Factores pronósticos pred y predictivos cáncer renal 2013-3

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  • Osaka Severa: menos de 134; mild: 135-137; no: más de 138
  • 3 pasos: screening, confirmación y validación
  • Molecular expression profiling
  • DFI: intervalo libre de enfermedad SR: VSG
  • En 463 pacientes tratados con interferón
  • La ganadora del Oscar en el año 2007 fue la película No es país para viejos.
  • VEGF = vascular endothelial growth factor; sVEGFR = soluble VEGFR receptor; CEC = circulating endothelial cell; CEP = circulating endothelial progenitor; CAF = cytokine and angiogenic factor; LDH = lactate dehydrogenase; VHL = von Hippel–Lindau; SNPs = single nucleotide polymorphism; DCE-MRI = dynamic contrast enhanced-magnetic resonance imaging; CE-CT = contrast-enhanced computed tomography
  • Both high and low VEGF pts benefit from sorafenib; The pts with high VEGF – i.e. poorer prognosis – may derive more
  • Evaluation of serum lactate dehydrogenase (LDH) as a predictive biomarker for mTOR inhibition in patients with metastatic renal cell carcinoma (RCC). Background: To date, no biomarkers are known that select for patients with cancer who are more likely to benefit from mTOR inhibition. LDH is a metabolic enzyme detectable in serum that is often elevated in multiple cancers including RCC, has prognostic importance, is regulated by the PI3K/AKT/mTOR pathway, and is associated with tumor hypoxia/necrosis. We sought to evaluate pretreatment total LDH as a predictive biomarker of overall survival (OS) in patients with RCC. Methods: We evaluated pretreatment serum total LDH in 404 poor-risk RCC subjects treated with temsirolimus or interferon alpha in an international phase III randomized trial. Survival curves were estimated using the Kaplan-Meier estimator and differences in survival distributions between the two treatment arms were evaluated by LDH categories using the log-rank test and proportional hazards model. Interaction between treatment effect and baseline LDH elevation was assessed by the proportional hazards model. Results: The mean baseline serum normalized LDH in this study was 1.23 times the upper limit of normal (range 0.05 to 28.5). LDH was prognostic in univariate analysis. Controlling for treatment group, the hazard ratio (HR) for death was 1.98 (95% confidence interval [CI]=1.6-2.5, p<0.0001) for patients with LDH >1 ULN compared to patients whose LDH ≤ 1ULN. Adjusting for known prognostic factors, the HR for death was 2.01 for patients with LDH >1 ULN (95% CI=1.6-2.6, p<0.0001) compared to patients whose LDH ≤ 1ULN. There was a statistically significant interaction effect noted between normalized LDH and treatment group (p=0.031). Among 264 subjects with normal LDH, OS was not improved with temsirolimus as compared to interferon therapy (11.7 vs. 10.4 months, log-rank p=0.514). Among 140 subjects with elevated LDH, OS was significantly improved with temsirolimus (6.9 vs. 4.2 months, HR 0.51, log-rank p<0.002). Conclusions: Serum LDH is a potential predictive biomarker for the survival benefit conferred by mTOR inhibition in poor risk RCC. Further investigation of mechanism, alterations in LDH levels with therapy, and the predictive role of LDH for mTOR inhibition in other tumor types is warranted.
  • P significativa
  • Gene polymorphism and clinical endpoints: IL8 + 276A >T was associated with PFS ( P =0.03): Wildtype (AA): 49 weeks Heterozygote (AT): 41 weeks TT: 28 weeks HIF1A+ 1790G>A (Ala588Thr) polymorphism was associated with better ORR ( P =0.02): Wildtype (GG): 43% Heterozygote (AG): 30% VEGFR2 +1416T>A (His472Gln) polymorphism was associated with average MAP ( P =0.005) AA: Experienced higher MAP change than wildtype (TT) and heterozygote AT genotypes Similar findings were also observed for polymorphisms of VEGFA gene ( P <0.05)
  • Concuerda con los artículos de Signoretti y Choueiri
  • B7H1 ha demostrado asociarse a respuesta con antiPD1 en diferentes tumores
  • target tumor vasculature and thus parameters derived from contrast-enhanced imaging have been evaluated as biomarkers. The simplest incorporation of these observations Coronal (C) T2-weighted and (D) perfusion images of the same patient obtained 8 days after initiation of antiangiogenic therapy with sorafenib and bevacizumab revealed a minimal decrease in the size of the lesion but a marked decrease in tumor vascularity.
