A proteogenomic portrait of lung squamous cell carcinoma

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.

Depletion of squalene epoxidase in synergy with glutathione peroxidase 4 inhibitor RSL3 overcomes oxidative stress resistance in lung squamous cell carcinoma

Background

Lung squamous cell carcinoma (LUSC) lacks effective targeted therapies and has a poor prognosis. Disruption of squalene epoxidase (SQLE) has been implicated in metabolic disorders and cancer. However, the role of SQLE as a monooxygenase involved in oxidative stress remains unclear.

Methods

We analyzed the expression and prognosis of lung adenocarcinoma (LUAD) and LUSC samples from GEO and TCGA databases. The proliferative activity of the tumors after intervention of SQLE was verified by cell and animal experiments. JC-1 assay, flow cytometry, and Western blot were used to show changes in apoptosis after intervention of SQLE. Flow cytometry and fluorescence assay of ROS levels were used to indicate oxidative stress status.

Results

We investigated the unique role of SQLE expression in the diagnosis and prognosis prediction of LUSC. Knockdown of SQLE or treatment with the SQLE inhibitor terbinafine can suppress the proliferation of LUSC cells by inducing apoptosis and reactive oxygen species accumulation. However, depletion of SQLE also results in the impairment of lipid peroxidation and ferroptosis resistance such as upregulation of glutathione peroxidase 4. Therefore, prevention of SQLE in synergy with glutathione peroxidase 4 inhibitor RSL3 effectively mitigates the proliferation and growth of LUSC.

Conclusion

Our study indicates that the low expression of SQLE employs adaptive survival through regulating the balance of apoptosis and ferroptosis resistance. In future, the combinational therapy of targeting SQLE and ferroptosis could be a promising approach in treating LUSC.

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.

Arginine deprivation enriches lung cancer proteomes with cysteine by inducing arginine-to-cysteine substitutants

Many types of human cancers suppress the expression of argininosuccinate synthase 1 (ASS1), a rate-limiting enzyme for arginine production. Although dependency on exogenous arginine can be harnessed by arginine-deprivation therapies, the impact of ASS1 suppression on the quality of the tumor proteome is unknown. We therefore interrogated proteomes of cancer patients for arginine codon reassignments (substitutants) and surprisingly identified a strong enrichment for cysteine (R>C) in lung tumors specifically. Most R>C events did not coincide with genetically encoded R>C mutations but were likely products of tRNA misalignments. The expression of R>C substitutants was highly associated with oncogenic kelch-like epichlorohydrin (ECH)-associated protein 1 (KEAP1)-pathway mutations and suppressed by intact-KEAP1 in KEAP1-mutated cancer cells. Finally, functional interrogation indicated a key role for R>C substitutants in cell survival to cisplatin, suggesting that regulatory codon reassignments endow cancer cells with more resilience to stress. Thus, we present a mechanism for enriching lung cancer proteomes with cysteines that may affect therapeutic decisions.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

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.

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.

Polycomb repressive complex 2 and its core component EZH2: potential targeted therapeutic strategies for head and neck squamous cell carcinoma

The polycomb group (PcG) comprises a set of proteins that exert epigenetic regulatory effects and play crucial roles in diverse biological processes, ranging from pluripotency and development to carcinogenesis. Among these proteins, enhancer of zeste homolog 2 (EZH2) stands out as a catalytic component of polycomb repressive complex 2 (PRC2), which plays a role in regulating the expression of homologous (Hox) genes and initial stages of x chromosome inactivation. In numerous human cancers, including head and neck squamous cell carcinoma (HNSCC), EZH2 is frequently overexpressed or activated and has been identified as a negative prognostic factor. Notably, EZH2 emerges as a significant gene involved in regulating the STAT3/HOTAIR axis, influencing HNSCC proliferation, differentiation, and promoting metastasis by modulating related oncogenes in oral cancer. Currently, various small molecule compounds have been developed as inhibitors specifically targeting EZH2 and have gained approval for treating refractory tumors. In this review, we delve into the epigenetic regulation mediated by EZH2/PRC2 in HNSCC, with a specific focus on exploring the potential roles and mechanisms of EZH2, its crucial contribution to targeted drug therapy, and its association with cancer markers and epithelial-mesenchymal transition. Furthermore, we aim to unravel its potential as a therapeutic strategy for oral squamous cell carcinoma.

