Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets

Epithelial-mesenchymal transition promotes metabolic reprogramming to suppress ferroptosis

Epithelial-mesenchymal transition (EMT) is a cellular de-differentiation process that provides cells with the increased plasticity and stem cell-like traits required during embryonic development, tissue remodeling, wound healing and metastasis. Morphologically, EMT confers tumor cells with fibroblast-like properties that lead to the rearrangement of cytoskeleton (loss of stiffness) and decrease of membrane rigidity by incorporating high level of poly-unsaturated fatty acids (PUFA) in their phospholipid membrane. Although large amounts of PUFA in membrane reduces rigidity and offers capabilities for tumor cells with the unbridled ability to stretch, bend and twist in metastasis, these PUFA are highly susceptible to lipid peroxidation, which leads to the breakdown of membrane integrity and, ultimately results in ferroptosis. To escape the ferroptotic risk, EMT also triggers the rewiring of metabolic program, particularly in lipid metabolism, to enforce the epigenetic regulation of EMT and mitigate the potential damages from ferroptosis. Thus, the interplay among EMT, lipid metabolism, and ferroptosis highlights a new layer of intricated regulation in cancer biology and metastasis. Here we summarize the latest findings and discuss these mutual interactions. Finally, we provide perspectives of how these interplays contribute to cellular plasticity and ferroptosis resistance in metastatic tumor cells that can be explored for innovative therapeutic interventions.

Phosphoproteomics predict response to midostaurin plus chemotherapy in independent cohorts of FLT3-mutated acute myeloid leukaemia

BACKGROUND

Acute myeloid leukaemia (AML) is a bone marrow malignancy with poor prognosis. One of several treatments for AML is midostaurin combined with intensive chemotherapy (MIC), currently approved for FLT3 mutation-positive (FLT3-MP) AML. However, many patients carrying FLT3 mutations are refractory or experience an early relapse following MIC treatment, and might benefit more from receiving a different treatment. Development of a stratification method that outperforms FLT3 mutational status in predicting MIC response would thus benefit a large number of patients.

METHODS

We employed mass spectrometry phosphoproteomics to analyse 71 diagnosis samples of 47 patients with FLT3-MP AML who subsequently received MIC. We then used machine learning to identify biomarkers of response to MIC, and validated the resulting predictive model in two independent validation cohorts (n = 20).

FINDINGS

We identified three distinct phosphoproteomic AML subtypes amongst long-term survivors. The subtypes showed similar duration of MIC response, but different modulation of AML-implicated pathways, and exhibited distinct, highly-predictive biomarkers of MIC response. Using these biomarkers, we built a phosphoproteomics-based predictive model of MIC response, which we called MPhos. When applied to two retrospective real-world patient test cohorts (n = 20), MPhos predicted MIC response with 83% sensitivity and 100% specificity (log-rank p < 7∗10-5, HR = 0.005 [95% CI: 0-0.31]).

INTERPRETATION

In validation, MPhos outperformed the currently-used FLT3-based stratification method. Our findings have the potential to transform clinical decision-making, and highlight the important role that phosphoproteomics is destined to play in precision oncology.

FUNDING

This work was funded by Innovate UK grants (application numbers: 22217 and 10054602) and by Kinomica Ltd.

Application of AI on cholangiocarcinoma

Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

Construction of an ER stress-related prognostic signature for predicting prognosis and screening the effective anti-tumor drug in osteosarcoma

BACKGROUND

Osteosarcoma is the most common malignant primary bone tumor in infants and adolescents. The lack of understanding of the molecular mechanisms underlying osteosarcoma progression and metastasis has contributed to a plateau in the development of current therapies. Endoplasmic reticulum (ER) stress has emerged as a significant contributor to the malignant progression of tumors, but its potential regulatory mechanisms in osteosarcoma progression remain unknown.

METHODS

In this study, we collected RNA sequencing and clinical data of osteosarcoma from The TCGA, GSE21257, and GSE33382 cohorts. Differentially expressed analysis and the least absolute shrinkage and selection operator regression analysis were conducted to identify prognostic genes and construct an ER stress-related prognostic signature (ERSRPS). Survival analysis and time dependent ROC analysis were performed to evaluate the predictive performance of the constructed prognostic signature. The "ESTIMATE" package and ssGSEA algorithm were utilized to evaluate the differences in immune cells infiltration between the groups. Cell-based assays, including CCK-8, colony formation, and transwell assays and co-culture system were performed to assess the effects of the target gene and small molecular drug in osteosarcoma. Animal models were employed to assess the anti-osteosarcoma effects of small molecular drug.

