LGALS3 is connected to CD74 in a previously unknown protein network that is associated with poor survival in patients with AML

Reverse Phase Proteomic Array Profiling of Asparagine Synthetase Expression in Newly Diagnosed Acute Myeloid Leukemia

Asparaginase-based therapy is a cornerstone in acute lymphoblastic leukemia (ALL) treatment, capitalizing on the methylation status of the asparagine synthetase (ASNS) gene, which renders ALL cells reliant on extracellular asparagine. Contrastingly, ASNS expression in acute myeloid leukemia (AML) has not been thoroughly investigated, despite studies suggesting that AML with chromosome 7/7q deletions might have reduced ASNS levels. Here, we leverage reverse phase protein arrays to measure ASNS expression in 810 AML patients and assess its impact on outcomes. We find that AML with inv(16) has the lowest overall ASNS expression. While AML with deletion 7/7q had ASNS levels slightly lower than those of AML without deletion 7/7q, this observation was not significant. Low ASNS expression correlated with improved overall survival (46 versus 54 weeks, respectively, p = 0.011), whereas higher ASNS levels were associated with better response to venetoclax-based therapy. Protein correlation analysis demonstrated association between ASNS and proteins involved in methylation and DNA repair. In conclusion, while ASNS expression was not lower in patients with deletion 7/7q as initially predicted, ASNS levels were highly variable across AML patients. Further studies are needed to assess whether patients with low ASNS expression are susceptible to asparaginase-based therapy due to their inability to augment compensatory ASNS expression upon asparagine depletion.

Immunotherapeutic targeting of surfaceome heterogeneity in AML

Immunotherapy remains underexploited in acute myeloid leukemia (AML) compared to other hematological malignancies. Currently, gemtuzumab ozogamicin is the only therapeutic antibody approved for this disease. Here, to identify potential targets for immunotherapeutic intervention, we analyze the surface proteome of 100 genetically diverse primary human AML specimens for the identification of cell surface proteins and conduct single-cell transcriptome analyses on a subset of these specimens to assess antigen expression at the sub-population level. Through this comprehensive effort, we successfully identify numerous antigens and markers preferentially expressed by primitive AML cells. Many identified antigens are targeted by therapeutic antibodies currently under clinical evaluation for various cancer types, highlighting the potential therapeutic value of the approach. Importantly, this initiative uncovers AML heterogeneity at the surfaceome level, identifies several antigens and potential primitive cell markers characterizing AML subgroups, and positions immunotherapy as a promising approach to target AML subgroup specificities.

CD44: a cancer stem cell marker and therapeutic target in leukemia treatment

CD44 is a ubiquitous leukocyte adhesion molecule involved in cell-cell interaction, cell adhesion, migration, homing and differentiation. CD44 can mediate the interaction between leukemic stem cells and the surrounding extracellular matrix, thereby inducing a cascade of signaling pathways to regulate their various behaviors. In this review, we focus on the impact of CD44s/CD44v as biomarkers in leukemia development and discuss the current research and prospects for CD44-related interventions in clinical application.

Unveiling novel insights in acute myeloid leukemia through single-cell RNA sequencing

Acute myeloid leukemia (AML) is a complex and heterogeneous group of aggressive hematopoietic stem cell disease. The presence of diverse and functionally distinct populations of leukemia cells within the same patient's bone marrow or blood poses a significant challenge in diagnosing and treating AML. A substantial proportion of AML patients demonstrate resistance to induction chemotherapy and a grim prognosis upon relapse. The rapid advance in next generation sequencing technologies, such as single-cell RNA-sequencing (scRNA-seq), has revolutionized our understanding of AML pathogenesis by enabling high-resolution interrogation of the cellular heterogeneity in the AML ecosystem, and their transcriptional signatures at a single-cell level. New studies have successfully characterized the inextricably intertwined interactions among AML cells, immune cells and bone marrow microenvironment and their contributions to the AML development, therapeutic resistance and relapse. These findings have deepened and broadened our understanding the complexity and heterogeneity of AML, which are difficult to detect with bulk RNA-seq. This review encapsulates the burgeoning body of knowledge generated through scRNA-seq, providing the novel insights and discoveries it has unveiled in AML biology. Furthermore, we discuss the potential implications of scRNA-seq in therapeutic opportunities, focusing on immunotherapy. Finally, we highlight the current limitations and future direction of scRNA-seq in the field.

