Development and validation of prognostic models in patients with metastatic renal cell carcinoma (mRCC) treated with sunitinib: A Greek-French collaboration.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15040-e15040
Author(s):  
Vasilios Karavasilis ◽  
Kimon Tzanis ◽  
Christina Bamia ◽  
Reza-Thierry Elaidi ◽  
Efthimios Kostouros ◽  
...  
Keyword(s):  
Kinase Inhibitors ◽  
Cox Regression ◽  
External Validation ◽  
Prognostic Index ◽  
Roc Curves ◽  
Risk Groups ◽  
High Risk Group ◽  
Prognostic Models ◽  
Clinical Factors ◽  
Complicated Model

e15040 Background: The use of tyrosine kinase inhibitors (TKIs) in mRCC has improved prognosis but the individual outcome remains largely unpredictable. The MSKCC model, used to identify risk groups, was developed in cytokine-treated patients and has not been externally validated in the TKI era. It contains 3 laboratory factors (total 5), making its application to retrospective series somewhat problematic. Subsequently, a more complicated model, using 4 laboratory factors (total 6) has been described. The Hellenic Cooperative Oncology Group recently described a simpler model with only 3 clinical factors. We are describing the application and external validation of this model. Methods: 128 Greek patients with mRCC treated with 1st line sunitinib were included. All had had nephrectomy. Previous interferon was allowed. Cox regression was used to develop a predictive model for overall survival (OS). Our model was compared to that of MSKCC and Heng’s using ROC curves and Harrell’s Concordance Index. Risk groups were defined by the calculated prognostic index and by clinical factors. External validation was done using a sample of 226 French patients. The Royston and Sauerbrei D statistic was used as a measure of discrimination of the survival model. Results: Time from diagnosis of RCC to start of sunitinib (<12), PS (>1) and number of metastatic sites (>1) were independent adverse prognostic factors in the Greek dataset. The co-efficients for each factor were: 0.51, 0.97, 0.61, respectively. The 3 risk groups were defined by the 25th and 75th percentiles of the prognostic index values (Table 1). The model was of equal prognostic value to the MSKCC (p=.272) and Heng’s (p=.075). French had better survival than Greek patients especially in the high risk group (for all models). Validation of our model in the French data showed that it was applicable (R2 D: 0.14, SE: 0.09), especially for the low/medium risk groups. Conclusions: Our model is the only one externally validated in TKI-treated patients. It may be considered as a simpler alternative to those currently applied. [Table: see text]

scholarly journals A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongdong Zhou ◽  
Xiaoli Liu ◽  
Xinhui Wang ◽  
Fengna Yan ◽  
Peng Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Surgical Therapy ◽  
Calibration Curve ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Alpha Fetoprotein ◽  
Clinicopathologic Characteristics

Abstract Background Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC. Methods A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort. Results The C-index of nomogram1was 0.708 (95%CI: 0.673–0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606–0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690–0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691–0.813; AUC: 0.784, 95%CI: 0.709–0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively. Conclusions Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

scholarly journals Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Bo Xiao ◽  
Liyan Liu ◽  
Zhuoyuan Chen ◽  
Aoyu Li ◽  
Pingxiao Wang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

A prognostic nomogram in AFP-negative hepatocellular carcinoma based on LASSO Cox regression

2020 ◽  
Author(s):  
Dong dong Zhou ◽  
Xiao li Liu ◽  
Xin hui Wang ◽  
Feng na Yan ◽  
Peng Wang ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Hazard Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Clinicopathologic Characteristics ◽  
Actual Observation ◽  
Prognostic Prediction

Abstract PurposeHepatocellular carcinoma (HCC) patients with alpha-fetoprotein (AFP)-negative (<8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of HCC patients with AFP-negative.Materials and MethodsA total of 410 AFP-negative patients with clinical diagnosed with HCC as a primary cohort; 148 AFP-negative HCC patients as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by univariate and multivariate Cox hazard analysis were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort.ResultsThe C-index of nomogram1was 0.708 (95%CI: 0.673-0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606-0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690-0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691-0.813; AUC: 0.784, 95%CI: 0.709-0.847) and good calibration. The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively.ConclusionsNovel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-negative HCC. This model could help AFP-negative HCC facilitate a personalized prognostic evaluation.

