Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes

Genomic characterization and immunotherapy for microsatellite instability-high in cholangiocarcinoma

BACKGROUND

Microsatellite instability-high (MSI-H) is a unique genomic status in many cancers. However, its role in the genomic features and immunotherapy in cholangiocarcinoma (CCA) is unclear. This study aimed to systematically investigate the genomic characterization and immunotherapy efficacy of MSI-H patients with CCA.

METHODS

We enrolled 887 patients with CCA in this study. Tumor samples were collected for next-generation sequencing. Differences in genomic alterations between the MSI-H and microsatellite stability (MSS) groups were analyzed. We also investigated the survival of PD-1 inhibitor-based immunotherapy between two groups of 139 patients with advanced CCA.

RESULTS

Differential genetic alterations between the MSI-H and MSS groups included mutations in ARID1A, ACVR2A, TGFBR2, KMT2D, RNF43, and PBRM1 which were enriched in MSI-H groups. Patients with an MSI-H status have a significantly higher tumor mutation burden (TMB) (median 41.7 vs. 3.1 muts/Mb, P < 0.001) and more positive programmed death ligand 1 (PD-L1) expression (37.5% vs. 11.9%, P < 0.001) than those with an MSS status. Among patients receiving PD-1 inhibitor-based therapy, those with MSI-H had a longer median overall survival (OS, hazard ratio (HR) = 0.17, P = 0.001) and progression-free survival (PFS, HR = 0.14, P < 0.001) than patients with MSS. Integrating MSI-H and PD-L1 expression status (combined positive score ≥ 5) could distinguish the efficacy of immunotherapy.

CONCLUSIONS

MSI-H status was associated with a higher TMB value and more positive PD-L1 expression in CCA tumors. Moreover, in patients with advanced CCA who received PD-1 inhibitor-based immunotherapy, MSI-H and positive PD-L1 expression were associated with improved both OS and PFS.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov on 07/01/2017 (NCT03892577).

Incorporating graph information in Bayesian factor analysis with robust and adaptive shrinkage priors

There has been an increasing interest in decomposing high-dimensional multi-omics data into a product of low-rank and sparse matrices for the purpose of dimension reduction and feature engineering. Bayesian factor models achieve such low-dimensional representation of the original data through different sparsity-inducing priors. However, few of these models can efficiently incorporate the information encoded by the biological graphs, which has been already proven to be useful in many analysis tasks. In this work, we propose a Bayesian factor model with novel hierarchical priors, which incorporate the biological graph knowledge as a tool of identifying a group of genes functioning collaboratively. The proposed model therefore enables sparsity within networks by allowing each factor loading to be shrunk adaptively and by considering additional layers to relate individual shrinkage parameters to the underlying graph information, both of which yield a more accurate structure recovery of factor loadings. Further, this new priors overcome the phase transition phenomenon, in contrast to existing graph-incorporated approaches, so that it is robust to noisy edges that are inconsistent with the actual sparsity structure of the factor loadings. Finally, our model can handle both continuous and discrete data types. The proposed method is shown to outperform several existing factor analysis methods through simulation experiments and real data analyses.

The trajectory of prognostication and risk stratification for patients with myelodysplastic syndromes

ABSTRACT

Risk stratification and prognostication are crucial for the appropriate management of patients with myelodysplastic syndromes (MDSs) or myelodysplastic neoplasms, for whom the expected survival can vary from a few months to >10 years. For the past 5 decades, patients with MDS have been classified into higher-risk vs lower-risk disease phenotypes using sequentially developed clinical prognostic scoring systems. Factors such as morphologic dysplasia, clinical hematologic parameters, cytogenetics, and, more recently, mutational information have been captured in prognostic scoring systems that refine risk stratification and guide therapeutic management in patients with MDS. This review describes the progressive evolution and improvement of these systems which has led to the current Molecular International Prognostic Scoring System.

Assessing the clinical utility of multi-omics data for predicting serous ovarian cancer prognosis

Ovarian cancer (OC) is characterised by heterogeneity that complicates the prediction of patient survival and treatment outcomes. Here, we conducted analyses to predict the prognosis of patients from the Genomic Data Commons database and validated the predictions by fivefold cross-validation and by using an independent dataset in the International Cancer Genome Consortium database. We analysed the somatic DNA mutation, mRNA expression, DNA methylation, and microRNA expression data of 1203 samples from 599 serous ovarian cancer (SOC) patients. We found that principal component transformation (PCT) improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than the decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. Our study provides perspective on building reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC.Impact statementWhat is already known on this subject? Recent studies have focussed on predicting cancer outcomes based on omics data. But the limitation is the performance of single-platform genomic analyses or the small numbers of genomic analyses.What do the results of this study add? We analysed multi-omics data, found that principal component transformation (PCT) significantly improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than did decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes.What are the implications of these findings for clinical practice and/or further research? Our study provides perspective on how to build reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC for future studies.

Identification of a Complex Karyotype Signature with Clinical Implications in AML and MDS-EB Using Gene Expression Profiling

Complex karyotype (CK) is associated with a poor prognosis in both acute myeloid leukemia (AML) and myelodysplastic syndrome with excess blasts (MDS-EB). Transcriptomic analyses have improved our understanding of the disease and risk stratification of myeloid neoplasms; however, CK-specific gene expression signatures have been rarely investigated. In this study, we developed and validated a CK-specific gene expression signature. Differential gene expression analysis between the CK and non-CK groups using data from 348 patients with AML and MDS-EB from four cohorts revealed enrichment of the downregulated genes localized on chromosome 5q or 7q, suggesting that haploinsufficiency due to the deletion of these chromosomes possibly underlies CK pathogenesis. We built a robust transcriptional model for CK prediction using LASSO regression for gene subset selection and validated it using the leave-one-out cross-validation method for fitting the logistic regression model. We established a 10-gene CK signature (CKS) predictive of CK with high predictive accuracy (accuracy 94.22%; AUC 0.977). CKS was significantly associated with shorter overall survival in three independent cohorts, and was comparable to that of previously established risk stratification models for AML. Furthermore, we explored of therapeutic targets among the genes comprising CKS and identified the dysregulated expression of superoxide dismutase 1 (SOD1) gene, which is potentially amenable to SOD1 inhibitors.

Predicting leukemic transformation in myelodysplastic syndrome using a transcriptomic signature

Background: For prediction on leukemic transformation of MDS patients, emerging model based on transcriptomic datasets, exhibited superior predictive power to traditional prognostic systems. While these models were lack of external validation by independent cohorts, and the cell origin (CD34+ sorted cells) limited their feasibility in clinical practice. Methods: Transformation associated co-expressed gene cluster was derived based on GSE58831 ('WGCNA' package, R software). Accordingly, the least absolute shrinkage and selection operator algorithm was implemented to establish a scoring system (i.e., MDS15 score), using training set (GSE58831 originated from CD34+ cells) and testing set (GSE15061 originated from unsorted cells). Results: A total of 68 gene co-expression modules were derived, and the 'brown' module was recognized to be transformation-specific (R2 = 0.23, p = 0.005, enriched in transcription regulating pathways). After 50,000-times LASSO iteration, MDS15 score was established, including the 15-gene expression signature. The predictive power (AUC and Harrison's C index) of MDS15 model was superior to that of IPSS/WPSS in both training set (AUC/C index 0.749/0.777) and testing set (AUC/C index 0.933/0.86). Conclusion: By gene co-expression analysis, the crucial gene module was discovered, and a novel prognostic system (MDS15) was established, which was validated not only by another independent cohort, but by a different cell origin.

