Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Integrated Lactylome Characterization Reveals the Molecular Dynamics of Protein Regulation in Gastrointestinal Cancers

Lysine lactylation (Kla) plays a vital role in several physiological processes. However, the cancer-specific modulation of Kla in gastrointestinal (GI) tumors requires systematic elucidation. Here, global lactylome profiling of cancerous and adjacent tissues is conducted from 40 patients with GI cancer and identified 11698 Kla sites. Lactylome integration revealed that Kla affects proteins involved in hallmark cancer processes, including epigenetic rewiring, metabolic perturbations, and genome instability. Moreover, the study revealed pan-cancer patterns of Kla alterations, among which 37 Kla sites are consistently upregulated in all four GI cancers and are involved in gene regulation. It is further verified that lactylation of CBX3 at K10 mediates its interaction of CBX3 with the epigenetic marker H3K9me3 and facilitates GI cancer progression. Overall, this study provides an invaluable resource for understanding the lactylome landscape in GI cancers, which may provide new paths for drug discovery for these devastating diseases.

Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer

Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current tumor-node-metastasis or Veterans Administration Lung Study Group staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent cohort, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggested potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better progression-free survival and overall survival after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Hypomethylation-associated LINC00987 downregulation induced lung adenocarcinoma progression by inhibiting the phosphorylation-mediated degradation of SND1

DNA methylation, an epigenetic regulatory mechanism dictating gene transcription, plays a critical role in the occurrence and development of cancer. However, the molecular underpinnings of LINC00987 methylation in the regulation of lung adenocarcinoma (LUAD) remain elusive. This study investigated LINC00987 expression in LUAD patients through analysis of The Cancer Genome Atlas data sets. Quantitative real-time polymerase chain reaction (RT-qPCR) and fluorescence in situ hybridization assays were used to assess LINC00987 expression in LUAD. The bisulfite genomic sequence PCR (BSP) assay was used to determine the methylation levels of the LINC00987 promoter. The interaction between LINC00987 and SND1 was elucidated via immunoprecipitation and RNA pull-down assays. The functional significance of LINC00987 and SND1 in Calu-3 and NCI-H1688 cells was evaluated in vitro through CCK-8, EdU, Transwell, flow cytometry, and vasculogenic mimicry (VM) tube formation assays. LINC00987 expression decreased in LUAD concomitant with hypermethylation of the promoter region, while hypomethylation of the LINC00987 promoter in LUAD tissues correlated with tumor progression. Treatment with 5-Aza-CdR augmented LINC00987 expression and inhibited tumor growth. Mechanistically, LINC00987 overexpression impeded LUAD progression and VM through direct binding with SND1, thereby facilitating its phosphorylation and subsequent degradation. Additionally, overexpression of SND1 counteracted the adverse effects of LINC00987 downregulation on cell proliferation, apoptosis, cell migration, invasion, and VM in LUAD in vitro. In conclusion, this pioneering study focuses on the expression and function of LINC00987 and reveals that hypermethylation of the LINC00987 gene may contribute to LUAD progression. LINC00987 has emerged as a potential tumor suppressor gene in tumorigenesis through its binding with SND1 to facilitate its phosphorylation and subsequent degradation.

The proteogenomic landscape of multiple myeloma reveals insights into disease biology and therapeutic opportunities

Multiple myeloma (MM) is a plasma cell malignancy of the bone marrow. Despite therapeutic advances, MM remains incurable, and better risk stratification as well as new therapies are therefore highly needed. The proteome of MM has not been systematically assessed before and holds the potential to uncover insight into disease biology and improved prognostication in addition to genetic and transcriptomic studies. Here we provide a comprehensive multiomics analysis including deep tandem mass tag-based quantitative global (phospho)proteomics, RNA sequencing, and nanopore DNA sequencing of 138 primary patient-derived plasma cell malignancies encompassing treatment-naive MM, plasma cell leukemia and the premalignancy monoclonal gammopathy of undetermined significance, as well as healthy controls. We found that the (phospho)proteome of malignant plasma cells are highly deregulated as compared with healthy plasma cells and is both defined by chromosomal alterations as well as posttranscriptional regulation. A prognostic protein signature was identified that is associated with aggressive disease independent of established risk factors in MM. Integration with functional genetics and single-cell RNA sequencing revealed general and genetic subtype-specific deregulated proteins and pathways in plasma cell malignancies that include potential targets for (immuno)therapies. Our study demonstrates the potential of proteogenomics in cancer and provides an easily accessible resource for investigating protein regulation and new therapeutic approaches in MM.

Characterization of an expanded set of assays for immunomodulatory proteins using targeted mass spectrometry

Immunotherapies are revolutionizing cancer care, but many patients do not achieve durable responses and immune-related adverse events are difficult to predict. Quantifying the hundreds of proteins involved in cancer immunity has the potential to provide biomarkers to monitor and predict tumor response. We previously developed robust, multiplexed quantitative assays for immunomodulatory proteins using targeted mass spectrometry, providing measurements that can be performed reproducibly and harmonized across laboratories. Here, we expand upon those efforts in presenting data from a multiplexed immuno-oncology (IO)-3 assay panel targeting 43 peptides representing 39 immune- and inflammation-related proteins. A suite of novel monoclonal antibodies was generated as assay reagents, and the fully characterized antibodies are made available as a resource to the community. The publicly available dataset contains complete characterization of the assay performance, as well as the mass spectrometer parameters and reagent information necessary for implementation of the assay. Quantification of the proteins will provide benefit to correlative studies in clinical trials, identification of new biomarkers, and improve understanding of the immune response in cancer.

