Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance

Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.

Zanubrutinib delays selinexor resistance evolution in biopsy sample-derived primary central nervous system lymphoma models

Primary central nervous system lymphoma (PCNSL) is a rare and aggressive lymphoma of the brain with poor prognosis. The scarcity of cell lines established using PCNSL makes it difficult to conduct preclinical studies on new drugs. We aimed to explore the effect of selinexor combined with zanubrutinib in PCNSL using established PCNSL cells and an orthotopic PCNSL model. Primary PCNSL cells were successfully cultured. Selinexor inhibited proliferation, induced G1 phase arrest, and promoted apoptosis, however, induced drug resistance in PCNSL. Selinexor combined with zanubrutinib had a synergistic effect on PCNSL and prevented the onset of selinexor resistance in PCNSL by inhibiting AKT signaling. Moreover, selinexor combined with zanubrutinib notably slowed tumor growth and prolonged survival compared to that of the control. Overall, the addition of zanubrutinib to selinexor monotreatment had a synergistic effect in vitro and prolonged survival in vivo.

The nuclear export protein exportin-1 in solid malignant tumours: From biology to clinical trials

BACKGROUND

Exportin-1 (XPO1), a crucial protein regulating nuclear-cytoplasmic transport, is frequently overexpressed in various cancers, driving tumor progression and drug resistance. This makes XPO1 an attractive therapeutic target. Over the past few decades, the number of available nuclear export-selective inhibitors has been increasing. Only KPT-330 (selinexor) has been successfully used for treating haematological malignancies, and KPT-8602 (eltanexor) has been used for treating haematologic tumours in clinical trials. However, the use of nuclear export-selective inhibitors for the inhibition of XPO1 expression has yet to be thoroughly investigated in clinical studies and therapeutic outcomes for solid tumours.

METHODS

We collected numerous literatures to explain the efficacy of XPO1 Inhibitors in preclinical and clinical studies of a wide range of solid tumours.

RESULTS

In this review, we focus on the nuclear export function of XPO1 and results from clinical trials of its inhibitors in solid malignant tumours. We summarized the mechanism of action and therapeutic potential of XPO1 inhibitors, as well as adverse effects and response biomarkers.

CONCLUSION

XPO1 inhibition has emerged as a promising therapeutic strategy in the fight against cancer, offering a novel approach to targeting tumorigenic processes and overcoming drug resistance. SINE compounds have demonstrated efficacy in a wide range of solid tumours, and ongoing research is focused on optimizing their use, identifying response biomarkers, and developing effective combination therapies.

KEY POINTS

Exportin-1 (XPO1) plays a critical role in mediating nucleocytoplasmic transport and cell cycle. XPO1 dysfunction promotes tumourigenesis and drug resistance within solid tumours. The therapeutic potential and ongoing researches on XPO1 inhibitors in the treatment of solid tumours. Additional researches are essential to address safety concerns and identify biomarkers for predicting patient response to XPO1 inhibitors.

Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics

Improving coverage, robustness, and sensitivity is crucial for routine phosphoproteomics analysis by single-shot liquid chromatography-tandem mass spectrometry (LC-MS/MS) from minimal peptide inputs. Here, we systematically optimized key experimental parameters for automated on-bead phosphoproteomics sample preparation with a focus on low-input samples. Assessing the number of identified phosphopeptides, enrichment efficiency, site localization scores, and relative enrichment of multiply-phosphorylated peptides pinpointed critical variables influencing the resulting phosphoproteome. Optimizing glycolic acid concentration in the loading buffer, percentage of ammonium hydroxide in the elution buffer, peptide-to-beads ratio, binding time, sample, and loading buffer volumes allowed us to confidently identify >16,000 phosphopeptides in half-an-hour LC-MS/MS on an Orbitrap Exploris 480 using 30 μg of peptides as starting material. Furthermore, we evaluated how sequential enrichment can boost phosphoproteome coverage and showed that pooling fractions into a single LC-MS/MS analysis increased the depth. We also present an alternative phosphopeptide enrichment strategy based on stepwise addition of beads thereby boosting phosphoproteome coverage by 20%. Finally, we applied our optimized strategy to evaluate phosphoproteome depth with the Orbitrap Astral MS using a cell dilution series and were able to identify >32,000 phosphopeptides from 0.5 million HeLa cells in half-an-hour LC-MS/MS using narrow-window data-independent acquisition (nDIA).