Interpretable deep learning for improving cancer patient survival based on personal transcriptomes

Precision medicine chooses the optimal drug for a patient by considering individual differences. With the tremendous amount of data accumulated for cancers, we develop an interpretable neural network to predict cancer patient survival based on drug prescriptions and personal transcriptomes (CancerIDP). The deep learning model achieves 96% classification accuracy in distinguishing short-lived from long-lived patients. The Pearson correlation between predicted and actual months-to-death values is as high as 0.937. About 27.4% of patients may survive longer with an alternative medicine chosen by our deep learning model. The median survival time of all patients can increase by 3.9 months. Our interpretable neural network model reveals the most discriminating pathways in the decision-making process, which will further facilitate mechanistic studies of drug development for cancers.

Ribosomal protein control of hematopoietic stem cell transformation through direct, non-canonical regulation of metabolism

We report here that expression of the ribosomal protein, RPL22, is frequently reduced in human myelodysplastic syndrome (MDS) and acute myelogenous leukemia (AML); reduced RPL22 expression is associated with worse outcomes. Mice null for Rpl22 display characteristics of an MDS-like syndrome and develop leukemia at an accelerated rate. Rpl22-deficient mice also display enhanced hematopoietic stem cell (HSC) self-renewal and obstructed differentiation potential, which arises not from reduced protein synthesis but from increased expression of the Rpl22 target, ALOX12, an upstream regulator of fatty acid oxidation (FAO). The increased FAO mediated by Rpl22-deficiency also persists in leukemia cells and promotes their survival. Altogether, these findings reveal that Rpl22 insufficiency enhances the leukemia potential of HSC via non-canonical de-repression of its target, ALOX12, which enhances FAO, a process that may serve as a therapeutic vulnerability of Rpl22 low MDS and AML leukemia cells.

Highlights

RPL22 insufficiency is observed in MDS/AML and is associated with reduced survivalRpl22-deficiency produces an MDS-like syndrome and facilitates leukemogenesisRpl22-deficiency does not impair global protein synthesis by HSCRpl22 controls leukemia cell survival by non-canonical regulation of lipid oxidation eTOC: Rpl22 controls the function and transformation potential of hematopoietic stem cells through effects on ALOX12 expression, a regulator of fatty acid oxidation.

Real-World Validation of Molecular International Prognostic Scoring System for Myelodysplastic Syndromes

PURPOSE

Myelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms in which a risk-adapted treatment strategy is needed. Recently, a new clinical-molecular prognostic model, the Molecular International Prognostic Scoring System (IPSS-M) was proposed to improve the prediction of clinical outcome of the currently available tool (Revised International Prognostic Scoring System [IPSS-R]). We aimed to provide an extensive validation of IPSS-M.

METHODS

A total of 2,876 patients with primary MDS from the GenoMed4All consortium were retrospectively analyzed.

RESULTS

IPSS-M improved prognostic discrimination across all clinical end points with respect to IPSS-R (concordance was 0.81 v 0.74 for overall survival and 0.89 v 0.76 for leukemia-free survival, respectively). This was true even in those patients without detectable gene mutations. Compared with the IPSS-R based stratification, the IPSS-M risk group changed in 46% of patients (23.6% and 22.4% of subjects were upstaged and downstaged, respectively).In patients treated with hematopoietic stem cell transplantation (HSCT), IPSS-M significantly improved the prediction of the risk of disease relapse and the probability of post-transplantation survival versus IPSS-R (concordance was 0.76 v 0.60 for overall survival and 0.89 v 0.70 for probability of relapse, respectively). In high-risk patients treated with hypomethylating agents (HMA), IPSS-M failed to stratify individual probability of response; response duration and probability of survival were inversely related to IPSS-M risk.Finally, we tested the accuracy in predicting IPSS-M when molecular information was missed and we defined a minimum set of 15 relevant genes associated with high performance of the score.

CONCLUSION

IPSS-M improves MDS prognostication and might result in a more effective selection of candidates to HSCT. Additional factors other than gene mutations can be involved in determining HMA sensitivity. The definition of a minimum set of relevant genes may facilitate the clinical implementation of the score.

Mutational landscape of primary spinal cord astrocytoma

Primary spinal cord astrocytoma (SCA) is a rare disease. Knowledge about the molecular profiles of SCAs mostly comes from intracranial glioma; the pattern of genetic alterations of SCAs is not well understood. Herein, we describe genome-sequencing analyses of primary SCAs, aiming to characterize the mutational landscape of primary SCAs. We utilized whole exome sequencing (WES) to analyze somatic nucleotide variants (SNVs) and copy number variants (CNVs) among 51 primary SCAs. Driver genes were searched using four algorithms. GISTIC2 was used to detect significant CNVs. Additionally, recurrently mutated pathways were also summarized. A total of 12 driver genes were identified. Of those, H3F3A (47.1%), TP53 (29.4%), NF1 (19.6%), ATRX (17.6%), and PPM1D (17.6%) were the most frequently mutated genes. Furthermore, three novel driver genes seldom reported in glioma were identified: HNRNPC, SYNE1, and RBM10. Several germline mutations, including three variants (SLC16A8 rs2235573, LMF1 rs3751667, FAM20C rs774848096) that were associated with risk of brain glioma, were frequently observed in SCAs. Moreover, 12q14.1 (13.7%) encompassing the oncogene CDK4 was recurrently amplified and negatively affected patient prognosis. Besides frequently mutated RTK/RAS pathway and PI3K pathway, the cell cycle pathway controlling the phosphorylation of retinoblastoma protein (RB) was mutated in 39.2% of patients. Overall, a considerable degree of the somatic mutation landscape is shared between SCAs and brainstem glioma. Our work provides a key insight into the molecular profiling of primary SCAs, which might represent candidate drug targets and complement the molecular atlas of glioma. © 2023 The Pathological Society of Great Britain and Ireland.

Glutaminase inhibition in combination with azacytidine in myelodysplastic syndromes: Clinical efficacy and correlative analyses

Malignancies can become reliant on glutamine as an alternative energy source and as a facilitator of aberrant DNA methylation, thus implicating glutaminase (GLS) as a potential therapeutic target. We demonstrate preclinical synergy of telaglenastat (CB-839), a selective GLS inhibitor, when combined with azacytidine (AZA), in vitro and in vivo, followed by a phase Ib/II study of the combination in patients with advanced MDS. Treatment with telaglenastat/AZA led to an ORR of 70% with CR/mCRs in 53% patients and a median overall survival of 11.6 months. scRNAseq and flow cytometry demonstrated a myeloid differentiation program at the stem cell level in clinical responders. Expression of non-canonical glutamine transporter, SLC38A1, was found to be overexpressed in MDS stem cells; was associated with clinical responses to telaglenastat/AZA and predictive of worse prognosis in a large MDS cohort. These data demonstrate the safety and efficacy of a combined metabolic and epigenetic approach in MDS.

Extracellular Vesicles and MicroRNA in Myelodysplastic Syndromes

The bone marrow niche plays an increasing role in the pathophysiogenesis of myelodysplastic syndromes. More specifically, mesenchymal stromal cells, which can secrete extracellular vesicles and their miRNA contents, modulate the fate of hematopoietic stem cells leading to leukemogenesis. Extracellular vesicles can mediate their miRNA and protein contents between nearby cells but also in the plasma of the patients, being potent tools for diagnosis and prognostic markers in MDS. They can be targeted by antisense miRNA or by modulators of the secretion of extracellular vesicles and could lead to future therapeutic directions in MDS.

