tendían a la significación estadística

    Aplicación de Machine Learning y análisis PK/PD a programas de tratamiento neoadyuvante en pacientes con adenocarcinoma de páncreas localmente avanzado

    Get PDF
    INTRODUCCIÓN El cáncer de páncreas (CP) constituye la cuarta causa de muerte por cáncer en Europa y Estados Unidos. El 50% de los pacientes con CP presentan enfermedad metastásica al diagnóstico, mientras que tan solo el 10-20% se consideran resecables. El abordaje quirúrgico continúa siendo el único tratamiento potencialmente curativo en CP. Sin embargo, incluso en los pacientes operados, el pronóstico sigue siendo desalentador. La búsqueda de estrategias de mejora en este contexto oncológico se ha convertido en un foco de estudio prioritario. ANÁLISIS RETROSPECTIVO DE LA EFICACIA DEL TRATAMIENTO NEOADYUVANTE EN PACIENTES CON CP RESECABLE Entre octubre de 2005 y mayo de 2016, 40 pacientes con CP resecable fueron tratados con neoadyuvancia (poli-quimioterapia, seguido, en caso de respuesta o estabilidad de la enfermedad, de quimio-radioterapia). En relación con la poli-quimioterapia (PQT), en 14 pacientes (35%) se administró mFOLFOXIRI, en 21 pacientes (52,5%) GEMOX-Capecitabina, y en 5 pacientes (12,5%) GEMOX. Durante la PQT, todos los pacientes experimentaron algún tipo de toxicidad grado 1 2 y 16 pacientes (40%) presentaron al menos una toxicidad grado 3 - 4. Treinta y siete pacientes (92,5%) recibieron quimio-radioterapia (QRT). En el 54% de los casos la radioterapia se administró con técnica 3D y en el 46% de los casos mediante técnica IMRT. Durante la QRT, 11 pacientes (29,7%) presentaron algún tipo de toxicidad grado 3. En total, 36 pacientes fueron intervenidos (90%), y en todos ellos se consiguió una cirugía R0. Tras la cirugía, 9 pacientes (22,5%) recibieron tratamiento adyuvante. La mediana de seguimiento fue de 33,5 meses. La mediana de supervivencia libre de progresión (SLP) y de supervivencia global (SG) fue de 18 meses y 39 meses, respectivamente. ANÁLISIS IN SILICO MEDIANTE ALGORITMOS DE MACHINE LEARNING PARA GENERAR UN MODELO POBLACIONAL DE PREDICCIÓN INDIVIDUAL DE RECAÍDA A 2 AÑOS EN PACIENTES CON CP RESECADOS TRAS TRATAMIENTO NEOADYUVANTE Entre septiembre de 2005 y noviembre de 2016, 45 pacientes con CP fueron intervenidos tras neoadyuvancia. El mejor modelo poblacional de predicción de recaída a dos años fue el de Regresión Logística, con una Tasa de Acierto del 71%, una Sensibilidad del 70%, una Especificidad del 73% y una media de ABC de 0,75. Las variables que deben formar parte del modelo son: Factores estimulantes de colonias granulocíticas (G-CSF) durante la quimioterapia neoadyuvante: SI / NO Número de ganglios linfáticos resecados Invasión perineural: SI / NO Márgenes quirúrgicos: R0 / R1 Sobre el modelo de Regresión Logística se realizó una validación externa, determinando una capacidad predictiva a nivel individual del 64%. ANÁLISIS RETROSPECTIVO DE LA MONITORIZACIÓN FARMACOCINÉTICA Y FARMACODINÁMICA DEL 5-FLUOROURACILO ADMINISTRADO DENTRO DEL ESQUEMA mFOLFOXIRI EN CP Entre diciembre de 2011 y septiembre de 2016, 25 pacientes con CP fueron intervenidos tras neoadyuvancia con monitorización cinética del 5-Fluorouracilo. Veinte pacientes (80%) recibieron mFOLFOXIRI seguido de quimio-radioterapia, y 5 pacientes (20%) recibieron mFOLFOXIRI como única modalidad de tratamiento neoadyuvante. Por extrapolación de estudios en cáncer de colon y ORL, se estableció un rango diana de Área Bajo la Curva (ABC) de entre 20 y 35 mcg*h/mL. El estudio determinó que la monitorización terapéutica del 5-Fluorouracilo dentro del esquema mFOLFOXIRI en CP es factible en la práctica clínica diaria, permitiendo ajustes posológicos estrechos del mismo dentro del rango de ABC preestablecido. No se encontró una asociación estadística entre ABC, perfil de toxicidad y SG. Sin embargo, los pacientes con un ABC >/= 27 mcg*h/mL tras el segundo ciclo monitorizado presentaron una SLP de 33,5 meses frente a los 14,5 meses de aquellos pacientes con un ABC < 27 mcg*h/mL, diferencias que tendían a la significación estadística