scholarly journals The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Bufu Tang ◽  
Jinyu Zhu ◽  
Jie Li ◽  
Kai Fan ◽  
Yang Gao ◽  
...  
Keyword(s):  
High Risk ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Models ◽  
Integrated Analysis ◽  
Immune Microenvironment ◽  
Diagnostic Models

Abstract Background In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results Four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients. Graphical abstract

Mantle Cell Lymphoma: Prognostic Capacity of the Follicular Lymphoma International Prognostic Index.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1910-1910
Author(s):  
Michael B. Moller ◽  
Niels T. Pedersen ◽  
Bjarne E. Christensen
Keyword(s):  
Overall Survival ◽  
Multivariate Analysis ◽  
Mantle Cell Lymphoma ◽  
Cell Lymphoma ◽  
International Prognostic Index ◽  
Prognostic Index ◽  
Risk Groups ◽  
High Risk Group ◽  
Predictive Capacity ◽  
Mantle Cell

Abstract Background: The International Prognostic Index (IPI) is the most commonly used prognostic model in mantle cell lymphoma. However, the prognostic value of IPI is limited. The recently published Follicular Lymphoma International Prognostic Index (FLIPI) is built on variables (age, stage, lactic dehydrogenase, anemia, and nodal disease) which also are pertinent to mantle cell lymphoma. This study was conducted to evaluate the prognostic value of FLIPI in patients with mantle cell lymphoma. Patients and Methods: A population-based series of 93 patients with mantle cell lymphoma diagnosed in a 7-year period were studied. End points of the study were response to therapy, overall survival, and failure-free survival according to IPI and FLIPI. Results: Applied to the whole series, FLIPI identified 3 risk groups with markedly different outcome with 5-year overall survival rates of 65%, 42%, and 8%, respectively (P < .0001; log-rank 28.13; figure below). Notably, the high-risk group comprised 53% of patients. In contrast, IPI only allocated 16% of cases to the high-risk group and had a lower overall predictive capacity (log-rank 24.8). When both FLIPI and IPI were included in a multivariate analysis, only FLIPI was related to survival. In patients treated with CHOP-based regimens (n = 45) FLIPI also had superior predictive capacity compared to IPI (log-rank, 18.51 versus 11.37), and again only FLIPI retained significance in multivariate analysis. Multivariate analysis of failure-free survival also identified FLIPI, and not IPI, as independently significant. Conclusion: FLIPI is the superior prognostic model as compared to IPI and should be the preferred clinical prognostic index in mantle cell lymphoma. Overall survival according to FLIPI risk groups Overall survival according to FLIPI risk groups

Reduced Intensity Conditioning with Thiotepa and Fludarabine for Allogeneic Transplantation: Evidence for Low Toxicity and Long-Lasting Disease Control in MDS with Low/Intermediate-1 IPSS Score and in AML from MDS in Complete Remission.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3285-3285
Author(s):  
Emilio Paolo Alessandrino ◽  
Luca Malcovati ◽  
Giorgio La Nasa ◽  
Paolo de Fabritiis ◽  
Massimo Bernardi ◽  
...  
Keyword(s):  
High Risk ◽  
Complete Remission ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Score ◽  
Conditioning Regimen ◽  
Disease Status ◽  
Risk Groups ◽  
High Risk Group ◽  
Valvular Stenosis