The International Consensus Classification of myelodysplastic syndromes and related entities

The International Consensus Classification (ICC) of myeloid neoplasms and acute leukemia has updated the classification of myelodysplastic syndromes (MDSs) and placed MDS in a broader group of clonal cytopenias that includes clonal cytopenia of undetermined significance (CCUS) and related entities. Although subject to some interobserver variability and lack of specificity, morphologic dysplasia remains the main feature that distinguishes MDS from other clonal cytopenias and defines MDS as a hematologic malignancy. The ICC has introduced some changes in the definition of MDS whereby some cases categorized as MDS based on cytogenetic abnormalities are now classified as CCUS, while SF3B1 and multi-hit TP53 mutations are now considered to be MDS-defining in a cytopenic patient. The ICC has also recognized several cytogenetic and molecular abnormalities that reclassify some cases of MDS with excess blasts as acute myeloid leukemia (AML) and has introduced a new MDS/AML entity that encompasses cases with 10-19% blasts that lie on the continuum between MDS and AML. Two new genetically defined categories of MDS have been introduced: MDS with mutated SF3B1 and MDS with mutated TP53, the latter requiring bi-allelic aberrations in the TP53 gene. The entity MDS, unclassifiable has been eliminated. These changes have resulted in an overall simplification of the MDS classification scheme from 8 separate entities (including 1 that was genetically defined) in the revised 4th edition WHO classification to 7 separate entities (including 3 that are genetically defined) in the ICC.

Splicing factor mutations in the myelodysplastic syndromes: Role of key aberrantly spliced genes in disease pathophysiology and treatment

Mutations of splicing factor genes (including SF3B1, SRSF2, U2AF1 and ZRSR2) occur in more than half of all patients with myelodysplastic syndromes (MDS), a heterogeneous group of myeloid neoplasms. Splicing factor mutations lead to aberrant pre-mRNA splicing of many genes, some of which have been shown in functional studies to impact on hematopoiesis and to contribute to the MDS phenotype. This clearly demonstrates that impaired spliceosome function plays an important role in MDS pathophysiology. Recent studies that harnessed the power of induced pluripotent stem cell (iPSC) and CRISPR/Cas9 gene editing technologies to generate new iPSC-based models of splicing factor mutant MDS, have further illuminated the role of key downstream target genes. The aberrantly spliced genes and the dysregulated pathways associated with splicing factor mutations in MDS represent potential new therapeutic targets. Emerging data has shown that IRAK4 is aberrantly spliced in SF3B1 and U2AF1 mutant MDS, leading to hyperactivation of NF-κB signaling. Pharmacological inhibition of IRAK4 has shown efficacy in pre-clinical studies and in MDS clinical trials, with higher response rates in patients with splicing factor mutations. Our increasing knowledge of the effects of splicing factor mutations in MDS is leading to the development of new treatments that may benefit patients harboring these mutations.

The transcription factor DDIT3 is a potential driver of dyserythropoiesis in myelodysplastic syndromes

Myelodysplastic syndromes (MDS) are hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis, with increased incidence in older individuals. Here we analyze the transcriptome of human HSCs purified from young and older healthy adults, as well as MDS patients, identifying transcriptional alterations following different patterns of expression. While aging-associated lesions seem to predispose HSCs to myeloid transformation, disease-specific alterations may trigger MDS development. Among MDS-specific lesions, we detect the upregulation of the transcription factor DNA Damage Inducible Transcript 3 (DDIT3). Overexpression of DDIT3 in human healthy HSCs induces an MDS-like transcriptional state, and dyserythropoiesis, an effect associated with a failure in the activation of transcriptional programs required for normal erythroid differentiation. Moreover, DDIT3 knockdown in CD34⁺ cells from MDS patients with anemia is able to restore erythropoiesis. These results identify DDIT3 as a driver of dyserythropoiesis, and a potential therapeutic target to restore the inefficient erythroid differentiation characterizing MDS patients.

SWI/SNF complex gene variations are associated with a higher tumor mutational burden and a better response to immune checkpoint inhibitor treatment: a pan-cancer analysis of next-generation sequencing data corresponding to 4591 cases

Background

Genes related to the SWItch/sucrose nonfermentable (SWI/SNF) chromatin remodeling complex are frequently mutated across cancers. SWI/SNF-mutant tumors are vulnerable to synthetic lethal inhibitors. However, the landscape of SWI/SNF mutations and their associations with tumor mutational burden (TMB), microsatellite instability (MSI) status, and response to immune checkpoint inhibitors (ICIs) have not been elucidated in large real-world Chinese patient cohorts.

Methods

The mutational rates and variation types of six SWI/SNF complex genes (ARID1A, ARID1B, ARID2, SMARCA4, SMARCB1, and PBRM1) were analyzed retrospectively by integrating next-generation sequencing data of 4591 cases covering 18 cancer types. Thereafter, characteristics of SWI/SNF mutations were depicted and the TMB and MSI status and therapeutic effects of ICIs in the SWI/SNF-mutant and SWI/SNF-non-mutant groups were compared.

Results

SWI/SNF mutations were observed in 21.8% of tumors. Endometrial (54.1%), gallbladder and biliary tract (43.4%), and gastric (33.9%) cancers exhibited remarkably higher SWI/SNF mutational rates than other malignancies. Further, ARID1A was the most frequently mutated SWI/SNF gene, and ARID1A D1850fs was identified as relatively crucial. The TMB value, TMB-high (TMB-H), and MSI-high (MSI-H) proportions corresponding to SWI/SNF-mutant cancers were significantly higher than those corresponding to SWI/SNF-non-mutant cancers (25.8 vs. 5.6 mutations/Mb, 44.3% vs. 10.3%, and 16.0% vs. 0.9%, respectively; all p  < 0.0001). Furthermore, these indices were even higher for tumors with co-mutations of SWI/SNF genes and MLL2/3. Regarding immunotherapeutic effects, patients with SWI/SNF variations showed significantly longer progression-free survival (PFS) rates than their SWI/SNF-non-mutant counterparts (hazard ratio [HR], 0.56 [95% confidence interval {CI} 0.44–0.72]; p < 0.0001), and PBRM1 mutations were associated with relatively better ICI treatment outcomes than the other SWI/SNF gene mutations (HR, 0.21 [95% CI 0.12–0.37]; p = 0.0007). Additionally, patients in the SWI/SNF-mutant + TMB-H (HR, 0.48 [95% CI 0.37–0.54]; p  < 0.0001) cohorts had longer PFS rates than those in the SWI/SNF-non-mutant + TMB-low cohort.

Conclusions

SWI/SNF complex genes are frequently mutated and are closely associated with TMB-H status, MSI-H status, and superior ICI treatment response in several cancers, such as colorectal cancer, gastric cancer, and non-small cell lung cancer. These findings emphasize the necessity and importance of molecular-level detection and interpretation of SWI/SNF complex mutations.

The genetics of myelodysplastic syndromes and the opportunities for tailored treatments

Genomic instability, microenvironmental aberrations, and somatic mutations contribute to the phenotype of myelodysplastic syndrome and the risk for transformation to AML. Genes involved in RNA splicing, DNA methylation, histone modification, the cohesin complex, transcription, DNA damage response pathway, signal transduction and other pathways constitute recurrent mutational targets in MDS. RNA-splicing and DNA methylation mutations seem to occur early and are reported as driver mutations in over 50% of MDS patients. The improved understanding of the molecular landscape of MDS has led to better disease and risk classification, leading to novel therapeutic opportunities. Based on these findings, novel agents are currently under preclinical and clinical development and expected to improve the clinical outcome of patients with MDS in the upcoming years. This review provides a comprehensive update of the normal gene function as well as the impact of mutations in the pathogenesis, deregulation, diagnosis, and prognosis of MDS, focuses on the most recent advances of the genetic basis of myelodysplastic syndromes and their clinical relevance, and the latest targeted therapeutic approaches including investigational and approved agents for MDS.

Development and validation of a novel prognosis prediction model for patients with myelodysplastic syndrome

Background

Somatic mutations are widespread in patients with Myelodysplastic Syndrome (MDS) and are associated with prognosis. However, a practical prognostic model for MDS that incorporates somatic mutations urgently needs to be developed.

