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Purpose: Prediction models for acute myeloid leukemia (AML) are useful, but have considerable inaccuracy and imprecision. No current model includes covariates related to immune cells in the AML microenvironment. Here, an immune risk score was explored to predict the survival of patients with AML. Experimental Design: We evaluated the predictive accuracy of several in silico algorithms for immune composition in AML based on a reference of multi-parameter flow cytometry. CIBERSORTx was chosen to enumerate immune cells from public datasets and develop an immune risk score for survival in a training cohort using least absolute shrinkage and selection operator Cox regression model. Results: Six flow cytometry–validated immune cell features were informative. The model had high predictive accuracy in the training and four external validation cohorts. Subjects in the training cohort with low scores had prolonged survival compared with subjects with high scores, with 5-year survival rates of 46% versus 19% (P < 0.001). Parallel survival rates in validation cohorts-1, -2, -3, and -4 were 46% versus 6% (P < 0.001), 44% versus 18% (P = 0.041), 44% versus 24% (P = 0.004), and 62% versus 32% (P < 0.001). Gene set enrichment analysis indicated significant enrichment of immune relation pathways in the low-score cohort. In multivariable analyses, high-risk score independently predicted shorter survival with HRs of 1.45 (P = 0.005), 2.12 (P = 0.004), 2.02 (P = 0.034), 1.66 (P = 0.019), and 1.59 (P = 0.001) in the training and validation cohorts, respectively. Conclusions: Our immune risk score complements current AML prediction models.

Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814‐7.630) and 2.047 (1.013‐4.138) in the training cohort and validation cohort, respectively. The AUC of 3‐year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high‐risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.

Background: The present prognosis for AML mainly rely on the cyto- and molecular genetic features. However, the bone marrow microenvironment also plays a critical role in the development and progression of the disease, and the immune signature within the bone marrow microenvironment of AML remains unknown, and should be clarified. Patients and methods: Here, we applied the robust algorithm CIBERSORT method to generate the distribution and frequency of 22 immune subsets in the bone marrow of AML from public datasets. The associations between the overall survival and proportions of putative marrow immune cell types were analyzed to generate an immune risk score in a public training cohort according to LASSO cox regression model and validated in other validation cohorts. Gene Set Enrichment Analysis was adopted to analyze the immune risk score associated biological phenotypes. Results: Data from GSE37642 (n = 531) were used for immune model estimation, and samples from GSE425 (n =81) and TCGA (n =111) were served as external cohorts for validation. We first derived the 22 immune subsets through CIBERSORT, and found that monocytes, resting mast cells, eosinophils, CD8+T cells, resting NK cells, resting dendritic cells and resting memory CD4+ T cells were the most represented cell fractions within AML bone marrow microenvironment. LASSO cox regression was then applied to identify a total of 10 immune subsets to generate the immune risk score that may predict the survival prognosis of AML patients. This model has an excellent reproducibility, with the area under curve of the immune risk score at 5- year OS of 0.65, 0.84 and 0.74 in the training, external 1 and external 2 cohort. Patients with low immune risk score had a significant survival advantage than those with high immune risk score (5-year OS: 39.2 vs 19.6%, P < 0.001 for the training cohort; 77.4 vs 20.1%, P = 0.004 for the external cohort 1; 27.2 vs 0.0%, P = 0.006 for the external cohort 2). After adjusting the confounding variables of the classic ELN risk category and other clinical factors, the multivariable analysis revealed that immune risk score remained an independent prognostic factor for OS in all the three cohorts (HR: 1.34, 5.42 and 2.16 for the training, external cohort1 and 2 cohorts; all P < 0.05). Next, we explored the related biological phenotypes to immune risk score and found that the immune risk score was significantly associated with the markers of immune checkpoint (HMGB1, CEACAM1, LAG3 and CTLA-4), cell proliferation (NOTCH1, LYN, TSC2, SETBP1, TGFBR2, MAP3K14, TP53, NFKB2 and FOXO1), DNA repairment (RAD50 and BRCA1), epigenetic regulation and transcriptional regulation (SRSF6, DNMT3, TLE1). Furthermore, GSEA analysis indicated a significant enrichment of active immune-related pathways in low immune risk group. Finally, we noted that the immune risk score consistently increased follow the increased cytogenetic risk level. Conclusions: The study identified a novel immune prognostic category of AML, which may be used as another prognostic tool that complements the existing genetic risk classification. Figure Disclosures No relevant conflicts of interest to declare.

