T Cell-Mediated Tumor Killing Sensitivity Gene Signature-Based Prognostic Score For Acute Myeloid Leukemia

T Cell-Mediated Tumor Killing Sensitivity Gene Signature-Based Prognostic Score For Acute Myeloid Leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article T Cell-Mediated Tumor Killing Sensitivity Gene Signature-Based Prognostic Score For Acute Myeloid Leukemia Yiyun Pan, FangFang Xie, Wen Zeng, Hailong Chen, Zhengcong Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3854251/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background and Objective: Acute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI). Methods: Publicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan-Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes. Results: From 401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75%-96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML. Conclusion : A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches. Acute Myeloid Leukaemia cancer immunotherapy prognostic score T cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Acute myeloid leukemia (AML) refers to heterogenous hematopoetic neoplasms that are well defined and may occur spontaneously or secondarily in response to chemoradiotherapy for other malignancies ( 1 ). Although rare, with incidence rates ranging from 0.4–11 cases per 100,000, the incidence of AML is anticipated to increase globally ( 2 ). The risk factors of de-novo AML include obesity, smoking, solvent exposure and the incidence increases with age with the median age of occurrence over 60 ( 3 ). In case of secondary AML, exposure to DNA damaging chemotherapy such as alkylating agents, platinum-based chemotherapeutics, topoisomerase inhibitors and antimetabolites and its duration increase disease risk ( 4 ). Over the last two decades, the five year survival rate of AML has remained low at 28.7% ( 5 ). Moreover, the survival rate in older patients has been dismally low ( 6 ). Older, frail patients may receive palliative care ( 7 ). Treatment toxicity and relapse have been common complications in AML, with relapse rates up to 50% ( 8 ). The standard treatment of AML has involved chemotherapy and allogeneic hematopoietic stem cell transplantation until recently ( 5 , 9 ). In the last few years, tremendous advances have been made in the treatment of AML, with the advent of clinically approved targeted therapeutics including FLT3, IDH1, and IDH2 inhibitors ( 10 – 11 ). Next generation hypomethylating agents (HMA) that overcome the issues of resistance and HMA combination agents are increasingly applied in AML management ( 12 ). The tumor microenvironment in AML is immunosuppressive and marked by T cell hypofunctions ( 13 , 14 ). These include T-cell senescence and T-cell exhaustion due to apoptosis ( 15 , 16 ). T cell function suppression in AML correlates with patient survival ( 17 ) and T-cell deregulation is implicated in immune escape AML relapse after allogeneic hematopoietic cell transplantation ( 18 ). Therefore, immunotherapy, that harnesses the tumor killing potential of T-cells in AML, is an active research area in AML. Antibody-based approaches and chimeric antigen receptor T-cells (CAR T-cells) have shown early efficacy ( 19 , 20 ). T-cell based immunotherapy approaches under development include specific dual antigen targeting antibodies against CD123, CD33, CD13, CL-1 and T-cell immune checkpoint inhibitors targeting PD-1, PDL-1, CTLA4, TIMP3 and other emerging targets ( 21 ). T cell response in the tumor microenvironment can be reactivated by targeting immune checkpoints ( 22 ). Expression patterns of immune checkpoints in AML are associated with mutational status and overall survival, and bone marrow T cell populations in AML exhibit variable immune checkpoint inhibitor expression patterns ( 23 , 24 ). However, resistance to T-cell targeting immunotherapies in AML owing to variability in T cell phenotypes, remains a challenge ( 25 ). T-cell transcriptional signatures and markers that correspond to immunotherapy response and resistance in AML is an area of intensive research focus ( 26 ). In the present study, we constructed a signature-based T cell mediated tumor killing sensitivity genes (GSTTK) in AML and assessed its predictive performance for immunotherapy efficacy and prognosis. Further, we applied molecular docking techniques to identify novel potential agents targeting T cell-mediated tumor killing sensitivity genes in AML. Materials and Methods Data download and pre-processing The TCGA-LAML dataset was obtained from XENA ( https://xenabrowser.net/datapages/ ) and the GTEx dataset ( https://xenabrowser.net/datapages/?cohort=GTEX ) was downloaded as control. The two datasets were de-batched and combined into the training set data. GSE71014 and GSE37642 datasets were downloaded from GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) and utilized as the test dataset. The sample information is summarized in Table 1 . Table 2 depicts the clinical characteristics and subtypes of the TCGA-LAML data. Table 1 Datasets and sample information Database Tumor Normal Total TCGA-LAML 132 70(GTEx) 202 GSE71014 104 0 104 GSE37642 136 0 136 Table 2 Clinical characteristics and subtypes of the TCGA-LAML samples High(N = 66) Low(N = 65) Overall(N = 131) Age =60 35 (53.0%) 20 (30.8%) 55(42.0%) Gender Female 26 (39.4%) 34 (52.3%) 60 (45.8%) Male 40 (60.6%) 31 (47.7%) 71 (54.2%) History_of_neoadjuvant_treatment No 46 (69.7%) 54 (83.1%) 100 (76.3%) Yes 20 (30.3%) 11 (16.9%) 31 (23.7%) Subtype Subtype.1 8 (12.1%) 0(0%) 8 (6.1%) Subtype.2 9 (13.6%) 5 (7.7%) 14 (10.7%) Subtype.3 0(0%) 16 (24.6%) 16 (12.2%) Subtype.4 12 (18.2%) 16 (24.6%) 28 (21.4%) Subtype.5 13 (19.7%) 13(20.0%) 26 (19.8%) Subtype.6 17 (25.8%) 9 (13.8%) 26 (19.8%) Subtype.7 7 (10.6%) 6 (9.2%) 13 (9.9%) Next, 641 T cell-mediated tumor killing sensitivity-related genes (GSTTK) were downloaded from the TISIDB database ( http://cis.hku.hk/TISIDB/ ), of which 588 genes were present in the training set. Differential expression analysis Differentially expressed genes among the 588 GSTTK in the training set data were identified using the Wilcox. Test function in R (version 3.6.2), p value 1 as the screening threshold. Enrichment Analysis GO and KEGG enrichment analysis of the differentially expressed GSTKK was performed using the clusterProfiler package in R (version 3.6.2), with parameters p adjustment method = 'BH', p value cutoff = 0.05, q value cutoff = 0.2. Gene-Gene Interaction Network The STRING database ( https://cn.string-db.org/cgi/input?sessionId=bH2OGdjjRDkc&input_page_show_search=on ) was used protein-protein interaction (PPI) pair-based network construction using the differentially expressed GSTKK. The screening criteria included a minimum required interaction score = 0.7, with the removal of non-connected genes. The network was mapped using Cytoscape (v3.8.0). Construction of a T cell-mediated tumor killing sensitivity gene-based prognostic score index: TTKPI The ‘coxph’ function in the ‘surv’ R package was used to perform one-way COX regression analysis to predict survival outcome, with the 401 differentially expressed genes as predictor features, screened at a p value < 0.05. 57 genes with significant prognostic effects were obtained. Next, the ‘glmnet’ function in the ‘glmnet’ R package was used to perform LASSO regression analysis for the significant genes obtained from the step mentioned above, with parameters set at alpha = 1, nlamba = 100, and a significance level of p < 0.05. The resulting genes selected by LASSO were used to construct a risk score for tumor killing (TTKPI). The scores were computed using the equation: $${\text{R}\text{i}\text{s}\text{k} \text{s}\text{c}\text{o}\text{r}\text{e}}_{\text{i}} = \sum _{\text{j}=1}^{\text{n}}{\text{C}}_{\text{j}}\text{*}{\text{e}\text{x}\text{p}}_{\text{i}\text{j}}$$ This equation calculates the risk score value of the ith sample, where C_j is the regression coefficient of the jth prognostic factor in the model, and exp_ij denotes the expression of the jth prognostic factor for the ith sample. The risk score model for predicting sample survival was established by weighting the expression of the significant genes with LASSO regression coefficients (exp represents gene expression level, C represents lasso regression coefficient). Survival analysis with TTKPI scores : Samples were grouped into high and low TTKPI risk score groups based on the median level. Survival curves were generated using the Kaplan-Meier method to predict overall survival. Univariate and multifactorial Cox proportional hazard analyses were performed to determine the prognostic value of the risk scores in combination with other clinical characteristics. Receiver operating curves (ROCs) were used to estimate the predictive performance of the TTKPI risk model for 1-, 3-, and 5-year survival, correlation analyses were performed using the ‘cor.test’ function, and Fisher’s exact tests were performed, where a p value < 0.05 was considered a statistically significant difference. Association of TTKPI scores with