scholarly journals A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator

2017 ◽  
Vol 8 (1) ◽  
pp. 20160163 ◽  
Author(s):  
Eleftherios Ouzounoglou ◽  
Eleni Kolokotroni ◽  
Martin Stanulla ◽  
Georgios S. Stamatakos

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

2009 ◽  
Vol 144 (2) ◽  
pp. 223-229 ◽  
Author(s):  
Leandro F. F. Dalmazzo ◽  
Rafael H. Jácomo ◽  
André F. Marinato ◽  
Lorena L. Figueiredo-Pontes ◽  
Renato L. G. Cunha ◽  
...  

1986 ◽  
Vol 53 (2) ◽  
pp. 175-180 ◽  
Author(s):  
R E Marcus ◽  
D Catovsky ◽  
S A Johnson ◽  
W M Gregory ◽  
J G Talavera ◽  
...  

2003 ◽  
Vol 122 (2) ◽  
pp. 240-244 ◽  
Author(s):  
Richard Aplenc ◽  
Wendy Glatfelter ◽  
Peggy Han ◽  
Eric Rappaport ◽  
Mei La ◽  
...  

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 4267-4267
Author(s):  
J. Motwani ◽  
J. Jesson ◽  
E. Sturch ◽  
L. Eyre ◽  
P. Short ◽  
...  

Abstract Patients with acute lymphoblastic leukaemia (ALL) in morphological remission may still have up to 1010 residual malignant cells. Detection of minimal residual disease (MRD) at the end of induction therapy allows better estimation of the leukaemic burden and can help selection of appropriate therapeutic strategies. Flow cytometric (FC) detection of MRD is based on the identification of immunophenotypic combinations expressed on leukaemic cells but not on normal hematopoietic cells - leukaemia associated immunophenotypes (LAIPs). We prospectively analysed bone marrow samples from 77 patients who presented with ALL to our unit between 1999–2003 and attained morphological remission. These patients were treated on a standard protocol. Multiparameter FC identification of LAIPs was performed at various time points, as dictated by the treatment protocol. Our results show that flow cytometric MRD at the end of induction therapy is an independent and the most significant predictor of relapse, both on univariate and multivariate analysis. The relapse risk was 4% if day 28 MRD was <0.01% and 50% if day 28 MRD was >0.01% (p<0.05). We conclude that flow cytometric based MRD assays can be used to assess early response to treatment and predict relapse in a similar way to molecular MRD analysis at the end of induction therapy. Flow cytometric analysis of MRD offers the advantages of being cheaper, more widely available and has quicker turnaround times.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 805-805
Author(s):  
Amir Enshaei ◽  
Claire Schwab ◽  
Lucy Chilton ◽  
Rachel Wade ◽  
Jeremy Hancock ◽  
...  

