Learning from the microbes: exploiting the microbiome to enforce T cell immunotherapy

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

Comorbidities in transplant recipients with acute myeloid leukemia receiving low-intensity conditioning regimens: an ALWP EBMT study

Older age and a high burden of comorbidities often drive the selection of low-intensity conditioning regimens in allogeneic hematopoietic stem cell transplantation recipients. However, the impact of comorbidities in the low-intensity conditioning setting is unclear. We sought to determine the contribution of individual comorbidities and their cumulative burden on the risk of nonrelapse mortality (NRM) among patients receiving low-intensity regimens. In a retrospective analysis of adults (≥18 years) who underwent transplantation for acute myeloid leukemia in the first complete remission between 2008 and 2018, we studied recipients of low-intensity regimens as defined by the transplantation conditioning intensity (TCI) scale. Multivariable Cox models were constructed to study associations of comorbidities with NRM. Comorbidities identified as putative risk factors in the low-TCI setting were included in combined multivariable regression models assessed for overall survival, NRM, and relapse. A total of 1663 patients with a median age of 61 years received low-TCI regimens. Cardiac comorbidity (including arrhythmia/valvular disease) and psychiatric disease were associated with increased NRM risk (hazard ratio [HR], 1.54; 95% confidence interval [CI], 1.13-2.09 and HR, 1.69; 95% CI, 1.02-2.82, respectively). Moderate pulmonary dysfunction, though prevalent, was not associated with increased NRM. In a combined model, cardiac, psychiatric, renal, and inflammatory bowel diseases were independently associated with adverse transplantation outcomes. These findings may inform patient and regimen selection and reinforce the need for further investigation of cardioprotective transplantation approaches.</p

Clinical practice recommendation on hematopoietic stem cell transplantation for acute myeloid leukemia patients with FLT3 internal tandem duplication: a position statement from the Acute Leukemia Working Party of the European Society for Blood and Marrow Transplantation

The FMS-like tyrosine kinase 3 (FLT3) gene is mutated in 25-30% of patients with acute myeloid leukemia . Because of the poor prognosis associated with FMS-like tyrosine kinase 3 internal tandem duplication mutated Acute myeloid leukemia, allogeneic-hematopoietic stem-cell transplantation 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 stem-cell transplantation includes improvement of transplant techniques, the use of haplo-identical donors in patients lacking an HLA matched donor, and the introduction of FLT3 inhibitors as posttransplant 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 nucleophosmin-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 stem-cell transplantation for acute myeloid leukemia with FMS-like tyrosine kinase internal tandem duplication including indications and modalities of allogeneic-hematopoietic stem-cell transplantation and on potential optimization of post-transplant maintenance with FMS-like tyrosine kinase inhibitors

Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study

PURPOSE: 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. PATIENTS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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

BRCA mutational status shapes the stromal microenvironment of pancreatic cancer linking clusterin expression in cancer associated fibroblasts with HSF1 signaling

Tumors initiate by mutations in cancer cells, and progress through interactions of the cancer cells with non-malignant cells of the tumor microenvironment. Major players in the tumor microenvironment are cancer-associated fibroblasts (CAFs), which support tumor malignancy, and comprise up to 90% of the tumor mass in pancreatic cancer. CAFs are transcriptionally rewired by cancer cells. Whether this rewiring is differentially affected by different mutations in cancer cells is largely unknown. Here we address this question by dissecting the stromal landscape of BRCA-mutated and BRCA Wild-type pancreatic ductal adenocarcinoma. We comprehensively analyze pancreatic cancer samples from 42 patients, revealing different CAF subtype compositions in germline BRCA-mutated vs. BRCA Wild-type tumors. In particular, we detect an increase in a subset of immune-regulatory clusterin-positive CAFs in BRCA-mutated tumors. Using cancer organoids and mouse models we show that this process is mediated through activation of heat-shock factor 1, the transcriptional regulator of clusterin. Our findings unravel a dimension of stromal heterogeneity influenced by germline mutations in cancer cells, with direct implications for clinical research

Individualized prediction of leukemia-free survival after autologous stem cell transplantation in acute myeloid leukemia

Item does not contain fulltex