Colorectal cancer molecular signatures derived from omics data can be employed to stratify CRC patients and aid decisions about therapies or evaluate prognostic outcome. However, molecular biomarkers for identification of patients at increased risk of disease relapse are currently lacking. Here, we present a comprehensive multi-omics analysis of a Danish colorectal cancer tumor cohort composed of 412 biopsies from tumors of 371 patients diagnosed at TNM stage II or III. From mass spectrometry-based patient proteome profiles, we classified the tumors into four molecular subtypes, including a mesenchymal-like subtype. As the mesenchymal-rich tumors are known to represent the most invasive and metastatic phenotype, we focused on the protein signature defining this subtype to evaluate their potential as relapse risk markers. Among signature-specific proteins, we followed-up Caveolae-Associated Protein-1 (CAVIN1) and demonstrated its role in tumor progression in a 3D in vitro model of colorectal cancer. Compared to previous omics analyses of CRC, our multi-omics classification provided deeper insights into EMT in cancer cells with stronger correlations with risk of relapse.

Poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC) present major challenges in treatment owing to extreme aggressiveness and high heterogeneity. In this study, deep-scale analyses spanning genomic, proteomic, and phosphoproteomic data are performed on 348 thyroid-cancer and 119 tumor-adjacent samples. TP53 (48%), TERT promoter (36.5%), and BRAF (23%) are most frequently mutated in PDTC and ATC. Ribosome biogenesis is identified as a common hallmark of ATC, and RRP9 silencing dramatically inhibits tumor growth. Proteomic clustering identified three ATC/PDTC subtypes. Pro-I subtype is characterized with aberrant insulin signaling and low immune cell infiltration, and Pro-II is featured with DNA repair signaling, while Pro-III harbors high frequency of TP53 and BRAF mutation and intensive C5AR1+ myeloid infiltration. Targeting C5AR1 synergistically improves antitumor effect of PD-1 blockade against ATC cell-derived tumors. These findings provide systematic insights into tumor biology and opportunities for drug discovery, accelerating precision therapy for virulent thyroid cancers.

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and blood-based molecular profiles that inform cancer heterogeneity, metastasis, and therapeutic resistance. Machine learning and AI-driven models further enhance these datasets by stratifying patient populations, predicting therapeutic responses, and expediting the discovery of actionable targets and companion biomarkers. This synergy between technology, automation, and high-dimensional data analytics enables individualized treatment strategies in melanoma, lung, and other cancer types. Aligned with international programs such as the Cancer Moonshot and the ICPC, the Lund Center's approach fosters open collaboration and data sharing on a global scale. This scalable, patient-centric biobanking paradigm provides an adaptable model for institutions aiming to unify clinical, molecular, and computational resources for transformative cancer research.

The majority of neuroendocrine neoplasms in pancreas are non-functional pancreatic neuroendocrine tumors (NF-PanNETs), which exhibit a high occurrence of distant metastases with limited therapeutic options. Here, we perform a comprehensive molecular characterization of 108 NF-PanNETs through integrative analysis of genomic, transcriptomic, proteomic, and phosphoproteomic profiles. Proteogenomic analysis provides functional insights into the genomic driver alterations of NF-PanNETs, revealing a potential mediator of MEN1 alterations using Men1-conditional knockout mice. Machine-learning-based modeling uncovers a three-protein signature as an independent prognostic factor, which is validated by an independent external cohort. Proteomic and phosphoproteomic-based stratification identifies four subtypes with distinct molecular characteristics, immune microenvironments, and clinicopathological features. Drug screening using patient-derived tumor organoids identifies cyclin-dependent kinase (CDK) 5 and Calcium Voltage-Gated Channel Subunit Alpha1 D (CACNA1D) as ubiquitous and subtype-specific targets, respectively, with in vivo validation using xenograft models. Together, our proteogenomic analyses illustrate a comprehensive molecular landscape of NF-PanNETs, revealing biological insights and therapeutic vulnerabilities.

The functional programs of CD4+ T helper (Th) cell clones play a central role in shaping immune responses to different challenges. While advances in single-cell RNA sequencing (scRNA-Seq) have significantly improved our understanding of the diversity of Th cells, the relationship between scRNA-Seq clusters and the traditionally characterized Th subsets remains ambiguous.

In this study, we introduce TCR-Track, a method leveraging immune repertoire data to map phenotypically sorted Th subsets onto scRNA-Seq profiles.