Abstract This study investigated Thiotepa (TT) and Fludarabine (Fluda) as a preparative regimen for allogeneic peripheral stem cell transplantation in patients with myelodysplastic syndrome (MDS) or acute leukemia from MDS (MDS-AML) older than 50 or with comorbidities contraindicating standard conditioning. Patients were prepared with TT, given over 3 hours as an i.v. infusion at a dose of 10 mg/kg over two days (day -8 and day -7) and Fluda at the dose of 125 mg/m2 i.v. over five days ( from day -7 to day -3). Fresh or cryopreserved allogeneic peripheral stem cells were infused on day 0 or +1. Graft-versus-Host Disease (GvHD) prophylaxis consisted of cyclosporine A (CyA) at the dose of 1.5 mg/kg day as a continuous iv infusion from day -5 until engraftment. The CyA was then administered orally at the dose of 3 mg/kg twice a day. Doses were adjusted to maintain plasma level concentrations between 150–350 mg/dL. From day +60, in the absence of acute GvHD, the CyA was tapered down by 20% every 2 weeks until withdrawal. In addition, patients received methotrexate 10 mg/m2 on day +1, and 8 mg/m2 on days + 3, +6 and +11 after transplantation. At the time of transplantation, patients were classified in two risk groups (low vs high risk) according to IPSS score (low/intermediate-1 vs. intermediate-2/high) for MDS patients, and disease status (CR vs. not CR) for MDS-AML. Kaplan-Meier survival analysis was carried out to compare Overall Survival (OS), Transplant-Related Mortality (TRM) and probability of relapse. Fifty patients (29 males, 21 females) entered the study; the median age was 54 years (range 38–71). Sixteen MDS patients had a low/intermediate 1 score according to the International Prognostic Score System (IPSS), 16 had an intermediate 2/high IPSS score, 18 had MDS-AML. Thirty patients underwent transplantation as front-line therapy, 20 received one or more cycles of chemotherapy before transplant. Among the latter, nine with MDS-AML were in complete remission at the time of their transplant, while four were in a partial remission. The interval from diagnosis to transplantation ranged from 1 to 52 months (median value 11 months). Contraindications to a standard conditioning regimen were liver disease, hypertrophic cardiomyopathy secondary to hypertension or valvular stenosis, cardiac arrhythmia, diabetes mellitus, hypothyroidism, previous CNS bleeding, and a history of sepsis. All but one patient achieved engraftment, with full donor chimerism by day +30. Patients were followed up for a median time of 21 months (range 0.2–87). TRM at 1 and 2 years after transplantation was 25% and 33%; the 5-year probability of relapse was 27%. Twenty-six patients are alive in complete remission, and the 5-year OS is 50%. The 5-year OS was 73% and 28% in low- and high-risk patients respectively (p=0.002). TRM at 1 and 2 years after transplantation was 13% and 21% in the low-risk group and 39% and 45% in the high-risk group (p=0.046); the 5-year probability of relapse was 10% and 50% in the low- and high-risk group respectively (p=0.015). In a multivariate Cox regression, risk group retained a borderline significance (HR=2.6, p=0.07) when adjusted by age at transplantation (p=0.03) and interval from diagnosis to transplant (n.s.). The combination of Thiotepa and Fludarabine is an effective and well-tolerated conditioning regimen in patients with MDS or MDS-AML who are poor candidates for standard myeloablative transplantation, particularly in MDS patients with low/intermediate-1 IPSS score and MDS-AML patients in CR.

Identifying patients with non-small cell lung cancer (NSCLC) unlikely to benefit from erlotinib: An exploratory analysis of National Cancer of Institute of Canada Clinical Trials Group BR.21

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 7161-7161 ◽  
Author(s):  
M. Florescu ◽  
B. Hasan ◽  
F. A. Shepherd ◽  
L. Seymour ◽  
K. Ding ◽  
...  
Keyword(s):  
Weight Loss ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Index ◽  
Early Death ◽  
Risk Groups ◽  
Smoking History ◽  
Factors Associated ◽  
Significant Survival ◽  
High Benefit