Redoxhigh phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma

Metabolism reprogramming within the tumor microenvironment (TME) can have a profound impact on immune cells. Identifying the association between metabolic phenotypes and immune cells in lung adenocarcinoma (LUAD) may reveal mechanisms of resistance to immune checkpoint inhibitors (ICIs). Metabolic phenotypes were classified by expression of metabolic genes. Somatic mutations and transcriptomic features were compared across the different metabolic phenotypes. The metabolic phenotype of LUAD is predominantly determined by reductase-oxidative activity and is divided into two categories: redoxhigh LUAD and redoxlow LUAD. Genetically, redoxhigh LUAD is mainly driven by mutations in KEAP1, STK11, NRF2, or SMARCA4. These mutations are more prevalent in redoxhigh LUAD (72.5%) compared to redoxlow LUAD (17.4%), whereas EGFR mutations are more common in redoxlow LUAD (19.0% vs. 0.7%). Single-cell RNA profiling of pre-treatment and post-treatment samples from patients receiving neoadjuvant chemoimmunotherapy revealed that tissue-resident memory CD8+ T cells are responders to ICIs. However, these cells are significantly reduced in redoxhigh LUAD. The redoxhigh phenotype is primarily attributed to tumor cells and is positively associated with mTORC1 signaling. LUAD with the redoxhigh phenotype demonstrates a lower response rate (39.1% vs. 70.8%, p = 0.001), shorter progression-free survival (3.3 vs. 14.6 months, p = 0.004), and overall survival (12.1 vs. 31.2 months, p = 0.022) when treated with ICIs. The redoxhigh phenotype in LUAD is predominantly driven by mutations in KEAP1, STK11, NRF2, and SMARCA4. This phenotype diminishes the number of tissue-resident memory CD8+ T cells and attenuates the efficacy of ICIs.

A novel prognostic signature of coagulation-related genes leveraged by machine learning algorithms for lung squamous cell carcinoma

Coagulation-related genes (CRGs) have been demonstrated to be essential for the development of certain tumors; however, little is known about CRGs in lung squamous cell carcinoma (LUSC). In this study, we adopted CRGs to construct a coagulation-related gene prognostic signature (CRGPS) using machine learning algorithms. Using a set of 92 machine learning integrated algorithms, the CRGPS was determined to be the optimal prognostic signature (median C-index = 0.600) for predicting the prognosis of an LUSC patient. The CRGPS was not only superior to traditional clinical parameters (e.g., T stage, age, and gender) and its commutative genes but also outperformed 19 preexisting prognostic signatures for LUSC on predictive accuracy. The CRGPS score was positively correlated with poor prognoses in patients with LUSC (hazard ratio > 1, p < 0.05), indicating its suitability as a prognostic marker for this disease. The CRGPS was observed to be inversely correlated with the degree of infiltration of natural killer cells. For some tumors, patients with lower CRGPS scores are more likely to experience enhanced immunotherapy effects (area under the curve = 0.70), which implies that the CRGPS can potentially predict immunotherapy efficacy. A high CRGPS score is predictive of an LUSC patient being sensitive to several drugs. Collectively, these findings indicate that the CRGPS may be a reliable indicator of the prognoses of patients with LUSC and may be useful for the clinical management of such patients.

Integrated microRNA and proteome analysis of cancer datasets with MoPC

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPC computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPC returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases.

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.

Discovery of potent small molecule inhibitors of histone lysine methyltransferase NSDs

Nuclear receptor binding SET domain (NSD) proteins are a class of histone lysine methyltransferases and implicated in multiple cancer types with aberrant expression and involvement of cancer related signaling pathways. In this study, a series of small-molecule compounds including compound 2 and 3 are identified against the SET domain of NSDs through structure-based virtual screening. Our lead compound 3 exhibits potent inhibitory activities in vitro towards the NSD2-SET and NSD3-SET with an IC50 of 0.81 μM and 0.84 μM, respectively, and efficiently inhibits histone H3 lysine 36 dimethylation and decreases the expression of NSDs-targeted genes in non-small cell lung cancer cells at 100 nM. Compound 3 suppresses cell proliferation and reduces the clonogenicity in H460 and H1299 non-small cell lung cancer cells, and induces s-phase cell cycle arrest and apoptosis. These data establish our compounds as a valuable tool-kit for the study of the biological roles of NSDs in cancer.

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.

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.

A genome-wide study of gastric intramucosal neoplasia based on somatic copy number alterations, gene mutations, and mRNA expression patterns

We performed comprehensive analyses of somatic copy number alterations (SCNAs) and gene expression profiles of gastric intramucosal neoplasia (IMN) using array-based methods in 97 intestinal-type IMNs, including 39 low-grade dysplasias (LGDs), 37 high-grade dysplasias (HGDs), and 26 intramucosal carcinomas (IMCs) with stromal invasion of the lamina propria to identify the molecular mechanism of IMN. In addition, we examined gene mutations using gene panel analyses. We used cluster analyses for exclusion of arbitrariness to identify SCNA patterns and expression profiles. IMNs were classified into two distinct subgroups (subgroups 1 and 2) based on SCNA patterns. Subgroup 1 showed a genomic stable pattern due to the low frequency of SCNAs, whereas subgroup 2 exhibited a chromosomal instability pattern due to the high frequencies of SCNAs and TP53 mutations. Interestingly, although the frequencies of LGD and HGD were significantly higher in subgroup 1 than in subgroup 2, IMC was commonly found in both types. Although the expression profiles of specific mRNAs could be used to categorise subgroups 1 and 2, no clinicopathological findings correlated with either subgroup. We examined signalling pathways specific to subgroups 1 and 2 to identify the association of each subgroup with signalling pathways based on gene ontology tree visualisation: subgroups 1 and 2 were associated with haem metabolism and chromosomal instability, respectively. These findings reveal a comprehensive genomic landscape that highlights the molecular complexity of IMNs and provide a road map to facilitate our understanding of gastric IMNs.

