The
(
) gene is mutated in 25-30% of patients with acute myeloid leukemia (AML). Because of the poor prognosis associated with
-internal tandem duplication mutated AML, allogeneic hematopoietic ...stem-cell transplantation (SCT) was commonly performed in first complete remission. Remarkable progress has been made in frontline treatments with the incorporation of FLT3 inhibitors and the development of highly sensitive minimal/measurable residual disease assays. Similarly, recent progress in allogeneic hematopoietic SCT includes improvement of transplant techniques, the use of haploidentical donors in patients lacking an HLA matched donor, and the introduction of FLT3 inhibitors as post-transplant maintenance therapy. Nevertheless, current transplant strategies vary between centers and differ in terms of transplant indications based on the internal tandem duplication allelic ratio and concomitant nucleophos-min-1 mutation, as well as in terms of post-transplant maintenance/consolidation. This review generated by international leukemia or transplant experts, mostly from the European Society for Blood and Marrow Transplantation, attempts to develop a position statement on best approaches for allogeneic hematopoietic SCT for AML with
-internal tandem duplication including indications for and modalities of such transplants and on the potential optimization of post-transplant maintenance with FLT inhibitors.
Risk scores for prediction of mortality 30-days following a ST-segment elevation myocardial infarction (STEMI) have been developed using a conventional statistical approach.
To evaluate an array of ...machine learning (ML) algorithms for prediction of mortality at 30-days in STEMI patients and to compare these to the conventional validated risk scores.
This was a retrospective, supervised learning, data mining study. Out of a cohort of 13,422 patients from the Acute Coronary Syndrome Israeli Survey (ACSIS) registry, 2782 patients fulfilled inclusion criteria and 54 variables were considered.
Prediction models for overall mortality 30days after STEMI were developed using 6 ML algorithms. Models were compared to each other and to the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) scores.
Depending on the algorithm, using all available variables, prediction models' performance measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.64 to 0.91. The best models performed similarly to the Global Registry of Acute Coronary Events (GRACE) score (0.87 SD 0.06) and outperformed the Thrombolysis In Myocardial Infarction (TIMI) score (0.82 SD 0.06, p<0.05). Performance of most algorithms plateaued when introduced with 15 variables. Among the top predictors were creatinine, Killip class on admission, blood pressure, glucose level, and age.
We present a data mining approach for prediction of mortality post-ST-segment elevation myocardial infarction. The algorithms selected showed competence in prediction across an increasing number of variables. ML may be used for outcome prediction in complex cardiology settings.
Allogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data ...mining (DM) approach, may serve for transplantation-related mortality risk prediction.
This work is a retrospective DM study on a cohort of 28,236 adult HSCT recipients from the AL registry of the European Group for Blood and Marrow Transplantation. The primary objective was prediction of overall mortality (OM) at 100 days after HSCT. Secondary objectives were estimation of nonrelapse mortality, leukemia-free survival, and overall survival at 2 years. Donor, recipient, and procedural characteristics were analyzed. The alternating decision tree machine learning algorithm was applied for model development on 70% of the data set and validated on the remaining data.
OM prevalence at day 100 was 13.9% (n=3,936). Of the 20 variables considered, 10 were selected by the model for OM prediction, and several interactions were discovered. By using a logistic transformation function, the crude score was transformed into individual probabilities for 100-day OM (range, 3% to 68%). The model's discrimination for the primary objective performed better than the European Group for Blood and Marrow Transplantation score (area under the receiver operating characteristics curve, 0.701 v 0.646; P<.001). Calibration was excellent. Scores assigned were also predictive of secondary objectives.
The alternating decision tree model provides a robust tool for risk evaluation of patients with AL before HSCT, and is available online (http://bioinfo.lnx.biu.ac.il/∼bondi/web1.html). It is presented as a continuous probabilistic score for the prediction of day 100 OM, extending prediction to 2 years. The DM method has proved useful for clinical prediction in HSCT.
Survival of acute leukemia (AL) patients following umbilical cord blood transplantation (UCBT) is dependent on an array of individual features. Integrative models for risk assessment are lacking. We ...sought to develop a scoring system for prediction of overall survival (OS) and leukemia-free survival (LFS) at 2 years following UCBT in AL patients.
The study cohort included 3,140 pediatric and adult AL UCBT patients from the European Society of Blood and Marrow Transplantation and Eurocord registries. Patients received single or double cord blood units. The dataset was geographically split into a derivation (
= 2,362, 65%) and validation set (
= 778, 35%). Top predictors of OS were identified using the Random Survival Forest algorithm and introduced into a Cox regression model, which served for the construction of the UCBT risk score.
The score includes nine variables: disease status, diagnosis, cell dose, age, center experience, cytomegalovirus serostatus, degree of HLA mismatch, previous autograft, and anti-thymocyte globulin administration. Over the validation set an increasing score was associated with decreasing probabilities for 2 years OS and LFS, ranging from 70.21% 68.89-70.71, 95% confidence interval (CI) and 64.76% (64.33-65.86, 95% CI) to 14.78% (10.91-17.41) and 18.11% (14.40-22.30), respectively. It stratified patients into six distinct risk groups. The score's discrimination (AUC) over multiple imputations of the validation set was 68.76 (68.19-69.04, range) and 65.78 (65.20-66.28) for 2 years OS and LFS, respectively.
The UCBT score is a simple tool for risk stratification of AL patients undergoing UCBT. Widespread application of the score will require further independent validation.
