A 4-gene leukemic stem cell score can independently predict the prognosis of myelodysplastic syndrome patients

CPXM1 correlates to poor prognosis and immune cell infiltration in gastric cancer

Background

Gastric cancer (GC) is the fourth most common cause of cancer-related death and the fifth most frequent malignant cancer, especially advanced GC. Carboxypeptidase X member 1 (CPXM1) is an epigenetic factor involved in many physiological processes, including osteoclast differentiation and adipogenesis. Several studies have shown the association of CPXM1 with multiple tumors; however, the mechanism of CPXM1 involvement in the progression of GC is yet to be characterized.

Method

CPXM1 expression data were obtained from the Tumor Immune Estimation Resource. The Cancer Genome Atlas and the Gene Expression Omnibus databases were used to obtain patient-matched clinicopathological information, and the Kaplan-Meier plot database was utilized for the prognosis analysis of GC patients. The Catalog of Somatic Mutations in Cancer and cBioportal databases were adopted to study CPXM1 mutations in tumors. Next, we utilized the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis for mechanism research. Furthermore, we performed tumor microenvironment and immune infiltration analysis based on CPXM1. Finally, we predicted sensitivity to several targeted drugs in GC patients based on CPXM1.CPXM1 is upregulated in GC and is correlated with poor prognosis, gender, and tumor stage in GC patients. Gene enrichment analysis suggested that CPXM1 may regulate the occurrence and progression of GC via the PI3K-AKT and TGF-β pathway. Moreover, CPXM1 expression results in an increase in the proportion of immune and stromal cells. Additionally, the proportion of plasma cells was inversely related to the expression of CPXM1, whereas macrophage M2 expression was proportionate to CPXM1 expression. Finally, six small-molecule drugs that showed notable variations in IC50 between two groups were screened.

Conclusion

These results suggested that CPXM1 regulates the progression of GC and may represent a novel target for the detection and treatment of GC.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

A three-gene leukaemic stem cell signature score is robustly prognostic in chronic myelomonocytic leukaemia

Leukaemic stem cell (LSC) gene expression has recently been linked to prognosis in patients with acute myeloid leukaemia (17-gene LSC score, LSC-17) and myelodysplastic syndromes. Although chronic myelomonocytic leukaemia (CMML) is regarded as a stem cell disorder, the clinical and biological impact of LSCs on CMML patients remains elusive. Making use of multiple independent validation cohorts, we here describe a concise three-gene expression signature (LSC-3, derived from the LSC-17 score) as an independent and robust prognostic factor for leukaemia-free and overall survival in CMML. We propose that LSC-3 could be used to supplement existing risk stratification systems, to improve prognostic performance and guide management decisions.

Novel gene signature reveals prognostic model in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a type of hematological malignancy and has a poor prognosis. In our study, we aimed to construct a prognostic model of ALL by identifying important genes closely related to ALL prognosis. We obtained transcriptome data (RNA-seq) of ALL samples from the GDC TARGET database and identified differentially expressed genes (DEGs) using the “DESeq” package of R software. We used univariate and multivariate cox regression analyses to screen out the prognostic genes of ALL. In our results, the risk score can be used as an independent prognostic factor to predict the prognosis of ALL patients [hazard ratio (HR) = 2.782, 95% CI = 1.903–4.068, p < 0.001]. Risk score in clinical parameters has high diagnostic sensitivity and specificity for predicting overall survival of ALL patients, and the area under curve (AUC) is 0.864 in the receiver operating characteristic (ROC) analysis results. Our study evaluated a potential prognostic signature with six genes and constructed a risk model significantly related to the prognosis of ALL patients. The results of this study can help clinicians to adjust the treatment plan and distinguish patients with good and poor prognosis for targeted treatment.

Higher RUNX1 expression levels are associated with worse overall and leukaemia-free survival in myelodysplastic syndrome patients

RUNX1 mutations are frequently detected in various myeloid neoplasms and implicate unfavourable clinical outcomes in patients with myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML). On the other hand, high expression of RUNX1 is also correlated with poor prognosis in AML patients. However, the clinical relevancy of RUNX1 expression in MDS patients remains elusive. This study aimed to investigate the prognostic and biologic impacts of RUNX1 expression in MDS patients. We recruited 341 MDS patients who had sufficient bone marrow samples for next-generation sequencing. Higher RUNX1 expression occurred more frequently in the patients with Revised International Prognostic Scoring System (IPSS-R) higher-risk MDS than the lower-risk group. It was closely associated with poor-risk cytogenetics and mutations in ASXL1, NPM1, RUNX1, SRSF2, STAG2, TET2 and TP53. Furthermore, patients with higher RUNX1 expression had significantly shorter leukaemia-free survival (LFS) and overall survival (OS) than those with lower expression. Subgroups analysis revealed that higher-RUNX1 group consistently had shorter LFS and OS than the lower-RUNX1 group, no matter RUNX1 was mutated or not. The same findings were observed in IPSS-R subgroups. In multivariable analysis, higher RUNX1 expression appeared as an independent adverse risk factor for survival. The prognostic significance of RUNX1 expression was validated in two external public cohorts, GSE 114922 and GSE15061. In summary, we present the characteristics and prognosis of MDS patients with various RUNX1 expressions and propose that RUNX1 expression complement RUNX1 mutation in MDS prognostication, wherein patients with wild RUNX1 but high expression may need more proactive treatment.

