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

“…The results of this randomization have been previously reported (Matutes et al , 2013), but here we describe only the treatment given. All other second‐line and all third‐line and subsequent treatments were given according to the treating physician's choice.…”

Section: Methodsmentioning

confidence: 92%

“…After disease progression, a minority of trial patients (n = 84) were randomized to receive the results of an in-vitro assay measuring drug resistance, which could be used by the treating physician to guide the choice of second-line treatment. The results of this randomization have been previously reported (Matutes et al, 2013), but here we describe only the treatment given. All other second-line and all third-line and subsequent treatments were given according to the treating physician's choice.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2015

View full text Add to dashboard Cite

SummaryWith 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.

show abstract

“…Oncologists are already familiar with trials that aim to evaluate two treatments, using either factorial designs [22] or using sequential randomizations where nonresponders are randomized to different salvage therapies or responders are randomized to different consolidation or maintenance treatments or to maintenance vs. no intervention (e.g., [23,24]). Unfortunately, results of first and second randomizations are often published in separate articles (for example [25][26][27][28]). Consequently, insights which might emerge by considering the trajectories as a whole are lost.…”

Section: Algorithm Validationmentioning

confidence: 99%

Precision medicine: Statistical methods for estimating adaptive treatment strategies

Moodie

Krakow

2020

Bone Marrow Transplant

View full text Add to dashboard Cite

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 covariates: 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 t...

show abstract

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

Cited by 7 publications

References 15 publications

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

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

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

Precision medicine: Statistical methods for estimating adaptive treatment strategies

Contact Info

Product

Resources

About