Methods

A cohort of 201 MDS patients from the Gene Expression Omnibus (GEO) database was used to develop the model, and a single-center cohort of 115 MDS cohorts from Northwest China was used for external validation. Kaplan-Meier analysis was performed to compare the effects of karyotype classifications and gene mutations on the prognosis of MDS patients. Univariate and multivariate Cox regression analyses and Lasso regression were used to screen for key prognostic factors. The shinyapps website was used to create dynamic nomograms with multiple variables. The time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the model’s discrimination, accuracy and clinical utility.

Results

Six risk factors (age, bone morrow blast percentage, ETV6, TP53, EZH2, and ASXL1) were considered as predictor variables in the nomogram. The nomogram showed excellent discrimination, with respective the area under the ROC curve (AUC) values of 0.850, 0.839, 0.933 for the training cohort at 1 year, 3 years and 5 years; 0.715, 0.802 and 0.750 for the testing cohort at 1 year, 3 years and 5 years; and 0.668, 0.646 and 0.731 for the external validation cohort at 1 year, 3 years and 5 years. The calibration curves and decision curve showed that the nomogram had good consistency and clinical practical benefit. Finally, a stratified analysis showed that MDS patients with high risk had worse survival outcomes than patients with low risk.

Conclusion

We developed a nomogram containing six risk factors, which provides reliable and objective predictions of prognosis for MDS patients.

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development

Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.

Upregulation of FHL1, SPNS3, and MPZL2 predicts poor prognosis in pediatric acute myeloid leukemia patients with FLT3-ITD mutation

Chromosomal translocations and gene mutations are characteristics of the genomic profile of acute myeloid leukemia (AML). We aim to identify a gene signature associated with poor prognosis in AML patients with FLT3-ITD compared to AML patients with NPM1/CEBPA mutations. RNA-sequencing (RNA-Seq) count data were downloaded from the UCSC Xena browser. Samples were grouped by their mutation status into high and low-risk groups. Differential gene expression (DGE), machine learning (ML) and survival analyses were performed. A total of 471 differentially expressed genes (DEGs) were identified, of which 16 DEGs were used as features for the prediction of mutation status. An accuracy of 92% was obtained from the ML model. FHL1, SPNS3, and MPZL2 were found to be associated with overall survival in FLT3-ITD samples. FLT3-ITD mutation confers an indicative gene expression profile different from NPM1/CEBPA mutation, and the expression of FHL1, SPSN3, and MPZL2 can serve as prognostic indicators of unfavorable disease.

Stemness Analysis Uncovers That The Peroxisome Proliferator-Activated Receptor Signaling Pathway Can Mediate Fatty Acid Homeostasis In Sorafenib-Resistant Hepatocellular Carcinoma Cells

Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes, retinoid X receptor beta (RXRB), nuclear receptor subfamily 1 group H member 3 (NR1H3), cytochrome P450 family 8 subfamily B member 1 (CYP8B1) and stearoyl-CoA desaturase (SCD), as a signature to distinguish the response of sorafenib. We proposed and validated that the RXRB and NR1H3 could directly regulate NR1H3 and SCD, respectively. Our results suggest that the combined use of SCD inhibitors and sorafenib may be a promising therapeutic approach.

Prioritizing risk genes as novel stratification biomarkers for acute monocytic leukemia by integrative analysis

Acute myeloid leukemia (AML) is a blood cancer with high heterogeneity and stratified as M0-M7 subtypes in the French-American-British (FAB) diagnosis system. Improved diagnosis with leverage of key molecular inputs will assist precisive medicine. Through deep-analyzing the transcriptomic data and mutations of AML, we report that a modern clustering algorithm, t-distributed Stochastic Neighbor Embedding (t-SNE), successfully demarcates M2, M3 and M5 territories while M4 bias to M5 and M0 & M1 bias to M2, consistent with the traditional FAB classification. Combining with mutation profiles, the results show that top recurrent AML mutations were unbiasedly allocated into M2 and M5 territories, indicating the t-SNE instructed transcriptomic stratification profoundly outperforms mutation profiling in the FAB system. Further functional data mining prioritizes several myeloid-specific genes as potential regulators of AML progression and treatment by Venetoclax, a BCL2 inhibitor. Among them two encode membrane proteins, LILRB4 and LRRC25, which could be utilized as cell surface biomarkers for monocytic AML or for innovative immuno-therapy candidates in future. In summary, our deep functional data-mining analysis warrants several unappreciated immune signaling-encoding genes as novel diagnostic biomarkers and potential therapeutic targets.

A Novel Prognostic Scoring Model for Myelodysplastic Syndrome Patients With SF3B1 Mutation

The outcomes of myelodysplastic syndrome (MDS) patients with SF3B1 mutation, despite identified as a favorable prognostic biomarker, are variable. To comprehend the heterogeneity in clinical characteristics and outcomes, we reviewed 140 MDS patients with SF3B1 mutation in Zhejiang province of China. Seventy-three (52.1%) patients diagnosed as MDS with ring sideroblasts (MDS-RS) following the 2016 World Health Organization (WHO) classification and 118 (84.3%) patients belonged to lower risk following the revised International Prognostic Scoring System (IPSS-R). Although clonal hematopoiesis-associated mutations containing TET2, ASXL1 and DNMT3A were the most frequent co-mutant genes in these patients, RUNX1, EZH2, NF1 and KRAS/NRAS mutations had significant effects on overall survival (OS). Based on that we developed a risk scoring model as IPSS-R×0.4+RUNX1×1.1+EZH2×0.6+RAS×0.9+NF1×1.6. Patients were categorized into two subgroups: low-risk (L-R, score <= 1.4) group and high risk (H-R, score > 1.4) group. The 3-year OS for the L-R and H-R groups was 91.88% (95% CI, 83.27%-100%) and 38.14% (95% CI, 24.08%-60.40%), respectively (P<0.001). This proposed model distinctly outperformed the widely used IPSS-R. In summary, we constructed and validated a personalized prediction model of MDS patients with SF3B1 mutation that can better predict the survival of these patients.

Global Proteomics Analysis of Bone Marrow: Establishing Talin-1 and Centrosomal Protein of 55 kDa as Potential Molecular Signatures for Myelodysplastic Syndromes

Myelodysplastic syndrome (MDS) is a hematological disorder characterized by abnormal stem cell differentiation and a high risk of acute myeloid leukemia transformation. Treatment options for MDS are still limited, making the identification of molecular signatures for MDS progression a vital task. Thus, we evaluated the proteome of bone marrow plasma from patients (n = 28) diagnosed with MDS with ring sideroblasts (MDS-RS) and MDS with blasts in the bone marrow (MDS-EB) using label-free mass spectrometry. This strategy allowed the identification of 1,194 proteins in the bone marrow plasma samples. Polyubiquitin-C (UBC), moesin (MSN), and Talin-1 (TLN1) showed the highest abundances in MDS-EB, and centrosomal protein of 55 kDa (CEP55) showed the highest relative abundance in the bone marrow plasma of MDS-RS patients. In a follow-up, in the second phase of the study, expressions of UBC, MSN, TLN1, and CEP55 genes were evaluated in bone marrow mononuclear cells from 45 patients by using qPCR. This second cohort included only seven patients from the first study. CEP55, MSN, and UBC expressions were similar in mononuclear cells from MDS-RS and MDS-EB individuals. However, TLN1 gene expression was greater in mononuclear cells from MDS-RS (p = 0.049) as compared to MDS-EB patients. Irrespective of the MDS subtype, CEP55 expression was higher (p = 0.045) in MDS patients with abnormal karyotypes, while MSN, UBC, and TALIN1 transcripts were similar in MDS with normal vs. abnormal karyotypes. In conclusion, proteomic and gene expression approaches brought evidence of altered TLN1 and CEP55 expressions in cellular and non-cellular bone marrow compartments of patients with low-risk (MDS-RS) and high-risk (MDS-EB) MDSs and with normal vs. abnormal karyotypes. As MDS is characterized by disrupted apoptosis and chromosomal alterations, leading to mitotic slippage, TLN1 and CEP55 represent potential markers for MDS prognosis and/or targeted therapy.