Kinase activities in pancreatic ductal adenocarcinoma with prognostic and therapeutic avenues

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read-out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)-based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho-subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho-subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype-associated activity profiles can guide therapeutic combination approaches in tumor and stroma-enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Therapeutic targeting of nuclear export and import receptors in cancer and their potential in combination chemotherapy

Systemic modalities are crucial in the management of disseminated malignancies and liquid tumours. However, patient responses and tolerability to treatment are generally poor and those that enter remission often return with refractory disease. Combination therapies provide a methodology to overcome chemoresistance mechanisms and address dose-limiting toxicities. A deeper understanding of tumorigenic processes at the molecular level has brought a targeted therapy approach to the forefront of cancer research, and novel cancer biomarkers are being identified at a rapid rate, with some showing potential therapeutic benefits. The Karyopherin superfamily of proteins is soluble receptors that mediate nucleocytoplasmic shuttling of proteins and RNAs, and recently, nuclear transport receptors have been recognized as novel anticancer targets. Inhibitors against nuclear export have been approved for clinical use against certain cancer types, whereas inhibitors against nuclear import are in preclinical stages of investigation. Mechanistically, targeting nucleocytoplasmic shuttling has shown to abrogate oncogenic signalling and restore tumour suppressor functions through nuclear sequestration of relevant proteins and mRNAs. Hence, nuclear transport inhibitors display broad spectrum anticancer activity and harbour potential to engage in synergistic interactions with a wide array of cytotoxic agents and other targeted agents. This review is focussed on the most researched nuclear transport receptors in the context of cancer, XPO1 and KPNB1, and highlights how inhibitors targeting these receptors can enhance the therapeutic efficacy of standard of care therapies and novel targeted agents in a combination therapy approach. Furthermore, an updated review on the therapeutic targeting of lesser characterized karyopherin proteins is provided and resistance to clinically approved nuclear export inhibitors is discussed.

High Mitochondrial Protein Expression as a Potential Predictor of Relapse Risk in Acute Myeloid Leukemia Patients with the Monocytic FAB Subtypes M4 and M5

AML is a highly aggressive and heterogeneous form of hematological cancer. Proteomics-based stratification of patients into more refined subgroups may contribute to a more precise characterization of the patient-derived AML cells. Here, we reanalyzed liquid chromatography-tandem mass spectrometry (LC-MS/MS) generated proteomic and phosphoproteomic data from 26 FAB-M4/M5 patients. The patients achieved complete hematological remission after induction therapy. Twelve of them later developed chemoresistant relapse (RELAPSE), and 14 patients were relapse-free (REL_FREE) long-term survivors. We considered not only the RELAPSE and REL_FREE characteristics but also integrated the French-American-British (FAB) classification, along with considering the presence of nucleophosmin 1 (NPM1) mutation and cytogenetically normal AML. We found a significant number of differentially enriched proteins (911) and phosphoproteins (257) between the various FAB subtypes in RELAPSE patients. Patients with the myeloblastic M1/M2 subtype showed higher levels of RNA processing-related routes and lower levels of signaling related to terms like translation and degranulation when compared with the M4/M5 subtype. Moreover, we found that a high abundance of proteins associated with mitochondrial translation and oxidative phosphorylation, particularly observed in the RELAPSE M4/M5 NPM1 mutated subgroup, distinguishes relapsing from non-relapsing AML patient cells with the FAB subtype M4/M5. Thus, the discovery of subtype-specific biomarkers through proteomic profiling may complement the existing classification system for AML and potentially aid in selecting personalized treatment strategies for individual patients.