Abstract High hyperdiploidy (HeH) in acute lymphoblastic leukaemia (ALL) is defined by the non-random acquisition of multiple chromosomes, resulting in a modal number of 51-65 chromosomes. HeH is the most common primary abnormality in paediatric ALL and is associated with an excellent outcome. However, relapses do occur and prevalence of the subgroup means that they account for a sizeable proportion of relapsed cases. Hence identifying risk factors within this subtype remains important. Numerous studies have identified specific trisomies, modal numbers and structural rearrangements as prognostic factors. Despite these efforts, a consensus of the key risk factors has failed to emerge. We present a comprehensive analysis of 1181 children with HeH ALL, comprising discovery and validation cohorts: ALL97/99 (n=456) and UKALL2003 (n=725). The event-free survival (EFS), relapse risk (RR) and overall survival (OS) of HeH patients were: 84%/13%/93% and 91%/6%/95%, respectively. Initially, we examined previously reported risk factors in both cohorts separately. We did not observe any impact on outcome when patients were sub-divided using our copy number alteration (CNA) risk profile, which is based on the deletion status of the 8 most often affected genes. Similarly, there was no relationship between modal number and outcome whether we examined modal number as a continuous variable or in categories (51-53 v 54-57 v 58-65). Although the EFS hazard ratios (HZR) for patients with a double (+4, +10) or triple trisomy (+4, +10, +17) were <1, the difference was not statistically significant in either cohort. HeH karyotypes harbour 5-19 additional chromosomes and although some chromosomes are more likely to be gained than others, there are thousands of combinations. In order to assess the optimal number of chromosomes required for predicting outcome, we used the discovery cohort to calculate the area under the curve (AUC) associated with each individual trisomy as well as all possible combinations of 2-6 chromosomes. This analysis revealed that the average AUC (an estimate for predictive power) increased with the number of chromosomes, but only up to 4, suggesting that there was no advantage in considering 5 or more chromosomes. Next, we derived EFS HZR from univariate Cox regression models for each chromosome and ranked them in order of p value. The HZR for the top 4 chromosomes were +18 (0.43, p<0.001), +20 (2.33, p=0.01), +17 (0.68, p=0.09) and +5 (1.52, p=0.09). Therefore, patients with +17 and +18 but not +5 or +20 (HeH good risk, HeHgr) are predicted to have a better outcome compared to HeH poor risk (HeHpr) patients. This was true for patients in the discovery cohort but also, more importantly, in the validation cohort (table). In UKALL2003, the HeHpr group was associated with a higher incidence of end of induction (EOI) MRD (>0.01%) (56% v 47%, p=0.04) and IKZF1 deletions (12% v 2%, p=0.002). Factoring in EOI MRD, revealed that all UKALL2003 HeH patients had an excellent outcome except those with EOI MRD plus a poor risk HeH profile (table). This subset represented 33% of HeH patients but captured 71% (15/21) of the relapses recorded among UKALL2003 patients. In conclusion, we demonstrate that with the exception of EOI MRD the most important risk factor in HeH is the pattern of chromosomal gain. This result is in keeping with recent genomic studies, which have failed to identify a common underlying mutation, suggesting that the key driving event is disordered gene expression caused by the pattern of chromosomal gain. It is reassuring that the trisomies identified in this study have all previously been proposed as risk factors, providing a framework for further investigations aimed at elucidating precisely which genes are determining treatment response in HeH. Table.GroupHeH good riskHeH poor riskALL97/99Number of patients188268EFS (95% CI)91% (86-95)79% (74-84%)Hazard ratio (95% CI), p value12.81 (1.64-4.80%), p<0.001UKALL2003Number of patients306419EFS (95% CI)95% (91-97%)88% (83-91%)Hazard ratio (95% CI), p value12.07 (1.17-3.67%), p=0.01EOI MRD (>0.01%) Number of patients*95162EFS (95% CI)95%(88-98)83%(75-88)Hazard ratio (95% CI), p value13.55 (1.23-10.29), p=0.02EOI MRD (<0.01%) Number of patients*109127EFS (95% CI)97%(90-99)94%(87-97)Hazard ratio (95% CI), p value13.05 (0.63-14.73), p=0.2*An MRD result was only available for 493 UKALL2003 patients. Disclosures No relevant conflicts of interest to declare.


2016 ◽  
pp. 699-753
Author(s):  
Adele K. Fielding ◽  
Charles G. Mullighan ◽  
Dieter Hoelzer ◽  
Eytan M. Stein ◽  
Ghada Zakout ◽  
...  

This chapter covers acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL), and includes information on prognostic factors, current standard of care, basic biology, epidemiology, clinical presentation, diagnosis, pathophysiology, aetiology, and management. Although the majority of patients with acute myeloid leukaemia (AML) achieve complete remission with induction chemotherapy, relapse after achievement of clinical remission remains the most critical clinical challenge facing AML patients and clinicians today, with a pressing need to improve prognostication. Prognostic factors in acute lymphoblastic leukaemia is to stratify patients into good- and poor-risk groups and to adapt different treatment strategies accordingly. There are principally two phases to evaluating prognostic factors; the first is the patient characteristics at diagnosis and the second is the response to treatment.


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