  • are the Morphology, Attenuation, Size and Structure (MASS) criteria. Post treatment lesions are evaluated for central necrosis or decreased attenuation as well as size. MASS criteria favorable response is better correlated with progression and disease specific survival than standard RECIST response.
  • MATERIALS AND METHODS. Tumor long-axis measurements and volumetric mean tumor attenuation of target lesions on CECT images were correlated with time to progression in 53 patients with metastatic clear cell RCC treated with first-line sorafenib or sunitinib. The frequencies of specific patterns of tumor progression were assessed. The data were used to develop new imaging criteria, the size and attenuation CT (SACT) criteria. CECT findings were evaluated using the SACT criteria, Response Evaluation Criteria in Solid Tumors (RECIST), and modified Choi criteria, and the Kaplan-Meier method was used to estimate survival functions. RESULTS. One or more target metastatic lesions had decreased attenuation of 40 HU in 59% of patients with progression-free survival of > 250 days ( n = 44) after initiating targeted therapy; 0% of patients with earlier disease progression ( n = 9) had this finding. A favorable response based on SACT criteria had a sensitivity of 75% and specificity of 100% for identifying patients with progression-free survival of > 250 days, versus 16% and 100%, respectively, for RECIST and 93% and 44% for the modified Choi criteria. CONCLUSION. Objectively measuring changes in both tumor size and attenuation on the first CECT study after initiating targeted therapy for metastatic RCC markedly improves response assessment. Distinct patterns of disease recurrence are seen in patients with metastatic RCC on targeted therapy.
  • Sequential FDG-PET/CT as a surrogate marker of response to sunitinib in metastatic clear cell renal cancer. Background: The purpose of this study was to investigate sequential FDG PET-CT as a correlative marker in metastatic clear cell renal cancer (mRCC) patients treated with first line sunitinib. Three sequential scans were performed to determine the importance of the timing of scans. Methods: Forty-four untreated mRCC patients with MSKCC intermediate risk and poor risk disease were enrolled into a prospective study. FDG PET-CT scans were performed before (n=44), after 4 weeks (n=43) and 16 weeks (n=40) of sunitinib given at standard doses as the translational aspect of this trial (NCT01024205). The primary endpoint was to determine whether 18 F-FDG PET-CT response (defined as a 20% reduction in SUVmax) correlated with survival. Results: Forty-three (98%) patients had FDG PET-CT avid lesions at diagnosis (median SUVmax 6.8 range: <2.5-18.4). In multivariate analysis a high SUVmax and increased number of PET positive lesions correlated with worse overall survival (OS) (HR: 3.30 (95%CI: 1.36-8.45) and 3.67 (95%CI: 1.43-9.39) respectively[p<0.05]). After 4 weeks of sunitinib, metabolic responses occurred in 24 (57%) patients at 4 weeks, but this did not correlate with progression-free survival [PFS] (HR for responders= 0.87 [95%CI: 0.40-1.99]) or OS (HR for responders= 0.80 [95%CI: 0.34-1.85]) (p>0.05 for both). After 16 weeks of treatment, FDG PET-CT demonstrated disease progression in 28% (n=12) patients. At this time point, the FDG PET-CT correlated with both OS and PFS (HR 5.96 [95%CI: 2.43-19.02] and HR 12.13 [95%CI: 3.72-46.45] respectively). Conclusions: Baseline FDG PET prior to sunitinib yields prognostically significant data. FDG PET response at 16 weeks predicts outcome, which is not the case at 4 weeks. This subsets of patients with a poor prognosis at 16 weeks could be investigated within the context of a randomized clinical trial.
  • In contrast, vEGF SNP 1498 genotype frequencies in the study cohort was more congruent with the expected frequencies reported in the NCBI database for caucasian populations Multivariate analysis adjusting for baseline HTN and use of antihypertensive meds Importance of duration???