7161 Background: Despite a 9% response rate, BR.21 demonstrated significant survival benefit for patients receiving erlotinib as 2nd/3rd line therapy for NSCLC. We undertook to characterize, by exploratory subset analysis, patients less likely to benefit from erlotinib. To identify factors for consideration, we first identified baseline characteristics associated with early progression by eight wks and early death by 3 mos. Methods: Using stratification factors and potential prognostic factors from BR.21, the Cox regression model with stepwise selection was used to establish a prognostic model to separate erlotinib patients into 4 risk categories based on the 10th, 50th & 90th percentiles of prognostic index scores. 7 variables (smoking history, PS, weight loss, anemia, high LDH, response to prior chemo and time from diagnosis to randomization) were used in the final model. The hypothesis was that the characteristics of the treated patients in the highest risk group would also be predictive of lack of benefit from erlotinib when erlotinib and placebo patients with the same characteristics were compared. Results: Factors associated with PD by 8 wks were: PS2–3 (p = 0.009), weight loss (p = 0.0004), anemia (p = 0.008), PD to prior chemo (p = 0.006), non-Asian (p = 0.047), EGFR IHC-negative (p = 0.005), Factors associated with survival < 3 mos were: PS2–3 (p < 0.0001), weight loss (p < 0.0001), anemia (p < 0.0001), PD to prior chemo (p < 0.0001), non-Asian (p = 0.008), high LDH (p < 0.0001), time to randomization <12 mos (p = 0.0003). Comparison of overall survival for the 4 risk groups derived from prognostic index score as follows: high benefit (HR = 0.41, p = 0.007), 2 intermediate benefit (HR 0.79, p = 0.09; HR 0.80; p = 0.09); no benefit (HR 1.23; p = 0.42). Median survivals for erlotinib (placebo) patients in each group were 17.3 (8.3), 9.7 (7.5), 4.1 (3.7), 1.9 (2.7) mos. Conclusions: By establishing a prognostic model, we identified a small group of patients who are unlikely to benefit from 2nd/3rd line erlotinib therapy. This model requires prospective validation to confirm that it is both prognostic and predictive of outcome from treatment. [Table: see text]

Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score

Blood ◽  
2011 ◽  
Vol 118 (3) ◽  
pp. 686-692 ◽  
Author(s):  
Joerg Hasford ◽  
Michele Baccarani ◽  
Verena Hoffmann ◽  
Joelle Guilhot ◽  
Susanne Saussele ◽  
...  
Keyword(s):  
High Risk ◽  
Kinase Inhibitors ◽  
Prognostic Score ◽  
Progression Free Survival ◽  
Risk Groups ◽  
High Risk Group ◽  
Prognostic Scores ◽  
Imatinib Treatment ◽  
Free Survival ◽  
Eutos Score

AbstractThe outcome of chronic myeloid leukemia (CML) has been profoundly changed by the introduction of tyrosine kinase inhibitors into therapy, but the prognosis of patients with CML is still evaluated using prognostic scores developed in the chemotherapy and interferon era. The present work describes a new prognostic score that is superior to the Sokal and Euro scores both in its prognostic ability and in its simplicity. The predictive power of the score was developed and tested on a group of patients selected from a registry of 2060 patients enrolled in studies of first-line treatment with imatinib-based regimes. The EUTOS score using the percentage of basophils and spleen size best discriminated between high-risk and low-risk groups of patients, with a positive predictive value of not reaching a CCgR of 34%. Five-year progression-free survival was significantly better in the low- than in the high-risk group (90% vs 82%, P = .006). These results were confirmed in the validation sample. The score can be used to identify CML patients with significantly lower probabilities of responding to therapy and survival, thus alerting physicians to those patients who require closer observation and early intervention.

scholarly journals Validation of Clinical Prognostic Models and Integration of Genetic Biomarkers of Drug Resistance in CLL Patients Treated with Ibrutinib

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 186-186 ◽  
Author(s):  
Inhye E. Ahn ◽  
Xin Tian ◽  
Maher Albitar ◽  
Sarah E. M. Herman ◽  
Erika M. Cook ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Models ◽  
Discovery Cohort ◽  
Treatment Naïve ◽  
Equity Ownership