clinical phenotype in AML We further analysed the TCGA dataset by extracting the clinical characteristics of the samples including age, gender, adjuvant treatment status, and cancer staging. The TTKPI scores were compared between clinical subgroups. Univariate and multivariate Cox regression analyses (at a threshold of the p value < 0.05) were performed to test the predictive performance of TTKPI scores and clinical characteristics of the TCGA dataset. The results were plotted as forest plots. The TTKPI risk grouping was combined with the clinical characteristics that were significant in the univariate and multivariate Cox proportional hazards analysis, and the nomogram function in the R package ‘rms’ was applied to construct a column line plot for nomogram analysis. Calibration curves were plotted using the calibrate function and decision curve analysis (DCA) was performed by plotting decision curves. Tumor immune cell infiltration analysis and immune checkpoint expression in TTKPI groups Tumor infiltration analysis was based on the gene expression data of TCGA, and the proportion of tumor infiltrating immune cells (22 immune cells) in a sample was determined using CIBERSORT in R. The analysis was performed using the default parameters infiltration scores for 22 immune cells were obtained. The differences in immune checkpoint expression levels between high and low TTKPI groups were analysed. Molecular docking association with TTKPI to predict potential therapeutic agents. Corresponding compound structures were downloaded from the DrugBank database and screened according to Lipinski's rule (hydrogen bond acceptor < = 10, hydrogen bond donor < = 5, rotatable bond < = 10, logarithmic value of lipid-water partition coefficient < = 5, molecular weight of 180–480, polar surface area < = 140). The spatial structure information of key gene-encoded proteins was searched in the PDB database and the corresponding PDB files were downloaded. The approximate docking box range was determined based on the ligand information therein, and other relevant parameters of autodock-vina were set and used to dock small molecule compounds. Interaction force analysis was performed using Pymol and Ligplus. Results Aberrant expression and function of GSTTKs in AML Differential analysis of 588 GSTTKs in the test set data by rank sum test (screening criteria, p =1) yielded 401 DEG, where 171 were up-regulated and 230 were down-regulated. A volcano plot (Fig. 1 -A), differential gene expression heat map (screening the top 20 genes according to |log2FC| (Fig. 1 -B) and a PCA analysis plot (Fig. 1 -C) illustrating the GSTTKs that distinguish AML samples from controls (the control samples were de-batched GTEx samples) are presented. The relationship between the 401 differential GSTKK and clinical characteristics (gender, history of adjuvant therapy or not) was analysed separately, and six genes were selected for visualization (Fig. 2 -A, B). The results of GO and KEGG functional enrichment analyses were based on protein-related pathways, immune-related pathways including antigen-antibody-related, T-cell-related, and leukocyte-related pathways, including antigen processing and presentation of exogenous peptide. The results of GO enrichment analysis included more than 20 immune-related pathways including antigen processing and presentation of exogenous peptide via MHC class I, T cell extravasation, interleukin-1-mediated signalling pathway. The results of KEGG enrichment analysis included the Antigen processing and presentation pathway. The results are shown in Fig. 2 -C, D. The gene interaction network showed genes with high connectivity, included RPS19, RPL3, RPL23A, and the log2FC values were negative (Fig. 3 ). Construction and validation of TTKPI A one-way cox regression analysis of TCGA dataset with the 401 differential GSTTK was performed and 57 genes with significant prognostic value were further screened using LASSO regression. 24 key prognostic genes were determined using LASSO including GSTKK; DSCR3, MPG, OTOA, TGIF2LX, CBLL1, KLF2, C6orf1, ENPP2, COL23A1, NELFE, MLYCD, AVPR2, KANSL1L, HNRNPAB, TPST2, FADD, SCD, FAM53B, CRTC1, LBR, RGP1, HSD17B13, PSORS1C1, SPAG1 (Fig. 4 -A,B,C), and Kaplan-Meier survival curves were plotted for these 24 genes (see Appendix for details), and the survival curves for six of these are shown (Fig. 5 ). The risk score of each tumor tissue sample was calculated based on the TTKPI risk score model, and the samples were divided into high and low risk score groups based on the median score (Fig. 6 - A, B). Survival curve analysis for the training set data showed that the prognosis of the samples in the low-risk score group was better (p < 0.0001; Fig. 6 -C), and ROC analysis showed that the AUC of the samples reached 0.89, 0.9, and 0.96 at 1, 3, and 5 years, respectively, indicating that the model predicted well (Fig. 6 -D). In the independent dataset GSE71014, the prognosis was similarly significantly better in the low-risk score group (p < 0.0001; Fig. 6 -C). ROC analysis showed high AUC values at 1, 3, and 5 years was 0.88, 0.85, and 0.82 (Fig. 6 -D), respectively, indicating a good prognostic accuracy of the risk score model. Using the independent test dataset GSE37642, similar results showing significantly better prognosis of the samples in the low-risk score group (p < 0.0001; Fig. 6 -C), and the AUC values at 1, 3, and 5 years were 0.78, 0.75, and 0.76 (Fig. 6 -D), respectively, validating consistently high prognostic performance of the TTKPI risk scoring model. TTKPI scores were associated with clinical phenotype. The TTKPI risk score of samples with age > = 60 years was significantly higher than that of samples with age < 60 years, and the risk score of samples with AML staging type AML.1 was significantly higher than that of other samples, while the risk score of samples with cancer staging type AML.3 was significantly lower. These indicated that the TTKPI risk scoring model was significantly associated phenotype categories of age and AML cancer stage (Fig. 7 ). The results of univariate and multivariate Cox regression analysis are depicted in forest plots (Fig. 8 ). The TTPKI risk score showed significantly elevated hazard ratio in both analyses. The result of the nomogram analysis is depicted in Fig. 9 -A and the calibration curves are shown in Fig. 9 -B. The two curves showed minimal deviation from the ideal prediction line, indicating that the combined model (column line plot) had high predictive performance for the 1-year and 3-year periods, and the two curves in the DCA decision curve (Fig. 9 -C D) indicated that the column line plot constructed by the combined model showed better prognostic performance. TTKPI scores were associated with tumor immune cell infiltration. CIBERSORT was used to analyse immune cell infiltration and determine differences between high and low TTKPI groups. The results showed that 4 types of infiltrating immune cells, “Mast cells resting", "Monocytes", "T cells CD4 naive" and "T cells follicular helper", were significantly different between the two groups (Fig. 10 ). The correlation between TTKPI scores, its constituent genes, and TME cells was calculated and “Dendritic cells activated”, “Mast cells resting”, “T cells CD4 naïve”, “T cells follicular helper” showed negative correlation with TTKPI, and T cells follicular helper showed negative correlation with TTKPI. “Macrophages M2”, “Monocytes” and other immune infiltrating cells showed positive correlation with TTKPI. (Fig. 11 ). TTKPI score-based prediction of immune checkpoint expression and immunotherapy responses The differences in immune checkpoints between high and low TTKPI groups were analysed including C10orf54, CD160, CD200, CD276, CD47, CD86, CD96, KIR3DL1, LAG3, LGALS9, PDCD1, SELPLG, SIGLEC7, TMIGD2, TNFRSF8. The expression levels of CD160, CD200, CD47, CD96, TMIGD2 in the low-risk group were significantly higher than those in the high-risk group; the expression levels of C10orf54, CD276, CD86, KIR3DL1, LAG3, LGALS9, PDCD1, SELPLG, SIGLEC7, TNFRSF8 expression levels were significantly lower than those of the high-risk group. (Fig. 12 ). Molecular docking association TTKPI predicts potential therapeutic agents. Screening of the corresponding compound structures from the DrugBank database according to Lipinski's rule yielded 5462 small molecule compounds. AVPR2, ENPP2, SCD and TPST2 were found to have corresponding spatial information structure, and the corresponding PDB files were downloaded: 7DW9, 5MHP, 4ZYO and 3AP1. DB11791, DB12886 and DB14773 were found to have good docking scores with the four proteins with Affinity <-9.6. The complete docking score table is shown in Table 3 . Table 3 Small molecule docking score with 4 proteins. DrugBank_ID Name Protein Affinity (kcal/mol) DB11791 Capmatinib AVPR2 -9.8 ENPP2 -11.2 SCD -12.0 TPST2 -10.3 DB12886, GSK-1521498 AVPR2 -10.1 ENPP2 -12.7 SCD -11.5 TPST2 -9.7 DB14773 Lifirafenib AVPR2 -9.9 ENPP2 -12.8 SCD -13.1 TPST2 -10.8 The docking conformation and interaction force analysis for AVPR2, ENPP2, SCD, and TPST2 with