Tumor-associated macrophage (TAM)-secreted CCL22 confers cisplatin resistance of esophageal squamous cell carcinoma (ESCC) cells via regulating the activity of diacylglycerol kinase α (DGKα)/NOX4 axis

Tumor-associated macrophages (TAMs) are often associated with chemoresistance and resultant poor clinical outcome in solid tumors. Here, we demonstrated that TAMs-released chemokine-C-C motif chemokine 22 (CCL22) in esophageal squamous cell carcinoma (ESCC) stroma was tightly correlated with the chemoresistance of ESCC patients. TAMs-secreted CCL22 was able to block the growth inhibitory and apoptosis-promoting effects of cisplatin on ESCC cells. Mechanistically, CCL22 stimulated intratumoral diacylglycerol kinase α (DGKα) to produce phosphatidic acid (PA), which suppressed the activity of NADPH oxidase 4 (NOX4) and then blocked the overproduction of intratumoral reactive species oxygen (ROS) induced by cisplatin. CCL22 activated DGKα/nuclear factor-κB (NF-κB) axis to upregulate the level of several members of ATP binding cassette (ABC) transporter superfamily, including ABC sub-family G member 4 (ABCG4), ABC sub-family A member 3 (ABCA3), and ABC sub-family A member 5 (ABCA5), to lower the intratumoral concentration of cisplatin. Consequently, these processes induced the cisplatin resistance in ESCC cells. In xenografted models, targeting DGKα with 5'-cholesterol-conjugated small-interfering (si) RNA enhanced the chemosensitivity of cisplatin in ESCC treatment, especially in the context of TAMs. Our data establish the correlation between the TAMs-induced intratumoral metabolic product/ROS axis and chemotherapy efficacy in ESCC treatment and reveal relevant molecular mechanisms.

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.

Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study

The modification patterns of N6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (P = 0.01), and immune checkpoints including PD-L1 (P = 4.9 × 10-8) and PD-1 (P < 0.01). We identified 51 novel proteins associated with the multi-omics signature (PFDR < 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.

Proteome-wide Mendelian randomization identifies causal plasma proteins in lung cancer

Plasma proteins are promising biomarkers and potential drug targets in lung cancer. To evaluate the causal association between plasma proteins and lung cancer, we performed proteome-wide Mendelian randomization meta-analysis (PW-MR-meta) based on lung cancer genome-wide association studies (GWASs), protein quantitative trait loci (pQTLs) of 4,719 plasma proteins in deCODE and 4,775 in Fenland. Further, causal-protein risk score (CPRS) was developed based on causal proteins and validated in the UK Biobank. 270 plasma proteins were identified using PW-MR meta-analysis, including 39 robust causal proteins (both FDR-q < 0.05) and 78 moderate causal proteins (FDR-q < 0.05 in one and p < 0.05 in another). The CPRS had satisfactory performance in risk stratification for lung cancer (top 10% CPRS:Hazard ratio (HR) (95%CI):4.33(2.65-7.06)). The CPRS [AUC (95%CI): 65.93 (62.91-68.78)] outperformed the traditional polygenic risk score (PRS) [AUC (95%CI): 55.71(52.67-58.59)]. Our findings offer further insight into the genetic architecture of plasma proteins for lung cancer susceptibility.

Stabilizing the Proteomes of Acute Myeloid Leukemia Cells: Implications for Cancer Proteomics

Previous work has shown that inhibition of abundant myeloid azurophil granule-associated serine proteases (ELANE [neutrophil elastase], PRTN3 [protease 3], and CTSG [Cathepsin G]) is required to stabilize some proteins in myeloid cells. We therefore hypothesized that effective inhibition of these proteases may be necessary for quantitative proteomic analysis of samples containing myeloid cells. To test this hypothesis, we thawed viably preserved acute myeloid leukemia cells from cryovials in the presence or the absence of diisopropyl fluorophosphate (DFP), a cell-permeable and irreversible serine protease inhibitor. Global proteomic analysis was performed, using label-free and isobaric peptide-labeling quantitation. The presence of DFP resulted in an increase of tryptic peptides (14-57%) and proteins (9-31%). In the absence of DFP, 11 to 31% of peptide intensity came from nontryptic peptides; 52 to 75% had cleavage specificity consistent with activities of ELANE-PRTN3. Treatment with DFP reduced the intensity of nontryptic peptides to 4-8% of the total. ELANE inhibition was 95%, based on diisopropyl phosphate modification of active site serine residue. Overall, the relative abundance of 20% of proteins was significantly altered by DFP treatment. These results suggest that active myeloid serine proteases, released during sample processing, can skew quantitative proteomic measurements. Finally, significant ELANE activity was also detected in Clinical Proteomics Tumor Analysis Consortium datasets of solid tumors (many of which have known myeloid infiltration). In the pancreatic cancer dataset, the median percentage of nontryptic intensity detected across patient samples was 34%, with many patient samples having more than half of their detected peptide intensity from nontryptic cleavage events consistent with ELANE-PRTN3 cleavage specificity. Our study suggests that in vitro cleavage of proteins by myeloid serine proteases may be relevant for proteomic studies of any tumor that contains infiltrating myeloid cells.