Acid Ceramidase Inhibitor LCL-805 Antagonizes Akt Signaling and Promotes Iron-Dependent Cell Death in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy requiring urgent treatment advancements. Ceramide is a cell-death-promoting signaling lipid that plays a central role in therapy-induced cell death. We previously determined that acid ceramidase (AC), a ceramide-depleting enzyme, is overexpressed in AML and promotes leukemic survival and drug resistance. The ceramidase inhibitor B-13 and next-generation lysosomal-localizing derivatives termed dimethylglycine (DMG)-B-13 prodrugs have been developed but remain untested in AML. Here, we report the in vitro anti-leukemic efficacy and mechanism of DMG-B-13 prodrug LCL-805 across AML cell lines and primary patient samples. LCL-805 inhibited AC enzymatic activity, increased total ceramides, and reduced sphingosine levels. A median EC50 value of 11.7 μM was achieved for LCL-805 in cell viability assays across 32 human AML cell lines. As a single agent tested across a panel of 71 primary AML patient samples, a median EC50 value of 15.8 μM was achieved. Exogenous ceramide supplementation with C6-ceramide nanoliposomes, which is entering phase I/II clinical trial for relapsed/refractory AML, significantly enhanced LCL-805 killing. Mechanistically, LCL-805 antagonized Akt signaling and led to iron-dependent cell death distinct from canonical ferroptosis. These findings elucidated key factors involved in LCL-805 cytotoxicity and demonstrated the potency of combining AC inhibition with exogenous ceramide.

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a 'pseudo-positive' learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ('SnapKin') by incorporating the above two learning strategies into a 'snapshot' ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstrate that SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

Integrative analysis of cancer dependency data and comprehensive phosphoproteomics data revealed the EPHA2-PARD3 axis as a cancer vulnerability in KRAS-mutant colorectal cancer

Colorectal cancer (CRC), a common malignant tumour of the gastrointestinal tract, is a life-threatening cancer worldwide. Mutations in KRAS and BRAF, the major driver mutation subtypes in CRC, activate the RAS pathway, contribute to tumorigenesis in CRC and are being investigated as potential therapeutic targets. Despite recent advances in clinical trials targeting KRASG12C or RAS downstream signalling molecules for KRAS-mutant CRC, there is a lack of effective therapeutic interventions. Therefore, understanding the unique molecular characteristics of KRAS-mutant CRC is essential for identifying molecular targets and developing novel therapeutic interventions. We obtained in-depth proteomics and phosphoproteomics quantitative data for over 7900 proteins and 38 700 phosphorylation sites in cells from 35 CRC cell lines and performed informatic analyses, including proteomics-based coexpression analysis and correlation analysis between phosphoproteomics data and cancer dependency scores of the corresponding phosphoproteins. Our results revealed novel dysregulated protein-protein associations enriched specifically in KRAS-mutant cells. Our phosphoproteomics analysis revealed activation of EPHA2 kinase and downstream tight junction signalling in KRAS-mutant cells. Furthermore, the results implicate the phosphorylation site Y378 in the tight junction protein PARD3 as a cancer vulnerability in KRAS-mutant cells. Together, our large-scale phosphoproteomics and proteomics data across 35 steady-state CRC cell lines represent a valuable resource for understanding the molecular characteristics of oncogenic mutations. Our approach to predicting cancer dependency from phosphoproteomics data identified the EPHA2-PARD3 axis as a cancer vulnerability in KRAS-mutant CRC.

Proteomic Characterization of Acute Myeloid Leukemia for Precision Medicine

Acute myeloid leukemia (AML) is a highly heterogeneous cancer of the hematopoietic system with no cure for most patients. In addition to chemotherapy, treatment options for AML include recently approved therapies that target proteins with roles in AML pathobiology, such as FLT3, BLC2, and IDH1/2. However, due to disease complexity, these therapies produce very diverse responses, and survival rates are still low. Thus, despite considerable advances, there remains a need for therapies that target different aspects of leukemic biology and for associated biomarkers that define patient populations likely to respond to each available therapy. To meet this need, drugs that target different AML vulnerabilities are currently in advanced stages of clinical development. Here, we review proteomics and phosphoproteomics studies that aimed to provide insights into AML biology and clinical disease heterogeneity not attainable with genomic approaches. To place the discussion in context, we first provide an overview of genetic and clinical aspects of the disease, followed by a summary of proteins targeted by compounds that have been approved or are under clinical trials for AML treatment and, if available, the biomarkers that predict responses. We then discuss proteomics and phosphoproteomics studies that provided insights into AML pathogenesis, from which potential biomarkers and drug targets were identified, and studies that aimed to rationalize the use of synergistic drug combinations. When considered as a whole, the evidence summarized here suggests that proteomics and phosphoproteomics approaches can play a crucial role in the development and implementation of precision medicine for AML patients.

Recent advances in kinase signaling network profiling by mass spectrometry

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.