    Therapeutic drug monitoring of neoadjuvant mFOLFIRINOX in resected pancreatic ductal adenocarcinoma

    No full text
    Background: Despite a potentially curative treatment, the prognosis after upfront surgery and adjuvant chemotherapy for patients with resectable pancreatic ductal adenocarcinoma (PDAC) is poor. Modified FOLFIRINOX (mFOLFIRINOX) is a cornerstone in the systemic treatment of PDAC, including the neoadjuvant setting. Pharmacokinetic-guided (PKG) dosing has demonstrated beneficial effects in other tumors, but scarce data is available in pancreatic cancer. Methods: Forty-six patients with resected PDAC after mFOLFIRINOX neoadjuvant approach and included in an institutional protocol for anticancer drug monitoring were retrospectively analyzed. 5-Fluorouracil (5-FU) dosage was adjusted throughout neoadjuvant treatment according to pharmacokinetic parameters and Irinotecan (CPT-11) pharmacokinetic variables were retrospectively estimated. Results: By exploratory univariate analyses, a significantly longer progression-free survival was observed for patients with either 5-FU area under the curve (AUC) above 28 mcgh/mLorCPT11AUCvaluesbelow10mcgh/mL or CPT-11 AUC values below 10 mcgh/mL. In the multivariate analyses adjusted by age, gender, performance status and resectability after stratification according to both pharmacokinetic parameters, the risk of progression was significantly reduced in patients with 5-FU AUC 28 mcgh/mL[HR¼0.251,95andCPT11AUC<10mcgh/mL [HR ¼ 0.251, 95% CI 0.096e0.656; p ¼ 0.005] and CPT-11 AUC <10 mcgh/mL [HR ¼ 0.189, 95% CI 0.073e0.486, p ¼ 0.001]. Conclusions: Pharmacokinetically-guided dose adjustment of standard chemotherapy treatments might improve survival outcomes in patients with pancreatic ductal adenocarcinoma

    Omics approaches in pancreatic adenocarcinoma

    No full text
    Pancreatic ductal adenocarcinoma, which represents 80% of pancreatic cancers, is mainly diagnosed when treatment with curative intent is not possible. Consequently, the overall five-year survival rate is extremely dismal—around 5% to 7%. In addition, pancreatic cancer is expected to become the second leading cause of cancer-related death by 2030. Therefore, advances in screening, prevention and treatment are urgently needed. Fortunately, a wide range of approaches could help shed light in this area. Beyond the use of cytological or histological samples focusing in diagnosis, a plethora of new approaches are currently being used for a deeper characterization of pancreatic ductal adenocarcinoma, including genetic, epigenetic, and/or proteo-transcriptomic techniques. Accordingly, the development of new analytical technologies using body fluids (blood, bile, urine, etc.) to analyze tumor derived molecules has become a priority in pancreatic ductal adenocarcinoma due to the hard accessibility to tumor samples. These types of technologies will lead us to improve the outcome of pancreatic ductal adenocarcinoma patients

    Modification of breast cancer milieu with chemotherapy plus dendritic cell vaccine: an approach to select best therapeutic strategies

    No full text
    Background: The addition of dendritic cell vaccines (DCV) to NAC could induce immune responses in those patients with residual disease (RD) by transforming the tumor microenvironment. Methods: Core diagnostic biopsies and surgical specimens from 80 patients (38 in the vaccinated group plus NAC (VG) and 42 in the control group (CG, treated only with NAC) were selected. We quantify TILs (CD8, CD4 and CD45RO) using immunohistochemistry and the automated cellular imaging system (ACIS III) in paired samples. Results: A CD8 rise in TNBC samples was observed after NAC plus DCV, changing from 4.48% in the biopsy to 6.70% in the surgical specimen, not reaching statistically significant differences (p = 0.11). This enrichment was seen in up to 67% of TNBC patients in the experimental arm as compared with the CG (20%). An association between CD8 TILs before NAC (4% cut-off point) and pathological complete response in the VG was found in the univariate and multivariate analysis (OR = 1.41, IC95% 1.05-1.90; p = 0.02, and OR = 2.0, IC95% 1.05-3.9; p = 0.03, respectively). Conclusion: Our findings suggest that patients with TNBC could benefit from the stimulation of the antitumor immune system by using DCV together with NAC

    Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

    No full text
    Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56-0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations
    corecore