RESULTS

Five genes (BLC2, MAGEA3, MAP3K5, STC2, TXNDC12) were identified to construct an ERSRPS. The ER stress-related gene Stanniocalcin 2 (STC2) was identified as a risk gene in this signature. Additionally, STC2 knockdown significantly inhibited osteosarcoma cell proliferation, migration, and invasion. Furthermore, the ER stress-related gene STC2 was found to downregulate the expression of MHC-I molecules in osteosarcoma cells, and mediate immune responses through influencing the infiltration and modulating the function of CD8+ T cells. Patients categorized by risk scores showed distinct immune status, and immunotherapy response. ISOX was subsequently identified and validated as an effective anti-osteosarcoma drug through a combination of CMap database screening and in vitro and in vivo experiments.

CONCLUSION

The ERSRPS may guide personalized treatment decisions for osteosarcoma, and ISOX holds promise for repurposing in osteosarcoma treatment.

[Inhibition of Histone Deacetylase 6 Ameliorates Podocyte Injury in Diabetic Kidney Disease in Mice]

Objective

To investigate the role of histone deacetylase 6 (HDAC6) in podocyte injury in diabetic kidney disease (DKD) in mice.

Methods

1) The 8-week-old male CD-1 mice were selected to construct the model of DKD with streptozocin (STZ). After the model was established, the mice were intraperitoneally injected with HDAC6 inhibitor CAY10603 (5mg/kg/daily) or same volume vehicle as control. The mice were divided into four groups, control (CTL)+vehicle (Veh) (n=5), CLT+CAY10603 (n=3), STZ+Veh (n=9), and STZ+CAY10603 (n=7). Mice in STZ+Veh and STZ+CAY10603 groups developed DKD, while mice in the CTL+Veh and CTL+CAY10603 groups were served as normal controls. The therapeutic effect was evaluated through urine albumin-to-creatinine ratio (uACR) and renal pathology after the 2-week treatment with CAY10603. 2) Human podocytes were cultured in vitro and were divided into four groups as follows: CTL, transforming growth factor-β (TGFβ), TGFβ+CAY10603 (250 nmol/L), and TGFβ+CAY10603 (500 nmol/L) groups. The control group did not receive any treatment, the last three groups were given 36-h TGFβ treatment at 5 ng/µL, with or without CAY10603 as indicated for an additional 12 h. Western blot was performed to determine the inhibitory effect of CAY10603 on NLRP3 inflammasome. 3) HDAC6 knockout (KO) mice were generated and used to create STZ-induced model of DKD. The mice were divided into four groups: C57BL/6J wild type (WT) (n=6), HDAC6 KO (n=6), WT+STZ (n=10), and HDAC6 KO+STZ (n=9). Samples were collected 16 weeks after successful modeling and changes in uACR and renal pathology were evaluated accordingly.

Results

After 2 weeks of treatment, mice in the STZ+CAY10603 group exhibited reduction in uACR (P<0.05) and inhibition of glomerular mesangium expansion (P<0.05) compared with those of the mice in the STZ+Veh group. There was no statistically significant difference in the indicators between the CTL+Veh group and the CTL+CAY10603 group. In vitro cultured podocytes, compared with the control group, NLRP3 inflammasome activation was seen in the TGFβ group. CAY10603 treatment significantly inhibited the activation of NLRP3 in the dosage-dependent manner (P<0.05). Compared with those of the WT group, the WT+STZ group showed increased uACR (P<0.05), obvious glomerulosclerosis and loss of podocytes numbers. Compared with those of the WT+STZ group, the HDAC6 KO+STZ group showed effectively reduction of uACR (P<0.05) and improvement in the renal pathological changes in mice. There was no significant difference in these aspects between the WT and HDAC6 KO groups.

Conclusion

Inhibition of HDAC6 alleviates proteinuria and podocyte injury in the mouse model of DKD by suppressing the activation of NLRP3 inflammasome.

Elucidating the causal association between gut microbiota and intrahepatic cholangiocarcinoma through Mendelian randomization analysis

Background

Intrahepatic cholangiocarcinoma (ICC) is an aggressive liver cancer with poor prognosis. The gut microbiota has been linked to ICC, but evidence for causality is lacking. Elucidating causal gut microbiota-ICC links could inform prevention and treatment strategies.

Materials and methods

We performed a bidirectional two-sample Mendelian randomization (MR) study to investigate causal associations between gut microbiota and ICC risk. Genome-wide significant single nucleotide polymorphisms (SNPs) associated with gut microbiota abundances were utilized as instrumental variables (IVs). Multiple methods assessed causality and sensitivity analyses evaluated result robustness. Bioinformatics analysis of genetic loci linked to gut microbiota and ICC examined potential mechanisms.

Results

Genetically predicted increases in Veillonellaceae, Alistipes, Enterobacteriales, and Firmicutes were suggestively associated with higher ICC risk, while increases in Anaerostipes, Paraprevotella, Parasutterella, and Verrucomicrobia appeared protective. Bioinformatics analysis revealed differentially expressed genes near gut microbiota-associated loci may influence ICC through regulating pathways and tumor immune microenvironment.