Integrative analysis of drug response and clinical outcome in acute myeloid leukemia

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

The 17‐gene stemness score associates with relapse risk and long‐term outcomes following allogeneic haematopoietic cell transplantation in acute myeloid leukaemia

A 17‐gene stemness (LSC17) score determines risk in acute myeloid leukaemia patients treated with standard chemotherapy regimens. The present study further analysed the impact of the LSC17 score at diagnosis on outcomes following allogeneic haematopoietic cell transplantation (HCT). Out of 452 patients with available LSC17 score, 123 patients received allogeneic HCT. Transplant outcomes, including overall (OS), leukaemia‐free survival (LFS), relapse incidence (RI) and non‐relapse mortality (NRM), were compared according to the LSC17 scored group. The patients with a low LSC17 score had higher OS (56.2%) and LFS (54.4%) at 2 years compared to patients with high LSC17 score (47.2%, p = 0.0237 for OS and 46.0%, p = 0.0181 for LFS). The low LSC17 score group also had a lower relapse rate at 2 years (12.7%) compared to 25.3% in the high LSC17 score group (p = 0.017), but no difference in NRM (p = 0.674). Worse outcomes in the high LSC17 score group for OS, LFS and relapse were consistently observed across all stratified sub‐groups. The use of more intensive conditioning did not improve outcomes for either group. In contrast, chronic graft‐versus‐host‐disease was associated with more favourable outcomes in both groups. The 17‐gene stemness score is highly prognostic for survival and relapse risk following allogeneic HCT.

Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.

A CIBERSORTx-based immune cell scoring system could independently predict the prognosis of patients with myelodysplastic syndromes

Aside from cell intrinsic factors such as genetic alterations, immune dysregulation in the bone marrow (BM) microenvironment plays a role in the development and progression of myelodysplastic syndromes (MDS). However, the prognostic implications of various immune cells in patients with MDS remain unclear. We adopted CIBERSORTx to estimate the relative fractions of 22 subtypes of immune cells in the BM of 316 patients with MDS and correlated the results with clinical outcomes. A lower fraction of unpolarized M0 macrophages and higher fractions of M2 macrophages and eosinophils were significantly associated with inferior survival. An immune cell scoring system (ICSS) was constructed based on the proportion of these 3 immune cells in the BM. The ICSS high-risk patients had higher BM blast counts, higher frequencies of poor-risk cytogenetics, and more NPM1, TP53, and WT1 mutations than intermediate- and low-risk patients. The ICSS could stratify patients with MDS into 3 risk groups with distinct leukemia-free survival and overall survival among the total cohort and in the subgroups of patients with lower and higher disease risk based on the revised International Prognostic Scoring System (IPSS-R). The prognostic significance of ICSS was also validated in another independent cohort. Multivariable analysis revealed that ICSS independently predicted prognosis, regardless of age, IPSS-R, and mutation status. Bioinformatic analysis demonstrated a significant correlation between high-risk ICSS and nuclear factor κB signaling, oxidative stress, and leukemic stem cell signature pathways. Further studies investigating the mechanistic insight into the crosstalk between stem cells and immune cells are warranted.

Myelodysplastic syndromes: Biological and therapeutic consequences of the evolving molecular aberrations landscape

Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders with heterogeneous presentation, ranging from indolent disease courses to aggressive diseases similar to acute myeloid leukemia (AML). Approximately 90% of MDS patients harbor recurrent mutations , which – with the exception of mutated SF3B1 –have not (yet) been included into the diagnostic criteria or risk stratification for MDS. Accumulating evidence suggests their utility for diagnostic workup, treatment indication and prognosis. Subsequently, in patients with unexplained cytopenia or dysplasia identification of these mutations may lead to earlier diagnosis. The acquisition and expansion of additional driver mutations usually antecedes further disease progression to higher risk MDS or secondary AML and thus, can be clinically helpful to detect individuals that may benefit from aggressive treatment approaches. Here, we review our current understanding of somatic gene mutations, gene expression patterns and flow cytometry regarding their relevance for disease evolution from pre-neoplastic states to MDS and potentially AML.