Abstract Introduction: We previously reported a prognostic scoring system in CLL using pre-treatment factors in patients treated with ibrutinib [Ahn et al, 2016 ASH Annual Meeting]. Here we present long-term follow-up results and validation of the prognostic models in a large independent cohort of patients. We also determine the incidence of resistance-conferring mutations in BTK and PLCG2 genes in different clinical risk groups. Methods and Patients: The discovery cohort comprised 84 CLL patients on a phase II study with either TP53 aberration (deletion 17p or TP53 mutation) or age ≥65 years (NCT01500733). The validation cohort comprised 607 patients pooled from four phase II and III studies for ibrutinib in treatment-naïve or relapsed/refractory CLL (NCT01105247; NCT01578707; NCT01722487; NCT01744691). All patients received single-agent ibrutinib 420mg once daily. We used Cox regression models to identify independent predictors of PFS, Kaplan-Meier method to estimate probabilities of PFS, log-rank test to compare PFS, and Cochran-Armitage trend test to compare the incidence of mutation among subgroups. We used R version 3.5.0 or SAS® version 9.3 for statistical analyses. For biomarker correlation, we tested cellular DNA or cell-free DNA collected from patients in the discovery cohort with the targeted sequencing of BTK and PLCG2 genes. Result: At a median follow-up of 5.2 years, 28 (33.3%) of 84 patients in the discovery cohort progressed or died. 52 (61.9%) patients had treatment-naïve CLL. Independent factors of PFS on univariate analysis were; TP53 aberration, prior treatment, and β-2 microglobulin (B2M) >4mg/L (P<0.05 for all tests). Unmutated IGHV and advanced Rai stage (III/IV) showed a trend toward inferior outcome without reaching statistical significance. Because higher levels of B2M were associated with relapsed/refractory CLL, we performed two multivariate Cox regression models to assess B2M and prior treatment status separately. Risk groups were determined by the presence of TP53 aberration, advanced Rai stage, and B2M >4mg/L for Model 1, and TP53 aberration, advanced Rai stage, and relapsed/refractory CLL for Model 2 (Table 1). The high-risk group had all three adverse risk factors; the intermediate-risk group had two risk factors; and the low-risk group, none or one. The median PFS of the high-risk group was 38.9 months for Model 1 and 38.4 months for Model 2, and was significantly shorter than those of intermediate and low-risk groups. In the validation cohort, 254 (41.8%) of 607 patients progressed or died at a median follow-up of 4.2 years. 167 (27.5%) patients had treatment-naïve CLL. Both models showed statistically significant differences in PFS by risk groups (Table 1). For the high-risk group, 4-year PFS was 30.2% in Model 1 and 30.5% in Model 2, which were inferior to those of intermediate (53.4 and 52.4%) and low-risk groups (68.7 and 73.7%). Model 1 classified 20% of patients and Model 2 classified 28% of patients to the high-risk group. BTK and PLCG2 mutations are common genetic drivers of ibrutinib resistance in CLL. To determine whether the incidence of these mutations correlates with prognostic risk groups, we performed targeted sequencing of BTK and PLCG2 of samples collected from patients in the discovery cohort. We used cell-free DNA for patients who received long-term ibrutinib (≥3 years) and had low circulating tumor burden, and cellular DNA, for samples collected within 3 years on ibrutinib or at progression. Of 84 patients, 69 (82.1%) were tested at least once, and 37 (44.0%) were tested at least twice. The frequency of testing was similar across the risk groups by two models (P>0.05). The cumulative incidences of mutations at 5 years in the low-, intermediate-, and high-risk groups were: 21.4%, 44.8% and 50%, respectively, by Model 1 (P=0.02); and 22.6%, 41.4% and 66.7%, respectively, by Model 2 (P=0.01). Conclusion: We developed and validated prognostic models to predict the risk of disease progression or death in CLL patients treated with ibrutinib. Risk groups classified by three commonly available pre-treatment factors showed statistically significant differences in PFS. The clinically-defined high-risk disease was linked to higher propensity to develop clonal evolution with BTK and/or PLCG2 mutations, which heralded ibrutinib resistance. Disclosures Albitar: Neogenomics Laboratories: Employment. Ma:Neogenomics Laboratories: Employment. Ipe:Pharmacyclics, an AbbVie Company: Employment, Other: Travel; AbbVie: Equity Ownership. Tsao:Pharmacyclics LLC, an AbbVie Company: Employment. Cheng:Pharmacyclics LLC, an AbbVie Company: Employment. Dean:CTI BioPharma Corp.: Employment, Equity Ownership; Pharmacyclics LLC, an AbbVie Company: Employment, Equity Ownership. Wiestner:Pharmacyclics LLC, an AbbVie Company: Research Funding.

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