Prognosis of immunotherapy for non-small cell lung cancer with CDKN2A loss of function

Background

Immunotherapy has been widely used to treat non-small cell lung cancer (NSCLC) but is only effective in 20% of patients. Cyclin-dependent kinase inhibitor 2A (CDKN2A) is an important tumor suppressor gene, and its loss of function (LOF) is quite common in NSCLC. Pre-clinical studies suggest CDKN2A LOF promotes immune evasion; however, the results in relation to NSCLC are controversial, and debate continues as to the effect of CDKN2A LOF on immunotherapy.

Methods

In this study, we collected the data of 49 CDKN2A LOF and 173 CDKN2A wild-type NSCLC consecutive patients treated by any line of immunotherapy. Through immunohistochemical (IHC) and immunofluorescent (IF) staining, we analyzed the CDKN2A predominant transcription protein p16INK4A in the CDKN2A LOF and CDKN2A wild-type NSCLC patients. Using Kaplan-Meier curves, we also examined the relationship between CDKN2A LOF and immunotherapy.

Results

The IHC and IF staining results showed that most CDKN2A LOF patients were p16INK4A negative, while most CDKN2A wild-type patients were p16INK4A positive. In the LOF group, five patients had partial responses, 35 had stable disease, and nine had progressive disease after the first evaluation of immunotherapy. The LOF group had a median progression-free survival (PFS) time of 4.67 months, while the wild-type group had a median PFS time of 8.63 months [hazard ratio (HR): 0.54; 95% confidence interval (CI): 0.38-0.77; P<0.001]. The LOF group had a median overall survival (OS) time of 9.07 months, while the wild-type group had a median OS time of 21.37 months (HR: 0.42; 95% CI: 0.29-0.61; P<0.001).

Conclusions

Our study revealed that CDKN2A LOF NSCLC patients treated with immune checkpoint inhibitor (ICI) mono-therapy or combined therapy had a worse prognosis than those with CDKN2A wild-type NSCLC. However, our study also suggested that ICI could work quite effectively in selective CDKN2A LOF patients.

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

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

Phosphoprotein dynamics of interacting T cells and tumor cells by HySic

Functional interactions between cytotoxic T cells and tumor cells are central to anti-cancer immunity. However, our understanding of the proteins involved is limited. Here, we present HySic (hybrid quantification of stable isotope labeling by amino acids in cell culture [SILAC]-labeled interacting cells) as a method to quantify protein and phosphorylation dynamics between and within physically interacting cells. Using co-cultured T cells and tumor cells, we directly measure the proteome and phosphoproteome of engaged cells without the need for physical separation. We identify proteins whose abundance or activation status changes upon T cell:tumor cell interaction and validate our method with established signal transduction pathways including interferon γ (IFNγ) and tumor necrosis factor (TNF). Furthermore, we identify the RHO/RAC/PAK1 signaling pathway to be activated upon cell engagement and show that pharmacologic inhibition of PAK1 sensitizes tumor cells to T cell killing. Thus, HySic is a simple method to study rapid protein signaling dynamics in physically interacting cells that is easily extended to other biological systems.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

Exploring the potential of histone demethylase inhibition in multi-therapeutic approaches for cancer treatment

Histone demethylases play a critical role in gene transcription regulation and have been implicated in cancer. Numerous reports have highlighted the overexpression of histone demethylases, such as LSD1 and JmjC, in various malignant tumor tissues, identifying them as effective therapeutic targets for cancer treatment. Despite many histone demethylase inhibitors entering clinical trials, their clinical efficacy has been limited. Therefore, combination therapies based on histone demethylase inhibitors, along with other modulators like dual-acting inhibitors, have gained significant attention and made notable progress in recent years. In this review, we provide an overview of recent advances in drug discovery targeting histone demethylases, focusing specifically on drug combination therapy and histone demethylases-targeting dual inhibitors. We discuss the rational design, pharmacodynamics, pharmacokinetics, and clinical status of these approaches. Additionally, we summarize the co-crystal structures of LSD1 inhibitors and their target proteins as well as describe the corresponding binding interactions. Finally, we also provided the challenges and future directions for utilizing histone demethylases in cancer therapy, such as PROTACs and molecular glue etc.

