How to predict relapse in leukemia using time series data: A comparative in silico study

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients’ time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients

The blind men and the AML elephant:can we feel the progress?

The pharmacological therapy of non-promyelocytic acute myeloid leukemia (AML) has remained unchanged for over 40 years with an anthracycline–cytarabine combination forming the backbone of induction treatments. Nevertheless, the survival of younger patients has increased due to improved management of the toxicity of therapies including stem cell transplantation. Older patients and those with infirmity that precludes treatment-intensification have, however, not benefited from improvements in supportive care and continue to experience poor outcomes. An increased understanding of the genomic heterogeneity of AML raises the possibility of treatment-stratification to improve prognosis. Thus, efforts to identify agents with non-conventional anti-leukemic effects have paralleled those aiming to optimize leukemia cell-kill with conventional chemotherapy, resulting in a number of randomized controlled trials (RCT). In the last 18 months, RCTs investigating the effects of vosaroxin, azacitidine and gemtuzumab ozogamycin and daunorubicin dose have been reported with some studies indicating a statistically significant survival benefit with the investigational agent compared with standard therapy and potentially, a new era in AML therapeutics. Given the increasing costs of cancer care, a review of these studies, with particular attention to the magnitude of clinical benefit with the newer agents would be useful, especially for physicians treating patients in single-payer health systems

Dasatinib dose management for the treatment of chronic myeloid leukemia

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143669/1/cncr31232.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143669/2/cncr31232_am.pd

Epigenetic dysregulation in chronic myeloid leukaemia: A myriad of mechanisms and therapeutic options

The onset of global epigenetic changes in chromatin that drive tumor proliferation and heterogeneity is a hallmark of many forms cancer. Identifying the epigenetic mechanisms that govern these changes and developing therapeutic approaches to modulate them, is a well-established avenue pursued in translational cancer medicine. Chronic myeloid leukemia (CML) arises clonally when a hematopoietic stem cell (HSC) acquires the capacity to produce the constitutively active tyrosine kinase BCR-ABL1 fusion protein which drives tumor development. Treatment with tyrosine kinase inhibitors (TKI) that target BCR-ABL1 has been transformative in CML management but it does not lead to cure in the vast majority of patients. Thus novel therapeutic approaches are required and these must target changes to biological pathways that are aberrant in CML − including those that occur when epigenetic mechanisms are altered. These changes may be due to alterations in DNA or histones, their biochemical modifications and requisite ‘writer’ proteins, or to dysregulation of various types of non-coding RNAs that collectively function as modulators of transcriptional control and DNA integrity. Here, we review the evidence for subverted epigenetic mechanisms in CML and how these impact on a diverse set of biological pathways, on disease progression, prognosis and drug resistance. We will also discuss recent progress towards developing epigenetic therapies that show promise to improve CML patient care and may lead to improved cure rates

Towards homogenization of total body irradiation practices in pediatric patients across SIOPE affiliated centers: a survey by the SIOPE radiation oncology working group

Pediatric; Radiotherapy; Stem cell transplantationPediatria; Radioteràpia; Trasplantament de cèl·lules marePediatría; Radioterapia; Trasplante de células madreBackground and purpose To reduce relapse risk, Total Body Irradiation (TBI) is part of conditioning regimens for hematopoietic stem cell transplantation (HSCT) in pediatric acute leukemia. The study purpose was to evaluate clinical practices regarding TBI, such as fractionation, organ shielding and delivery techniques, among SIOPE affiliated radiotherapy centers. Methods An electronic survey was sent out to 233 SIOPE affiliated centers, containing 57 questions about clinical practice of TBI. Surveys could be answered anonymously. Results From over 25 countries, 82 responses were collected. For TBI-performing centers, 40/48 irradiated ≤10 pediatric patients annually (range: 1–2 to >25). Most indications concerned acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML). Four different fractionation schedules were used, of which 12 Gy in 6 fractions was applied in 91% for ALL and 86% for AML. Dose reduction to the lungs, mostly to a mean dose of 8–10 Gy, was applied by 28/33 centers for ALL and 19/21 centers for AML, in contrast to much less applied dose reduction to the kidneys (7/33 ALL and 7/21 AML), thyroid (2/33 ALL and 2/21 AML), liver (4/33 ALL and 3/21 AML) and lenses (4/33 ALL and 4/21 AML). Conventional TBI techniques were used by 24/29 responding centers, while 5/29 used advanced optimized planning techniques. Conclusion Across SIOPE, there is a high level of uniformity in fractionation and use of lung shielding. Practices vary regarding other organs-at-risk shielding and implementation of advanced techniques. A SIOPE radiotherapy working group will be established to define international guidelines for pediatric TBI

ESTRO ACROP and SIOPE recommendations for myeloablative Total Body Irradiation in children