Transcriptome Analysis of Diffuse Large B-Cell Lymphoma Cells Inducibly Expressing MyD88 L265P Mutation Identifies Upregulated CD44, LGALS3, NFKBIZ, and BATF as Downstream Targets of Oncogenic NF-κB Signaling

During innate immune responses, myeloid differentiation primary response 88 (MyD88) functions as a critical signaling adaptor protein integrating stimuli from toll-like receptors (TLR) and the interleukin-1 receptor (IL-1R) family and translates them into specific cellular outcomes. In B cells, somatic mutations in MyD88 trigger oncogenic NF-κB signaling independent of receptor stimulation, which leads to the development of B-cell malignancies. However, the exact molecular mechanisms and downstream signaling targets remain unresolved. We established an inducible system to introduce MyD88 to lymphoma cell lines and performed transcriptomic analysis (RNA-seq) to identify genes differentially expressed by MyD88 bearing the L265P oncogenic mutation. We show that MyD88L265P activates NF-κB signaling and upregulates genes that might contribute to lymphomagenesis, including CD44, LGALS3 (coding Galectin-3), NFKBIZ (coding IkBƺ), and BATF. Moreover, we demonstrate that CD44 can serve as a marker of the activated B-cell (ABC) subtype of diffuse large B-cell lymphoma (DLBCL) and that CD44 expression is correlated with overall survival in DLBCL patients. Our results shed new light on the downstream outcomes of MyD88L265P oncogenic signaling that might be involved in cellular transformation and provide novel therapeutical targets.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Prognostic characteristics of immune subtypes associated with acute myeloid leukemia and their identification in cell subsets based on single-cell sequencing analysis

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration. AML patients were divided into high and low immune cell infiltration clusters based on ssGSEA results. Immune-related pathways, AML pathways and glucose metabolism pathways were enriched in the high immune cell infiltration cluster. Then we screened the differential immune genes between the two immune cell infiltration clusters. Nine prognostic immune genes were finally identified in the train set by LASSO-Cox regression. We constructed a model in the train set based on the nine prognostic immune genes and validated the predictive capability in the test set. The areas under the ROC curve of the train set and the test set for ROC at 1, 3, 5 years were 0.807, 0.813, 0.815, and 0.731, 0.745, 0.830, respectively. The areas under ROC curve of external validation set in 1, 3, and 5 years were 0.564, 0.619, and 0.614, respectively. People with high risk scores accompanied by high TMB had been detected with the worst prognosis. Single-cell sequencing analysis revealed the expression of prognostic genes in AML cell subsets and pseudo-time analysis described the differentiation trajectory of cell subsets. In conclusion, our results reveal the characteristics of immune microenvironment and cell subsets of AML, while it still needs to be confirmed in larger samples studies. The prognosis model constructed with nine key immune genes can provide a new method to assess the prognosis of AML patients.

Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a heterogeneous and aggressive blood cancer that results from diverse genetic aberrations in the hematopoietic stem or progenitor cells (HSPCs) leading to the expansion of blasts in the hematopoietic system. The heterogeneity and evolution of cancer blasts can render therapeutic interventions ineffective in a yet poorly understood patient-specific manner. In this study, we investigated the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs at genetic and transcriptional levels, and unveiled the underlying pathways and genes contributing to recurrence.

Methods

Whole-exome sequencing was used to detect somatic mutations and large copy number variations (CNVs). Single cell RNA-seq was performed to investigate the clonal heterogeneity between Dx-Re pairs and amongst patients.

Results

scRNA-seq analysis revealed extensive expression differences between patients and Dx-Re pairs, even for those with the same -presumed- initiating events. Transcriptional differences between and within patients are associated with clonal composition and evolution, with the most striking differences in patients that gained large-scale copy number variations at relapse. These differences appear to have significant molecular implications, exemplified by a DNMT3A/FLT3-ITD patient where the leukemia switched from an AP-1 regulated clone at Dx to a mTOR signaling driven clone at Re. The two distinct AML1-ETO pairs share genes related to hematopoietic stem cell maintenance and cell migration suggesting that the Re leukemic stem cell-like (LSC-like) cells evolved from the Dx cells.

Conclusions

In summary, the single cell RNA data underpinned the tumor heterogeneity not only amongst patient blasts with similar initiating mutations but also between each Dx-Re pair. Our results suggest alternatively and currently unappreciated and unexplored mechanisms leading to therapeutic resistance and AML recurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01635-4.