A heterogeneous tumor immune microenvironment of uncommon epidermal growth factor receptor mutant non-small cell lung cancer

Common epidermal growth factor receptor (EGFR) mutations are usually not considered for immunotherapy in non-small cell lung cancer (NSCLC) due to poor efficacy. However, whether uncommon EGFR mutations are suitable for immunotherapy has not been thoroughly studied. Thus, we explored the tumor immune microenvironment (TME) features in uncommon EGFR mutant NSCLC. In this study, a total of 41 patients with EGFR mutations were included, the majority (85.4%) of whom were stage I. Among them, 22 patients harbored common mutations, while 19 patients presented with uncommon mutations. Compared with common mutations, uncommon mutations exhibited more infiltrating T cells and fewer M2 macrophages, upregulated expression of antigen processing and a presentation pathway. Unsupervised clustering based on the mIF profile identified two classes with heterogeneous TME in uncommon mutations. Class 1 featured the absence of PD-1+ cytotoxic T cell infiltration, and class 2 displayed a hotter TME because of the downregulated expression of hypoxia (p < 0.001), oxidative phosphorylation (p = 0.009), and transforming growth factor beta signaling (p = 0.01) pathways as well as increased expression of CTLA4 (p = 0.001) and PDCD1 (p = 0.004). The association of CTLA4 and PDCD1 with TME profiles was validated in a TCGA lung adenocarcinoma cohort with uncommon EGFR mutations. Our study reveals the distinct and heterogeneous TME features in uncommon EGFR mutant NSCLC.

PCAS: An Integrated Tool for Multi-Dimensional Cancer Research Utilizing Clinical Proteomic Tumor Analysis Consortium Data

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Extracellular Matrix Remodeling Alleviates Memory Deficits in Alzheimer's Disease by Enhancing the Astrocytic Autophagy-Lysosome Pathway

Extracellular matrix (ECM) remodeling is strongly linked to Alzheimer's disease (AD) risk; however, the underlying mechanisms are not fully understood. Here, it is found that the injection of chondroitinase ABC (ChABC), mimicking ECM remodeling, into the medial prefrontal cortex (mPFC) reversed short-term memory loss and reduced amyloid-beta (Aβ) deposition in 5xFAD mice. ECM remodeling also reactivated astrocytes, reduced the levels of aggrecan in Aβ plaques, and enhanced astrocyte recruitment to surrounding plaques. Importantly, ECM remodeling enhanced the autophagy-lysosome pathway in astrocytes, thereby mediating Aβ clearance and alleviating AD pathology. ECM remodeling also promoted Aβ plaque phagocytosis by astrocytes by activating the astrocytic phagocytosis receptor MERTK and promoting astrocytic vesicle circulation. The study identified a cellular mechanism in which ECM remodeling activates the astrocytic autophagy-lysosomal pathway and alleviates AD pathology. Targeting ECM remodeling may represent a potential therapeutic strategy for AD and serve as a reference for the treatment of this disease.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Integration of Proteomic and Metabolomic Data Reveals the Lipid Metabolism Disorder in the Liver of Rats Exposed to Simulated Microgravity

Long-term exposure to microgravity is considered to cause liver lipid accumulation, thereby increasing the risk of non-alcoholic fatty liver disease (NAFLD) among astronauts. However, the reasons for this persistence of symptoms remain insufficiently investigated. In this study, we used tandem mass tag (TMT)-based quantitative proteomics techniques, as well as non-targeted metabolomics techniques based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), to comprehensively analyse the relative expression levels of proteins and the abundance of metabolites associated with lipid accumulation in rat liver tissues under simulated microgravity conditions. The differential analysis revealed 63 proteins and 150 metabolites between the simulated microgravity group and the control group. By integrating differentially expressed proteins and metabolites and performing pathway enrichment analysis, we revealed the dysregulation of major metabolic pathways under simulated microgravity conditions, including the biosynthesis of unsaturated fatty acids, linoleic acid metabolism, steroid hormone biosynthesis and butanoate metabolism, indicating disrupted liver metabolism in rats due to weightlessness. Finally, we examined differentially expressed proteins associated with lipid metabolism in the liver of rats exposed to stimulated microgravity. These findings contribute to identifying the key molecules affected by microgravity and could guide the design of rational nutritional or pharmacological countermeasures for astronauts.

TMPRSS2 is a tumor suppressor and its downregulation promotes antitumor immunity and immunotherapy response in lung adenocarcinoma

BACKGROUND

TMPRSS2, a key molecule for SARS-CoV-2 invading human host cells, has an association with cancer. However, its association with lung cancer remains insufficiently unexplored.

METHODS

In five bulk transcriptomics datasets, one single-cell RNA sequencing (scRNA-seq) dataset and one proteomics dataset for lung adenocarcinoma (LUAD), we explored associations between TMPRSS2 expression and immune signatures, tumor progression phenotypes, genomic features, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we performed experimental validation of the bioinformatics findings.

RESULTS

TMPRSS2 expression levels correlated negatively with the enrichment levels of both immune-stimulatory and immune-inhibitory signatures, while they correlated positively with the ratios of immune-stimulatory/immune-inhibitory signatures. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor progression, and worse survival in LUAD. We further validated that TMPRSS2 was downregulated with tumor progression in the LUAD cohort we collected from Jiangsu Cancer Hospital, China. In vitro and in vivo experiments verified the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1.

CONCLUSIONS

TMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a potential link between lung cancer and pneumonia caused by SARS-CoV-2 infection.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

A liquid biopsy assay for the noninvasive detection of lymph node metastases in T1 lung adenocarcinoma

INTRODUCTION

Lung adenocarcinoma (LUAD) is a common pathological type of lung cancer. The presence of lymph node metastasis plays a crucial role in determining the overall treatment approach and long-term prognosis for early LUAD, therefore accurate prediction of lymph node metastasis is essential to guide treatment decisions and ultimately improve patient outcomes.

METHODS

We performed transcriptome sequencing on T1 LUAD patients with positive or negative lymph node metastases and combined this data with The Cancer Genome Atlas Program cohort to identify potential risk molecules at the tissue level. Subsequently, by detecting the expression of these risk molecules by real-time quantitative PCR in serum samples, we developed a model to predict the risk of lymph node metastasis from a training cohort of 96 patients and a validation cohort of 158 patients.