  • Polibromo-1: encontrado en el 41% de los CCR, es un gen que codifica un complejo de remodelación de la cromatina Proteína 1 asociada a BRCA1: presente en el 15% Solo 7 pacientes tienen las 2 alteraciones
  • Factores pronósticos pred y predictivos cáncer renal 2013-3

    1. 1. Factor pronóstico Factor predictivoCaracterística en al paciente o Marcador clínico, biológico o la enfermedad asociada con molecular asociado con la su evolución respuesta a un tratamiento en independientemente del particular tratamiento FuncionesFunciones -Individualización del-Identificación grupos de tratamientoriesgo -Evitar tratamientos ineficaces-Ensayos clínicos: diseño e -Ensayos con nuevas drogasinterpretación en enfermedad refractaria-Información
    2. 2. FACTORES CLÍNICOSCATEGORÍA Factor pronósticoRelacionados con el paciente Síntomas PSCrecimiento tumoral Sitio y nº de metástasis Fosfatasa alcalina Hiponatremia LDH Anemia Hipercalcemia Intervalo libre de enfermedadMarcadores proinflamatorios VSG PCR Neutrofilia TrombocitosisTratamiento Nefrectomía citorreductora
    3. 3. Suero/tejidoBiomarker Role CommentsVHL alterations Dudas como F. Pron Estudios más grandesHIF-1 Dudas como F. pron Tinción citoplasmática/nuclearVEGF-A Potencial f. pronóstico: Falta definir punto de VEGF alto asociado con corte peor SGCAIX Incierto Niveles de expresión en mets pueden no ser representativosPTEN InciertoB7-H1 Expresión + se asocia a Se necesitan estudios peor supervivencia prospectivos (nefrectomía)IMP3 Expresión +: peor supervivencia
    4. 4. Interferón: mejoría en la supervivencia en pacientes nefrectomizados OS N=85 17 VS 7 M Mickisch et al; Lancet 2001; 358:966-70 N=245 12.5 vs 8.1 m Flanigan RC. NEJM. 2001;345:1655–9
    5. 5. En la era de las terapias dirigidas… N=328 Aben KKH, European Journal of Cancer 2011;47(13):2023–32
    6. 6. N=113 N=201 Choueiri et al; J Urol 2011;185:60-66
    7. 7. KPS >80 KPS <80 Choueiri et al; J Urol 2011;185:60-66
    8. 8. BMI y supervivencia 475 pacientes en primera línea 23.4 m 16.7 m10 m Mueller et al; ASCO GU 2013 #454
    9. 9. Hiponatremia en pacientes con antiVEGF87 pacientes Normal: >138 Mild: 134-137 Severe: >134 Kawashima et al; Int J Urol 2012;19:1050-57
    10. 10. VEGF en el RECORD-1Medido los días de los 4 primeros ciclosEverolimus mejora PFS independientementede los niveles de biomarcadoresNiveles bajos de VEGF-A se asocian a una mejor PFS: HR 1.27 (95% CI 1.03-1.57, p=0.28) Oudard et al; ASCO GU 2013 #352
    11. 11. N=1816 Pronóstico Clinical Cancer Research. 2013 Feb 14;19(4):929–37
    12. 12. 3 pasos: 129 pac con respuesta en fase II; 215 pac del fase II y344 de fase IIIConcentraciones plasmáticas pretratamiento de factoresangiogénicos y citokinas (CAFs)Fase screening: 17 CAFs; 5 candidatos en el screening inicial: IL-6, IL-8, HGF, TIMP-1, E-selectine.Análisis confirmatorio: se añaden VEGF y osteopontina a IL-6, IL-8, E-selectina y HGF Lancet Oncol 2012;13:827-37
    13. 13. Pazopanib: Tran et al Lancet Oncol 2012;13:827-37Fase validatoria: fase III pac con pazopanib: altas concentraciones de IL-8, osteopontina, HGF y TIMP-1 se asociaron a menor SLP pac con placebo: altas concentraciones de IL-6, IL-8, osteopontina se asociaron a menor SLP Tran et al; Lancet Oncol 2012;13:827-37