Conclusion

Our findings provide suggestive evidence for causal links between specific gut microbiota and ICC risk.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

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.

Unraveling the actin cytoskeleton in the malignant transformation of cholangiocyte biology

Correct actin cytoskeleton organization is vital in the liver organ homeostasis and disease control. Rearrangements of the actin cytoskeleton may play a vital role in the bile duct cells cholangiocytes. An abnormal actin network leads to aberrant cell morphology, deregulated signaling networks and ultimately triggering the development of cholangiocarcinoma (CCA) and paving the route for cancer cell dissemination (metastasis). In this review, we will outline alterations of the actin cytoskeleton and the potential role of this dynamic network in initiating CCA, as well as regulating the course of this malignancy. Actin rearrangements not only occur because of signaling pathways, but also regulate and modify cellular signaling. This emphasizes the importance of the actin cytoskeleton itself as cause for aberrant signaling and in promoting tumorigenic phenotypes. We will highlight the impact of aberrant signaling networks on the actin cytoskeleton and its rearrangement as potential cause for CCA. Often, these exact mechanisms in CCA are limited understood and still must be elucidated. Indeed, focusing future research on how actin affects and regulates other signaling pathways may provide more insights into the mechanisms of CCA development, progression, and metastasis. Moreover, manipulation of the actin cytoskeleton organization highlights the potential for a novel therapeutic area.

A persistent neutrophil-associated immune signature characterizes post-COVID-19 pulmonary sequelae

Interstitial lung disease and associated fibrosis occur in a proportion of individuals who have recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through unknown mechanisms. We studied individuals with severe coronavirus disease 2019 (COVID-19) after recovery from acute illness. Individuals with evidence of interstitial lung changes at 3 to 6 months after recovery had an up-regulated neutrophil-associated immune signature including increased chemokines, proteases, and markers of neutrophil extracellular traps that were detectable in the blood. Similar pathways were enriched in the upper airway with a concomitant increase in antiviral type I interferon signaling. Interaction analysis of the peripheral phosphoproteome identified enriched kinases critical for neutrophil inflammatory pathways. Evaluation of these individuals at 12 months after recovery indicated that a subset of the individuals had not yet achieved full normalization of radiological and functional changes. These data provide insight into mechanisms driving development of pulmonary sequelae during and after COVID-19 and provide a rational basis for development of targeted approaches to prevent long-term complications.

Proteomic and Phosphoproteomic Profiling Reveals the Oncogenic Role of Protein Kinase D Family Kinases in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a lethal malignancy in the hepatobiliary system, with dysregulated protein expression and phosphorylation signaling. However, the protein and phosphorylation signatures of CCAs are little-known. Here, we performed the proteomic and phosphoproteomic profiling of tumors and normal adjacent tissues (NATs) from patients with CCA and predicted eleven PKs high-potentially related to CCA with a comprehensive inference of the functional protein kinases (PKs) (CifPK) pipeline. Besides the two known CCA-associated PKs, we screened the remaining candidates and uncovered five PKs as novel regulators in CCA. Specifically, the protein kinase D (PKD) family members, including PRKD1, PRKD2, and PRKD3, were identified as critical regulators in CCA. Moreover, the pan-inhibitor of the PKD family, 1-naphthyl PP1 (1-NA-PP1), was validated as a potent agent for inhibiting the proliferation, migration, and invasion ability of CCA cells. This study reveals new PKs associated with CCA and suggests PRKD kinases as novel treatment targets for CCA.

Temporal dynamics from phosphoproteomics using endoscopic biopsy specimens provides new therapeutic targets in stage IV gastric cancer

Phosphoproteomic analysis expands our understanding of cancer biology. However, the feasibility of phosphoproteomic analysis using endoscopically collected tumor samples, especially with regards to dynamic changes upon drug treatment, remains unknown in stage IV gastric cancer. Here, we conducted a phosphoproteomic analysis using paired endoscopic biopsy specimens of pre- and post-treatment tumors (Ts) and non-tumor adjacent tissues (NATs) obtained from 4 HER2-positive gastric cancer patients who received trastuzumab-based treatment and from pre-treatment Ts and NATs of 4 HER2-negative gastric cancer patients. Our analysis identified 14,622 class 1 phosphosites with 12,749 quantified phosphosites and revealed molecular changes by HER2 positivity and treatment. An inhibitory signature of the ErbB signaling was observed in the post-treatment HER2-positive T group compared with the pre-treatment HER2-positive T group. Phosphoproteomic profiles obtained by a case-by-case review using paired pre- and post-treatment HER2-positive T could be utilized to discover predictive or resistant biomarkers. Furthermore, these data nominated therapeutic kinase targets which were exclusively activated in the patient unresponded to the treatment. The present study suggests that a phosphoproteomic analysis of endoscopic biopsy specimens provides information on dynamic molecular changes which can individually characterize biologic features upon drug treatment and identify therapeutic targets in stage IV gastric cancer.