Dysregulated Immune and Metabolic Microenvironment Is Associated with the Post-Operative Relapse in Stage I Non-Small Cell Lung Cancer

Simple Summary

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remained poorly understood. This study highlights that both tumors and adjacent tissues from stage I NSCLC with relapse show a dysregulated immune and metabolic environment. Immune response shifts from an active state in primary tumors to a suppressive state in recurrent tumors. A model based on the enriched biological features in the primary tumors with relapse could effectively predict recurrence for stage I NSCLC. These results provide insights into the underpinning of the post-operative relapse and suggest that identifying NSCLC patients with a high risk of relapse could help the clinical decision of applying appropriate therapeutic interventions.

Abstract

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remains poorly understood. We enrolled 57 stage I NSCLC patients with or without relapse and performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) on available primary and recurrent tumors, as well as on matched tumor-adjacent tissues (TATs). The WES analysis revealed that primary tumors from patients with relapse were enriched with USH2A mutation and 2q31.1 amplification. RNA-seq data showed that the relapse risk was associated with aberrant immune response and metabolism in the microenvironment of primary lesions. TATs from the patients with relapse showed an immunosuppression state. Moreover, recurrent lesions exhibited downregulated immune response compared with their paired primary tumors. Genomic and transcriptomic features were further subjected to build a prediction model classifying patients into groups with different relapse risks. We show that the recurrence risk of stage I NSCLC could be ascribed to the altered immune and metabolic microenvironment. TATs might be affected by cancer cells and facilitate the invasion of tumors. The immune microenvironment in the recurrent lesions is suppressed. Patients with a high risk of relapse need active post-operative intervention.

A Prognostic Model for Acute Myeloid Leukemia Based on IL-2/STAT5 Pathway-Related Genes

Accurate prognostic stratification of patients can provide guidance for personalized therapy. Many prognostic models for acute myeloid leukemia (AML) have been reported, but most have considerable inaccuracies due to contained variables with insufficient capacity of predicting survival and lack of adequate verification. Here, 235 genes strongly related to survival in AML were systematically identified through univariate Cox regression analysis of eight independent AML datasets. Pathway enrichment analysis of these 235 genes revealed that the IL-2/STAT5 signaling pathway was the most highly enriched. Through Cox proportional-hazards regression model and stepwise algorithm, we constructed a six-gene STAT5-associated signature based on the most robustly survival-related genes related to the IL-2/STAT5 signaling pathway. Good prognostic performance was observed in the training cohort (GSE37642-GPL96), and the signature was validated in seven other validation cohorts. As an independent prognostic factor, the STAT5-associated signature was positively correlated with patient age and ELN2017 risk levels. An integrated score based on these three prognostic factors had higher prognostic accuracy than the ELN2017 risk category. Characterization of immune cell infiltration indicated that impaired B-cell adaptive immunity, immunosuppressive effects, serious infection, and weakened anti-inflammatory function tended to accompany high-risk patients. Analysis of in-house clinical samples revealed that the STAT5-assocaited signature risk scores of AML patients were significantly higher than those of healthy people. Five chemotherapeutic drugs that were effective in these high-risk patients were screened in silico. Among the five drugs, MS.275, a known HDAC inhibitor, selectively suppressed the proliferation of cancer cells with high STAT5 phosphorylation levels in vitro. Taken together, the data indicate that the STAT5-associated signature is a reliable prognostic model that can be used to optimize prognostic stratification and guide personalized AML treatments.

Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities

Myeloid neoplasms (MN) are heterogeneous clonal disorders arising from the expansion of hematopoietic stem and progenitor cells. In parallel with genetic and epigenetic dynamics, the immune system plays a critical role in modulating tumorigenesis, evolution and therapeutic resistance at the various stages of disease progression. Single-cell technologies represent powerful tools to assess the cellular composition of the complex tumor ecosystem and its immune environment, to dissect interactions between neoplastic and non-neoplastic components, and to decipher their functional heterogeneity and plasticity. In addition, recent progress in multi-omics approaches provide an unprecedented opportunity to study multiple molecular layers (DNA, RNA, proteins) at the level of single-cell or single cellular clones during disease evolution or in response to therapy. Applying single-cell technologies to MN holds the promise to uncover novel cell subsets or phenotypic states and highlight the connections between clonal evolution and immune escape, which is crucial to fully understand disease progression and therapeutic resistance. This review provides a perspective on the various opportunities and challenges in the field, focusing on key questions in MN research and discussing their translational value, particularly for the development of more efficient immunotherapies.

Bioinformatics analysis deciphering the transcriptomic signatures associated with signalling pathways and prognosis in the myelodysplastic syndromes

BACKGROUND

Several studies scatteredly identified the myelodysplastic syndromes' transcriptomic profiles (MDS). However, the exploration of transcriptional signatures, key signalling pathways, and their association with prognosis and diagnosis in the integrated multiple datasets remains lacking.

METHODS

We integrated the GSE4619, GSE19429, GSE30195, and GSE58831 microarray datasets of CD34 + cells for identifying the differentially expressed genes (DEGs) in the MDS. The series of bioinformatics methods are applied to identify the key hub genes, gene clusters, prognostic hub genes, and genes associated with diagnostic efficacy. Finally, we validated the expression differences of hub genes in the GSE114922 dataset.

RESULTS

We explored the DEGs related to gene ontology enrichment and KEGG pathways. We identified significant hub genes, including 168 upregulated hub genes (such as STAT1, IFIH1, EPRS, GRB2, RAC2, MAPK14, CASP1, and SPI1) and 52 downregulated hub genes (such as CREBBP, HIF1A, PIK3CA, EZH2, PIK3R1, MDM2, IRF4, CXCR4, PCNA, and CD19) in the MDS. In addition, we identified six significant molecular complex detection (MCODE)-derived upregulated gene clusters and one downregulated gene cluster, respectively. Moreover, we found that the higher expression level of MX2, GBP2, PXN, IFI44, FDXR, PLCB2, ASS1, ERCC4, PML, and RRAGD and the lower expression level of CD19, PAX5, TCF3, LEF1, NUSAP1, and TIMELESS hub genes are significantly correlated with shorter survival times of MDS patients. Furthermore, the area value under the ROC curve (AUC) of PXN, FDXR, PLCB2, PML, CD19, PAX5, and LEF1 prognostic genes are more than 0.80, indicating that these genes could be effectively used for the diagnostic efficacy of MDS patients.

CONCLUSIONS

Identifying key hub genes and their association with the prognosis and diagnostic efficacy may provide substantial clues for the treatment and diagnosis of MDS patients.

TRAF6 functions as a tumor suppressor in myeloid malignancies by directly targeting MYC oncogenic activity

Clonal hematopoiesis (CH) is an aging-associated condition characterized by the clonal outgrowth of pre-leukemic cells that acquire specific mutations. Although individuals with CH are healthy, they are at an increased risk of developing myeloid malignancies, suggesting that additional alterations are needed for the transition from a pre-leukemia stage to frank leukemia. To identify signaling states that cooperate with pre-leukemic cells, we used an in vivo RNAi screening approach. One of the most prominent genes identified was the ubiquitin ligase TRAF6. Loss of TRAF6 in pre-leukemic cells results in overt myeloid leukemia and is associated with MYC-dependent stem cell signatures. TRAF6 is repressed in a subset of patients with myeloid malignancies, suggesting that subversion of TRAF6 signaling can lead to acute leukemia. Mechanistically, TRAF6 ubiquitinates MYC, an event that does not affect its protein stability but rather represses its functional activity by antagonizing an acetylation modification.