NSD3 in Cancer: Unraveling Methyltransferase-Dependent and Isoform-Specific Functions

NSD3 (nuclear receptor-binding SET domain protein 3) is a member of the NSD histone methyltransferase family of proteins. In recent years, it has been identified as a potential oncogene in certain types of cancer. The NSD3 gene encodes three isoforms, the long version (NSD3L), a short version (NSD3S) and the WHISTLE isoforms. Importantly, the NSD3S isoform corresponds to the N-terminal region of the full-length protein, lacking the methyltransferase domain. The chromosomal location of NSD3 is frequently amplified across cancer types, such as breast, lung, and colon, among others. Recently, this amplification has been correlated to a chromothripsis event, that could explain the different NSD3 alterations found in cancer. The fusion proteins containing NSD3 have also been reported in leukemia (NSD3-NUP98), and in NUT (nuclear protein of the testis) midline carcinoma (NSD3-NUT). Its role as an oncogene has been described by modulating different cancer pathways through its methyltransferase activity, or the short isoform of the protein, through protein interactions. Specifically, in this review we will focus on the functions that have been characterized as methyltransferase dependent, and those that have been correlated with the expression of the NSD3S isoform. There is evidence that both the NSD3L and NSD3S isoforms are relevant for cancer progression, establishing NSD3 as a therapeutic target. However, further functional studies are needed to differentiate NSD3 oncogenic activity as dependent or independent of the catalytic domain of the protein, as well as the contribution of each isoform and its clinical significance in cancer progression.

CancerProteome: a resource to functionally decipher the proteome landscape in cancer

Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.

Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies

We performed comprehensive proteogenomic characterization of small cell lung cancer (SCLC) using paired tumors and adjacent lung tissues from 112 treatment-naive patients who underwent surgical resection. Integrated multi-omics analysis illustrated cancer biology downstream of genetic aberrations and highlighted oncogenic roles of FAT1 mutation, RB1 deletion, and chromosome 5q loss. Two prognostic biomarkers, HMGB3 and CASP10, were identified. Overexpression of HMGB3 promoted SCLC cell migration via transcriptional regulation of cell junction-related genes. Immune landscape characterization revealed an association between ZFHX3 mutation and high immune infiltration and underscored a potential immunosuppressive role of elevated DNA damage response activity via inhibition of the cGAS-STING pathway. Multi-omics clustering identified four subtypes with subtype-specific therapeutic vulnerabilities. Cell line and patient-derived xenograft-based drug tests validated the specific therapeutic responses predicted by multi-omics subtyping. This study provides a valuable resource as well as insights to better understand SCLC biology and improve clinical practice.

Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion, LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

Pulmonary squamous cell carcinoma and lymphoepithelial carcinoma - morphology, molecular characteristics and differential diagnosis

Squamous cell carcinoma (SCC) comprises one of the major groups of non-small-cell carcinoma of the lung, and is subtyped into keratinising, non-keratinising and basaloid SCC. SCC can readily be diagnosed using histomorphology alone in keratinising SCC. Confirmatory immunohistochemical analyses should always be applied in non-keratinising and basaloid tumours to exclude differential diagnoses, most prominently adenocarcinoma and high-grade neuroendocrine carcinoma, which may have important therapeutic consequences. According to the World Health Organisation (WHO) classification 2015, the diagnosis of SCC can be rendered in resections of morphologically ambiguous tumours with squamous immunophenotype. In biopsies and cytology preparations in the same setting the current guidelines propose a diagnosis of 'non-small-cell carcinoma, favour SCC' in TTF1-negative and p40-positive tumours to acknowledge a possible sampling bias and restrict extended immunohistochemical evaluation in order to preserve tissue for molecular testing. Most SCC feature a molecular 'tobacco-smoke signature' with enrichment in GG > TT mutations, in line with the strong epidemiological association of SCC with smoking. Targetable mutations are extremely rare but they do occur, in particular in younger and non- or light-smoking patients, warranting molecular investigations. Lymphoepithelial carcinoma (LEC) is a poorly differentiated SCC with a syncytial growth pattern and a usually prominent lymphoplasmacytic infiltrate and frequent Epstein-Barr virus (EBV) association. In this review, we describe the morphological and molecular characteristics of SCC and LEC and discuss the most pertinent differential diagnoses.