The treatment options for patients with acute myeloid leukemia (AML) under inferior performance status (i.e. senior patients, MDS transformed AML, and patients with proven invasive fungal disease) are limited. The conventional "3+7" (idarubicin plus cytarabine) induction for those patients can be either too toxic, which leads to higher mortality rate, or requires prolonged recovery time thus raise medical cost. And the delay of consolidation may compromise outcome thus cause early relapse in such patients. Giving the rationale from the designing art of CPX-351, the prolonged IV time requirement for cytarabine, as well as mini-transplantation for AML treatment, at least in part, reflects the philosophy of a longer exposure to the chemo agent can be a more effective treatment approach for leukemia. On the other hand, the D-CAG protocol, which indicated an encouraging result for elderly patients with AML, indicated effectiveness of the strategy with reduced intensity. However, the cyto-toxic effect of decitabine with standard dose (20mg/m2) could still lead to severe treatment adverse events (AEs) thus raise the need of optimization of D-CAG regimen for patients with inferior PS. When considering the low dose hypo-methylation agent (HMA) can trigger the innate immunity response, the unique effect of homoharringtonine, as well as the effectiveness of CAG, we designed this DHCAG protocol following the principle of "longer exposure, lower intensity preceded by priming" and observed an unexpected excellent outcome with high CR rate, low induction failure and treatment mortality, as well as a higher cost effective value for patients with AML, when compared to "3+7" protocol, whom under inferior PS. From March 2016 - January 2018, we initiated this pilot study and investigated the safety and efficiency of this DHCAG protocol in patients with AML under poor PS. We enrolled 25 patients and administer the regimen as followings: i) G-CSF: 5μg/kg used when WBC 1*10^9/L then increase the dosage of homoharringtonine to 2mg/m2); iv) Cytarabine:10mg/m2 q12h at day 1-14 subcutaneous injection (if WBC >20*10^9/L then increase to 100mg/m2 by 24h CIV, or 50mg/m2 q12h by subcutaneous injection). The primary end point was complete hematologic remission, defined as a bone marrow blast cells ≤5%, Neutrophils in peripheral blood ≥1.0X109 /L, hemoglobin≥90g/L, and platelets ≥100X109/L and no any evidence of extramedullary leukemic infiltration; Secondary end points included numbers of adverse events, length of hospital stay, medical costs, and quality of life as measured with the use of the Functional Assessment of Cancer Therapy - General (FACT-G) questionnaire. Among the 25 patients in the study, 23 completed the induction therapy. And 21/25 patients had a hematologic complete remission after a median time of 19 days. Once complete remission had occurred, all 21 patients received post-remission treatment for another 5 cycles of DHCAG. Median follow-up was 14 months (range, 8 to 19) by June 2018. Interestingly, regardless of grade 3-4 myelo-suppression occurred all of our patients during induction, eight patients did not experienced grade 3-4 myelo-suppression during the following cycles. The median hospital stay is 22 days. The median of total medical costs for induction was $9,815 (range, $5,053 to $16,336) vs $14,705 for "3+7" induction protocol (history control. Data unpublished). Patients resumed their usual lifestyle during post-remission therapy, and their quality of life was rated as nearly normal on the FACT-G questionnaire. At the time of the last follow-up, seven patients had had a hematologic relapse. The results of our pilot study, in which we tested a priming based, low dose and longer exposure in 25 patients with AML under poor PS, showed that the treatment was safe, effective, and economical. A prospective multicenter, randomized trial comparing DHCAG with IA is now under way in China. Disclosures No relevant conflicts of interest to declare.