DB11791 (Fig. 14-top), DB12886 (Fig. 14-centre), DB14773 (Fig. 14-bottom) are represented. Discussion The present study constructed and validated a T cell-mediated tumor killing sensitivity gene-based prognostic score, TTKPI, composed of 24 differentially expressed genes in AML. The TTKPI score showed good-excellent prognostic value for survival in the training and test datasets, with AUC values ranging from 75%-96%. Higher TTKPI scores were associated with older age and cancer stage and the combined model showed better prognostic function. Distinct AML tumor immunological profiles clustering with age, T-cell receptor clonality and survival outcomes have been shown earlier ( 27 ).The widely used ELN17 prognostic tool showed inadequate performance to predict long-term survival patients older than 60 years, despite accounting for mutational burden ( 28 ). Older patients with AML show different memory T cell subpopulations that young populations, owing to T cell senescence and terminal differentiation, particularly in case of CD8 + T cells ( 29 ). Together, these evidences support the application of combined prognostic tools integrating tumor immunological scores such as TTKPI with clinical parameters for improved prognostic performance. We mapped the individual survival probability curves of 6 of the 24 component GSTTK genes which were included in the TTKPI score system. Higher expression levels of C6orf1 (SMIM29), FAM53B, HNRNPAB predicted lower overall survival. C6orf1 is a prognostic gene for clear cell renal carcinoma ( 30 ). The FAM53B gene, implicated in Wnt signaling, is deregulated in T cell dysfunction, and found to truncated in the bone marrow tissue from acute lymphoblastic leukemia ( 31 , 32 , 33 ). HNRNPAB is an RNA binding protein documented as a prognostic biomarker in several solid tumors ( 34 , 35 ) and is considered as an important biomarker and therapeutic target ( 36 ). We also showed that the levels of lower levels of HSD17B13, KANSL 1L, and LBR predicted worse survival rates. Lower HSD17B13 expression, a protein primarily implicated in lipid metabolism in the liver, and also expressed in the bone marrow, similarly predicted worse survival rates in hepatocellular carcinoma ( 37 ).KANSL 1L encodes for a chromatin modifying protein and truncated mutations in this gene in AML are documented ( 38 ), with clonal mutations noted during relapse ( 39 ). A high TTKPI score predicted worse survival with good accuracy. T cell mediated tumor killing sensitivity genes have shown strong prognostic value in several solid tumor types including lung adenocarcinoma, head and neck cancer and hepatocellular carcinoma ( 40 , 41 , 42 ). AML is highly heterogenous at the molecular level with low mutational burden, which is believed to underpin modest responses to antibody dependent checkpoint inhibitor therapy as compared to solid tumors and receptor independent T-cell directed therapies assumes high significance ( 43 ). Identifying GSTTK based tumor immunological subtypes can also be very valuable for predicting responses to checkpoint inhibitors and emerging immunotherapies such as CAR-T cell and bispecific antibody therapy. Our data showed different patterns of checkpoint expression in the high and low TTKPI groups. The low TTKPI group showed higher levels of CD160, CD200, CD47, CD96, TMIGD2, and lower levels of C10orf54, CD276, CD86, KIR3DL1, LAG3, LGALS9, PDCD1, SELPLG, SIGLEC7, TNFRSF8. Classical immune checkpoint inhibitor drugs targeting PD-1, PD-L1 and CTLA4 show variable efficacy and additional checkpoint inhibitor drugs such as drugs targeting LAG3 are under current focus ( 44 , 45 ). Our data suggest that the TTKPI score pattern could potentially direct the selection of optimal checkpoint inhibitor drugs and combination. The TTKPI score component GSTTKs were enriched in immune-related pathways including antigen processing and presentation, T cell extravasation, interleukin-1-mediated signalling pathway; key pathways of tumor immune function, which maps the TTKPI score to functional variances in the tumor microenvironment. TTKPI negatively correlated with CD4 naïve and follicular helper T cell immune infiltration scores while M2 macrophages, monocytes were positively correlated, implying a T cell exhausted, comparatively pro-inflammatory, innate immune dominated milieu in high TTKPI states, which corresponded to low survival. T cell exhaustion and concurrent PD-1 resistance is a characteristic of severe AML ( 46 , 47 ). An AML tumor immune landscape marked by high M2 macrophage and monocyte infiltration corresponds to high inflammatory response and predicts poor survival ( 48 ), in alignment with our results. Our findings may have implications for immunotherapy selection based on TTKPI profiles. PD-1 immunotherapy may be applied as adjunct to HMA or in case of relapse after allogeneic hematopoietic stem cell transplantation ( 49 ) but considering its variable performance, biomarker/ transcriptome driven immunotherapy design and novel target discovery is under focus ( 50 ). Therefore, we sought to identify novel potential targets using molecular docking to seek relevant compounds from the DrugBank database. Our results identified three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) as potential agents targeting T-cell mediated tumor killing mechanisms in AML. Capmatinib is an inhibitor of cellular-mesenchymal-epithelial transition factor (c-Met), which belongs to the MET family. Early preclinical and clinical evidence shows that MET inhibition may be a valuable target for refractory and relapsed AML( 51 , 52 ) GSK-1521398 is a mu-opioid receptor antagonist primarily trialled in addiction and obesity ( 53 ), however mu-opioid receptors are understood to play significant roles in cancer immunoregulation, and positive effects of other mu-opioid antagonist drugs have been noted ( 54 ). Lifirafenib is a dual inhibitor of BRAF-kinase and EGFR that has shown positive effects in several solid tumors in preliminary investigations ( 55 ). RAF pathway activation and BRAF mutation is recognised in a proportion of AML cases, and RAF inhibition has shown positive anti-cancer effects ( 56 , 57 ). Collectively, these findings support the potential utility of these novel small molecule agents in AML. The strengths of the present study include a dual step selection for identification of a GSTTK genes for prognostic score construction and validation in an independent cohort. In addition, tumor immune cell infiltration analysis, checkpoint expression patterns and molecular docking were leveraged to shed light on functional and therapeutic implications of the constructed TTKPI score. The limitations of the present study include a small number of validation datasets and the lack of experimental studies to validate the GSTTK signature and its functional aspects identified in the in-silico study. Overall, our findings demonstrated the prognostic value of the TTKPI score and identified functional pathways, molecular targets, and potential agents that may be harnessed for tumor killing T-cell dependent therapies in AML. Conclusion The present study identified a 24 T-cell mediated tumor killing sensitivity gene signature of AML and constructed a comprehensive risk score TTKPI, which showed good-excellent prognostic value for overall survival in AML and was significantly associated with clinical phenotype features. Immune cell infiltration analysis showed that the TTKPI score corresponded to the tumor microenvironment and immune checkpoint expression patterns, and using drug docking, we identified 3 small molecule drugs with high potential for clinical translation. The utility of TTKPI score for prognosis and targeted biomarker-based immunotherapy should be tested in clinical studies. Declarations Data Availability Statement The two datasets were de-batched and combined into the training set data. GSE71014 and GSE37642 datasets were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/). All data generated or analysed during this study are included in this published article [and its supplementary information files]. Conflicts of Interest The authors declare that they have no competing interests Author Contributions Conception or design of the work: Yijian Chen and Dechang Xu. Acquisition, analysis, or interpretation of data for the work: Yiyun Pan, Fangfang Xie, Wen Zeng, Hailong Chen. Drafting of the work or revising it critically for important intellectual content: Yiyun Pan, Zhengcong Chen,. All authors approved the final version of the manuscript. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. Funding Funding program: Science and Technology Program of Jiangxi Provincial Administration of Traditional Chinese Medicine (2022B967, 2021B363); Science and Technology Plan Project of Jiangxi Provincial Health Care Commission (202212543). 