Molecular characterization of STEAP3 in lung squamous cell carcinoma: Regulating EGFR to affect cell proliferation and ferroptosis

Six-transmembrane epithelial antigen of the prostate 3 (STEAP3) has been reported to play a regulatory role in various types of cancers. However, its involvement in lung squamous cell carcinoma (LUSC) remains understudied. Here, we aimed to explore the biological functions and underlying mechanisms of STEAP3 in LUSC. Intersection genes associated with LUSC and ferroptosis were analyzed using the Venn method, STRING, GEPIA and UALCAN databases. The expression of STEAP3 was detected by qPCR and western blotting assay. Cell proliferation and viability were determined using the cell counting kit-8 assay and EDU staining. Oxidative stress and lipid peroxidation were measured by corresponding kits and DCFH-DA staining. Ferroptosis was evaluated by Phen Green SK and Western blot assay. The correlation between STEAP3 and EGFR was predicted by the TIMER and starBase database. Co-immunoprecipitation was conducted to verify the binding of STEAP3 and EGFR. The data demonstrated a significant upregulation of STEAP3 expression in LUSC cell lines. Silencing of STEAP3 suppressed H2170 cell viability and proliferation while promoting oxidative stress and lipid peroxidation through increased levels of MDA and ROS, as well as inhibited SOD activity. In addition, knockdown of STEAP3 induced ferroptosis through the regulation of ferroptosis-related proteins. Moreover, the binding between STEAP3 and EGFR was predicted and confirmed in LUSC. EGFR overexpression reversed the effects of STEAP3 silencing on H2170 cell viability, proliferation, oxidative stress, and ferroptosis. To summarize, the inhibition of STEAP3/EGFR may serve as a promising therapeutic target for LUSC treatment, as it can suppress LUSC proliferation and promote lipid peroxidation and ferroptosis.

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.

Tumor-immune microenvironment and NRF2 associate with clinical efficacy of PD-1 blockade combined with chemotherapy in lung squamous cell carcinoma

The RATIONALE-307 study (ClinicalTrials.gov: NCT03594747) demonstrates prolonged progression-free survival (PFS) with first-line tislelizumab plus chemotherapy versus chemotherapy in advanced lung squamous cell carcinoma (LUSC; N = 360). Here we describe an immune-related gene expression signature (GES), composed of genes involved in both innate and adaptive immunity, that appears to differentiate tislelizumab plus chemotherapy PFS benefit versus chemotherapy. In contrast, a tislelizumab plus chemotherapy PFS benefit is observed regardless of programmed death ligand 1 (PD-L1) expression or tumor mutational burden (TMB). Genetic analysis reveals that NRF2 pathway activation is enriched in PD-L1positive and TMBhigh patients. NRF2 pathway activation is negatively associated with PFS, which affects efficacy outcomes associated with PD-L1 and TMB status, impairing their predictive potential. Mechanistic studies demonstrate that NRF2 directly mediates PD-L1 constitutive expression independent of adaptive PD-L1 regulation in LUSC. In summary, the GES is an immune signature that might identify LUSC patients likely to benefit from first-line tislelizumab plus chemotherapy.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis

Disrupted protein phosphorylation due to genetic variation is a widespread phenomenon that triggers oncogenic transformation of healthy cells. However, few relevant phosphorylation disruption events have been verified due to limited biological experimental methods. Because of the lack of reliable benchmark datasets, current bioinformatics methods primarily use sequence-based traits to study variant impact on phosphorylation (VIP). Here, we increased the number of experimentally supported VIP events from less than 30 to 740 by manually curating and reanalyzing multi-omics data from 916 patients provided by the Clinical Proteomic Tumor Analysis Consortium. To predict VIP events in cancer cells, we developed VIPpred, a machine learning method characterized by multidimensional features that exhibits robust performance across different cancer types. Our method provided a pan-cancer landscape of VIP events, which are enriched in cancer-related pathways and cancer driver genes. We found that variant-induced increases in phosphorylation events tend to inhibit the protein degradation of oncogenes and promote tumor suppressor protein degradation. Our work provides new insights into phosphorylation-related cancer biology as well as novel avenues for precision therapy.

Isoliquiritigenin Inhibits Oral Squamous Cell Carcinoma and Overcomes Chemoresistance by Destruction of Survivin

The oncoprotein survivin plays a pivotal role in controlling cell division and preventing apoptosis by inhibiting caspase activation. Its significant contribution to tumorigenesis and therapeutic resistance has been well established. Isoliquiritigenin (ISL), a natural compound, has been recognized for its powerful inhibitory effects against various tumors. However, whether ISL exerts regulatory effects on survivin and its underlying mechanism in oral squamous cell carcinoma (OSCC) remains unclear. Here, we found that ISL inhibited the viability and colony formation of OSCC, and promoted their apoptosis. The immunoblotting data showed that ISL treatment significantly decreased survivin expression. Mechanistically, ISL suppressed survivin phosphorylation on Thr34 by deregulating Akt-Wee1-CDK1 signaling, which facilitated survivin for ubiquitination degradation. ISL inhibited CAL27 tumor growth and decreased p-Akt and survivin expression in vivo. Meanwhile, survivin overexpression caused cisplatin resistance of OSCC cells. ISL alone or combined with cisplatin overcame chemoresistance in OSCC cells. Overall, our results revealed that ISL exerted potent inhibitory effects via inducing Akt-dependent survivin ubiquitination in OSCC cells.