Phagocytosis checkpoints on hematopoietic stem cells in patients with myelodysplastic syndromes

BACKGROUND

The myelodysplastic syndrome (MDS) is a high-risk hemocytopenia easily converted to acute myeloid leukemia. CD47 plays an important role in regulating phagocytosis, and its role in the pathogenesis of MDS is unclear.

METHODS

CD47 and PI3K/AKT/mTOR on CD34⁺ CD38^- cells were detected by flow cytometry. NF-κB, PI3K, AKT, PTEN, and mTOR mRNA overexpressed in CD34⁺ CD38^- CD47⁺ cells were performed by real-time quantitative transcriptase-polymerase chain reaction. Phagocytic capacity of macrophages was measured with carboxyfluorescein succinimidyl ester and fluorescent microspheres. Sorted CD34⁺ CD38^- CD47⁺ cells were injected into NOD-Prkdc^scid Il2rg^null mice.

RESULTS

The expression of CD47 on CD34⁺ CD38^- cells of the patients in high-risk MDS based on IPSS-R/WPSS score was higher than that in low-risk MDS and controls. The signaling pathway of PI3K/AKT/mTOR is active in CD34⁺ CD38^- CD47⁺ cells of MDS patients. CD47 overexpressing CD34⁺ CD38^- cells has antiphagocytosis. CD47 overexpressing leukemia stem cell (LSC) -transplanted mice has a short survival time. The macrophages originated from MDS might elicit a pro-tumor response in MDS by inhibiting phagocytosis.

CONCLUSIONS

Phagocytosis checkpoints are impaired in MDS. High expression of CD47 on CD34+CD38^- cells indicates poor clinical prognosis in MDS.

Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling

Acute Myeloid Leukemia (AML) is a heterogeneous neoplasm characterized by cytogenetic and molecular alterations that drive patient prognosis. Currently established risk stratification guidelines show a moderate predictive accuracy, and newer tools that integrate multiple molecular variables have proven to provide better results. In this report, we aimed to create a new machine learning model of AML survival using gene expression data. We used gene expression data from two publicly available cohorts in order to create and validate a random forest predictor of survival, which we named ST-123. The most important variables in the model were age and the expression of KDM5B and LAPTM4B, two genes previously associated with the biology and prognostication of myeloid neoplasms. This classifier achieved high concordance indexes in the training and validation sets (0.7228 and 0.6988, respectively), and predictions were particularly accurate in patients at the highest risk of death. Additionally, ST-123 provided significant prognostic improvements in patients with high-risk mutations. Our results indicate that survival of patients with AML can be predicted to a great extent by applying machine learning tools to transcriptomic data, and that such predictions are particularly precise among patients with high-risk mutations.

Studying leukemia stem cell properties and vulnerabilities with human iPSCs

The reprogramming of cancer cells into induced pluripotent stem cells (iPSCs) can capture entire cancer genomes, and thus create genetically faithful models of human cancers. By providing stringent genetically clonal conditions, iPSC modeling can also unveil non-genetic sources of cancer heterogeneity and provide a unique opportunity to study them separately from genetic sources, as we recently showed in an iPSC-based model of acute myeloid leukemia (AML). Genetically clonal iPSCs, derived from a patient with AML, reproduce, upon hematopoietic differentiation, phenotypic and functional heterogeneity with all the hallmarks of a leukemia stem cell (LSC) hierarchy. Here we discuss the lessons that can be learned about the LSC state, its plasticity, stability and genetic and epigenetic determinants from iPSC modeling. We also discuss the practical and translational implications of exploiting AML-iPSCs to prospectively isolate large numbers of iLSCs for large-scale experiments, such as screens, and for discovery of new therapeutic targets specific to AML LSCs.

The expression levels of long non-coding RNA KIAA0125 are associated with distinct clinical and biological features in myelodysplastic syndromes

Long non-coding RNAs (lncRNAs) have important functions in cancer biology. Among them, lncRNA KIAA0125 is one of the genes proposed to play a critical role in leukaemia stem cell (LSC). In this study, we aimed to investigate the clinical relevance of the expression levels of lncRNA KIAA0125 in myelodysplastic syndromes (MDS), a disease with highly heterogeneous clinical and biological features. Using RNA arrays, we measured the expression of KIAA0125 in 176 primary MDS patients. We found that higher KIAA0125 expression was associated with higher risk MDS, based on the revised International Prognostic Scoring System (IPSS-R), mutations in ASXL1 and NRAS, and predicted poorer overall survival (OS) and leukaemia-free survival (LFS). Multivariate analysis revealed that higher KIAA0125 expression was an independent, unfavourable prognostic factor for OS and LFS, irrespective of IPSS-R and mutation status. Further global gene expression profile analysis suggested a close association of higher KIAA0125 expressions with LSC signatures. The expression of KIAA0125 may be a potential biomarker to guide the treatment choice in MDS patients, especially those with lower risk subtypes, in whom palliative treatment is usually used.