RESULTS

Through transcriptome sequencing analysis of tissue samples, we identified 11 RNA (miR-412, miR-219, miR-371, FOXC1, ID1, MMP13, COL11A1, PODXL2, CXCL13, SPOCK1 and MECOM) associated with positive lymph node metastases in T1 LUAD. As the expression of FOXC1 and COL11A1 was not detected in serum, we constructed a predictive model that accurately identifies patients with positive lymph node metastases using the remaining nine RNA molecules in the serum of T1 LUAD patients. In the training set, the model achieved an area under the curve (AUC) of 0.89, and in the validation set, the AUC was 0.91.

CONCLUSIONS

We have established a new risk prediction model using serum samples from T1 LUAD patients, enabling noninvasive identification of those with positive lymph node metastases.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

A "One-Step" Strategy for the Global Characterization of Core-Fucosylated Glycoproteome

Core fucosylation, a special type of N-linked glycosylation, is important in tumor proliferation, invasion, metastatic potential, and therapy resistance. However, the core-fucosylated glycoproteome has not been extensively profiled due to the low abundance and poor ionization efficiency of glycosylated peptides. Here, a "one-step" strategy has been described for protein core-fucosylation characterization in biological samples. Core-fucosylated peptides can be selectively labeled with a glycosylated probe, which is linked with a temperature-sensitive poly(N-isopropylacrylamide) (PNIPAM) polymer, by mutant endoglycosidase (EndoF3-D165A). The labeled probe can be further removed by wild-type endoglycosidase (EndoF3) in a traceless manner for mass spectrometry (MS) analysis. The feasibility and effectiveness of the "one-step" strategy are evaluated in bovine serum albumin (BSA) spiked with standard core-fucosylated peptides, H1299, and Jurkat cell lines. The "one-step" strategy is then employed to characterize core-fucosylated sites in human lung adenocarcinoma, resulting in the identification of 2494 core-fucosylated sites distributed on 1176 glycoproteins. Further data analysis reveals that 196 core-fucosylated sites are significantly upregulated in tumors, which may serve as potential drug development targets or diagnostic biomarkers. Together, this "one-step" strategy has great potential for use in global and in-depth analysis of the core-fucosylated glycoproteome to promote its mechanism research.

Proteome Analysis Related to Unsaturated Fatty Acid Synthesis by Interfering with Bovine Adipocyte ACSL1 Gene

Unsaturated fatty acids (UFAs) in beef play a vital role in promoting human health. Long-chain fatty acyl-CoA synthase 1 (ACSL1) is a crucial gene for UFA synthesis in bovine adipocytes. To investigate the protein expression profile during UFA synthesis, we performed a proteomic analysis of bovine adipocytes by RNA interference and non-interference with ACSL1 using label-free techniques. A total of 3558 proteins were identified in both the NC and si-treated groups, of which 1428 were differentially expressed proteins (DEPs; fold change ≥ 1.2 or ≤ 0.83 and p-value < 0.05). The enrichment analysis of the DEPs revealed signaling pathways related to UFA synthesis or metabolism, including cAMP, oxytocin, fatty acid degradation, glycerol metabolism, insulin, and the regulation of lipolysis in adipocytes (p-value < 0.05). Furthermore, based on the enrichment analysis of the DEPs, we screened 50 DEPs that potentially influence the synthesis of UFAs and constructed an interaction network. Moreover, by integrating our previously published transcriptome data, this study established a regulatory network involving differentially expressed long non-coding RNAs (DELs), highlighting 21 DEPs and 13 DELs as key genes involved in UFA synthesis. These findings present potential candidate genes for further investigation into the molecular mechanisms underlying UFA synthesis in bovines, thereby offering insights to enhance the quality of beef and contribute to consumer health in future studies.

Comparative analysis of integrative classification methods for multi-omics data

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

Proteomic features of soft tissue tumours in adolescents and young adults

BACKGROUND

Adolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes.

METHODS

To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8).

RESULTS

Biological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients.

CONCLUSIONS

Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.

Associations between immune cell phenotypes and lung cancer subtypes: insights from mendelian randomization analysis

INTRODUCTION

Lung cancer is a common malignant tumor, and different types of immune cells may have different effects on the occurrence and development of lung cancer subtypes, including lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). However, the causal relationship between immune phenotype and lung cancer is still unclear.

METHODS

This study utilized a comprehensive dataset containing 731 immune phenotypes from the European Bioinformatics Institute (EBI) to evaluate the potential causal relationship between immune phenotypes and LUSC and LUAD using the inverse variance weighted (IVW) method in Mendelian randomization (MR). Sensitivity analyses, including MR-Egger intercept, Cochran Q test, and others, were conducted for the robustness of the results. The study results were further validated through meta-analysis using data from the Transdisciplinary Research Into Cancer of the Lung (TRICL) data. Additionally, confounding factors were excluded to ensure the robustness of the findings.

RESULTS

Among the final selection of 729 immune cell phenotypes, three immune phenotypes exhibited statistically significant effects with LUSC. CD28 expression on resting CD4 regulatory T cells (OR 1.0980, 95% CI: 1.0627-1.1344, p < 0.0001) and CD45RA + CD28- CD8 + T cell %T cell (OR 1.0011, 95% CI: 1.0007; 1.0015, p < 0.0001) were associated with increased susceptibility to LUSC. Conversely, CCR2 expression on monocytes (OR 0.9399, 95% CI: 0.9177-0.9625, p < 0.0001) was correlated with a decreased risk of LUSC. However, no significant causal relationships were established between any immune cell phenotypes and LUAD.

CONCLUSION

This study demonstrates that specific immune cell types are associated with the risk of LUSC but not with LUAD. While these findings are derived solely from European populations, they still provide clues for a deeper understanding of the immunological mechanisms underlying lung cancer and may offer new directions for future therapeutic strategies and preventive measures.