Multi-omics analysis reveals PUS1 triggered malignancy and correlated with immune infiltrates in NSCLC

Non-small cell lung cancer (NSCLC) is the main pathological type of lung cancer. In this study, multi-omics analysis revealed a significant increase of pseudouridine synthase 1 (PUS1) in NSCLC and the high expression of PUS1 was associated with shorter OS (Overall Survival), PFS (Progression Free Survival), and PPS (Post Progression Survival) of NSCLC patients. Clinical subgroup analysis showed that PUS1 may be involved in the occurrence and development of NSCLC. Besides, TIMER, ESTIMATE, and IPS analysis suggested that PUS1 expression was associated with immune cell infiltration, and the expression of PUS1 was significantly negatively correlated with DC cell infiltration. GESA analysis also indicated PUS1 may involve in DNA_REPAIR, E2F_TARGETS, MYC_TARGETS_V2, G2M_CHECKPOINT and MYC_TARGETS_V1 pathways and triggered NSCLC malignancy through MCM5 or XPO1. Furthermore, PUS1 may be a potential target for NSCLC therapy.

Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma

Molecular subtyping of lung squamous cell carcinoma (LUSC) has been performed at the genomic, transcriptomic, and proteomic level. However, LUSC stratification based on tissue metabolomics is still lacking. Combining high-mass-resolution imaging mass spectrometry with consensus clustering, four tumor- and four stroma-specific subtypes with distinct metabolite patterns were identified in 330 LUSC patients. The first tumor subtype T1 negatively correlated with DNA damage and immunological features including CD3, CD8, and PD-L1. The same features positively correlated with the tumor subtype T2. Tumor subtype T4 was associated with high PD-L1 expression. Compared with the status of subtypes T1 and T4, patients with subtype T3 had improved prognosis, and T3 was an independent prognostic factor with regard to UICC stage. Similarly, stroma subtypes were linked to distinct immunological features and metabolic pathways. Stroma subtype S4 had a better prognosis than S2. Subsequently, analyses based on an independent LUSC cohort treated by neoadjuvant therapy revealed that the S2 stroma subtype was associated with chemotherapy resistance. Clinically relevant patient subtypes as determined by tissue-based spatial metabolomics are a valuable addition to existing molecular classification systems. Metabolic differences among the subtypes and their associations with immunological features may contribute to the improvement of personalized therapy.

KEAP1/NFE2L2 Pathway Signature Outperforms KEAP1/NFE2L2 Mutation Status and Reveals Alternative Pathway-Activating Mutations in NSCLC

INTRODUCTION

Activation of the antioxidant KEAP1/NFE2L2 (NRF2) pathway leads to increased glutamine dependence and an aggressive phenotype in NSCLC. Because this pathway has been explored as a clinical target, we developed a transcriptomic signature for identifying KEAP1/NFE2L2-activated tumors.

METHODS

A total of 971 NSCLC samples were used to train an expression signature (K1N2-score) to predict KEAP1/NFE2L2 mutations. There were 348 in-house NSCLCs that were analyzed using a NanoString expression panel for validation.

RESULTS

The 46-gene K1N2 score robustly predicted KEAP1/NFE2L2 mutations in the validation set irrespective of histology and mutation (area under the curve: 89.5, sensitivity: 90.2%), suggesting that approximately 90% of KEAP1/NFE2L2 mutations are pathway-activating. The K1N2-score outperformed KEAP1/NFE2L2 mutational status when predicting patient survival (score p = 0.047; mutation p = 0.215). In K1N2 score-positive but KEAP1/NFE2L2 wild-type samples, enrichment testing identified SMARCA4/BRG1 and CUL3 mutations as mimics of KEAP1/NFE2L2 mutations.

CONCLUSIONS

The K1N2-score identified KEAP1/NFE2L2-activated NSCLC by robustly detecting KEAP1/NFE2L2mut cases and discovering alternative genomic activators. It is a potential means for selecting patients with a constitutively active KEAP1/NFE2L2 pathway.

Acetylation in pathogenesis: Revealing emerging mechanisms and therapeutic prospects

Protein acetylation modifications play a central and pivotal role in a myriad of biological processes, spanning cellular metabolism, proliferation, differentiation, apoptosis, and beyond, by effectively reshaping protein structure and function. The metabolic state of cells is intricately connected to epigenetic modifications, which in turn influence chromatin status and gene expression patterns. Notably, pathological alterations in protein acetylation modifications are frequently observed in diseases such as metabolic syndrome, cardiovascular disorders, and cancer. Such abnormalities can result in altered protein properties and loss of function, which are closely associated with developing and progressing related diseases. In recent years, the advancement of precision medicine has highlighted the potential value of protein acetylation in disease diagnosis, treatment, and prevention. This review includes provocative and thought-provoking papers outlining recent breakthroughs in acetylation modifications as they relate to cardiovascular disease, mitochondrial metabolic regulation, liver health, neurological health, obesity, diabetes, and cancer. Additionally, it covers the molecular mechanisms and research challenges in understanding the role of acetylation in disease regulation. By summarizing novel targets and prognostic markers for the treatment of related diseases, we aim to contribute to the field. Furthermore, we discuss current hot topics in acetylation research related to health regulation, including N4-acetylcytidine and liquid-liquid phase separation. The primary objective of this review is to provide insights into the functional diversity and underlying mechanisms by which acetylation regulates proteins in disease contexts.

