Dynamically Track The Immune Reconstitution Pathway After Hematopoietic Stem Cell Transplantation And Establish A Composite Immune Risk Score To Predict Surviva

Background:Immune reconstitution after hematopoietic stem cell transplantation(HSCT)is complicated and of vital importance for survival.Previously researchers have established that:First,immune reconstitution after HSCT often follows the sequential order of innate immunity,CD8+T cells,and then CD4+T and B cells.Second,the earlier the immune cell counts are normalized,the lower the risk for infection,and the better the overall survival.Third,while different stem cell sources have different profiles of immune reconstitution after transplant,so far there is no cell source that is decidedly superior.The previous studies,however,often contained few patients(<200),focus on the prognostic significance of single immune variables,or had short durations of immune monitoring.Therefore,there is no consensus on how to integrate multivariate immune data to visualize the time course of immune reconstitution or establish a scoring system that can be applied broadly at the clinic.Purposes:We aimed to integrate multivariate immune data,visualize the trajectory of post-HSCT immune reconstitution,study the kinetics of the multivariate immune status,and discover a ’high-risk’ composite immune signature that is predictive of early mortality.Additionally,we aim to develop an online tool to facilitate the clinicians to compute an immune signature for prognosis.Methods:We retrospectively analyzed 11150 post-transplant immune profiles of 1945 patients who underwent HSCT between 2012 and 2020 at two medical centers in China.1838(94.5%)of the cases were allogeneic HSCT.We utilized manifold learning to visualize the temporal profiles of immune cell repopulation(integrating 20 immune cells features in the peripheral blood),used grid-search optimization to identify a composite immune signature that was associated with mortality.Then simplified the composite immune signature into a formula for a Composite Immune Risk Score and discovered and verified it in two independent subsets of the patients.Results:On average,recovery of absolute cell counts was concentrated on the repopulation of CD8+T cells during the initial two months after transplant.Inter-patient variance of immune status peaked at around day 60 post-transplant,and broad-ranged recovery in NK,CD8+T,CD4+T,and B cells initiated afterwards.Using the training set of patients(n=729),we identified a composite immune signature during days 91-180 after allogeneic HSCT that was predictive of early mortality.Moreover,we simplified it into a formula for a Composite Immune Risk Score which could classify patients as high-risk and low-risk.When we verified the Composite Immune Risk Score in the validation(n=284)and test(n=391)sets of patients,a high score value was found to be associated with hazard ratios of 3.64(95%CI 1.55-8.51;P=0.0014)and 2.44(95%CI,1.224.87;P=0.0087),respectively,for early mortality.The high-risk patients were significantly more likely to suffer from non-relapse mortality(hazard ratio(HR),3.28;95%CI,2.28-4.73;P<0.0001),including infection-related mortality(HR,3.12;95%CI,1.97-4.96;P<0.0001).Importantly,a high Composite Immune Risk Score during days 91-180 remained an independent risk factor for early mortality after allogeneic HSCT(HR,1.80;95%CI,1.28-2.55;P=0.00085)in multivariate analysis.Severe aGVHD,infectious episodes and patient age were all risk factors for having Composite Immune Risk Scores during days 91-180 after allogeneic HSCT.In addition,the SKIRT Calculator that enables clinicians to evaluate the patient’s progress in immune reconstitution after transplantation was developed.Conclusions:We broke through the limitations of previous univariate studies,combined unsupervised machine learning and survival modeling to visualize the dynamic patterns of post-transplant immune reconstitution and formulate a multivariate Composite Immune Risk Score during days 91-180 post-transplant that can predict early mortality after allo-HSCT even in independent cohort.The Composite Immune Risk Score is easy to compute and could identify the high-risk patients of allogeneic HSCT who require targeted effort for prevention and control of infection.Future researches should focus on devising clinical strategies to improve the overall survival of the high-score patients.Background:Allogeneic hematopoietic stem cell transplantation(allo-HSCT)is an important treatment of hematological diseases.Acute graft-versus-host disease(aGVHD)is one of the main complications of allo-HSCT.The incidence of grade 2-4 aGVHD is as high as 50%,which seriously leads to non-relapse mortality(NRM)after allo-HSCT and affects succuss of allo-HSCT.Although great progress has been made in the treatment of aGVHD in the past 20 years,severe aGVHD(grade 3-4)after allo-HSCT still threatens the survival of patients.Therefore,establishing a prediction method for severe aGVHD,identifying and intervening patients at high risk of severe aGVHD in advance is a problem that needs to be solved urgently and has great clinical significance.Purposes:We aimed to establish a predictive method for severe aGVHD by detecting the levels of peripheral blood inflammatory proteins in allo-HSCT patients 14 days after transplantation.Methods:In this study,peripheral blood plasma samples of patients at 14 days after allo-HSCT