Longitudinal single-cell transcriptomics reveals distinct patterns of recurrence in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a heterogeneous and aggressive blood cancer that results from diverse genetic aberrations in the hematopoietic stem or progenitor cells (HSPCs) leading to the expansion of blasts in the hematopoietic system. The heterogeneity and evolution of cancer blasts can render therapeutic interventions ineffective in a yet poorly understood patient-specific manner. In this study, we investigated the clonal heterogeneity of diagnosis (Dx) and relapse (Re) pairs at genetic and transcriptional levels, and unveiled the underlying pathways and genes contributing to recurrence.

METHODS

Whole-exome sequencing was used to detect somatic mutations and large copy number variations (CNVs). Single cell RNA-seq was performed to investigate the clonal heterogeneity between Dx-Re pairs and amongst patients.

RESULTS

scRNA-seq analysis revealed extensive expression differences between patients and Dx-Re pairs, even for those with the same -presumed- initiating events. Transcriptional differences between and within patients are associated with clonal composition and evolution, with the most striking differences in patients that gained large-scale copy number variations at relapse. These differences appear to have significant molecular implications, exemplified by a DNMT3A/FLT3-ITD patient where the leukemia switched from an AP-1 regulated clone at Dx to a mTOR signaling driven clone at Re. The two distinct AML1-ETO pairs share genes related to hematopoietic stem cell maintenance and cell migration suggesting that the Re leukemic stem cell-like (LSC-like) cells evolved from the Dx cells.

CONCLUSIONS

In summary, the single cell RNA data underpinned the tumor heterogeneity not only amongst patient blasts with similar initiating mutations but also between each Dx-Re pair. Our results suggest alternatively and currently unappreciated and unexplored mechanisms leading to therapeutic resistance and AML recurrence.

Comprehensive Analysis to Identify SPP1 as a Prognostic Biomarker in Cervical Cancer

Background: SPP1, secreted phosphoprotein 1, is a member of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. Previous studies have proven SPP1 overexpressed in a variety of cancers and can be identified as a prognostic factor, while no study has explored the function and carcinogenic mechanism of SPP1 in cervical cancer. Methods: We aimed to demonstrate the relationship between SPP1 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated SPP1 expression of cervical cancer in the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of SPP1 as a differentiating factor by the area under curve (AUC) score. Cox regression and logistic regression were performed to evaluate factors associated with prognosis. The SPP1-binding protein network was built by the STRING tool. Enrichment analysis by the R package clusterProfiler was used to explore potential function of SPP1. The single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER database were used to investigate the association between the immune infiltration level and SPP1 expression in cervical cancer. Results: Pan-cancer data analysis showed that SPP1 expression was higher in most cancer types, including cervical cancer, and we got the same result in the GEO database. The ROC curve suggested that SPP1 could be a potential diagnostic biomarker (AUC = 0.877). High SPP1 expression was associated with poorer overall survival (OS) (P = 0.032). Further enrichment and immune infiltration analysis revealed that high SPP1 expression was correlated with regulating the infiltration level of neutrophil cells and some immune cell types, including macrophage and DC. Conclusion: SPP1 expression was higher in cervical cancer tissues than in normal cervical epithelial tissues. It was significantly associated with poor prognosis and immune cell infiltration. Thus, SPP1 may become a promising prognostic biomarker for cervical cancer patients.

Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

Background

The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD.

Methods

With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset.

Results

The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival.

Conclusion

We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

Galectin-3 Inhibitors Suppress Anoikis Resistance and Invasive Capacity in Thyroid Cancer Cells

Accumulating evidence suggests that galectin-3 is a histologic marker of thyroid cancer. However, the pharmacological lectin-based approach has not been well studied. In the present study, we aimed to investigate the therapeutic potential of novel galectin-3 inhibitors by treating thyroid cancer cells with different concentrations of GB1107 or TD139. At high doses, TD139, but not GB1107, reduced cell viability and clonogenicity of thyroid cancer cells. TD139 induced apoptosis of thyroid cancer cells, as evident by an increase in the percentage of sub-G1 cells on cell cycle analysis, caspase-3 activation, and PARP1 cleavage. Either GB1107 or TD139 significantly inhibited cell coherence and counteracted anoikis resistance. Both inhibitors decreased migratory and invasive abilities in a dose-dependent manner. Furthermore, GB1107 and TD139 treatment attenuated AKT phosphorylation and decreased the expression of β-catenin and MMP2. In conclusion, these novel galectin-3 inhibitors suppressed the anoikis resistance, motility, and invasive capacity of thyroid cancer cells at least partly through the AKT/β-catenin pathway. Galectin-3 inhibitors are potentially suitable for preclinical evaluation of treatment and/or prevention of metastatic spread in thyroid cancer.