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

Functional Impact of Genomic Complexity on the Transcriptome of Multiple Myeloma

PURPOSE

Multiple myeloma is a biologically heterogenous plasma-cell disorder. In this study, we aimed at dissecting the functional impact on transcriptome of gene mutations, copy-number abnormalities (CNA), and chromosomal rearrangements (CR). Moreover, we applied a geno-transcriptomic approach to identify specific biomarkers for personalized treatments.

EXPERIMENTAL DESIGN

We analyzed 514 newly diagnosed patients from the IA12 release of the CoMMpass study, accounting for mutations in multiple myeloma driver genes, structural variants, copy-number segments, and raw-transcript counts. We performed an in silico drug sensitivity screen (DSS), interrogating the Cancer Dependency Map (DepMap) dataset after anchoring cell lines to primary tumor samples using the Celligner algorithm.

RESULTS

Immunoglobulin translocations, hyperdiploidy and chr(1q)gain/amps were associated with the highest number of deregulated genes. Other CNAs and specific gene mutations had a lower but very distinct impact affecting specific pathways. Many recurrent genes showed a hotspot (HS)-specific effect. The clinical relevance of double-hit multiple myeloma found strong biological bases in our analysis. Biallelic deletions of tumor suppressors and chr(1q)-amplifications showed the greatest impact on gene expression, deregulating pathways related to cell cycle, proliferation, and expression of immunotherapy targets. Moreover, our in silico DSS showed that not only t(11;14) but also chr(1q)gain/amps and CYLD inactivation predicted differential expression of transcripts of the BCL2 axis and response to venetoclax.

CONCLUSIONS

The multiple myeloma genomic architecture and transcriptome have a strict connection, led by CNAs and CRs. Gene mutations impacted especially with HS-mutations of oncogenes and biallelic tumor suppressor gene inactivation. Finally, a comprehensive geno-transcriptomic analysis allows the identification of specific deregulated pathways and candidate biomarkers for personalized treatments in multiple myeloma.

Myelodysplastic Syndromes and Modalities of Treatment: An Updated Literature Review

Myelodysplastic syndromes (MDS) represent a large group of rare and diverse clonal stem cell disorders. These are classified into several different phenotypes and typically arise following a multistep genetic process, whereby genetic mutations alter the DNA damage and cellular stress responses, impacting transcription, RNA splicing, epigenetics, and cytokine signaling. However, despite the advances made regarding molecular pathophysiology and prognostic criteria and the influx of new treatment modalities, management is primarily based on prognostic scores, such as the Revised International Prognostic Scoring System. This poses a significant challenge to current healthcare professionals due to poor comprehension of the underlying pathophysiology. Hence, this review integrates the latest research and treatment modalities for MDS and discusses the different genetic mutations outlined in the revised World Health Organization 2016 MDS classification system and the associated treatment modalities. Additionally, future directions of research and clinical management of MDS are discussed.

COSMIC Cancer Gene Census 3D database: understanding the impacts of mutations on cancer targets

Mutations in hallmark genes are believed to be the main drivers of cancer progression. These mutations are reported in the Catalogue of Somatic Mutations in Cancer (COSMIC). Structural appreciation of where these mutations appear, in protein-protein interfaces, active sites or deoxyribonucleic acid (DNA) interfaces, and predicting the impacts of these mutations using a variety of computational tools are crucial for successful drug discovery and development. Currently, there are 723 genes presented in the COSMIC Cancer Gene Census. Due to the complexity of the gene products, structures of only 87 genes have been solved experimentally with structural coverage between 90% and 100%. Here, we present a comprehensive, user-friendly, web interface (https://cancer-3d.com/) of 714 modelled cancer-related genes, including homo-oligomers, hetero-oligomers, transmembrane proteins and complexes with DNA, ribonucleic acid, ligands and co-factors. Using SDM and mCSM software, we have predicted the impacts of reported mutations on protein stability, protein-protein interfaces affinity and protein-nucleic acid complexes affinity. Furthermore, we also predicted intrinsically disordered regions using DISOPRED3.

TP53 Co-Mutational Features and NGS-Calibrated Immunohistochemistry Threshold in Gastric Cancer

Purpose

TP53 is the most frequently mutated gene in gastric cancer and it can be potentially used for gastric cancer diagnosis and screening. However, standardized clinical approaches that could accurately and cost-effectively detect TP53 mutations in gastric cancer are largely lagged behind.

Patients and Methods

We conducted next-generation sequencing (NGS) analysis of 425 cancer-related genes in 42 gastric cancer patients in our cohort. A 1313-patient cohort derived from the cBioPortal database was used for validation. We performed immunohistochemistry (IHC) staining with four commonly used p53 antibodies, and the NGS results were used as the gold standard to optimize the IHC threshold for each antibody.

Results

By NGS analysis, we found that around 80% of gastric cancer patients in our cohort harbored TP53 alterations. Genetic alterations of BRCA1/2 or KMT2B were mostly exclusive with TP53 mutations, so were the MSI status or low grade of tumors. These results were further validated using the data from cBioPortal. We then used the NGS-derived TP53 status to optimize four commonly used IHC antibodies for detecting TP53 mutations. We showed that all antibodies could achieve more than 93% accuracy when proper IHC positivity thresholds were used, especially for the SP5 antibody that could reach 100% sensitivity and specificity with the 20% threshold.

Conclusion

Our results indicated that exclusivity between TP53 and BRCA mutations could be potentially used as a cost-effective way to predict BRCA status. Also, setting proper IHC thresholds for each specific antibody is critical to accurately detect TP53 mutations and facilitate disease diagnosis.

Pan-cancer evaluation of gene expression and somatic alteration data for cancer prognosis prediction

BACKGROUND

Over the past decades, approaches for diagnosing and treating cancer have seen significant improvement. However, the variability of patient and tumor characteristics has limited progress on methods for prognosis prediction. The development of high-throughput omics technologies now provides multiple approaches for characterizing tumors. Although a large number of published studies have focused on integration of multi-omics data and use of pathway-level models for cancer prognosis prediction, there still exists a gap of knowledge regarding the prognostic landscape across multi-omics data for multiple cancer types using both gene-level and pathway-level predictors.

METHODS

In this study, we systematically evaluated three often available types of omics data (gene expression, copy number variation and somatic point mutation) covering both DNA-level and RNA-level features. We evaluated the landscape of predictive performance of these three omics modalities for 33 cancer types in the TCGA using a Lasso or Group Lasso-penalized Cox model and either gene or pathway level predictors.

RESULTS

We constructed the prognostic landscape using three types of omics data for 33 cancer types on both the gene and pathway levels. Based on this landscape, we found that predictive performance is cancer type dependent and we also highlighted the cancer types and omics modalities that support the most accurate prognostic models. In general, models estimated on gene expression data provide the best predictive performance on either gene or pathway level and adding copy number variation or somatic point mutation data to gene expression data does not improve predictive performance, with some exceptional cohorts including low grade glioma and thyroid cancer. In general, pathway-level models have better interpretative performance, higher stability and smaller model size across multiple cancer types and omics data types relative to gene-level models.

CONCLUSIONS

Based on this landscape and comprehensively comparison, models estimated on gene expression data provide the best predictive performance on either gene or pathway level. Pathway-level models have better interpretative performance, higher stability and smaller model size relative to gene-level models.

