Unavailability of thymidine kinase does not preclude the use of German comprehensive prognostic index: results of an external validation analysis in early chronic lymphocytic leukemia and comparison with MD Anderson Cancer Center model

miR-197, miR-26a and miR-27a analysis in chronic lymphocytic leukemia

Aims: Chronic lymphocytic leukemia (CLL) involves the proliferation and increase of B-lymphocytes in the peripheral blood, bone marrow and lymphoid organs. This study evaluated the microRNAs miR-197, miR-26a and miR-27a as potential biomarkers for CLL. Patients & Methods: Eighty-two patients with CLL and 62 control subjects (CT) were investigated for these targets, using quantitative PCR (qPCR). Results: A significant reduction of all microRNAs was observed in CLL compared to the controls (p < 0.001). Significant negative correlations were observed for the clinical staging groups. After adjusting for multiple logistic regression analysis, miR-197 and miR-26a remained as possible independent risk factors related to the CLL. Conclusions: Our data indicated good performance of this microRNAs as potential biomarkers in CLL.

Artificial intelligence models in chronic lymphocytic leukemia - recommendations toward state-of-the-art

Artificial intelligence (AI), machine learning and predictive modeling are becoming enabling technologies in many day-to-day applications. Translation of these advances to the patient's bedside for AI assisted interventions is not yet the norm. With specific emphasis on CLL, here, we review the progress of prognostic models in hematology and highlight sources of stagnation that may be limiting significant improvements in prognostication in the near future. We discuss issues related to performance, trust, modeling simplicity, and prognostic marker robustness and find that the major limiting factor in progressing toward state-of-the-art prognostication within the hematological community, is not the lack of able AI algorithms but rather, the lack of their adoption. Current models in CLL still deal with the 'average' patient while the use of patient-centric approaches remains absent. Using lessons from research areas where machine learning has become an enabling technology, we derive recommendations and propose methods for achieving state-of-the-art predictions in modeling health data, that can be readily adopted by the CLL modeling community.

Lymphocyte Doubling Time As A Key Prognostic Factor To Predict Time To First Treatment In Early-Stage Chronic Lymphocytic Leukemia

The prognostic role of lymphocyte doubling time (LDT) in chronic lymphocytic leukemia (CLL) was recognized more than three decades ago when the neoplastic clone’s biology was almost unknown. LDT was defined as the time needed for the peripheral blood lymphocyte count to double the of the initial observed value. Herein, the LDT prognostic value for time to first treatment (TTFT) was explored in our prospective O-CLL cohort and validated in in two additional CLL cohorts. Specifically, newly diagnosed Binet stage A CLL patients from 40 Italian Institutions, representative of the whole country, were prospectively enrolled into the O-CLL1-GISL protocol (clinicaltrial.gov identifier: NCT00917540). Two independent cohorts of newly diagnosed CLL patients recruited respectively at the Division of Hematology in Novara, Italy, and at the Hospital Clinic in Barcelona, Spain, were utilized as validation cohorts. In the training cohort, TTFT of patients with LDT >12 months was significantly longer related to those with a shorter LDT. At Cox multivariate regression model, LDT ≤ 12 months maintained a significant independent relationship with shorter TTFT along with IGHV unmutated (IGHVunmut) status, 11q and 17p deletions, elevated β2M, Rai stage I-II, and NOTCH1 mutations. Based on these statistics, two regression models were constructed including the same prognostic factors with or without the LDT. The model with the LTD provided a significantly better data fitting (χ² = 8.25, P=0.0041). The risk prediction developed including LDT had better prognostic accuracy than those without LDT. Moreover, the Harrell’C index for the scores including LDT were higher than those without LDT, although the accepted 0.70 threshold exceeded in both cases. These findings were also confirmed when the same analysis was carried out according to TTFT’s explained variation. When data were further analyzed based on the combination between LDT and IGHV mutational status in the training and validation cohorts, IGHVunmut and LDT>12months group showed a predominant prognostic role over IGHVmut LTD ≤ 12 months (P=0.006) in the O-CLL validation cohort. However, this predominance was of borden-line significance (P=0.06) in the Barcelona group, while the significant prognostic impact was definitely lost in the Novara group. Overall, in this study, we demonstrated that LDT could be re-utilized together with the more sophisticated prognostic factors to manage the follow-up plans for Binet stage A CLL patients.

Recent progress of prognostic biomarkers and risk scoring systems in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is the most prevalent adult leukemia with high heterogeneity in the western world. Thus, investigators identified a number of prognostic biomarkers and scoring systems to guide treatment decisions and validated them in the context of immunochemotherapy. A better understanding of prognostic biomarkers, including serum markers, flow cytometry outcomes, IGHV mutation status, microRNAs, chromosome aberrations and gene mutations, have contributed to prognosis in CLL. Del17p/ TP53 mutation, NOTCH1 mutation, CD49d, IGHV mutation status, complex karyotypes and microRNAs were reported to be of predictive values to guide clinical decisions. Based on the biomarkers above, classic prognostic models, such as the Rai and Binet staging systems, MDACC nomogram, GCLLSG model and CLL-IPI, were developed to improve risk stratification and tailor treatment intensity. Considering the presence of novel agents, many investigators validated the conventional prognostic biomarkers in the setting of novel agents and only TP53 mutation status/del 17p and CD49d expression were reported to be of prognostic value. Whether other prognostic indicators and models can be used in the context of novel agents, further studies are required.

Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis

BACKGROUND

Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL.

OBJECTIVES

To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances.

SEARCH METHODS

We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies.

SELECTION CRITERIA

We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models.

MAIN RESULTS

From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies: CLL International Prognostic Index (CLL-IPI) This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77.

AUTHORS' CONCLUSIONS

Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely.

Comparative assessment of prognostic models in chronic lymphocytic leukemia: evaluation in Indian cohort

Prognostic indices combining several clinical and laboratory parameters have been proposed for prognostication in chronic lymphocytic leukemia (CLL). Recently, international consortium on CLL proposed an international prognostic index (CLL-IPI) integrating clinical, molecular, and genetic parameters. The present study was designed to evaluate the reproducibility of CLL-IPI in Indian CLL cohort. The prognostic ability of CLL-IPI in terms of overall survival (OS) and time to first treatment (TTFT) was investigated in treatment-naive CLL patients and also compared with other existing prognostic scores. For assigning scores, clinical and laboratory details were obtained from medical records, and IGHV gene mutation status, β2-microglobulin levels, and copy number variations were determined using c-DNA, ELISA, and multiplex ligation-dependent probe amplification (MLPA), respectively. The scores were generated as per the weighted grades assigned to each variable involved in score categorization. The predictive value of prognostic models was assessed and compared using Harrell's C-index and Akaike's information criterion (AIC). Stratification of patients according to CLL-IPI yielded significant differences in terms of OS and TTFT (p < 0.001). Comparative assessment of scores for OS suggested better performance of CLL-IPI (C = 0.64, AIC = 740) followed by Barcelona-Brno (C = 0.61, AIC = 754) and MDACC score (C = 0.59, AIC = 759). Comparison of predictive value of prognostic scores for TTFT illustrated better performance of CLL-IPI (C = 0.72, AIC = 726) followed by Barcelona-Brno (C = 0.68, AIC = 743), modified GCLLSG (C = 0.66, AIC = 744), and O-CLL1 index (C = 0.55, AIC = 773). The results suggest better performance of CLL-IPI in terms of both OS and TTFT as compared to other available scores in our cohort.

The prognostic role of HBV infection in chronic lymphocytic leukemia

PURPOSE

We attempt to assess the impact of hepatis-B virus (HBV) status on the prognosis of chronic lymphocytic leukemia (CLL) using a Chinese case cohort.

METHODS

Five hundred and one consecutive newly diagnosed subjects with CLL were enrolled in this case cohort. HBV infection was defined as hepatitis B surface antigen (HBsAg) positive or hepatitis-B core antibody (HBcAb) positive. Univariate and stepwise multivariate Cox regression analyses were used to screen the prognostic risk factors associated with the end point of time-to-treatment (TTT) or overall survival (OS). Bootstrap re-sampling method was used to evaluate the model's internal validity. The discriminative ability of the models was evaluated using time-dependent receiver-operator characteristic (ROC) curves and corresponding areas under the curve (AUC).

RESULTS

One hundred and twenty-one subjects (24%) among 501 patients were HBV positive. HBV infection was an independent predictor for the prognosis of TTT (HR = 1.37; 95% CI 1.04-1.80) or OS (HR =2.85; 95% CI 1.80-4.52). The AUCs for HBV infection were 0.62 (95% CI 0.58-0.66) for TTT and 0.69 (95% CI 0.66-0.72) for OS, respectively. When we combined HBV infection with the traditional clinical and biological factors, significant improvements for model's discrimination were observed for TTT [AUC: 0.81 (95% CI: 0.77-0.85) vs. 0.78 (95% CI: 0.74-0.82), P < 0.001] and OS [AUC: 0.81 (95% CI 0.76-0.86) vs. 0.76 (95% CI 0.71-0.82), P < 0.001). Further bootstrap re-sampling method revealed good internal consistence for the final optimal models (Average AUC: 0.78 for TTT and 0.79 for OS based on 1000 bootstraps).

CONCLUSIONS

Our results indicated that HBV infection should be served as an important risk predictor for prognosis of CLL (TTT and OS).

The International Prognostic Index for Patients with Chronic Lymphocytic Leukemia Has the Higher Value in Predicting Overall Outcome Compared with the Barcelona-Brno Biomarkers Only Prognostic Model and the MD Anderson Cancer Center Prognostic Index

In recent years, new prognostic indexes (PIs) for chronic lymphocytic leukemia (CLL), which include clinical, biological, and genetic variables, have been validated, highlighting the MD Anderson Cancer Center prognostic index (MDACC PI), the CLL-international prognostic index (CLL-IPI), and the Barcelona-Brno biomarkers only prognostic model. The aim of this study is to compare the utility of these PIs in a cohort of Spanish patients. A retrospective analysis of 696 unselected CLL patients newly diagnosed and previously untreated from different Spanish institutions was performed. The MDACC PI, the CLL-IPI, and the biomarkers only PI were applied to these patients, and a comparison of the three PIs was performed. With a median follow-up time of 46 months, 394 patients were alive and 187 had received treatment. The median overall survival (OS) was 173 months and the median time to first therapy (TTFT) was 32 months. Significant differences were obtained in OS and TTFT for all subgroups when applying these PIs, with the CLL-IPI being the one with the higher c-index (0.676 for OS and 0.757 for TTFT). The three PIs were able to discriminate patients in different prognostic subgroups. In our cohort, the CLL-IPI showed higher power in predicting TTFT and OS.