Synergy of EGFR and AURKA Inhibitors in KRAS-mutated Non-small Cell Lung Cancers

The most common oncogenic driver mutations for non-small cell lung cancer (NSCLC) activate EGFR or KRAS. Clinical trials exploring treatments for EGFR- or KRAS-mutated (EGFRmut or KRASmut) cancers have focused on small-molecule inhibitors targeting the driver mutations. Typically, these inhibitors perform more effectively based on combination with either chemotherapies, or other targeted therapies. For EGFRmut NSCLC, a combination of inhibitors of EGFR and Aurora-A kinase (AURKA), an oncogene commonly overexpressed in solid tumors, has shown promising activity in clinical trials. Interestingly, a number of recent studies have indicated that EGFR activity supports overall viability of tumors lacking EGFR mutations, and AURKA expression is abundant in KRASmut cell lines. In this study, we have evaluated dual inhibition of EGFR and AURKA in KRASmut NSCLC models. These data demonstrate synergy between the EGFR inhibitor erlotinib and the AURKA inhibitor alisertib in reducing cell viability and clonogenic capacity in vitro, associated with reduced activity of EGFR pathway effectors, accumulation of enhanced aneuploid cell populations, and elevated cell death. Importantly, the erlotinib-alisertib combination also synergistically reduces xenograft growth in vivo. Analysis of signaling pathways demonstrated that the combination of erlotinib and alisertib was more effective than single-agent treatments at reducing activity of EGFR and pathway effectors following either brief or extended administration of the drugs. In sum, this study indicates value of inhibiting EGFR in KRASmut NSCLC, and suggests the specific value of dual inhibition of AURKA and EGFR in these tumors.

SIGNIFICANCE

The introduction of specific KRAS G12C inhibitors to the clinical practice in lung cancer has opened up opportunities that did not exist before. However, G12C alterations are only a subtype of all KRAS mutations observed. Given the high expression of AURKA in KRASmut NSCLC, our study could point to a potential therapeutic option for this subgroup of patients.

Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK Alterations

Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations, particularly in women. EGFR mutations and anaplastic lymphoma kinase (ALK) fusions are major genetic alterations observed in NSLA, and NSLA with these alterations have been well studied and can be treated with targeted therapies. To provide insights into the molecular profile of NSLA without EGFR and ALK alterations (NENA), we selected 141 NSLA tissues and performed proteogenomic characterization, including whole genome sequencing (WGS), transcriptomic, methylation EPIC array, total proteomic, and phosphoproteomic analyses. Forty patients with NSLA harboring EGFR and ALK alterations and seven patients with NENA with microsatellite instability were excluded. Genome analysis revealed that TP53 (25%), KRAS (22%), and SETD2 (11%) mutations and ROS1 fusions (14%) were the most frequent genetic alterations in NENA patients. Proteogenomic impact analysis revealed that STK11 and ERBB2 somatic mutations had broad effects on cancer-associated genes in NENA. DNA copy number alteration analysis identified 22 prognostic proteins that influenced transcriptomic and proteomic changes. Gene set enrichment analysis revealed estrogen signaling as the key pathway activated in NENA. Increased estrogen signaling was associated with proteogenomic alterations, such as copy number deletions in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14. Finally, saracatinib, an Src inhibitor, was identified as a potential drug for targeting activated estrogen signaling in NENA and was experimentally validated in vitro. Collectively, this study enhanced our understanding of NENA NSLA by elucidating the proteogenomic landscape and proposed saracatinib as a potential treatment for this patient population that lacks effective targeted therapies.

SIGNIFICANCE

The proteogenomic landscape in never-smoker lung cancer without known driver mutations reveals prognostic proteins and enhanced estrogen signaling that can be targeted as a potential therapeutic strategy to improve patient outcomes.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics

BACKGROUND

Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application.

METHODS

Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system.

RESULTS

The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis.

CONCLUSIONS

PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

Proteomic profiling of tumor microenvironment and prognosis risk prediction in stage I lung adenocarcinoma

OBJECTIVES

With the increasing popularity of CT screening, more cases of early-stage lung cancer are being diagnosed. However, 24.5% of stage I non-small-cell lung cancer (NSCLC) patients still experience treatment failure post-surgery. Biomarkers to predict lung cancer patients at high risk of recurrence are needed.

MATERIALS AND METHODS

We collected protein mass spectrometry data from the Taiwan Lung Cancer Moonshot Project and performed bioinformatics analysis on proteins with differential expressions between tumor and adjacent normal tissues in 74 stage I lung adenocarcinoma (LUAD) cases, aiming to explore the tumor microenvironment related prognostic biomarkers. Findings were further validated in 6 external cohorts.

RESULTS

The analysis of differentially expressed proteins revealed that the most enriched categories of diseases and biological functions were cellular movement, immune cell trafficking, and cancer. Utilizing proteomic profiling of the tumor microenvironment, we identified five prognostic biomarkers (ADAM10, MIF, TEK, THBS2, MAOA). We then developed a risk score model, which independently predicted recurrence-free survival and overall survival in stage I LUAD. Patients with high risk scores experienced worse recurrence-free survival (adjusted hazard ratio = 8.28, p < 0.001) and overall survival (adjusted hazard ratio = 6.88, p = 0.013). Findings had been also validated in the external cohorts.

CONCLUSION

The risk score model derived from proteomic profiling of tumor microenvironment can be used to predict recurrence risk and prognosis of stage I LUAD.

Proteomics-Derived Biomarker Panel Facilitates Distinguishing Primary Lung Adenocarcinomas With Intestinal or Mucinous Differentiation From Lung Metastatic Colorectal Cancer

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical (IHC), and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging-based shotgun proteomics to characterize proteomes of formalin-fixed, paraffin-embedded tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest, and the Least Absolute Shrinkage and Selection Operator, were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by IHC to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection. The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of ten candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

MHD-Net: Memory-Aware Hetero-Modal Distillation Network for Thymic Epithelial Tumor Typing With Missing Pathology Modality

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.