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Supplementary Files SupplementaryTable1.xls Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Feb, 2024 Reviews received at journal 04 Feb, 2024 Reviewers agreed at journal 01 Feb, 2024 Reviewers agreed at journal 24 Jan, 2024 Reviewers invited by journal 24 Jan, 2024 Editor assigned by journal 22 Jan, 2024 Submission checks completed at journal 22 Jan, 2024 First submitted to journal 11 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3854251","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268608674,"identity":"8a7273e9-e200-451d-8ec4-f36c4214417f","order_by":0,"name":"Yiyun Pan","email":"","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yiyun","middleName":"","lastName":"Pan","suffix":""},{"id":268608675,"identity":"ffc0f983-5b69-414c-8f8c-8b1d9afb2bcb","order_by":1,"name":"FangFang Xie","email":"","orcid":"","institution":"Ganzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"FangFang","middleName":"","lastName":"Xie","suffix":""},{"id":268608676,"identity":"90f8c297-1021-4c20-aa3e-a6654e46dd93","order_by":2,"name":"Wen Zeng","email":"","orcid":"","institution":"Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Zeng","suffix":""},{"id":268608677,"identity":"c2e7a917-9c4d-4783-b78b-be96477d4a89","order_by":3,"name":"Hailong Chen","email":"","orcid":"","institution":"Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Chen","suffix":""},{"id":268608678,"identity":"9a6af4f2-f896-4e7e-bd4b-ce06e8230e9a","order_by":4,"name":"Zhengcong Chen","email":"","orcid":"","institution":"Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhengcong","middleName":"","lastName":"Chen","suffix":""},{"id":268608679,"identity":"f534c304-d0ff-499e-9393-0f78780d0fde","order_by":5,"name":"Dechang Xu","email":"","orcid":"","institution":"Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dechang","middleName":"","lastName":"Xu","suffix":""},{"id":268608680,"identity":"c6faaa03-17e8-4107-a319-54040ef4078c","order_by":6,"name":"Yijian Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCRDBY8PDJn/4AETkAHFa0uT4JdgSSNHCcNhYcgaPAXFa+Gc3H3vMI8OcuOF2z+dXN9sY5PhuJDB+LsBnyZ1j6cY8PGyJG+6c3Wad28ZgLHkjgVl6Bh4tBhI5ZtI8PDyJGw7kbjMGaknccCOBjZkHr5b8b0AtEkAtOc9AWuqJ0JLDBtRiAPR+DvNjoJYEA0JaJG6kmUnO4UmQ4+c5Zsacc07CcOaZh83S+LTwz0h+JvG25z8PG3vz4885ZTbyfMeTD37GpwUEmHh7wDSbBCSaGBsIaAAq+fEDTDN/IKh0FIyCUTAKRiQAAL6VSoKoaHtBAAAAAElFTkSuQmCC","orcid":"","institution":"Suzhou Medical College of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Yijian","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-01-11 17:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3854251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3854251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50139920,"identity":"db6d48be-e8f9-4d35-86d9-eb8280d38cf0","added_by":"auto","created_at":"2024-01-25 06:29:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":947500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Differential gene volcano map, B. Differential gene heat map (TOP20), C. PCA analysis map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/1cca9cd5e292df943aaf916c.jpg"},{"id":50138822,"identity":"70e912ad-6509-4f60-83a9-63fb30f40286","added_by":"auto","created_at":"2024-01-25 06:13:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1406183,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. 6 differentially expressed GSTTK associated with sex, B. 6 differentially expressed GSTTK associated with whether adjuvant therapy was performed, C. GO enrichment analysis, D. KEGG enrichment analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/ff42828d227c8d43a5ae3ed5.jpg"},{"id":50138818,"identity":"7bc4844d-6e99-473a-84cf-c2afff77eb45","added_by":"auto","created_at":"2024-01-25 06:13:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1312922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene interaction network for the differentially expressed GSTTK constructed using Cytoscape. Node size corresponds to the connectivity and the color corresponds to the log2FC value.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/c90bde22cff6b3bfca8b48e3.jpg"},{"id":50139921,"identity":"c59ff79a-4d24-489f-9be3-42fd7f2941c7","added_by":"auto","created_at":"2024-01-25 06:29:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":741695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. LASSO analysis to screen redundant genes., B. LASSO analysis and distribution of lambda values. C. coefficients statistics of 24 genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/50fc00a305c6764ccc13ff3c.jpg"},{"id":50140495,"identity":"523888a8-601e-4403-8128-af9e7b81219f","added_by":"auto","created_at":"2024-01-25 06:37:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1668308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan Meier survival curves for 6 of the 24 GSTKK genes selected by LASSO regression analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/23a98ea4b26d85e1065bbd18.jpg"},{"id":50138825,"identity":"a994b338-e85d-4545-bab6-8240222991a0","added_by":"auto","created_at":"2024-01-25 06:13:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3448709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic performance of the TTKPI risk score model. A. Heatmap depicting gene expression, risk scores and survival times in high and low risk group samples, red represents death events and green represents survival events; B. Differences between high and low risk TTKPI risk groups in survival outcomes; C. Kaplan Meier survival analysis showing survival probability curve; D. Receiver operating curve analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/8d3f4efb0f9490d8e74629e0.jpg"},{"id":50139401,"identity":"425c6ae1-3fe4-452e-b034-0f9fee501c06","added_by":"auto","created_at":"2024-01-25 06:21:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":709693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntergroup comparison of TTKPI scores in clinical phenotype categories.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/94a4852916d13f1965feca1d.jpg"},{"id":50139398,"identity":"535ee2b4-531e-4e75-95fd-59186e3f65a7","added_by":"auto","created_at":"2024-01-25 06:21:16","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":438153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plots of the results of single-factor COX and multi-factor COX analysis of clinical characteristics and TTKPI (ns:p\u0026gt;0.05,*:p\u0026lt;0.05,**:p\u0026lt;0.01,***:p\u0026lt;0.001,****:p\u0026lt;0.0001)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/0e45b41787a63b8f14ee19f2.jpg"},{"id":50139924,"identity":"df039efd-0596-474b-8de8-b03166d83a20","added_by":"auto","created_at":"2024-01-25 06:29:16","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1748686,"visible":true,"origin":"","legend":"\u003cp\u003eA shows the nomogram column line plot ; B shows the calibration curve for the nomogram, with the 1 (blue line) and 3 (red line)-year survival periods, the dotted line represent perfect predictive performance; C and D show the decision curves for the 1-year and 3-year periods, the y-axis represents the net benefit, coloured line represents the nomogram (red: 1 year period, blue: 3 year period), grey line represents the assumption that all patients have adverse events, black line represents the assumption that no patients have adverse events.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/7a638d5f73d4d8998ed63160.jpg"},{"id":50139403,"identity":"177df654-3bc4-4cb6-904a-3a338bca7c0c","added_by":"auto","created_at":"2024-01-25 06:21:16","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3114065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in immune cell infiltration between high and low TTKPI subgroups. Differences in immune cell infiltration between high and low TTKPI subgroups. t-test *:p\u0026lt;0.05,**:p\u0026lt;0.01,***:p\u0026lt;0.001,****:p\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/74dabe1e53bf4d82f38615ab.jpg"},{"id":50139404,"identity":"c6c51e9b-94cf-43c6-b228-a43b98c5f0fd","added_by":"auto","created_at":"2024-01-25 06:21:16","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1453307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of TTKPI and its component genes with TME cells\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/2a2f25298cf822a83bcd0a04.jpg"},{"id":50138828,"identity":"16c02b83-4724-447a-bf5a-b112f36536b7","added_by":"auto","created_at":"2024-01-25 06:13:16","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":3903938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in immune checkpoint expression between high and low TTKPI subgroups. Differences in immune checkpoint expression between high and low TTKPI subgroups. t-test *:p\u0026lt;0.05,**:p\u0026lt;0.01,***:p\u0026lt;0.001,****:p\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/db41cff5a90e132fd1892d92.jpg"},{"id":50138826,"identity":"e075d373-d8a6-425b-916e-3665e46f57a1","added_by":"auto","created_at":"2024-01-25 06:13:16","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":2543150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA Docking conformation and interaction force analysis of DB11791 (top), DB12886 (centre): DB14773 (bottom); with AVPR2 (A), ENPP2 (B), SCD (C) and TPST2(D). Docking conformation and hydrogen bonding shown by Pymol (upper section), Ligplus interaction force analysis (lower section). Upper section: Pymol shows the docking conformation and hydrogen bonding, cyan represents the small molecule, yellow dashed line represents the hydrogen bond, blue represents the amino acid residues forming hydrogen bonds with the small molecule; lower section: Ligplus force analysis, the small molecule is seen the centre surrounded by the associated protein amino acid residues, green dashed line represents the hydrogen bonds formed, amino acid names for the amino acid residues forming hydrogen bonds are in green font.