were prospectively collected.The samples for detection were selected according to the condition of aGVHD after transplantation.A total of 33 cases of severe aGVHD,17 cases of grade 2 aGVHD and 38 cases of grade 0-1 aGVHD were selected.92 inflammatory proteins were quantitatively detected in a total of 88 plasma samples.A method for predicting severe aGVHD after allo-HSCT was established by differential analysis and Logistic regression model.Whether inflammatory proteins could predict NRM was also analyzed.Results:Among the 88 patients,60(68.2%)underwent haploidentical HSCT and 28(31.8%)underwent matched sibling donor HSCT.The median time of aGVHD onset was 32(IQR 25-45)days after transplantation.Compared with patients without severe aGVHD,the plasma levels of IL-17A,IL-18,IL-10,PD-L1,CXCL9,uPA,TNFB,CD244,CXCL10,ADA,and IFN-y were significantly upregulated in patients with severe aGVHD,and FGF-19 levels were significantly downregulated.Among these differential proteins,IFN-y and CXCL10,CXCL10 and CXCL9,IFN-γ and CXCL9,IL-10 and IL-17A had obvious positive correlations.A Logistic regression model was established using the five most significantly different proteins,namely IL-17A,IL-18,IL-10,PD-L1 and CXCL9.In 88 patients,the model predicted severe aGVHD with an area under the receiver operating characteristic curve(AUROC)of 0.78.Using the Bootstrap method to internally validate the model,the AUROC was 0.759(95%Cl,0.757-0.761),and the precision was 0.717(95%CI,0.713-0.715).The calibration curve showed that the probability of severe aGVHD predicted by the model was similar to the actual probability of severe aGVHD.The results of clinical decision curve analysis showed that preemptive intervention for patients with high risk of severe aGVHD after transplantation would have high benefits,except in rare cases when the damage to the patient was much greater than the benefit of the intervention.In addition,the levels of the detected inflammatory proteins had no significant effect on the occurrence of NRM in patients with severe aGVHD.Conclusions:The results of this study showed that the Logistic regression model established based on the levels of IL-17A,IL-18,IL-10,PD-L1 and CXCL9 in the peripheral blood 14 days after allo-HSCT could predict severe aGVHD.Through large-scale cohort research and full verification,it is expected to become an effective method applied clinically for predicting severe aGVHD.Background:Haploidentical hematopoietic cell transplantation(haplo-HSCT)is increasingly used to treat life-threatening blood disorders.Patients benefit from donors with favorable factors such as young male donors.Recently,HLA-B leader was found to inform risk predictions after unrelated HSCT,cord blood HSCT and haplo-HSCT with post-transplant cyclophosphamide.However,whether the HLA-B leader matching status could differentially influence clinical outcomes in haplo-HSCT with antithymocyte globulin(ATG)has yet to be elucidated.Purposes:This study sought to evaluate the impact of HLA-B leader matching on clinical outcomes after haplo-HSCT with ATG,which may facilitate optimization of donor selection.Methods:We conducted a single-center retrospective analysis of 570 patients who received haplo-HSCT for the treatment of hematological malignancies between Sep,2012 and May,2021.All patients received a myeloablative conditioning regimen with ATG for the prevention of acute graft-versus-host-disease.Probabilities of survival were estimated by the Kaplan-Meier method.Cumulative incidences of competing risk outcomes were estimated by the competing risk model.Cox proportional hazard regression and the Fine-Gray competing risks regression model were used to define hazard ratio(HR)and conduct multivariate analysis.Results:Median follow-up was 649 days(interquartile range 398-965 days)for patients that survived.Mismatch in HLA-A,HLA-B,HLA-C,HLA-DRB1,and HLA-DQB1 did not have a statistically significant impact on clinical outcomes.Relative to HLA-B leader-matched transplantation,HLA-B leader-mismatched transplantation had a lower overall-survival(OS)(HR,1.44;95%CI,1.02-2.01;P=0.04;Figure 1A)and higher non-relapse mortality(NRM)(HR,1.56;95%CI,1.04-2.35,P=0.03).With respect to specific causes of NRM,the group of patients with mismatched HLA-B leader had a higher risk of infection-related death than patients with matched HLA-B leader(HR,2.30;95%CI,1.25-4.23;P=0.01).Relapse was adversely affected by a shared M relative to a shared T allotype in the leader-matched subgroup(HR,1.92;95%CI,1.07-3.47,P=0.03).Patients benefited from HLA-B leader match with a shared T,which was associated with superior OS(HR,0.71;95%CI,0.51-0.98;P=0.035)and modestly increased DFS(HR,0.74;95%CI,0.55-1.00;P=0.051)compared with the others.A similar association was observed in multivariable models——compared to leader match with shared T,leader match with shared M was associated with increased relapse(HR,1.85;95%CI.1.02-3.36;P=0.045),while leader mismatch with shared T was associated with reduced OS(HR,1.57;95%CI,1.08-2.28;P=0.017).Conclusions:In conclusion,findings from the current study suggested that matched HLA-B leader informed a decreased risk of NRM and higher OS in comparison with mismatched HLA-B leader.Furthermore,patients benefited from leader-matched donors with shared T.A paradigm inclusive of HLA-B leader might facilitate optimization of donor selection and improve clinical outcomes of haplo-HSCT with ATG.