Multi-omics characterization of esophageal squamous cell carcinoma identifies molecular subtypes and therapeutic targets

Esophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal cancer and is characterized by an unfavorable prognosis. To elucidate the distinct molecular alterations in ESCC and investigate therapeutic targets, we performed a comprehensive analysis of transcriptomics, proteomics, and phosphoproteomics data derived from 60 paired treatment-naive ESCC and adjacent nontumor tissue samples. Additionally, we conducted a correlation analysis to describe the regulatory relationship between transcriptomic and proteomic processes, revealing alterations in key metabolic pathways. Unsupervised clustering analysis of the proteomics data stratified patients with ESCC into 3 subtypes with different molecular characteristics and clinical outcomes. Notably, subtype III exhibited the worst prognosis and enrichment in proteins associated with malignant processes, including glycolysis and DNA repair pathways. Furthermore, translocase of inner mitochondrial membrane domain containing 1 (TIMMDC1) was validated as a potential prognostic molecule for ESCC. Moreover, integrated kinase-substrate network analysis using the phosphoproteome nominated candidate kinases as potential targets. In vitro and in vivo experiments further confirmed casein kinase II subunit α (CSNK2A1) as a potential kinase target for ESCC. These underlying data represent a valuable resource for researchers that may provide better insights into the biology and treatment of ESCC.

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.

Germline USP36 Mutation Confers Resistance to EGFR-TKIs by Upregulating MLLT3 Expression in Patients with Non-Small Cell Lung Cancer

PURPOSE

Although somatic mutations were explored in depth, limited biomarkers were found to predict the resistance of EGFR tyrosine kinase inhibitors (EGFR-TKI). Previous studies reported N6-methyladenosine (m6A) levels regulated response of EGFR-TKIs; whether the germline variants located in m6A sites affected resistance of EGFR-TKIs is still unknown.

EXPERIMENTAL DESIGN

Patients with non-small cell lung cancer (NSCLC) with EGFR-activating mutation were enrolled to investigate predictors for response of EGFR-TKIs using a genome-wide-variant-m6A analysis. Bioinformatics analysis and series of molecular biology assays were used to uncover the underlying mechanism.

RESULTS

We identified the germline mutation USP36 rs3744797 (C > A, K814N) was associated with survival of patients with NSCLC treated with gefitinib [median progression-free survival (PFS): CC vs. CA, 16.30 vs. 10.50 months, P < 0.0001, HR = 2.45] and erlotinib (median PFS: CC vs. CA, 14.13 vs. 9.47 months, P = 0.041, HR = 2.63). Functionally, the C > A change significantly upregulated USP36 expression by reducing its m6A level. Meanwhile, rs3744797_A (USP36 MUT) was found to facilitate proliferation, migration, and resistance to EGFR-TKIs via upregulating MLLT3 expression in vitro and in vivo. More importantly, MLLT3 and USP36 levels are tightly correlated in patients with NSCLC, which were associated with prognosis of patients. Mechanistically, USP36 MUT stabilized MLLT3 by deubiquitinating MLLT3 in nucleoli and consequently activating its downstream signaling (HIF1α and Snai). Furthermore, inhibition of MLLT3 alleviated USP36 variant-induced EGFR-TKIs resistance in EGFR-mutant NSCLC.

CONCLUSIONS

These findings characterized rs3744797 as an oncogenic variant in mediating EGFR-TKI resistance and tumor aggressiveness through deubiquitinating MLLT3, highlighting the variant as a predictive biomarker for EGFR-TKI response in NSCLC.

Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, but limited knowledge about the regulation and function of most phosphosites restricts our ability to extract meaningful biological insights from phosphoproteomics data. To address this, we combine machine learning and phosphoproteomic data from 1,195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network mapping the co-regulation of 26,280 phosphosites. Integrating network features from CoPheeMap into a machine learning model, CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA reveals 24,015 associations between 9,399 phosphosites and 104 serine/threonine kinases, including many unannotated phosphosites and under-studied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. By applying CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and cancer-associated phosphosites, we demonstrate the effectiveness of these tools in systematically illuminating phosphosites of interest, revealing dysregulated signaling processes in human cancer, and identifying under-studied kinases as putative therapeutic targets.

Widespread alteration of protein autoinhibition in human cancers

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins. Reduced autoinhibition underlies the tumorigenic effect of some known cancer drivers, but whether autoinhibition is altered generally in cancer remains elusive. Here, we demonstrate that cancer-associated missense mutations, in-frame insertions/deletions, and fusion breakpoints are enriched within inhibitory allosteric switches (IASs) across all cancer types. Selection for IASs that are recurrently mutated in cancers identifies established and unknown cancer drivers. Recurrent missense mutations in IASs of these drivers are associated with distinct, cancer-specific changes in molecular signaling. For the specific case of PPP3CA, the catalytic subunit of calcineurin, we provide insights into the molecular mechanisms of altered autoinhibition by cancer mutations using biomolecular simulations, and demonstrate that such mutations are associated with transcriptome changes consistent with increased calcineurin signaling. Our integrative study shows that autoinhibition-modulating genetic alterations are positively selected for by cancer cells.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

A platform-independent AI tumor lineage and site (ATLAS) classifier

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

Integrated proteogenomic characterization of glioblastoma evolution

The evolutionary trajectory of glioblastoma (GBM) is a multifaceted biological process that extends beyond genetic alterations alone. Here, we perform an integrative proteogenomic analysis of 123 longitudinal glioblastoma pairs and identify a highly proliferative cellular state at diagnosis and replacement by activation of neuronal transition and synaptogenic pathways in recurrent tumors. Proteomic and phosphoproteomic analyses reveal that the molecular transition to neuronal state at recurrence is marked by post-translational activation of the wingless-related integration site (WNT)/ planar cell polarity (PCP) signaling pathway and BRAF protein kinase. Consistently, multi-omic analysis of patient-derived xenograft (PDX) models mirror similar patterns of evolutionary trajectory. Inhibition of B-raf proto-oncogene (BRAF) kinase impairs both neuronal transition and migration capability of recurrent tumor cells, phenotypic hallmarks of post-therapy progression. Combinatorial treatment of temozolomide (TMZ) with BRAF inhibitor, vemurafenib, significantly extends the survival of PDX models. This study provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance, highlighting promising therapeutic strategies for clinical intervention.