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/dd1c36886d08a57d4a5e1a2b.jpg"},{"id":50141046,"identity":"4126c3fc-5fc7-4999-aba1-8a57c0cf9337","added_by":"auto","created_at":"2024-01-25 06:45:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2282817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/50070833-6c4e-43d8-bb18-1d115e2f2bd1.pdf"},{"id":50138831,"identity":"b3d2b52d-a734-4e6c-b880-69e3dfbe00f1","added_by":"auto","created_at":"2024-01-25 06:13:16","extension":"xls","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":9185,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xls","url":"https://assets-eu.researchsquare.com/files/rs-3854251/v1/1aa4c267fd9481b172fc5ad2.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"T Cell-Mediated Tumor Killing Sensitivity Gene Signature-Based Prognostic Score For Acute Myeloid Leukemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) refers to heterogenous hematopoetic neoplasms that are well defined and may occur spontaneously or secondarily in response to chemoradiotherapy for other malignancies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although rare, with incidence rates ranging from 0.4\u0026ndash;11 cases per 100,000, the incidence of AML is anticipated to increase globally (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The risk factors of de-novo AML include obesity, smoking, solvent exposure and the incidence increases with age with the median age of occurrence over 60 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In case of secondary AML, exposure to DNA damaging chemotherapy such as alkylating agents, platinum-based chemotherapeutics, topoisomerase inhibitors and antimetabolites and its duration increase disease risk (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Over the last two decades, the five year survival rate of AML has remained low at 28.7% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Moreover, the survival rate in older patients has been dismally low (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Older, frail patients may receive palliative care (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Treatment toxicity and relapse have been common complications in AML, with relapse rates up to 50% (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The standard treatment of AML has involved chemotherapy and allogeneic hematopoietic stem cell transplantation until recently (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In the last few years, tremendous advances have been made in the treatment of AML, with the advent of clinically approved targeted therapeutics including FLT3, IDH1, and IDH2 inhibitors (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Next generation hypomethylating agents (HMA) that overcome the issues of resistance and HMA combination agents are increasingly applied in AML management (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe tumor microenvironment in AML is immunosuppressive and marked by T cell hypofunctions (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These include T-cell senescence and T-cell exhaustion due to apoptosis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). T cell function suppression in AML correlates with patient survival (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and T-cell deregulation is implicated in immune escape AML relapse after allogeneic hematopoietic cell transplantation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Therefore, immunotherapy, that harnesses the tumor killing potential of T-cells in AML, is an active research area in AML. Antibody-based approaches and chimeric antigen receptor T-cells (CAR T-cells) have shown early efficacy (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). T-cell based immunotherapy approaches under development include specific dual antigen targeting antibodies against CD123, CD33, CD13, CL-1 and T-cell immune checkpoint inhibitors targeting PD-1, PDL-1, CTLA4, TIMP3 and other emerging targets (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). T cell response in the tumor microenvironment can be reactivated by targeting immune checkpoints (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Expression patterns of immune checkpoints in AML are associated with mutational status and overall survival, and bone marrow T cell populations in AML exhibit variable immune checkpoint inhibitor expression patterns (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, resistance to T-cell targeting immunotherapies in AML owing to variability in T cell phenotypes, remains a challenge (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). T-cell transcriptional signatures and markers that correspond to immunotherapy response and resistance in AML is an area of intensive research focus (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In the present study, we constructed a signature-based T cell mediated tumor killing sensitivity genes (GSTTK) in AML and assessed its predictive performance for immunotherapy efficacy and prognosis. Further, we applied molecular docking techniques to identify novel potential agents targeting T cell-mediated tumor killing sensitivity genes in AML.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData download and pre-processing\u003c/h2\u003e \u003cp\u003eThe TCGA-LAML dataset was obtained from XENA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the GTEx dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/?cohort=GTEX\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/?cohort=GTEX\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was downloaded as control. The two datasets were de-batched and combined into the training set data. GSE71014 and GSE37642 datasets were downloaded from GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and utilized as the test dataset. The sample information is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the clinical characteristics and subtypes of the TCGA-LAML data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDatasets and sample information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCGA-LAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(GTEx)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE71014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE37642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics and subtypes of the TCGA-LAML samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh(N\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow(N\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall(N\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76(58.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55(42.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71 (54.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory_of_neoadjuvant_treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100 (76.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubtype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtype.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, 641 T cell-mediated tumor killing sensitivity-related genes (GSTTK) were downloaded from the TISIDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), of which 588 genes were present in the training set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes among the 588 GSTTK in the training set data were identified using the Wilcox. Test function in R (version 3.6.2), p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC| \u0026gt;1 as the screening threshold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analysis\u003c/h2\u003e \u003cp\u003eGO and KEGG enrichment analysis of the differentially expressed GSTKK was performed using the clusterProfiler package in R (version 3.6.2), with parameters p adjustment method = 'BH', p value cutoff\u0026thinsp;=\u0026thinsp;0.05, q value cutoff\u0026thinsp;=\u0026thinsp;0.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGene-Gene Interaction Network\u003c/h2\u003e \u003cp\u003eThe STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/cgi/input?sessionId=bH2OGdjjRDkc\u0026amp;input_page_show_search=on\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/cgi/input?sessionId=bH2OGdjjRDkc\u0026amp;input_page_show_search=on\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used protein-protein interaction (PPI) pair-based network construction using the differentially expressed GSTKK. The screening criteria included a minimum required interaction score\u0026thinsp;=\u0026thinsp;0.7, with the removal of non-connected genes. The network was mapped using Cytoscape (v3.8.0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a T cell-mediated tumor killing sensitivity gene-based prognostic score index: TTKPI\u003c/h2\u003e \u003cp\u003eThe \u0026lsquo;coxph\u0026rsquo; function in the \u0026lsquo;surv\u0026rsquo; R package was used to perform one-way COX regression analysis to predict survival outcome, with the 401 differentially expressed genes as predictor features, screened at a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. 