    14. 14. Pazopanib: Tran et al Lancet Oncol 2012;13:827-37 Tran et al; Lancet Oncol 2012;13:827-37
    15. 15. Pazopanib: Tran et al Lancet Oncol 2012;13:827-37 Niveles de IL-6 y PFS24 m9.9 m Low High Tran et al; Lancet Oncol 2012;13:827-37
    16. 16. Rini ASCO 2010 #4501N=931Nefrectomía; RT-PCR732 genes examinados: 16 asociados a SLPEntre los 16 genes, la expresión aumentada se asoció a un riesgomás bajo de recidiva para los genes relacionados con laangiogénesis (EMCN, NOS3) y la inmunidad (CCL5, CXCL9)
    17. 17. Expression of epithelial-mesenchymal transitionmarkers in RCC: impact on prognosis outcomesN=122; enfermedad localizadaAnalizan 11 marcadores de EMT4 en análisis multivariante: clustering, twist, CRP, invasión microvascular Harada et al; BJU International. 2012 Jun 19;110(11c):E1131–7
    18. 18. AC IX y sunitinib52 pacientes del ensayo sunitinib vs IFN OS CAIX elevada 94 semanas CAIX baja 115 semanas p= 0.026 Sunitinib > IFN independientemente de los niveles de CIX Lamparella et al; ASCO GU 2013 #405
    19. 19. Modelospronósticos
    20. 20. MSKCC Group Heng Patil Choueiri Motzer Français CFCPatient 463 pac 782 patients 645 patients 375 patients 120 patientspopulation treated with with with treated with treated with IFN on inmunoth on suni/sora/be sunitinib beva/sora/su prospective trials va at multiple ni/axi on clinical trials NA centers prospective clinical trials; single centreCommon KPS <80% ECOG PS KPS <80 ECOG ECOGprognostic LDH >1.5 ULN Hb level <LLN Ca >ULN LDH Ca < 8.5/>10factors Calcium>10 DFI > 1 year Hb level <LLN Calcium DFI < 2 yearscompared DFI < 1 year Hemoglobinwith Motzer DFI < 1yearcriteriaSpecific N of met Neutr >ULN Bone mets Neutr >4500 sites Plat >ULN Plat >300 SR >100 or CRP >50
    21. 21. Motzer N=463Motzer RJ, Journal of Clinical Oncology 2002;20: 289-296
    22. 22. IKCWGEstudio internacional; datos de 3748 pacientes en ensayos clínicos Criterios Grupos de riesgo PS Riesgo bajo: Tiempo desde diagnóstico a tratº Risk score < -2.755 Nº sitios mets Inmunoterapia previa Riesgo intermedio Hemoglobina Risk score >2.755 to <- Calcemia 1.253 LDH Riesgo alto Leucocitos Fosfatasa alcalina Risk score >-1253 Manola J, et al. Clinical Cancer Research. 2011 Aug 14;17(16):5443–50.
    23. 23. IKCWGManola J, et al. Clinical Cancer Research. 2011 Aug 14;17(16):5443–50.
    24. 24. Risk stratification within TKI treatment subset (validation dataset)
    25. 25. Heng N=645Variable n %Inmunoterapia previa 1ª línea anti-VEGF 431 66.8 2ª línea anti-VEGF 214 33.2Tratamiento Sunitinib 396 61.4 Pazopanib 200 31 Bevacizumab 49 7.6 Heng DYC, Journal of Clinical Oncology 2009;27(34):5794–9
    26. 26. Heng N=645Riesgo favorable: 0 factoresRiesgo intermedio: 1-2 factoresRiesgo alto: 3-6 factores Heng DYC, Journal of Clinical Oncology 2009;27(34):5794–9
    27. 27. HengHeng DYC, Journal of Clinical Oncology 2009;27(34):5794–9
    28. 28. Porcentaje de pacientes supervivientes a 2 años PronósticoBuen pronóstico Mal pronóstico intermedio
    29. 29. 1028 pacientes; 13 centros internacionales Heng et al; Lancet Oncolo 2013;14:141-48