Comprehensive multi-omics analysis reveals WEE1 as a synergistic lethal target with hyperthermia through CDK1 super-activation

Hyperthermic intraperitoneal chemotherapy's role in ovarian cancer remains controversial, hindered by limited understanding of hyperthermia-induced tumor cellular changes. This limits developing potent combinatory strategies anchored in hyperthermic intraperitoneal therapy (HIPET). Here, we perform a comprehensive multi-omics study on ovarian cancer cells under hyperthermia, unveiling a distinct molecular panorama, primarily characterized by rapid protein phosphorylation changes. Based on the phospho-signature, we pinpoint CDK1 kinase is hyperactivated during hyperthermia, influencing the global signaling landscape. We observe dynamic, reversible CDK1 activity, causing replication arrest and early mitotic entry post-hyperthermia. Subsequent drug screening shows WEE1 inhibition synergistically destroys cancer cells with hyperthermia. An in-house developed miniaturized device confirms hyperthermia and WEE1 inhibitor combination significantly reduces tumors in vivo. These findings offer additional insights into HIPET, detailing molecular mechanisms of hyperthermia and identifying precise drug combinations for targeted treatment. This research propels the concept of precise hyperthermic intraperitoneal therapy, highlighting its potential against ovarian cancer.

Whole-genome doubling is a double-edged sword: the heterogeneous role of whole-genome doubling in various cancer types

Whole-genome doubling (WGD), characterized by the duplication of an entire set of chromosomes, is commonly observed in various tumors, occurring in approximately 30-40% of patients with different cancer types. The effect of WGD on tumorigenesis varies depending on the context, either promoting or suppressing tumor progression. Recent advances in genomic technologies and large-scale clinical investigations have led to the identification of the complex patterns of genomic alterations underlying WGD and their functional consequences on tumorigenesis progression and prognosis. Our comprehensive review aims to summarize the causes and effects of WGD on tumorigenesis, highlighting its dualistic influence on cancer cells. We then introduce recent findings on WGD-associated molecular signatures and genetic aberrations and a novel subtype related to WGD. Finally, we discuss the clinical implications of WGD in cancer subtype classification and future therapeutic interventions. Overall, a comprehensive understanding of WGD in cancer biology is crucial to unraveling its complex role in tumorigenesis and identifying novel therapeutic strategies. [BMB Reports 2024; 57(3): 125-134].

Towards a general-purpose foundation model for computational pathology

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

FBXL16 promotes cell growth and drug resistance in lung adenocarcinomas with KRAS mutation by stabilizing IRS1 and upregulating IRS1/AKT signaling

Lung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinomas (LUADs) are a major subtype of non-small-cell lung cancers (NSCLCs). About 25% of LUADs harbor GTPase KRAS mutations associated with poor prognosis and limited treatment options. While encouraging tumor response to novel covalent inhibitors specifically targeting KRASG12C has been shown in the clinic, either intrinsic resistance exists or acquired therapeutic resistance arises upon treatment. There is an unmet need to identify new therapeutic targets for treating LUADs with activating KRAS mutations, particularly those with resistance to KRASG12C inhibitor(s). In this study, we have revealed that F-box/LRR-repeat protein 16 (FBXL16) is selectively upregulated in LUAD with KRAS mutations. It promotes LUAD cell growth and transforms lung epithelial cells. Importantly, FBXL16 depletion greatly enhances sensitivity to the KRASG12C inhibitor (sotorasib) in resistant cells by downregulating phosphatidylinositol 3-kinase (PI3K)/protein kinase B (PKB; also known as AKT) signaling. Mechanistically, FBXL16 upregulates insulin receptor substrate 1 (IRS1) protein stability, leading to an increase of IGF1/AKT signaling, thereby promoting cell growth and migration. Taken together, our study highlights the potential of FBXL16 as a therapeutic target for treating LUAD with KRAS activating mutations.

A partial epithelial-mesenchymal transition signature for highly aggressive colorectal cancer cells that survive under nutrient restriction

Partial epithelial-mesenchymal transition (p-EMT) has recently been identified as a hybrid state consisting of cells with both epithelial and mesenchymal characteristics and is associated with the migration, metastasis, and chemoresistance of cancer cells. Here, we describe the induction of p-EMT in starved colorectal cancer (CRC) cells and identify a p-EMT gene signature that can predict prognosis. Functional characterisation of starvation-induced p-EMT in HCT116, DLD1, and HT29 cells showed changes in proliferation, morphology, and drug sensitivity, supported by in vivo studies using the chorioallantoic membrane model. An EMT-specific quantitative polymerase chain reaction (qPCR) array was used to screen for deregulated genes, leading to the establishment of an in silico gene signature that was correlated with poor disease-free survival in CRC patients along with the CRC consensus molecular subtype CMS4. Among the significantly deregulated p-EMT genes, a triple-gene signature consisting of SERPINE1, SOX10, and epidermal growth factor receptor (EGFR) was identified. Starvation-induced p-EMT was characterised by increased migratory potential and chemoresistance, as well as E-cadherin processing and internalisation. Both gene signature and E-cadherin alterations could be reversed by the proteasomal inhibitor MG132. Spatially resolving EGFR expression with high-resolution immunofluorescence imaging identified a proliferation stop in starved CRC cells caused by EGFR internalisation. In conclusion, we have gained insight into a previously undiscovered EMT mechanism that may become relevant when tumour cells are under nutrient stress, as seen in early stages of metastasis. Targeting this process of tumour cell dissemination might help to prevent EMT and overcome drug resistance. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Prognostic and predictive biomarkers in non-small cell lung carcinoma

Lung cancer is the most common cause of cancer-related deaths globally, with the highest mortality rates among both men and women. Most lung cancers are diagnosed at late stages, necessitating systemic therapy. Modern clinical management of lung cancer relies heavily upon application of biomarkers, which guide the selection of systemic treatment. Here, we provide an overview of currently approved and emerging biomarkers of non-small cell lung cancer (NSCLC), including EGFR, ALK, ROS1, RET, NTRK1-3, KRAS, BRAF, MET, ERBB2/HER2, NRG1, PD-L1, TROP2, and CEACAM5. For practical purposes, we divide these biomarkers into genomic and protein markers, based on the tested substrate. We review the biology and epidemiology of the genomic and proteomic biomarkers, discuss optimal diagnostic assays for their detection, and highlight their contribution to the contemporary clinical management of NSCLC.

Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma

Adjuvant chemotherapy benefits patients with resected pancreatic ductal adenocarcinoma (PDAC), but the compromised physical state of post-operative patients can hinder compliance. Biomarkers that identify candidates for prompt adjuvant therapy are needed. In this prospective observational study, 1,171 patients with PDAC who underwent pancreatectomy were enrolled and extensively followed-up. Proteomic profiling of 191 patient samples unveiled clinically relevant functional protein modules. A proteomics-level prognostic risk model was established for PDAC, with its utility further validated using a publicly available external cohort. More importantly, through an interaction effect regression analysis leveraging both clinical and proteomic datasets, we discovered two biomarkers (NDUFB8 and CEMIP2), indicative of the overall sensitivity of patients with PDAC to adjuvant chemotherapy. The biomarkers were validated through immunohistochemistry on an internal cohort of 386 patients. Rigorous validation extended to two external multicentic cohorts-a French multicentric cohort (230 patients) and a cohort from two grade-A tertiary hospitals in China (466 patients)-enhancing the robustness and generalizability of our findings. Moreover, experimental validation through functional assays was conducted on PDAC cell lines and patient-derived organoids. In summary, our cohort-scale integration of clinical and proteomic data demonstrates the potential of proteomics-guided prognosis and biomarker-aided adjuvant chemotherapy for PDAC.

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.

Surrogate Biomarker Prediction from Whole-Slide Images for Evaluating Overall Survival in Lung Adenocarcinoma

BACKGROUND

Recent advances in computational pathology have shown potential in predicting biomarkers from haematoxylin and eosin (H&E) whole-slide images (WSI). However, predicting the outcome directly from WSIs remains a substantial challenge. In this study, we aimed to investigate how gene expression, predicted from WSIs, could be used to evaluate overall survival (OS) in patients with lung adenocarcinoma (LUAD).

METHODS

Differentially expressed genes (DEGs) were identified from The Cancer Genome Atlas (TCGA)-LUAD cohort. Cox regression analysis was performed on DEGs to identify the gene prognostics of OS. Attention-based multiple instance learning (AMIL) models were trained to predict the expression of identified prognostic genes from WSIs using the TCGA-LUAD dataset. Models were externally validated in the Clinical Proteomic Tumour Analysis Consortium (CPTAC)-LUAD dataset. The prognostic value of predicted gene expression values was then compared to the true gene expression measurements.

RESULTS

The expression of 239 prognostic genes could be predicted in TCGA-LUAD with cross-validated Pearson's R > 0.4. Predicted gene expression demonstrated prognostic performance, attaining a cross-validated concordance index of up to 0.615 in TCGA-LUAD through Cox regression. In total, 36 genes had predicted expression in the external validation cohort that was prognostic of OS.

CONCLUSIONS

Gene expression predicted from WSIs is an effective method of evaluating OS in patients with LUAD. These results may open up new avenues of cost- and time-efficient prognosis assessment in LUAD treatment.

Proteomics-based clustering of lung adenocarcinoma identifies three subtypes with significantly different clinical and molecular features

BACKGROUND

Lung adenocarcinoma (LUAD) is a predominant subtype of lung cancer. Although molecular classification of LUAD has been widely explored, proteomics-based subtyping of LUAD remains scarce.

METHODS

We proposed a subtyping method for LUAD based on the expression profiles of 500 proteins with the largest expression variability across LUAD. Furthermore, we comprehensively compared molecular and clinical features among the LUAD subtypes.

RESULTS

Consensus clustering identified three subtypes of LUAD, namely MtE, DrE, and StE. We demonstrated this subtyping method to be reproducible by analyzing two independent LUAD cohorts. MtE was characterized by high enrichment of metabolic pathways, high EGFR mutation rate, low stemness, proliferation, invasion, metastasis and inflammation signatures, favorable prognosis; DrE was characterized by high enrichment of DNA repair pathways, high TP53 mutation rate, and high levels of genomic instability, stemness, proliferation, and intratumor heterogeneity (ITH); and StE was characterized by high enrichment of stroma-related pathways, high KRAS mutation rate, and low levels of genomic instability.

CONCLUSIONS

The proteomics-based clustering analysis identified three LUAD subtypes with significantly different molecular and clinical properties. The novel subtyping method offers new perspectives on the cancer biology and holds promise in improving the clinical management of LUAD.

Transcriptional analysis of landmark molecular pathways in lung adenocarcinoma results in a clinically relevant classification with potential therapeutic implications

Lung adenocarcinoma (LUAD) is a molecularly heterogeneous disease. In addition to genomic alterations, cancer transcriptional profiling can be helpful to tailor cancer treatment and to estimate each patient's outcome. Transcriptional activity levels of 50 molecular pathways were inferred in 4573 LUAD patients using Gene Set Variation Analysis (GSVA) method. Seven LUAD subtypes were defined and independently validated based on the combined behavior of the studied pathways: AD (adenocarcinoma subtype) 1-7. AD1, AD4, and AD5 subtypes were associated with better overall survival. AD1 and AD4 subtypes were enriched in epidermal growth factor receptor (EGFR) mutations, whereas AD2 and AD6 showed higher tumor protein p53 (TP53) alteration frequencies. AD2 and AD6 subtypes correlated with higher genome instability, proliferation-related pathway expression, and specific sensitivity to chemotherapy, based on data from LUAD cell lines. LUAD subtypes were able to predict immunotherapy response in addition to CD274 (PD-L1) gene expression and tumor mutational burden (TMB). AD2 and AD4 subtypes were associated with potential resistance and response to immunotherapy, respectively. Thus, analysis of transcriptomic data could improve patient stratification beyond genomics and single biomarkers (i.e., PD-L1 and TMB) and may lay the foundation for more personalized treatment avenues, especially in driver-negative LUAD.