Cohesin mutations in myeloid malignancies

Cohesin is a multisubunit protein complex that forms a ring-like structure around DNA. It is essential for sister chromatid cohesion, chromatin organization, transcriptional regulation, and DNA damage repair and plays a major role in dynamically shaping the genome architecture and maintaining DNA integrity. The core complex subunits STAG2, RAD21, SMC1, and SMC3, as well as its modulators PDS5A/B, WAPL, and NIPBL, have been found to be recurrently mutated in hematologic and solid malignancies. These mutations are found across the full spectrum of myeloid neoplasia, including pediatric Down syndrome-associated acute megakaryoblastic leukemia, myelodysplastic syndromes, chronic myelomonocytic leukemia, and de novo and secondary acute myeloid leukemias. The mechanisms by which cohesin mutations act as drivers of clonal expansion and disease progression are still poorly understood. Recent studies have described the impact of cohesin alterations on self-renewal and differentiation of hematopoietic stem and progenitor cells, which are associated with changes in chromatin and epigenetic state directing lineage commitment, as well as genomic integrity. Herein, we review the role of the cohesin complex in healthy and malignant hematopoiesis. We discuss clinical implications of cohesin mutations in myeloid malignancies and discuss opportunities for therapeutic targeting.

The Prognostic Value of Early Detection of Minimal Residual Disease as Defined by Flow Cytometry and Gene Mutation Clearance for Myelodysplastic Syndrome Patients After Myeloablative Allogeneic Hematopoietic Stem-Cell Transplantation

High relapse incidence remains a major problem for myelodysplastic syndrome (MDS) patients who have received an allogeneic hematopoietic stem-cell transplantation (allo-HSCT). We retrospectively analyzed the correlations between clinical outcomes and minimal residual disease (MRD) by using mutations (MUT) and flow cytometry (FCM) analysis of 115 MDS patients with allo-HSCT. We divided 115 MDS patients into four groups based on molecular genetics and FCM MRD results at day 30 post-HSCT. There were significant differences in the 2-year progression-free survival (PFS) between the FCM^high MUT^pos and FCM^low MUT^neg groups (20% vs 79%, P < 0.001). In addition, by univariate analysis, we found that an IPSS-R score ≥4 pre-HSCT (HR, 5.061; P=0.007), DNMT3A mutations (HR, 2.291; P=0.052), TP53 mutations (HR, 3.946; P=0.011), and poor and very poor revised International Prognostic Scoring System (IPSS-R) cytogenetic risk (HR, 4.906; P < 0.001) were poor risk factors for PFS. In multivariate analysis, we found that an IPSS-R score ≥ 4 pre-HSCT (HR, 4.488; P=0.015), DNMT3A mutations (HR, 2.385; P=0.049), positive FCM MRD combined with persistence gene mutations at day 30 (HR, 5.198; P=0.013) were independent risk factors for disease progression. In conclusion, our data indicated that monitoring MRD by FCM combined with gene mutation clearance at day 30 could help in the prediction of disease progression for MDS patients after transplantation.

Methylation of dual-specificity phosphatase 4 controls cell differentiation

Mitogen-activated protein kinases (MAPKs) are inactivated by dual-specificity phosphatases (DUSPs), the activities of which are tightly regulated during cell differentiation. Using knockdown screening and single-cell transcriptional analysis, we demonstrate that DUSP4 is the phosphatase that specifically inactivates p38 kinase to promote megakaryocyte (Mk) differentiation. Mechanistically, PRMT1-mediated methylation of DUSP4 triggers its ubiquitinylation by an E3 ligase HUWE1. Interestingly, the mechanistic axis of the DUSP4 degradation and p38 activation is also associated with a transcriptional signature of immune activation in Mk cells. In the context of thrombocytopenia observed in myelodysplastic syndrome (MDS), we demonstrate that high levels of p38 MAPK and PRMT1 are associated with low platelet counts and adverse prognosis, while pharmacological inhibition of p38 MAPK or PRMT1 stimulates megakaryopoiesis. These findings provide mechanistic insights into the role of the PRMT1-DUSP4-p38 axis on Mk differentiation and present a strategy for treatment of thrombocytopenia associated with MDS.

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

Myelodysplastic syndrome (MDS) comprised a heterogeneous group of diseases. The prognosis of patients varies even in the same risk groups. Searching for novel prognostic markers is warranted. Leukemic stem cells (LSCs) are responsible for chemoresistance and relapse in leukemia. Recently, expressions of 17 genes related to stemness of LSCs were found to be associated with prognosis in acute myeloid leukemia patients. However, the clinical impact of LSC genes expressions in MDS, a disorder arising from hematopoietic stem cells, remains unclear. We analyzed expression profile of the 17 stemness-related genes in primary MDS patients and identified expression of 4 genes (LAPTM4B, NGFRAP1, EMP1, and CPXM1) were significantly correlated with overall survival (OS). We constructed an LSC4 scoring system based on the weighted sums of the expression of 4 genes and explored its clinical implications in MDS patients. Higher LSC4 scores were associated with higher revised International Prognostic Scoring System (IPSS-R) scores, complex cytogenetics, and mutations in RUNX1, ASXL1, and TP53. High-score patients had significantly shorter OS and leukemia-free survival (LFS), which was also confirmed in 2 independent validation cohorts. Subgroup analysis revealed the prognostic significance of LSC4 scores for OS remained valid across IPSS-R lower- and higher-risk groups. Furthermore, higher LSC4 score was an independent adverse risk factor for OS and LFS in multivariate analysis. In summary, LSC4 score can independently predict prognosis in MDS patients irrespective of IPSS-R risks and may be used to guide the treatment of MDS patients, especially lower-risk group in whom usually only supportive treatment is given.

Horizontal meta-analysis identifies common deregulated genes across AML subgroups providing a robust prognostic signature

Advances in transcriptomics have improved our understanding of leukemic development and helped to enhance the stratification of patients. The tendency of transcriptomic studies to combine AML samples, regardless of cytogenetic abnormalities, could lead to bias in differential gene expression analysis because of the differential representation of AML subgroups. Hence, we performed a horizontal meta-analysis that integrated transcriptomic data on AML from multiple studies, to enrich the less frequent cytogenetic subgroups and to uncover common genes involved in the development of AML and response to therapy. A total of 28 Affymetrix microarray data sets containing 3940 AML samples were downloaded from the Gene Expression Omnibus database. After stringent quality control, transcriptomic data on 1534 samples from 11 data sets, covering 10 AML cytogenetically defined subgroups, were retained and merged with the data on 198 healthy bone marrow samples. Differentially expressed genes between each cytogenetic subgroup and normal samples were extracted, enabling the unbiased identification of 330 commonly deregulated genes (CODEGs), which showed enriched profiles of myeloid differentiation, leukemic stem cell status, and relapse. Most of these genes were downregulated, in accordance with DNA hypermethylation. CODEGs were then used to create a prognostic score based on the weighted sum of expression of 22 core genes (CODEG22). The score was validated with microarray data of 5 independent cohorts and by quantitative real time-polymerase chain reaction in a cohort of 142 samples. CODEG22-based stratification of patients, globally and into subpopulations of cytologically healthy and elderly individuals, may complement the European LeukemiaNet classification, for a more accurate prediction of AML outcomes.

A clinical transcriptome approach to patient stratification and therapy selection in acute myeloid leukemia

As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we develop and validate a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of ribonucleic acid sequencing (RNA-Seq) to whole genome and exome sequencing reveals that a standalone RNA-Seq assay offers the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data from 154 AML patients are used to develop a novel AML prognostic score, which is strongly associated with patient outcomes across 620 patients from three independent cohorts, and 42 patients from a prospective cohort. When combined with molecular risk guidelines, the risk score allows for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identify a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase, encoded by PTK2, demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy.