57 genes with significant prognostic effects were obtained. Next, the \u0026lsquo;glmnet\u0026rsquo; function in the \u0026lsquo;glmnet\u0026rsquo; R package was used to perform LASSO regression analysis for the significant genes obtained from the step mentioned above, with parameters set at alpha\u0026thinsp;=\u0026thinsp;1, nlamba\u0026thinsp;=\u0026thinsp;100, and a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The resulting genes selected by LASSO were used to construct a risk score for tumor killing (TTKPI). The scores were computed using the equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\text{R}\\text{i}\\text{s}\\text{k} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e}}_{\\text{i}} = \\sum _{\\text{j}=1}^{\\text{n}}{\\text{C}}_{\\text{j}}\\text{*}{\\text{e}\\text{x}\\text{p}}_{\\text{i}\\text{j}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis equation calculates the risk score value of the ith sample, where C_j is the regression coefficient of the jth prognostic factor in the model, and exp_ij denotes the expression of the jth prognostic factor for the ith sample. The risk score model for predicting sample survival was established by weighting the expression of the significant genes with LASSO regression coefficients (exp represents gene expression level, C represents lasso regression coefficient).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eSurvival analysis with TTKPI scores\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eSamples were grouped into high and low TTKPI risk score groups based on the median level. Survival curves were generated using the Kaplan-Meier method to predict overall survival. Univariate and multifactorial Cox proportional hazard analyses were performed to determine the prognostic value of the risk scores in combination with other clinical characteristics. Receiver operating curves (ROCs) were used to estimate the predictive performance of the TTKPI risk model for 1-, 3-, and 5-year survival, correlation analyses were performed using the \u0026lsquo;cor.test\u0026rsquo; function, and Fisher\u0026rsquo;s exact tests were performed, where a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered a statistically significant difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of TTKPI scores with clinical phenotype in AML\u003c/h2\u003e \u003cp\u003eWe further analysed the TCGA dataset by extracting the clinical characteristics of the samples including age, gender, adjuvant treatment status, and cancer staging. The TTKPI scores were compared between clinical subgroups. Univariate and multivariate Cox regression analyses (at a threshold of the p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were performed to test the predictive performance of TTKPI scores and clinical characteristics of the TCGA dataset. The results were plotted as forest plots. The TTKPI risk grouping was combined with the clinical characteristics that were significant in the univariate and multivariate Cox proportional hazards analysis, and the nomogram function in the R package \u0026lsquo;rms\u0026rsquo; was applied to construct a column line plot for nomogram analysis. Calibration curves were plotted using the calibrate function and decision curve analysis (DCA) was performed by plotting decision curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune cell infiltration analysis and immune checkpoint expression in TTKPI groups\u003c/h2\u003e \u003cp\u003eTumor infiltration analysis was based on the gene expression data of TCGA, and the proportion of tumor infiltrating immune cells (22 immune cells) in a sample was determined using CIBERSORT in R. The analysis was performed using the default parameters infiltration scores for 22 immune cells were obtained. The differences in immune checkpoint expression levels between high and low TTKPI groups were analysed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular docking association with TTKPI to predict potential therapeutic agents.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCorresponding compound structures were downloaded from the DrugBank database and screened according to Lipinski's rule (hydrogen bond acceptor\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;10, hydrogen bond donor\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;5, rotatable bond\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;10, logarithmic value of lipid-water partition coefficient\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;5, molecular weight of 180\u0026ndash;480, polar surface area\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;140). The spatial structure information of key gene-encoded proteins was searched in the PDB database and the corresponding PDB files were downloaded. The approximate docking box range was determined based on the ligand information therein, and other relevant parameters of autodock-vina were set and used to dock small molecule compounds. Interaction force analysis was performed using Pymol and Ligplus.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAberrant expression and function of GSTTKs in AML\u003c/h2\u003e \u003cp\u003eDifferential analysis of 588 GSTTKs in the test set data by rank sum test (screening criteria, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC| \u0026gt;=1) yielded 401 DEG, where 171 were up-regulated and 230 were down-regulated. A volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-A), differential gene expression heat map (screening the top 20 genes according to |log2FC| (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B) and a PCA analysis plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-C) illustrating the GSTTKs that distinguish AML samples from controls (the control samples were de-batched GTEx samples) are presented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relationship between the 401 differential GSTKK and clinical characteristics (gender, history of adjuvant therapy or not) was analysed separately, and six genes were selected for visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A, B).\u003c/p\u003e \u003cp\u003eThe results of GO and KEGG functional enrichment analyses were based on protein-related pathways, immune-related pathways including antigen-antibody-related, T-cell-related, and leukocyte-related pathways, including antigen processing and presentation of exogenous peptide. The results of GO enrichment analysis included more than 20 immune-related pathways including antigen processing and presentation of exogenous peptide via MHC class I, T cell extravasation, interleukin-1-mediated signalling pathway. The results of KEGG enrichment analysis included the Antigen processing and presentation pathway. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C, D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe gene interaction network showed genes with high connectivity, included RPS19, RPL3, RPL23A, and the log2FC values were negative (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of TTKPI\u003c/h2\u003e \u003cp\u003eA one-way cox regression analysis of TCGA dataset with the 401 differential GSTTK was performed and 57 genes with significant prognostic value were further screened using LASSO regression. 24 key prognostic genes were determined using LASSO including GSTKK; DSCR3, MPG, OTOA, TGIF2LX, CBLL1, KLF2, C6orf1, ENPP2, COL23A1, NELFE, MLYCD, AVPR2, KANSL1L, HNRNPAB, TPST2, FADD, SCD, FAM53B, CRTC1, LBR, RGP1, HSD17B13, PSORS1C1, SPAG1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A,B,C), and Kaplan-Meier survival curves were plotted for these 24 genes (see Appendix for details), and the survival curves for six of these are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe risk score of each tumor tissue sample was calculated based on the TTKPI risk score model, and the samples were divided into high and low risk score groups based on the median score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e- A, B). Survival curve analysis for the training set data showed that the prognosis of the samples in the low-risk score group was better (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-C), and ROC analysis showed that the AUC of the samples reached 0.89, 0.9, and 0.96 at 1, 3, and 5 years, respectively, indicating that the model predicted well (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-D). In the independent dataset GSE71014, the prognosis was similarly significantly better in the low-risk score group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-C). ROC analysis showed high AUC values at 1, 3, and 5 years was 0.88, 0.85, and 0.82 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-D), respectively, indicating a good prognostic accuracy of the risk score model. Using the independent test dataset GSE37642, similar results showing significantly better prognosis of the samples in the low-risk score group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-C), and the AUC values at 1, 3, and 5 years were 0.78, 0.75, and 0.76 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e-D), respectively, validating consistently high prognostic performance of the TTKPI risk scoring model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTTKPI scores were associated with clinical phenotype.