The use of individualized tumor response testing in treatment selection: second randomization results from the LRF CLL4 trial and the predictive value of the test at trial entry

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.

Precision medicine: Statistical methods for estimating adaptive treatment strategies

SERIES EDITORS' NOTE

The beauty of science is that all the important things are unpredictable. Freeman Dyson In the typescript which follows, Moodie and Krakow tackle the topical issue of precision medicine and statistical methods for estimating adaptive treatment strategies. This may be the most difficult typescript in our series so far for non-statisticians to understand. It even has equations! But please bear with the authors and give it a chance. One needs not to understand the equations to get the thrust of the strategy.Precision medicine as we discuss elsewhere, is misnamed. In statistics and mathematics precision refers to getting the same answer again and again. It does not mean getting the correct answer, the term for which is accuracy, not precision. However, precision is the current buzz word so there's no point trying to get this straight. When we think about precision we need to consider two elements, reproducibility and replicability. Reproducibility means you give me your data and computer code and I come to the same conclusion you did. Replicability is another matter. I try to replicate your experiment and hopefully reach the same conclusion. In medicine, replicability is obviously more important than reproducibility but things which cannot be reproduced are unlikely to be replicated.As the authors discuss, one can think about precision medicine as one does a family vacation. A best vacation depends on several co-variates: where you live, your prior travel experiences, advice from family and friends, online reviews, Wikitravel, cost, your travel budget, if you have kids and many other co-variates. Consequently, there is unlikely to be a best vacation for everyone. Yours might be a week at the Ritz Carlton Cancun with dinner at Careyes and ours, a week at the Pfister Hotel in Milwaukee with dinner at Mader's German Restaurant (bring simvastatin). Similarly, it is unlikely there is a best therapy of acute myeloid leukemia, a best donor, a best conditioning regimen, a best posttransplant immune suppressive regimen etc. and certainly no best combination of these co-variates for your patient.The question Moodie and Krakow tackle is how we can determine the best therapy or combination of therapies for someone receiving a haematopoietic cell transplant. Although the default answer is typically: randomized clinical trials are the gold standard, these inform us of the outcome of a cohort of subjects, not individuals. In many instances, although a new therapy may be shown to be better than an old one in a controlled randomized trial the benefit is not uniformly distributed. Some subjects in the experimental cohort may do worse with the new therapy compared with controls, others better. The question is who are the winners and losers? We cannot do a controlled randomized trial of one person. Moodie and Krakow discuss statistical tools to help us sort this out.Again, please do not be put off by the equations; forgetaboutit. The overriding message is not so complex, and important. We are always standing by on twitter @BMTStats to help. But don't confuse us with Match.com. And, by the way, Freeman Dyson was a professor at the Institute for Advanced Studies at Princeton but never got his PhD.Robert Peter Gale, Imperial College London, and Mei-Jie Zhang, Medical College of Wisconsin, Center for International Blood and Marrow Research (CIBMTR).

The Challenges of Informed Consent in High-Stakes, Randomized Oncology Trials: A Systematic Review

Importance. Oncology trials often entail high-stakes interventions where potential for morbidity and fatal side effects, and for life-prolongation or cure, intensify bioethical issues surrounding informed consent. These challenges are compounded in multistage randomized trials, which are prevalent in oncology. Objective. We sought to elucidate the major barriers to informed consent in high-stakes oncology trials in general and the best consent practices for multistage randomized trials. Evidence Review. We queried PubMed for original studies published from January 1, 1990, to April 5, 2018, that focused on readability, quality, complexity or length of consent documents, motivation and sickness level of participants, or interventions and enhancements that influence informed consent for high-stakes oncologic interventions. Exclusion criteria included articles focused on populations outside industrialized countries, minors or other vulnerable populations, physician preferences, cancer screening and prevention, or recruitment strategies. Additional articles were identified through comprehensive bibliographic review. Findings. Twenty-seven articles were retained; 19 enrolled participants and 8 examined samples of consent documents. Methodologic quality was variable. This body of literature identified certain challenges that can be readily remedied. For example, the average length of the consent forms has increased 10-fold from 1987 to 2010, and patient understanding was shown to be inversely proportional to page count; shortening forms, or providing a concise summary as mandated by the revised Common Rule, might help. However, barriers to understanding that stem from deeply ingrained and flawed sociocultural perceptions of medical research seem more difficult to surmount. Although no studies specifically addressed problems posed by multiple sequential randomizations (such as change in risk-benefit ratio due to time-varying treatment responses or organ toxicities), the findings are likely applicable and especially relevant in that context. Concrete suggestions for improvement are proposed.