Expression of Viral microRNAs in Serum and White Blood Cells of Cows Exposed to Bovine Leukemia Virus

Bovine leukemia virus (BLV) affects the health and productivity of cattle. The virus causes abnormal immune function and immunosuppression. MicroRNAs (miRNAs) are involved in gene expression, having been associated with stress and immune response, tumor growth, and viral infection. The objective of this study was to determine the expression of circulating miRNAs produced by BLV in animals exposed to the virus. Sera from 14 animals were collected to establish IgG reactivity to BLV by ELISA, where seven animals were seropositive and seven were seronegative for BLV exposure. White blood cells (WBC) from each animal were also collected and miRNAs were identified by sequencing from sera and WBC. The seropositive group had higher counts of BLV miRNAs when compared to seronegative group in sera and WBC. Blv-miR-1-3p, blv-miR-B2-5p, blv-miR-B4-3p, and blv-miR-B5-5p were statistically significant (P < 0.00001) in serum with an average of 7 log2 fold difference between seropositive and seronegative groups. Blv-miR-B1-3p, blv-miR-B1-5p, blv-miR-B3, blv-miR-B4-3p, blv-miR-B4-5p, blv-miR-B5-5p were statistically significant (P < 1.08e⁻⁹) in WBC with an average of 7 log2 fold difference between the seropositive and the seronegative groups. Blv-miR-B2-3p and blv-miR-B2-5p were also statistically significant in WBC (P < 2.79e-17), with an average of 27 log2 fold difference between the seropositive and the seronegative groups. There were 18 genes identified as being potential targets for blv-miR-B1-5p, and 3 genes for blv-miR-B4-5p. Gene ontology analysis indicated that the target genes are mainly involved in the response to stress and in the immune system process. Several of the identified genes have been associated with leukemia development in humans and cattle. Differential expression of genes targeted by BLV miRNAs should be evaluated to determine their effect in BLV replication.

The Challenge of Classifying Metastatic Cell Properties by Molecular Profiling Exemplified with Cutaneous Melanoma Cells and Their Cerebral Metastasis from Patient Derived Mouse Xenografts

The prediction of metastatic properties from molecular analyses still poses a major challenge. Here we aimed at the classification of metastasis-related cell properties by proteome profiling making use of cutaneous and brain-metastasizing variants from single melanomas sharing the same genetic ancestry. Previous experiments demonstrated that cultured cells derived from these xenografted variants maintain a stable phenotype associated with a differential metastatic behavior: The brain metastasizing variants produce more spontaneous micro-metastases than the corresponding cutaneous variants. Four corresponding pairs of cutaneous and metastatic cells were obtained from four individual patients, resulting in eight cell-lines presently investigated. Label free proteome profiling revealed significant differences between corresponding pairs of cutaneous and cerebellar metastases from the same patient. Indeed, each brain metastasizing variant expressed several apparently metastasis-associated proteomic alterations as compared with the corresponding cutaneous variant. Among the differentially expressed proteins we identified cell adhesion molecules, immune regulators, epithelial to mesenchymal transition markers, stem cell markers, redox regulators and cytokines. Similar results were observed regarding eicosanoids, considered relevant for metastasis, such as PGE2 and 12-HETE. Multiparametric morphological analysis of cells also revealed no characteristic alterations associated with the cutaneous and brain metastasis variants. However, no correct classification regarding metastatic potential was yet possible with the present data. We thus concluded that molecular profiling is able to classify cells according to known functional categories but is not yet able to predict relevant cell properties emerging from networks consisting of many interconnected molecules. The presently observed broad diversity of molecular patterns, irrespective of restricting to one tumor type and two main classes of metastasis, highlights the important need to develop meta-analysis strategies to predict cell properties from molecular profiling data. Such base knowledge will greatly support future individualized precision medicine approaches.