Consensus recommendations; Pediatric; Total Body IrradiationRecomanacions de consens; Pediatria; Irradiació corporal totalRecomendaciones de consenso; Pediatría; Irradiación corporal totalBackground and purpose Myeloablative Total Body Irradiation (TBI) is an important modality in conditioning for allogeneic hematopoietic stem cell transplantation (HSCT), especially in children with high-risk acute lymphoblastic leukemia (ALL). TBI practices are heterogeneous and institution-specific. Since TBI is associated with multiple late adverse effects, recommendations may help to standardize practices and improve the outcome versus toxicity ratio for children. Material and methods The European Society for Paediatric Oncology (SIOPE) Radiotherapy TBI Working Group together with ESTRO experts conducted a literature search and evaluation regarding myeloablative TBI techniques and toxicities in children. Findings were discussed in bimonthly virtual meetings and consensus recommendations were established. Results Myeloablative TBI in HSCT conditioning is mostly performed for high-risk ALL patients or patients with recurring hematologic malignancies. TBI is discouraged in children <3–4 years old because of increased toxicity risk. Publications regarding TBI are mostly retrospective studies with level III–IV evidence. Preferential TBI dose in children is 12–14.4 Gy in 1.6–2 Gy fractions b.i.d. Dose reduction should be considered for the lungs to <8 Gy, for the kidneys to ≤10 Gy, and for the lenses to <12 Gy, for dose rates ≥6 cGy/min. Highly conformal techniques i.e. TomoTherapy and VMAT TBI or Total Marrow (and/or Lymphoid) Irradiation as implemented in several centers, improve dose homogeneity and organ sparing, and should be evaluated in studies. Conclusions These ESTRO ACROP SIOPE recommendations provide expert consensus for conventional and highly conformal myeloablative TBI in children, as well as a supporting literature overview of TBI techniques and toxicities

Application of mathematical modelling to describe and predict treatment dynamics in patients with NPM1-mutated Acute Myeloid Leukaemia (AML)

Background: Acute myeloid leukaemia (AML) is a severe form of blood cancer, which in many cases can not be cured. Although chemotherapeutic treatment is effective in most cases, often the disease relapses. To monitor the course of disease, as well as to early identify a relapse, the leukaemic cell burden in the bone marrow is measured. In the genome of these cells certain mutations can be found, which lead to the occurrence of leukaemia. One of those mutations is in the neucleophosmin 1 (NPM1) gene. This mutation is found in about one third of all AML patients. The burden of leukaemic cells can be derived from the proportion of NPM1 transcripts carrying this mutation in a bone marrow sample. These values are measured routinely at specific time points during treatment and are then used to categorise the patients into defined risk groups. In the studies, the data for this work originates from, the NPM1 burden was measured beyond the treatment period. That leads to a more comprehensive picture of the molecular course of disease of the patients. Hypothesis: My hypothesis is that the risk group categorisation can be improved by taking into account the dynamic time course information of the patients. Another hypothesis of this work is that with the help of statistical methods and computer models the time course data can be used to describe the course of disease of AML patients and assess whether they will experience a relapse or not. Materials and Methods: For these investigations I was provided with a dataset consisting of quantitative NPM1 time course measurements of 340 AML patients (with a median of 6 mea- surements per patient). To analyse this data I used statistical methods, such as correlation, logistic regression and survival time analysis. For a better understanding of the course of disease I developed a mechanistic model describing the dynamics of the cell numbers in the bone marrow of an AML patient. This model can be fitted to the measurements of a patient by adjusting two parameters, which represent the individual severity of disease. To predict a possible relapse within 2 years after beginning of treatment, I used data that was generated using the mechanistic model (synthetic data). For the prediction three different methods were compared: the mechanistic model, a recurrent neural network (RNN) and a generalised linear model (GLM). Both, the RNN and the GLM were trained and tuned on part of the synthetic data. Afterwards all three methods were tested using the so far unseen part of the data set (test data). Results: Following the analysis of the data I found that the decreasing slope of NPM1 burden during primary treatment as well as the absolute burden after the treatment harbour information about the further course of disease. Specifically, I found that a faster decrease of NPM1 burden and a lower final burden lead to a better prognosis. Further, I could show that the developed simple mechanistic model is able to describe the course of disease of most patients. When I divided the patients into two different risk groups using the fitted parameters from the model I could show that the patients in those groups show distinct relapse-free survival times. The categorisation using the parameters lead to a better distinction of groups than using current categorisation by the WHO. Further, I tried to predict a 2-year relapse using synthetic data and three different prediction methods. I could show that it had nearly no impact at all which method I used. Much more important, however, was the quality of data. Especially the sparseness of data, which we find in the time courses of AML patients, has a considerable negative effect on the predictability of relapse. Using a synthetic data set with measurement times oriented on the times of chemotherapy I could show that a sophisticated measurement scheme could improve the relapse predictability. Conclusions: In conclusion, I suggest to include the dynamic molecular course of the NPM1 burden of AML patients in clinical routine, as this harbours additional information about the course of disease. The involvement of a mechanistic model to asses the risk of AML patients can help to make more accurate predictions about their general prognosis. An accurate prediction of the time of relapse is not possible. All three used methods (mechanistic model, statistical model and neural network) are in general suitable to predict relapse of AML patients. For reliable predictions, however, the quality of the data needs to be drastically improved