The long-term outcome of patients in the LRF CLL4 trial: the effect of salvage treatment and biological markers in those surviving 10 years

With 10+ years follow‐up in the Leukaemia Research Fund (LRF) CLL4 trial, we report the effect of salvage therapy, and the clinical/biological features of the 10‐year survivors treated for chronic lymphocytic leukaemia (CLL). Overall survival (OS) was similar in the three randomized arms. With fludarabine‐plus‐cyclophosphamide (FC), progression‐free survival (PFS) was significantly longer (P < 0·0001), but OS after progression significantly shorter, than in the chlorambucil or fludarabine arms (P < 0·0001). 614/777 patients progressed; 524 received second‐line and 260 third‐line therapy, with significantly better complete remission (CR) rates compared to first‐line in the chlorambucil arm (7% vs. 13% after second‐, 18% after third‐line), but worse in the FC arm (38% vs. 15% after both second and third‐line). OS 10 years after progression was better after a second‐line CR versus a partial response (36% vs. 16%) and better with FC‐based second‐line therapy (including rituximab in 20%) or a stem cell transplant (28%) versus all other treatments (10%, P < 0·0001). The 176 (24%) 10‐year survivors tended to be aged <70 years, with a “good risk” prognostic profile, stage A‐progressive, achieving at least one CR, with a first‐line PFS >3 years and receiving ≤2 lines of treatment. In conclusion, clinical/biological features and salvage treatments both influence the long‐term outcome. Second‐line therapies that induce a CR can improve OS in CLL patients.

Relationship between in vitro drug sensitivity and clinical response of patients to treatment in chronic lymphocytic leukemia

To improve the efficacy of therapeutic options in chronic lymphocytic leukemia (CLL) an in vitro system to determine the response of mononuclear blood cells from blood of patients was elaborated. The study combines four approaches, i.e., cell viability, apoptosis rate, differential scanning calorimetry (DSC), and immunoblotting to develop personalized therapy protocols based on the cell sensitivity to drug exposure of individual CLL patients. The complementary analyses were performed on 28 peripheral blood samples from previously untreated CLL patients before therapy. The induction and progress of apoptosis in CLL cells exposed in vitro to purine analogs combined with mafosfamide, i.e., cladribine + mafosfamide (CM) and fludarabine + mafosfamide (FM) were assessed using the above approaches. The changes in thermal profiles (decrease/loss of transition at 95±5˚C) coincided with an accumulation of apoptotic cells, a decrease in the number of viable cells, and differences in the expression of the apoptosis‑related protein PARP‑1. No significant changes were observed in the thermal profiles of nuclei isolated from CLL cells resistant to the treatment. The complementary assays revealed a strong relationship between both the in vitro sensitivity of leukemia cells to drugs and the clinical response of the patients, determined usually after the sixth course of treatment (after ~6 months of therapy). As a summary of studies followed by complementary tests, our findings demonstrate the value of in vitro exposure of CLL cell samples to drugs intended to treat CLL patients, before their administration in order to recommend the most suitable and effective therapy for individual patients.

Towards the Application of Atorvastatin to Intensify Proapoptotic Potential of Conventional Antileukemic AgentsIn Vitro

It has been previously revealed that statins used at high concentrations display antileukemic potential towards chronic lymphocytic leukemia (CLL) cells. However, their usage alone in clinical practice may be limited due to possible side effects of high doses of these drugs. On the other hand, combined treatment of leukemia with statins and the conventional chemotherapeutics is questionable because of unknown influence of the first on the standard treatment results. This study has revealed thatin vitroatorvastatin increases the proapoptotic potential of cladribine and mafosfamide in CLL cells isolated from peripheral blood of patients. Moreover, a preincubation with the above statin sensitizes leukemic cells to CM-induced apoptosis even at small concentrations of the drug. The usage of atorvastatin together with or followed by the conventional chemotherapy should be considered as therapeutic option for the treatment for this leukemia. Interestingly, CM-resistant patients might have the biggest benefits from atorvastatin administration.

CYP2B6*6 is an independent determinant of inferior response to fludarabine plus cyclophosphamide in chronic lymphocytic leukemia

Therapeutic response in CLL can be influenced by host pharmacogenetics.