Targeted Proteomic Quantitation of NRF2 Signaling and Predictive Biomarkers in HNSCC

The NFE2L2 (NRF2) oncogene and transcription factor drives a gene expression program that promotes cancer progression, metabolic reprogramming, immune evasion, and chemoradiation resistance. Patient stratification by NRF2 activity may guide treatment decisions to improve outcome. Here, we developed a mass spectrometry-based targeted proteomics assay based on internal standard-triggered parallel reaction monitoring to quantify 69 NRF2 pathway components and targets, as well as 21 proteins of broad clinical significance in head and neck squamous cell carcinoma (HNSCC). We improved an existing internal standard-triggered parallel reaction monitoring acquisition algorithm, called SureQuant, to increase throughput, sensitivity, and precision. Testing the optimized platform on 27 lung and upper aerodigestive cancer cell models revealed 35 NRF2 responsive proteins. In formalin-fixed paraffin-embedded HNSCCs, NRF2 signaling intensity positively correlated with NRF2-activating mutations and with SOX2 protein expression. Protein markers of T-cell infiltration correlated positively with one another and with human papilloma virus infection status. CDKN2A (p16) protein expression positively correlated with the human papilloma virus oncogenic E7 protein and confirmed the presence of translationally active virus. This work establishes a clinically actionable HNSCC protein biomarker assay capable of quantifying over 600 peptides from frozen or formalin-fixed paraffin-embedded archived tissues in under 90 min.

Microdissection of cancer-associated fibroblast infiltration subtypes unveils the secreted SERPINE2 contributing to immunosuppressive microenvironment and immuotherapeutic resistance in gastric cancer: A large-scale study integrating bulk and single-cell transcriptome profiling

In the era of immunotherapy, the suboptimal response rate and the development of acquired resistance among the initial beneficiaries continue to present significant challenges across multiple malignancies, including gastric cancer (GC). Considering that the interactions of tumor stroma, especially the cancer-associated fibroblasts (CAFs), with immune and tumor cells, play indispensable roles in tumor progression, tumor microenvironment remodeling and therapeutic responsiveness, in-depth exploration on the roles of CAFs and pivotal mediators of their functions may provide novel clues to increase the effectiveness of current immunotherapeutic drugs and further achieve synergistic antitumor response. Herein, through the consensus clustering of canonical biomarkers, three GC subclasses with different abundance of CAFs were virtually microdissected in four integrated bulk cohorts encompassing 2148 GC patients from 11 independent datasets. An extensive immunogenomic analysis revealed that tumors with high CAFs infiltration were characterized with unfavorable outcomes, aggressive phenotypes, decreased tumor immunogenicity, high risk of immune evasion and thus immunotherapeutic resistance. By leveraging large-scale single-cell transcriptomic profiling, a series of CAF-secreted proteins were identified, among which the SERPINE2 was confirmed to be restrictively enriched in stromal fibroblasts of GC tissues and contribute to promoting a protumor milieu and fostering an immunosuppressive microenvironment via bioinformatics computations and tissue microarray analysis. Moreover, pan-cancer investigations generalized the immunological roles of SERPINE2, especially in pan-gastrointestinal malignancies, with multiple real-world immunotherapy cohorts further confirming its implications on predicting immunotherapeutic efficacy. In conclusion, these findings suggest that the CAF-derived SERPINE2 is a promising immune-oncology target with therapeutic implications to further synergize the immunotherapeutic combinations.

Exploring the pan-cancer landscape of posttranscriptional regulation

Understanding the mechanisms underlying cancer gene expression is critical for precision oncology. Posttranscriptional regulation is a key determinant of protein abundance and cancer cell behavior. However, to what extent posttranscriptional regulatory mechanisms impact protein levels and cancer progression is an ongoing question. Here, we exploit cancer proteogenomics data to systematically compare mRNA-protein correlations across 14 different human cancer types. We identify two clusters of genes with particularly low mRNA-protein correlations across all cancer types, shed light on the role of posttranscriptional regulation of cancer driver genes and drug targets, and unveil a cohort of 55 mutations that alter systems-wide posttranscriptional regulation. Surprisingly, we find that decreased levels of posttranscriptional control in patients correlate with shorter overall survival across multiple cancer types, prompting further mechanistic studies into how posttranscriptional regulation affects patient outcomes. Our findings underscore the importance of a comprehensive understanding of the posttranscriptional regulatory landscape for predicting cancer progression.