The mutational landscape of chronic lymphocytic leukemia and its impact on prognosis and treatment

The typical genome of chronic lymphocytic leukemia (CLL) carries ∼2000 molecular lesions. Few mutations recur across patients at a frequency >5%, whereas a large number of biologically and clinically uncharacterized genes are mutated at lower frequency. Approximately 80% of CLL patients carry at least 1 of 4 common chromosomal alterations, namely deletion 13q14, deletion 11q22-23, deletion 17p12, and trisomy 12. Knowledge of the CLL genome has translated into the availability of molecular biomarkers for prognosis and treatment prediction. Prognostic biomarkers do not affect treatment choice, and can be integrated into prognostic scores that are based on both clinical and biological variables. Molecular predictive biomarkers affect treatment choice, and currently include TP53 disruption by mutation and/or deletion and IGHV mutation status. TP53 disruption by gene mutation and/or deletion associates with chemoimmunotherapy failure and mandates treatment with innovative drugs, including ibrutinib, idelalisib, or venetoclax. The mutation status of IGHV genes represents a predictive biomarker for identifying patients that may benefit the most from chemoimmunotherapy with fludarabine, cyclophosphamide, and rituximab. Assessment of these biomarkers at the time of treatment requirement is recommended by most current guidelines for CLL management. Other molecular predictors are under investigation, but their application in clinical practice is premature.

PPAR-delta promotes survival of chronic lymphocytic leukemia cells in energetically unfavorable conditions

Targeting the mechanisms that allow chronic lymphocytic leukemia (CLL) cells to survive in harsh cancer microenvironments should improve patient outcomes. The nuclear receptor peroxisome proliferator activated receptor delta (PPARδ) sustains other cancers, and in silico analysis showed higher PPARD expression in CLL cells than normal lymphocytes and other hematologic cancers. A direct association was found between PPARδ protein levels in CLL cells and clinical score. Transgenic expression of PPARδ increased the growth and survival of CD5⁺ Daudi cells and primary CLL cells in stressful conditions including exhausted tissue culture media, low extracellular glucose, hypoxia and exposure to cytotoxic drugs. Glucocorticoids and synthetic PPARδ agonists up-regulated PPARD expression and also protected Daudi and primary CLL cells from metabolic stressors. Survival in low glucose was related to increased antioxidant expression, substrate utilization and mitochondrial performance, and was reversed by genetic deletion and synthetic PPARδ antagonists. These findings suggest PPARδ conditions CLL cells to survive in harsh microenvironmental conditions by reducing oxidative stress and increasing metabolic efficiency. Targeting PPARδ may be beneficial in the treatment of CLL.

Predictive and prognostic biomarkers in the era of new targeted therapies for chronic lymphocytic leukemia

Treatment options for chronic lymphocytic leukemia (CLL) have improved with the introduction of the B-cell receptor inhibitors ibrutinib and idelalisib, and of the BCL2 inhibitor venetoclax. While awaiting the results of head to head comparisons between novel agents and chemoimmunotherapy, predictive biomarkers can assist physicians in treatment tailoring. Though novel agents have modified the landscape of predictors at the time of treatment requirement, the usefulness of historical CLL prognostic biomarkers is still up-to-date when considering anticipation of time to first treatment. This review discusses: (i) disease-related (TP53 defects, immunoglobulin gene mutations), therapy-related (duration of remission), and patient-related (age, comorbidities) biomarkers that can be used in the clinical practice to inform CLL treatment decision either at the time of first line therapy and disease relapse; and (ii) the need of new biomarkers to re-define high-risk CLL because of the questioning by novel agents of historical prognostic factors.

The HOVON68 CLL trial revisited: performance status and comorbidity affect survival in elderly patients with chronic lymphocytic leukemia

In the HOVON68 CLL trial, patients 65 to 75 years of age had no survival benefit from the addition of low-dose alemtuzumab to fludarabine and cyclophosphamide (FC) in contrast to younger patients. The reasons are explored in this 5-year trial update using both survival analysis and competing risk analysis on non-CLL-related mortality. Elderly FCA patients died more frequently from causes not related to CLL, and more often related to comorbidity (mostly cardiovascular) than to infection. In a Cox multivariate analysis, del(17p), performance status >0, and comorbidity were associated with a higher non-CLL-related mortality in the elderly independent of the treatment modality. Thus, while the 'fit' elderly with no comorbidity or performance status of 0 might potentially benefit from chemo-immunotherapy with FC, caution is warranted, when considering alemtuzumab treatment in elderly patients with cardiovascular comorbidity.