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe TTKPI risk score of samples with age\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;60 years was significantly higher than that of samples with age\u0026thinsp;\u0026lt;\u0026thinsp;60 years, and the risk score of samples with AML staging type AML.1 was significantly higher than that of other samples, while the risk score of samples with cancer staging type AML.3 was significantly lower. These indicated that the TTKPI risk scoring model was significantly associated phenotype categories of age and AML cancer stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of univariate and multivariate Cox regression analysis are depicted in forest plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The TTPKI risk score showed significantly elevated hazard ratio in both analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe result of the nomogram analysis is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e-A and the calibration curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e-B. The two curves showed minimal deviation from the ideal prediction line, indicating that the combined model (column line plot) had high predictive performance for the 1-year and 3-year periods, and the two curves in the DCA decision curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e-C D) indicated that the column line plot constructed by the combined model showed better prognostic performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTTKPI scores were associated with tumor immune cell infiltration.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCIBERSORT was used to analyse immune cell infiltration and determine differences between high and low TTKPI groups. The results showed that 4 types of infiltrating immune cells, \u0026ldquo;Mast cells resting\", \"Monocytes\", \"T cells CD4 naive\" and \"T cells follicular helper\", were significantly different between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The correlation between TTKPI scores, its constituent genes, and TME cells was calculated and \u0026ldquo;Dendritic cells activated\u0026rdquo;, \u0026ldquo;Mast cells resting\u0026rdquo;, \u0026ldquo;T cells CD4 na\u0026iuml;ve\u0026rdquo;, \u0026ldquo;T cells follicular helper\u0026rdquo; showed negative correlation with TTKPI, and T cells follicular helper showed negative correlation with TTKPI. \u0026ldquo;Macrophages M2\u0026rdquo;, \u0026ldquo;Monocytes\u0026rdquo; and other immune infiltrating cells showed positive correlation with TTKPI. (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTTKPI score-based prediction of immune checkpoint expression and immunotherapy responses\u003c/h2\u003e \u003cp\u003eThe differences in immune checkpoints between high and low TTKPI groups were analysed including C10orf54, CD160, CD200, CD276, CD47, CD86, CD96, KIR3DL1, LAG3, LGALS9, PDCD1, SELPLG, SIGLEC7, TMIGD2, TNFRSF8. The expression levels of CD160, CD200, CD47, CD96, TMIGD2 in the low-risk group were significantly higher than those in the high-risk group; the expression levels of C10orf54, CD276, CD86, KIR3DL1, LAG3, LGALS9, PDCD1, SELPLG, SIGLEC7, TNFRSF8 expression levels were significantly lower than those of the high-risk group. (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular docking association TTKPI predicts potential therapeutic agents.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eScreening of the corresponding compound structures from the DrugBank database according to Lipinski's rule yielded 5462 small molecule compounds. AVPR2, ENPP2, SCD and TPST2 were found to have corresponding spatial information structure, and the corresponding PDB files were downloaded: 7DW9, 5MHP, 4ZYO and 3AP1. DB11791, DB12886 and DB14773 were found to have good docking scores with the four proteins with Affinity \u0026lt;-9.6. The complete docking score table is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSmall molecule docking score with 4 proteins.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugBank_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAffinity\u003c/p\u003e \u003cp\u003e (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDB11791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCapmatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVPR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENPP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPST2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDB12886,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGSK-1521498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVPR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENPP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPST2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDB14773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLifirafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVPR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENPP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPST2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe docking conformation and interaction force analysis for AVPR2, ENPP2, SCD, and TPST2 with DB11791 (Fig.\u0026nbsp;14-top), DB12886 (Fig.\u0026nbsp;14-centre), DB14773 (Fig.\u0026nbsp;14-bottom) are represented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study constructed and validated a T cell-mediated tumor killing sensitivity gene-based prognostic score, TTKPI, composed of 24 differentially expressed genes in AML. The TTKPI score showed good-excellent prognostic value for survival in the training and test datasets, with AUC values ranging from 75%-96%. Higher TTKPI scores were associated with older age and cancer stage and the combined model showed better prognostic function. Distinct AML tumor immunological profiles clustering with age, T-cell receptor clonality and survival outcomes have been shown earlier (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).The widely used ELN17 prognostic tool showed inadequate performance to predict long-term survival patients older than 60 years, despite accounting for mutational burden (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Older patients with AML show different memory T cell subpopulations that young populations, owing to T cell senescence and terminal differentiation, particularly in case of CD8 + T cells (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Together, these evidences support the application of combined prognostic tools integrating tumor immunological scores such as TTKPI with clinical parameters for improved prognostic performance.\u003c/p\u003e \u003cp\u003eWe mapped the individual survival probability curves of 6 of the 24 component GSTTK genes which were included in the TTKPI score system. Higher expression levels of C6orf1 (SMIM29), FAM53B, HNRNPAB predicted lower overall survival. C6orf1 is a prognostic gene for clear cell renal carcinoma (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The FAM53B gene, implicated in Wnt signaling, is deregulated in T cell dysfunction, and found to truncated in the bone marrow tissue from acute lymphoblastic leukemia (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). HNRNPAB is an RNA binding protein documented as a prognostic biomarker in several solid tumors (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and is considered as an important biomarker and therapeutic target (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). We also showed that the levels of lower levels of HSD17B13, KANSL 1L, and LBR predicted worse survival rates. Lower HSD17B13 expression, a protein primarily implicated in lipid metabolism in the liver, and also expressed in the bone marrow, similarly predicted worse survival rates in hepatocellular carcinoma (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).KANSL 1L encodes for a chromatin modifying protein and truncated mutations in this gene in AML are documented (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), with clonal mutations noted during relapse (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA high TTKPI score predicted worse survival with good accuracy. T cell mediated tumor killing sensitivity genes have shown strong prognostic value in several solid tumor types including lung adenocarcinoma, head and neck cancer and hepatocellular