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Clinical decisions in allogeneic hematopoietic stem cell transplantation (allo-HSCT) are supported by the use of prognostic scores for outcome prediction. Scores vary in their features and in the ...composition of development cohorts. We sought to externally validate and compare the performance of 8 commonly applied scoring systems on a cohort of allo-HSCT recipients. Among 528 patients studied, acute myeloid leukemia was the leading transplant indication (44%) and 46% of patients had a matched sibling donor. Most models successfully grouped patients into higher and lower risk strata, supporting their use for risk classification. However, discrimination varied (2-year overall survival area under the receiver operating characteristic curve AUC: revised Pretransplantation Assessment of Mortality rPAM, 0.64; PAM, 0.63; revised Disease Risk Index rDRI, 0.62; Endothelial Activation and Stress Index EASIx, 0.60; combined European Society for Blood and Marrow Transplantation EBMT/Hematopoietic Cell Transplantation-specific Comorbidity Index HCT-CI, 0.58; EBMT, 0.58; Comorbidity-Age, 0.58; HCT-CI, 0.55); AUC ranges from 0.5 (random) to 1.0 (perfect prediction). rPAM and PAM, which had the greatest predictive capacity across all outcomes, are comprehensive models including patient, disease, and transplantation information. Interestingly, EASIx, a biomarker-driven model, had comparable performance for nonrelapse mortality (NRM; 2-year AUC, 0.65) but no predictive value for relapse (2-year AUC, 0.53). Overall, allo-HSCT prognostic systems may be useful for risk stratification, but individual prediction remains a challenge, as reflected by the scores' limited discriminative capacity.
•Prognostic capacity varied across 8 allogeneic transplantation scores, with rPAM showing modest benefit across several outcomes.•EASIx, a biomarker-based prediction model, is among the strongest predictive scores of NRM.
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We present three patients with aggressive non-Hodgkin's B-cell lymphoma (NHL) who received anti-CD19 chimeric antigen receptor T (CAR T) cells therapy after failure of several lines of chemotherapy ...that developed pseudo-progression. One-week clinical and radiological findings were consistent with tumor progression. Positron emission tomography-computed tomography (PET-CT) at 1 month post CAR T cells administration was consistent with treatment response. The rapid tumor growth and subsequent resolution are suggestive of tumor pseudo-progression mediated secondary to infiltration and immune activation of CAR T cells. Overall, 56 adult patients with NHL were enrolled in a phase 1b/2 in house clinical study with CD19 CAR T cells. Out of them 22/56 patients progressed as per PET-CT the 1 month post CAR T cells. In 14 patients, signs of progression started 7-10 days after CAR T cells infusion. In 11/14 patients, it was true progression, while in 3 it was pseudo-progression. Additional studies are warranted to describe the extent of this phenomenon and evaluate correlation with the CAR T activity and long-term disease control.
The opportunities genetic engineering has created in the field of adoptive cellular therapy for cancer are accelerating the development of novel treatment strategies using chimeric antigen receptor ...(CAR) and T cell receptor (TCR) T cells. The great success in the context of hematologic malignancies has made especially CAR T cell therapy a promising approach capable of achieving long-lasting remission. However, the causalities involved in mediating resistance to treatment or relapse are still barely investigated. Research on T cell exhaustion and dysfunction has drawn attention to host-derived factors that define both the immune and tumor microenvironment (TME) crucially influencing efficacy and toxicity of cellular immunotherapy. The microbiome, as one of the most complex host factors, has become a central topic of investigations due to its ability to impact on health and disease. Recent findings support the hypothesis that commensal bacteria and particularly microbiota-derived metabolites educate and modulate host immunity and TME, thereby contributing to the response to cancer immunotherapy. Hence, the composition of microbial strains as well as their soluble messengers are considered to have predictive value regarding CAR T cell efficacy and toxicity. The diversity of mechanisms underlying both beneficial and detrimental effects of microbiota comprise various epigenetic, metabolic and signaling-related pathways that have the potential to be exploited for the improvement of CAR T cell function. In this review, we will discuss the recent findings in the field of microbiome-cancer interaction, especially with respect to new trajectories that commensal factors can offer to advance cellular immunotherapy.
Risk stratification is important for balancing potential risks and benefits of allogeneic hematopoietic stem cell transplantation (HSCT) for hematological malignancies. We retrospectively studied ...1119 patients undergoing allogenic-HSCT in a single center for five hematological indications assessing the prognostic role of LDH at admission for survival (OS), progression-free survival (PFS), relapse incidence (RI), and nonrelapse mortality (NRM). In non-Hodgkin lymphoma (NHL) and acute myeloid leukemia (AML), higher than median LDH had an adverse effect on survival. The prognostic significance was strongest in AML, with higher LDH levels having lower 1-and 3-year survival 69.2% vs. 50.8%, P < 0.001 and 51.9% vs. 39.2%, P < 0.001, respectively, reduced 1-and 3-year PFS 62.4% vs. 42.1%, P < 0.001 48% vs. 35.2%, P < 0.001, respectively, higher cumulative incidence of 1-and 3-year NRM 11% vs. 17.3%, p = 0.01 and 15.7% vs. 19.6%, P = 0.04, and higher 1-and 3-year relapse incidence (RI) 26.7% vs. 40.7%, p < .0001 36.2% vs. 40.7%, respectively, P < 0.0001). In multivariate analysis LDH maintained significant prognostic capacity in OS, PFS and RI. These findings in AML, validated in an independent cohort, suggest that LDH is a readily available tool that could be integrated into transplant risk assessments to aid decision-making and identify high-risk patients who may benefit from post-transplant pharmacological or cellular strategies.
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