Identification of the consistently differential expressed hub mRNAs and proteins in lung adenocarcinoma and construction of the prognostic signature: a multidimensional analysis

BACKGROUND

This study aimed to elucidate the consistency of differentially expressed hub mRNAs and proteins in lung adenocarcinoma (LUAD) across populations and to construct a comprehensive LUAD prognostic signature.

METHODS

The transcriptomic and proteomics data from different populations were standardized and analyzed using the same criteria to identify the consistently differential expressed mRNAs and proteins across genders and races. We then integrated prognosis-related mRNAs with clinical, pathological, and EGFR (epidermal growth factor receptor) mutation data to construct a survival model, subsequently validating it across populations. Through plasma proteomics, plasma proteins that consistently differential expressed with LUAD tissues were screened and validated, with their associations discerned by measuring expressions in tumor tissues and tumor vascular normalization.

RESULTS

The consistency rate of differentially expressed mRNAs and proteins was ~20-40%, with ethnic factors leading to about 40-60% consistency of differentially expressed mRNA or protein across populations. The survival model based on the identified eight hub mRNAs as well as stage, smoking status, and EGFR mutations, demonstrated good prognostic prediction capabilities in both Western and East Asian populations, with a higher number of unfavorable variables indicating poorer LUAD prognosis. Notably, GPI expression in tumor tissues was inversely correlated with vascular normalization and positively correlated with plasma GPI expression.

CONCLUSION

Our study underscores the significance of integrating transcriptomics and proteomics data, emphasizing the need to account for genetic diversity among ethnic groups. The developed survival model may offer a holistic perspective on LUAD progression, enhancing prognosis and therapeutic strategies.

The oral administration of Lacticaseibacillus casei Shirota alleviates acetaminophen-induced liver injury through accelerated acetaminophen metabolism via the liver-gut axis in mice

Acetaminophen is a widely used antipyretic and analgesic drug, and its overdose is the leading cause of drug-induced acute liver failure. This study aimed to investigate the effect and mechanism of Lacticaseibacillus casei Shirota (LcS), an extensively used and highly studied probiotic, on acetaminophen-induced acute liver injury. C57BL/6 mice were gavaged with LcS suspension or saline once daily for 7 days before acute liver injury was induced via intraperitoneal injection of 300 mg/kg acetaminophen. The results showed that LcS significantly decreased acetaminophen-induced liver and ileum injury, as demonstrated by reductions in the increases in aspartate aminotransferase, total bile acids, total bilirubin, indirect bilirubin, and hepatic cell necrosis. Moreover, LcS alleviated acetaminophen-induced intestinal mucosal permeability, decreased serum IL-1α and lipopolysaccharide levels, and elevated serum eosinophil chemokine (eotaxin) and hepatic glutathione levels. Furthermore, analysis of the gut microbiota and metabolome showed that LcS reduced the acetaminophen-enriched levels of Cyanobacteria, Oxyphotobacteria, long-chain fatty acids, cholesterol, and sugars in the gut. Additionally, the transcriptomic and proteomic results showed that LcS mitigated the decrease in metabolic and immune pathways as well as glutathione formation during acetaminophen-induced acute liver injury. This is the first study showing that pretreatment with LcS alleviates acetaminophen-enriched acute liver injury, and it provides a reference for the application of LcS.IMPORTANCEAcetaminophen is the most frequently used antipyretic analgesic worldwide. As a result, overdoses easily occur and lead to drug-induced acute liver injury, which quickly progresses to liver failure with a mortality of 60%-80% if not corrected in time. The current emergency treatment for overused acetaminophen needs to be administered within 8 hours to avoid liver injury or even liver failure. Therefore, developing preventive strategies for liver injury during planned acetaminophen medication is particularly important, preferably nonpharmacological methods. Lacticaseibacillus casei Shirota (LcS) is a famous probiotic that has been used for many years. Our study found that LcS significantly alleviated acetaminophen-induced acute liver injury, especially acetaminophen-induced liver injury toward fulminant hepatic failure. Here, we elucidated the function and potential mechanisms of LcS in alleviating acetaminophen-induced acute liver injury, hoping it will provide preventive strategies to people during acetaminophen treatment.

VISTA promotes the metabolism and differentiation of myeloid-derived suppressor cells by STAT3 and polyamine-dependent mechanisms

Myeloid-derived suppressor cells (MDSCs) impair antitumor immune responses. Identifying regulatory circuits during MDSC development may bring new opportunities for therapeutic interventions. We report that the V-domain suppressor of T cell activation (VISTA) functions as a key enabler of MDSC differentiation. VISTA deficiency reduced STAT3 activation and STAT3-dependent production of polyamines, which causally impaired mitochondrial respiration and MDSC expansion. In both mixed bone marrow (BM) chimera mice and myeloid-specific VISTA conditional knockout mice, VISTA deficiency significantly reduced tumor-associated MDSCs but expanded monocyte-derived dendritic cells (DCs) and enhanced T cell-mediated tumor control. Correlated expression of VISTA and arginase-1 (ARG1), a key enzyme supporting polyamine biosynthesis, was observed in multiple human cancer types. In human endometrial cancer, co-expression of VISTA and ARG1 on tumor-associated myeloid cells is associated with poor survival. Taken together, these findings unveil the VISTA/polyamine axis as a central regulator of MDSC differentiation and warrant therapeutically targeting this axis for cancer immunotherapy.