carcinoma (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). AML is highly heterogenous at the molecular level with low mutational burden, which is believed to underpin modest responses to antibody dependent checkpoint inhibitor therapy as compared to solid tumors and receptor independent T-cell directed therapies assumes high significance (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Identifying GSTTK based tumor immunological subtypes can also be very valuable for predicting responses to checkpoint inhibitors and emerging immunotherapies such as CAR-T cell and bispecific antibody therapy. Our data showed different patterns of checkpoint expression in the high and low TTKPI groups. The low TTKPI group showed higher levels of CD160, CD200, CD47, CD96, TMIGD2, and lower levels of C10orf54, CD276, CD86, KIR3DL1, LAG3, LGALS9, PDCD1, SELPLG, SIGLEC7, TNFRSF8. Classical immune checkpoint inhibitor drugs targeting PD-1, PD-L1 and CTLA4 show variable efficacy and additional checkpoint inhibitor drugs such as drugs targeting LAG3 are under current focus (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Our data suggest that the TTKPI score pattern could potentially direct the selection of optimal checkpoint inhibitor drugs and combination. The TTKPI score component GSTTKs were enriched in immune-related pathways including antigen processing and presentation, T cell extravasation, interleukin-1-mediated signalling pathway; key pathways of tumor immune function, which maps the TTKPI score to functional variances in the tumor microenvironment. TTKPI negatively correlated with CD4 naïve and follicular helper T cell immune infiltration scores while M2 macrophages, monocytes were positively correlated, implying a T cell exhausted, comparatively pro-inflammatory, innate immune dominated milieu in high TTKPI states, which corresponded to low survival. T cell exhaustion and concurrent PD-1 resistance is a characteristic of severe AML (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). An AML tumor immune landscape marked by high M2 macrophage and monocyte infiltration corresponds to high inflammatory response and predicts poor survival (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), in alignment with our results.\u003c/p\u003e \u003cp\u003eOur findings may have implications for immunotherapy selection based on TTKPI profiles. PD-1 immunotherapy may be applied as adjunct to HMA or in case of relapse after allogeneic hematopoietic stem cell transplantation (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) but considering its variable performance, biomarker/ transcriptome driven immunotherapy design and novel target discovery is under focus (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Therefore, we sought to identify novel potential targets using molecular docking to seek relevant compounds from the DrugBank database. Our results identified three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) as potential agents targeting T-cell mediated tumor killing mechanisms in AML. Capmatinib is an inhibitor of cellular-mesenchymal-epithelial transition factor (c-Met), which belongs to the MET family. Early preclinical and clinical evidence shows that MET inhibition may be a valuable target for refractory and relapsed AML(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) GSK-1521398 is a mu-opioid receptor antagonist primarily trialled in addiction and obesity (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), however mu-opioid receptors are understood to play significant roles in cancer immunoregulation, and positive effects of other mu-opioid antagonist drugs have been noted (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Lifirafenib is a dual inhibitor of BRAF-kinase and EGFR that has shown positive effects in several solid tumors in preliminary investigations (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). RAF pathway activation and BRAF mutation is recognised in a proportion of AML cases, and RAF inhibition has shown positive anti-cancer effects (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Collectively, these findings support the potential utility of these novel small molecule agents in AML.\u003c/p\u003e \u003cp\u003eThe strengths of the present study include a dual step selection for identification of a GSTTK genes for prognostic score construction and validation in an independent cohort. In addition, tumor immune cell infiltration analysis, checkpoint expression patterns and molecular docking were leveraged to shed light on functional and therapeutic implications of the constructed TTKPI score. The limitations of the present study include a small number of validation datasets and the lack of experimental studies to validate the GSTTK signature and its functional aspects identified in the in-silico study. Overall, our findings demonstrated the prognostic value of the TTKPI score and identified functional pathways, molecular targets, and potential agents that may be harnessed for tumor killing T-cell dependent therapies in AML.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study identified a 24 T-cell mediated tumor killing sensitivity gene signature of AML and constructed a comprehensive risk score TTKPI, which showed good-excellent prognostic value for overall survival in AML and was significantly associated with clinical phenotype features. Immune cell infiltration analysis showed that the TTKPI score corresponded to the tumor microenvironment and immune checkpoint expression patterns, and using drug docking, we identified 3 small molecule drugs with high potential for clinical translation. The utility of TTKPI score for prognosis and targeted biomarker-based immunotherapy should be tested in clinical studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two datasets were de-batched and combined into the training set data. GSE71014 and GSE37642 datasets were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/). All data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception or design of the work: Yijian Chen and Dechang Xu. Acquisition, analysis, or interpretation of data for the work: Yiyun Pan, Fangfang Xie, Wen Zeng, Hailong Chen. Drafting of the work or revising it critically for important intellectual content: Yiyun Pan, Zhengcong Chen,. All authors approved the final version of the manuscript. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding program: Science and Technology Program of Jiangxi Provincial Administration of Traditional Chinese Medicine (2022B967, 2021B363); Science and Technology Plan Project of Jiangxi Provincial Health Care Commission (202212543).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLubeck DP, Danese M, Jennifer D, Miller K, Richhariya A, Garfin PM. Systematic literature review of the global incidence and prevalence of myelodysplastic syndrome and acute myeloid leukemia. Blood. 2016 Dec 2;128(22):5930.\u003c/li\u003e\n\u003cli\u003eHughes M. 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Cancers (Basel). 2020 Nov 25;12(12):3511. doi: 10.3390/cancers12123511.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Appendix","content":"\u003cp\u003eAppendix is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute Myeloid Leukaemia, cancer immunotherapy, prognostic score, T cell","lastPublishedDoi":"10.21203/rs.3.rs-3854251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3854251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objective: \u003c/strong\u003eAcute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003ePublicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan-Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFrom\u003cstrong\u003e \u003c/strong\u003e401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75%-96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches.\u003c/p\u003e","manuscriptTitle":"T Cell-Mediated Tumor Killing Sensitivity Gene Signature-Based Prognostic Score For Acute Myeloid Leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 06:13:11","doi":"10.21203/rs.3.rs-3854251/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-12T12:58:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-04T12:07:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92473a17-ae51-41ba-ad5e-f3be25071682","date":"2024-02-01T12:55:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"353241d4-d69f-412f-884e-71a7a0bcd63c","date":"2024-01-24T10:25:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-24T07:08:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-22T17:27:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-22T17:26:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-01-11T17:13:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e895ad01-f093-4574-8707-1a9cf0452fe8","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-03-29T08:12:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 06:13:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3854251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3854251","identity":"rs-3854251","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}