    30. 30. Edad > 65 años 55 %KPS <80% 27%>1 sitio mets 77%Mets SNC 10%Mets hepáticas 20%No células claras 13%Sarcomatoide 12%<1 año tras diagnóstico 55%Hemoglobina < LN 56%Hipercalcemia 10%LDH >1.5 x LN 12%Neutrofilia 19%Trombocitosis 21% Heng et al; Lancet Oncolo 2013;14:141-48
    31. 31. Inmunoterapia previa 24%Nefrectomía previa 78%TratamientoSunitinib 82%Sorafenib 13%Axitinib <1%Bevacizumab 5%Pazopanib <1% Heng et al; Lancet Oncolo 2013;14:141-48
    32. 32. Concordance: Database Consortium Model Original model (n=564) Validation (n=849) HR (95% CI) p HR (95% CI) pKPS <80% 2.51 <0.0001 2.08 <0.0001 (1.92-3.29) (1.71-2.55)<1 año desde 1.42 0.0098 1.27 0.0122diagnóstico (1.09-1.84) (1.05-1.53)Hemoglobina <LN 1.72 0.0001 1.69 0<0.0001 (1.31-2.26) (1.05-1.53)Hipercalcemia 1.81 0.0006 1.45 0.0087 (1.29-2.53) (1.10-1.92)Neutrofilia 2.42 <0.0001 1.64 <0.0001 (1.72-3.39) (1.28-2.01)Trombocitosis 1.49 0.0121 1.60 <0-0001 (1.09-2.03) (1.28-2.01)
    33. 33. Kaplan-Meier for OS 0 factores 1-2 factores 3-6 factores 17% 51% 31%
    34. 34. Concordancia con otros modelos Concordance Generalised R Index (rank)DCM 0.664 0.185 (1)CFC 0.662 0.161 (3)French 0.640 0.136 (5)IKCWG 0.668 1.149 (4)MSKCC 0.657 0.163 (2)
    35. 35. Predicción fallecimiento a los 2 años Favorable Intermedio PobreDCM 0.30 0.53 0.88CFC 0.37 0.60 0.86French 0.19 0.52 0.86IKCWG 0.35 0.50 0.84MSKCC 0.30 0.58 0.91
    36. 36. N=336; criterios Motzer < Procopio et al; BJC 2012;107(8):1227–32
    37. 37. Tras metastasectomíaN=5595 factores: resección incompleta, mets SNC,proteína C reactiva >1, PS>1, peor grado nuclear 105 m 24 m Naito ASCO 2012 #e15702
    38. 38. En segunda línea: Heng321 pacientes Median OS Riesgo bajo (n=32) NRTratamientos en 2ª línea: Riesgo intermedio (n=179) 13 m - sunitinib: 32% Riesgo pobre (n=74) 5.5 m - sorafenib 43% - temsirolimus 11% Duración de tratº previo Median OS - everolimus 6% > 8 meses 14.3 m < 8 meses 9.9 m Heng et al; ASCO 2010 #4523
    39. 39. En tercera línea N=252 pacientes Análisis multivariante: solo PS >2 juega papel independiente Cleveland Clinic French Heng MSKCCGrupo de Pts PFS Pts PFS Pts PFS Pts PFSriesgo (%) (m) (%) (m) (%) (m) (%) (m)Good 20 NR 6 NR 32 17.5 21 NRIntermediate 37 15.3 81 14.3 62 15.2 65 15.2Poor 43 10.2 13 9.1 6 5.5 19 6.4 p:<0.001 p:0.008 p:<0.001 p:<0.001 Iacovelli et al; ASCO-GU 2013 #470
    40. 40. Un factor es predictivo si el efecto del tratamiento es diferente en pacientes con el marcador positivo Superv en el grupo M- Superv en el grupo M+Se comparan pacientes tratados y Se comparan pacientes tratados yno tratados : EM- no tratados: EM+100 100 +15% 50 50 -5% Sv M-T+ Sv M+T+ Sv M-T- Sv M+T- 0 0 0 1 2 3 4 5 0 1 2 3 4 5 El marcador es un factor predictivo si EM- & EM+ son significativamente diferentes
    41. 41. Marcadores potenciales en RCC • Circulating biomarkers – VEGF, sVEGFR, CEC/CEPs, CAFs, LDH • Tissue-based biomarkers – VHL status, SNPs, RNA gene expression • Radiographic biomarkers – DCE-MRI, CE-CT, PET • Clinical biomarkers – HypertensionVEGF = vascular endothelial growth factor; sVEGFR = soluble VEGFR receptor; CEC = circulating endothelial cell;CEP = circulating endothelial progenitor; CAF = cytokine and angiogenic factor; LDH = lactate dehydrogenase;VHL = von Hippel–Lindau; SNPs = single nucleotide polymorphism; DCE-MRI = dynamic contrast enhanced-magnetic resonanceimaging; CE-CT = contrast-enhanced computed tomography