LRP1B mutation is associated with tumor HPV status and promotes poor disease outcomes with a higher mutation count in HPV-related cervical carcinoma and head & neck squamous cell carcinoma

Human papillomavirus (HPV) infection and gene mutations were reputed as key factors in cervical carcinoma (CC) and head and neck squamous cell carcinoma (HNSCC). However, the associations of HPV status and gene mutations remain to be determined. This study aims to identify molecular patterns of LRP1B mutation and HPV status via rewiring tumor samples of HNSCC (n=1478) and CC (n=178) from the TCGA dataset. Here, we found that LRP1B mutation was associated with HPV status in CC (P=0.040) and HNSCC (P=0.044), especially in HPV 16 integrated CC (P=0.036). Cancer survival analysis demonstrated that samples with LRP1B mutation showed poor disease outcomes in CC (P=0.013) and HNSCC (P=0.0124). In addition, the expression status of LPR1B was more favorable for prediction than TP53 or RB1 in CC and HNSCC. Mutation clustering analysis showed that samples with LRP1B mutation showed higher mutation count in CC (P=1.76e-67) and HNSCC (P<10e-10). Further analysis identified 289 co-occurrence genes in these two cancer types, which were enriched in PI3K signaling, cell division process, and chromosome segregation process, et al. The 289-co-occurrence gene signature identified a cluster of patients with a higher portion of copy number variation (CNV) lost in the genome, different tumor HPV status (P<10e-10), higher mutation count (P<10e-10), higher fraction genome altered value (P=2.078e-4), higher aneuploidy score (P=3.362e-4), and earlier started the smoking year (P=2.572e-4), which were associated with shorter overall survival (P=0.0103) in CC and HNSCC samples. Overall, LRP1B mutation was associated with tumor HPV status and was an unfavorable prognostic biomarker for CC and HNSCC.

ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network

Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy.

Cellular functions are carried out by a set of gene products. Mutation of a single gene is often sufficient to disrupt certain biological functions and promote tumorigenesis. Therefore, genes participating in the same function are less likely to mutate in the same sample. Such phenomenon is called “mutual exclusivity”. In this study, our algorithm (ORN) has utilized this property to identify gene-level mutations that affect similar biological functions. It also considers mutations’ impact on mRNA expression. Functional modules identified by ORN tends to be mutually exclusive while causing similar differential expression profiles. When we applied ORN to lower-grade glioma and liver cancer datasets, we have identified gene modules significantly related to patient survival. Furthermore, across different types of cancer, ORN has connected well-known cancer driver mutations with genes whose functions remain unclear. These connections, once validated, can generate novel hypothesis for biologist to further investigate cancer mechanism and develop targeted therapy.

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.

Cis-regulatory mutations with driver hallmarks in major cancers

Despite the recent availability of complete genome sequences of tumors from thousands of patients, isolating disease-causing (driver) non-coding mutations from the plethora of somatic variants remains challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, genetic data, and allele-specific gene expression from TCGA, we identified 320 somatic non-coding mutations that affect gene expression in cis (FDR<0.25). These mutations cluster into 47 cis-regulatory elements that modulate expression of their subject genes through diverse molecular mechanisms. We further show that these mutations have hallmark features of non-coding drivers; namely, that they preferentially disrupt transcription factor binding motifs, are associated with a selective advantage, increased oncogene expression and decreased tumor suppressor expression.

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Enrichment of functional non-coding somatic mutations predicts drivers

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Elevated variant allele frequencies are consistent with roles in tumorigenesis

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Putative non-coding drivers disrupt transcription factor binding motifs

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Predicted drivers associate with increased oncogene and decreased TSG expression

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications

BACKGROUND

Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor types that are optimally responsive. Open data on the gene expression of the NCI-60 cell lines, provides a unique opportunity to add another dimension to the preclinical development of such drugs by interrogating correlations with gene expression patterns. Machine learning can be used to reduce the complexity of whole genome gene expression patterns to derive manageable signatures of response. Application of machine learning in early phases of preclinical development is likely to allow a better positioning and ultimate clinical success of molecules. LP-184 is a highly potent novel alkylating agent where the preclinical development is being guided by a dedicated machine learning-derived response signature. We show the feasibility and the accuracy of such a signature of response by accurately predicting the response to LP-184 validated using wet lab derived IC50s on a panel of cell lines.

RESULTS

We applied our proprietary RADR® platform to an NCI-60 discovery dataset encompassing LP-184 IC50s and publicly available gene expression data. We used multiple feature selection layers followed by the XGBoost regression model and reduced the complexity of 20,000 gene expression values to generate a 16-gene signature leading to the identification of a set of predictive candidate biomarkers which form an LP-184 response gene signature. We further validated this signature and predicted response to an additional panel of cell lines. Considering fold change differences and correlation between actual and predicted LP-184 IC50 values as validation performance measures, we obtained 86% accuracy at four-fold cut-off, and a strong (r = 0.70) and significant (p value 1.36e-06) correlation between actual and predicted LP-184 sensitivity. In agreement with the perceived mechanism of action of LP-184, PTGR1 emerged as the top weighted gene.

CONCLUSION

Integration of a machine learning-derived signature of response with in vitro assessment of LP-184 efficacy facilitated the derivation of manageable yet robust biomarkers which can be used to predict drug sensitivity with high accuracy and clinical value.

Navigating Myelodysplastic and Myelodysplastic/Myeloproliferative Overlap Syndromes

Myelodysplastic syndromes (MDS) and MDS/myeloproliferative neoplasms (MPNs) are clonal diseases that differ in morphologic diagnostic criteria but share some common disease phenotypes that include cytopenias, propensity to acute myeloid leukemia evolution, and a substantially shortened patient survival. MDS/MPNs share many clinical and molecular features with MDS, including frequent mutations involving epigenetic modifier and/or spliceosome genes. Although the current 2016 World Health Organization classification incorporates some genetic features in its diagnostic criteria for MDS and MDS/MPNs, recent accumulation of data has underscored the importance of the mutation profiles on both disease classification and prognosis. Machine-learning algorithms have identified distinct molecular genetic signatures that help refine prognosis and notable associations of these genetic signatures with morphologic and clinical features. Combined geno-clinical models that incorporate mutation data seem to surpass the current prognostic schemes. Future MDS classification and prognostication schema will be based on the portfolio of genetic aberrations and traditional features, such as blast count and clinical factors. Arriving at these systems will require studies on large patient cohorts that incorporate advanced computational analysis. The current treatment algorithm in MDS is based on patient risk as derived from existing prognostic and disease classes. Luspatercept is newly approved for patients with MDS and ring sideroblasts who are transfusion dependent after erythropoietic-stimulating agent failure. Other agents that address red blood cell transfusion dependence in patients with lower-risk MDS and the failure of hypomethylating agents in higher-risk disease are in advanced testing. Finally, a plethora of novel targeted agents and immune checkpoint inhibitors are being evaluated in combination with a hypomethylating agent backbone to augment the depth and duration of response and, we hope, improve overall survival.

A Deep Look into the Program of Rapid Tumor Growth of Hepatocellular Carcinoma

BACKGROUND AND AIMS

Great efforts have been made towards increasing our understanding of the pathogenesis involved in hepatocellular carcinoma (HCC), but the rapid growth inherent to such tumor development remains to be explored.

METHODS

We identified distinct gene coexpression modes upon liver tumor growth using weighted gene coexpression network analysis. Modeling of tumor growth as signaling activity was employed to understand the main cascades responsible for the growth. Hub genes in the modules were determined, examined in vitro, and further assembled into the growth signature.

RESULTS

We revealed modules related to the different growth states in HCC, especially the fastest growth module, which is preserved among different HCC cohorts. Moreover, signaling flux in the cell cycle pathway was found to act as a driving force for rapid growth. Twenty hub genes in the module were identified and assembled into the growth signature, and two genes (NCAPH, and RAD54L) were tested for their growth potential in vitro. Genetic alteration of the growth signature affected the global gene expression. The activity of the signature was associated with tumor metabolism and immunity in HCC. Finally, the prognosis effect of the growth signature was reproduced in nine cancers.

CONCLUSIONS

These results collectively demonstrate the molecule organization of rapid tumor growth in HCC, which is a highly synergistic process, with implications for the future management of patients.