    42. 42. Sorafenib phase III (TARGET): Biomarker analysis Low baseline VEGF (≤131 pg/mL) High baseline VEGF (>131 pg/mL) 100 100 Sorafenib (n=180): Sorafenib (n=184): 75 75 5.5 months 5.5 months PFS (% patients)PFS (% patients) Placebo (n=176): Placebo (n=172): 3.3 months 2.7 months 50 50 HR=0.64 HR=0.48 95% CI: 0.49–0.83 95% CI: 0.38–0.62 25 25 0 0 0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20 Time (months) Time (months) VEGF levels are NOT predictive for sorafenib PFS in RCC Escudier B, et al. J Clin Oncol 2009
    43. 43. A cytokine and angiogenic factor (CAF) analysis in plasma for selection of sorafenib therapy in patients with metastatic renal cell carcinomaN=69; 6 marcadores:OPN, VEGF, sCA9, collagen VI, sVEGFR-2, TNF related apopt-inducing ligand Zurita AJ, Annals of Oncology. 2012;23(1):46–52.
    44. 44. Evaluation of serum LDH as a predictive biomarker for mTOR inhibition in patients with mRCC• LDH is regulated by the PI3K/AKT/mTOR pathway, and is associated with tumor hypoxia/necrosis• Pretreatment LDH was assessed in 404 poor-risk mRCC patients treated with temsirolimus or interferon-alpha in a phase III trial LDH normal LDH elevada Armstrong et al. J Clin Oncol 2012;30:3402-3407
    45. 45. Interleukina-6 y pazopanib Tran et al; Lancet Oncol 2012;13:827-37
    46. 46. Clinical Cancer Research. 2013 Feb 14;19(4):929–37
    47. 47. CTC as early markers Estudio Circles CECs/4 mlPac que progresan durante 28el estudio (11 pac)Pacientes que no 73progresan (28 pac) P=0.002 Oyarvides et al; ASCO GU 2013 #436
    48. 48. Association of SNPs in IL-8, HIF1α, VEGFA and VEGFR2 with treatment response to pazopanib in RCC Association between SNPs and efficacy / toxicity Reference SNPEndpoint Gene Polymorphism (rs) Number P-valuePFS IL-8 2767A>T rs1126647 0.03RR HIF1α 1790G>A rs11549467 0.02MAP VEGFA –2578C>A rs699947 0.04MAP VEGFA –1498C>T rs833061 0.03MAP VEGFA –634G>C rs2010963 0.04MAP VEGFR2 1416T>A (H472Q) rs1870377 0.005HIF-1α = hypoxia-inducible factor-1 alpha; MAP = mean arterial pressure Ball HA, et al. ASCO 2010
    49. 49. SNP analysis397 pacientes tratados con pazopanib27 polimorfismos en 13 genes 3 pol 3 pol HIF1A IL-8 PFS 5 pol RR VEGF A 5 pol NR1I2 5 pol HIF1a Xu et al; J Clin Oncol 2011; 29:2557-64
    50. 50. Progression-free survival Kaplan-Meier curves for each genotype group of pazopanib and placebo-treated patients Pazopanib Placebo(A) IL8 2767A>T (rs1126647)(B) IL8 −251T>A (rs4073)(C) HIF1A 1790G>A (rs11549467)
    51. 51. Evaluation of different polymorphisms as markers ofsunitinib efficacy and toxicity in first line treatment of renal clear cell carcinoma VEGFR3 y SLP García-Donas; Lancet Oncol 2011;12:1143-50
    52. 52. Predictive factors for response to treatment in patients with advanced renal cell carcinoma Muriel et al; Invest New Drugs 2012;30:2443-2449
    53. 53. B7H1 y respuesta a sunitinibN=20 Expresión de RR PFS B7H1 Positiva 30% 6.2 m (55.5%) Negativa 50% 19.3 m (44.5%) p=0.63 p=0.56 Análisis multivariante: no correlación Kim et al; ASCO GU 2013; #416
    54. 54. Marcador Tratamiento SG con SG sin p toxicidad toxicidadHTA s Bevacizumab 30.9 m 7.2 m <0.0001Rini 2010 SunitinibHTA d Bevacizumab 32.2 m 14.9 m <0.0001Rini 2010 SunitinibHipotiroidismo Sorafenib SLP SLP 0.018Schmidinger Sunitinib 17 m 10.8 m2010HFS Sunitinib 38.2 m 18.9 m <0.0001Michaelson 2011Neumonitis mTOR inh EE EEDabydeen 2011 86% 43%
    55. 55. Hypertension as a biomarker in VEGF-targeted therapy Disease Anti-VEGF HypertensionStudy Results (N) agent definitionRini et al.1 RCC Sunitinib SBP >140 mmHg and OS: 30.9 vs 7.2 months (n=544) DBP ≥90 mmHg (p<0.0001) PFS: 12.5 vs 2.5 months (p<0.0001) ORR: 55% vs 9% (p<0.0001)Harzstark et al.2 RCC Bevacizumab ≥CTC Grade 2 OS: 41.6 vs 16.2 months (n=366) (+IFN) (p<0.0001) PFS: 13.2 vs 8.0 months (p=0.0009) ORR: 13% vs 9% (p=ns)Escudier et al.3 RCC Bevacizumab ≥CTC Grade 2 PFS: 10.2 vs 8.4 months (p=ns) (n=337) (+IFN)Schneider et al.4 Breast Ca Bevacizumab ≥CTC Grade 3 OS: 38.7 vs 25.3 months (n=345) (+chemo) (p=0.002)Dahlberg et al.5 NSC Lung Ca Bevacizumab >150/100 mmHg, OR OS: 15.9 vs 11.5 months (n=741) (+chemo) >20 mmHg increase (p=0.0002) vs baseline by end of C#1 PFS: 7.0 vs 5.5 months (p<0.0001) 1. Rini B, et al. J Natl Cancer Inst 2011 (Epub ahead of print); 2. Harzstark AL, et al. ASCO GU 2010 3. Escudier B, et al. ASCO 2008; 4. Schneider BP, et al. J Clin Oncol 2008 5. Dahlberg SE, et al. J Clin Oncol 2010
    56. 56. Comparative assesment of sunutinib-associated Aes as potential biomarkers of efficacy in mRCC Endpoint AE at any time point AE by the 12-wk landmark HR (95% CI) p HR (95% CI) p HTN during treatment PFS 0.291 (0.220-0.399) <0.0001 - NS OS 0.296 (0.237-0.427) <0.0001 0.654 (0.511- 0.0008 0.838) HFS during treatment PFS 0.750 (0.595-0.945) 0.0148 - NS OS 0.578 (0.437-0.766) 0.0001 0.674 (0.462- 0.0415 0.985) Asthenia/fatigue during treatment PFS 0.491 (0.375-0.644) <0.0001 - NS OS 0.720 (0.541-0.959) 0.0245 - NS Donskov et al; ESMO 2012 #785
    57. 57. Subpoblación de pacientes a tratº con sunitinib y larga supervivencia Experiencia SOG-GU N=46 PFS Con HTA 44.8 Sin HTA 31.1 Con astenia 35.5 Sin astenia 32.3 Con hipotiroidismo 44.8 Sin Hipotiroidismo 31.0 Esteban E, et al. ESMO 2012 #862
    58. 58. MASS criteria (CECT) Morphology, attenuation, size and structureResponse category MASS criteria descriptionFavorable response No new lesions and either of the following: 1. Decrease in tumor size of ≥20% 2. One or more predominantly solid enhancing lesions with marked central necrosis or marked decreased attenuation (≥40 HU)Indeterminate response Does not fit criteria for favorable response or unfavorable responseUnfavorable response Either of the following: 1. Increase in tumor size of ≥20% in the absence of marked central necrosis or marked decreased attenuation 2. New metastases, marked central fill-in, or new enhancement of a previously homogeneously hypo-attenuating non-enhancing massMASS = morphology, attenuation, size and structure Smith A, et al. Am J Roentgenol 2010;194:1470-8
    59. 59. 89 pacientes a tratº con sunitinib y sorafenibPerformance PFS > 250 díasmeasure foridentifying patients MASS SCAT Mod Choi RECISTwith a good clinical Favorable Favorable Good Partialoutcome response response response responseSensibilidad 86 75 93 16Especificidad 100 100 44 100VPP 100 100 89 100VPN 60 45 57 20Exactitud 89 79 85 30
    60. 60. Central fill-in or new enhancement signals eventual radiographic PD New metastasis group Never progresses group (N=6 patients) (N=21 patients)Patients with central fill-in orchange from homogeneously 83% 14%low density to enhancing Pre-PD = 236 days PD = 343 days Central fill-in Smith A, et al. AJR Am J Roentgenol
    61. 61. Sequential FDG-PET/CT as a surrogate marker of response to sunitinib in met clear cell renal cancerFDGPET response at 16 weeks predicts outcome Powles J Clin Oncol 29: 2011 (suppl 7; abstr 301)
    62. 62. Predicting survival in metastatic RCC on sunitinib using MASS criteria: evaluation of a large multicenter prospective phase III trial Respuesta por: N=213 RECIST 1.1 5 target lesions Choi PRIMER TAC DE EVALUACIÓN Choi mod MASS Correlacionan mejor/peor respuesta en cada sistema con PFS y OS PFS (HR) OS (HR) CHOI mod MASS RECIST MASS 0.6/2.6 0.46/14.8 0.46/9.78 0.56/7.18 Smith et al; ASCO GU 2013 #407
    63. 63. Exactitud para detectar una buena evolución clínica (PFS > 250 días) RECIST 1.1 51% Choi 62% Choi mod 67% MASS 76%Exactitud para detectar una mala evolución clínica (PFS < 250 días) RECIST 1.1 58% Choi 57% Choi mod 58% MASS 58% Smith et al; ASCO GU 2013 #407
    64. 64. DCE-USN=539 (157 con CCR)DCE-US en los días: basal-7-14-30-60Disminución en el AUC>40% al mes fuepredictivo de TTP y OS Lassau et al; ASCO 2012 #4618
    65. 65. Predictivos detoxicidad
    66. 66. VEGF SNP-634 predicts incidence of hypertension in sunitinib-treated mRCC patientsFrequency of hypertension (%) p=0.03 94% 81% 67% Genotype (# of pts) Kim JJ, et al. ASCO 2009
    67. 67. Predicción de HFS >2 con sorafenibFactor predictivo Start sorafenib 451 pacientesScore inicial 20Mujer +6PS >1 -7Mts pulmonares -7Mts hepáticas +62 o más órganos afectos +9WBC basal >5.5 +5Semana 1 +4Semana 2 +7Semana 3 + 10Semana 4 + 11Semana 5 + 12Semana 6 + 11Semana 7 +10 Dranitsaris et al;Semana 8 +8 Ann Oncol 2012;23:2013-2108
    68. 68. Score Event Sensitivity Specificity Correctly LikelihoodCut Point Incidence 1 Classified Ratio20 to ≤ 20 1.4% 100% 0.0% 8.5% 1.0> 20 to ≤ 30 3.7% 99.6% 2.2% 10.4% 1.02> 30 to ≤ 40 8.3% 88.9% 28.1% 33.3% 1.24> 40 to ≤ 50 15.0% 36.3% 81.7% 77.9% 2.00> 50 24.3% 3.1% 99.1% 91.0% 3.5
    69. 69. Evaluation of different polymorphisms as markers ofsunitinib efficacy and toxicity in first line treatment of renal clear cell carcinoma García-Donas; Lancet Oncol 2011;12:1143-50
    70. 70. 63-69% de lasmutaciones somáticasno detectables encada región tumoral
    71. 71. Estudio retrospectivo; 176 pacientes nefrectomizadosUniversidad de Texas; Cancer Genome Atlas Lancet Oncology; 2013 Jan 25;14(2):159–67
    72. 72. 1 ¿tenemos factores/modelos pronó sticos aplicables a la práctica diaria?
    73. 73.
    74. 74. 2 ¿tenemos factores/modelospredictivos aplicables a la práctica diaria?
    75. 75. N

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