This approach accurately positions the Th1, Th1-17, Th17, Th22, Th2a, Th2, T follicular helper (Tfh), and regulatory T-cell (Treg) subsets, outperforming mapping based on CITE-Seq. Remarkably, the mapping is tightly focused on specific scRNA-Seq clusters, despite 4-year interval between subset sorting and the effector CD4+ scRNA-Seq experiment. These findings highlight the intrinsic program stability of Th clones circulating in peripheral blood. Repertoire overlap analysis at the scRNA-Seq level confirms that the circulating Th1, Th2, Th2a, Th17, Th22, and Treg subsets are clonally independent. However, a significant clonal overlap between the Th1 and cytotoxic CD4+ T-cell clusters suggests that cytotoxic CD4+ T cells differentiate from Th1 clones. In addition, this study resolves a longstanding ambiguity: we demonstrate that, while CCR10+ Th cells align with a specific Th22 scRNA-Seq cluster, CCR10-CCR6+CXCR3-CCR4+ cells, typically classified as Th17, represent a mixture of bona fide Th17 cells and clonally unrelated CCR10low Th22 cells. The clear distinction between the Th17 and Th22 subsets should influence the development of vaccine- and T-cell-based therapies. Furthermore, we show that severe acute SARS-CoV-2 infection induces systemic type 1 interferon (IFN) activation of naive Th cells. An increased proportion of effector IFN-induced Th cells is associated with a moderate course of the disease but remains low in critical COVID-19 cases. Using integrated scRNA-Seq, TCR-Track, and CITE-Seq data from 122 donors, we provide a comprehensive Th scRNA-Seq reference that should facilitate further investigation of Th subsets in fundamental and clinical studies.

Racial disparities in the clinical outcomes of triple-negative breast cancer (TNBC) have been well-documented, but the underlying biological mechanisms remain poorly understood. To investigate these disparities, we employed a multi-omic approach integrating imaging mass cytometry and spatial transcriptomics to characterize the tumor microenvironment (TME) in self-identified Black American (BA) and White American (WA) TNBC patients. Our analysis revealed that the TME in BA patients is marked by a network of endothelial cells, macrophages, and mesenchymal-like cells, which correlates with reduced patient survival. In contrast, the WA TNBC microenvironment is enriched in T-cells and neutrophils, indicative of T-cell exhaustion and suppressed immune responses. Ligand-receptor and pathway analyses further demonstrated that BA TNBC tumors exhibit a relatively "immune-cold" profile, while WA TNBC tumors display features of an "inflamed" TME, suggesting the evolution of a unique immunosuppressive mechanism. These findings provide insight into racially distinct tumor-promoting and immunosuppressive microenvironments, which may contribute to the observed differences in clinical outcomes among BA and WA TNBC patients.

This study identifies distinct tumor microenvironment (TME) profiles in Black and White American TNBC patients, providing new insights into the biological mechanisms driving outcome disparities. Our findings highlight the role of the tumor-endothelial-macrophage niche in these disparities, offering a potential therapeutic target for race-inclusive strategies aimed at improving clinical outcomes. By revealing racial differences in treatment response profiles, this work underscores the necessity for tailored therapies in TNBC. These insights lay the groundwork for the development of inclusive, precision-driven treatment approaches that may help mitigate racial disparities and enhance patient outcomes.

Patient sex influences a wide range of cancer phenotypes, including prevalence, response to therapy and survival endpoints. Molecular sex differences have been identified at all levels of the central dogma. It is hypothesized that these molecular differences may drive the observed clinical sex differences. Yet despite a growing catalog of molecular sex differences in a range of cancer types, their specific functional consequences remain unclear. To directly assess how patient sex impacts cancer cell function, we evaluated 1,209 cell lines subjected to CRISPR knockout, RNAi knockdown or drug exposures. Despite limited statistical power, we identified pan- and per-cancer sex differences in gene essentiality in six sex-linked and fourteen autosomal genes, and in drug sensitivity for two compounds. These data fill a gap in our understanding of the link between sex-differential molecular effects and patient phenotypes. They call for much more careful and systematic consideration of sex-specific effects in mechanistic and functional studies.

Esophageal adenocarcinomas (EACs) represent an evolving tumor entity with high mortality rates. MET amplification is a recurrent driver in EACs and is associated with decreased patient survival. However, the response to MET inhibitors is limited. Recent studies have identified several mechanisms that lead to resistance against MET inhibitors in different tumor entities. Nonetheless, a characterization of additional vulnerable targets beyond MET has not been conducted in MET-amplified EACs.

In this study, we determined the MET amplification status in a cohort of more than 900 EACs using fluorescence in situ hybridization (FISH) and compared the proteomes of MET-amplified (n = 20) versus non-amplified tumors (n = 39) by mass spectrometry.

We identified a phenotype, present in almost all MET-amplified tumors, which shows an enrichment of alternative RNA splicing, and androgen receptor signaling proteins, as well as decreased patient survival. Additionally, our analyses revealed a negative correlation between MET expression and patient survival in MET-amplified EACs, indicating biological heterogeneity with clinical relevance despite the presence of MET amplification as the predominant oncogenic driver. Furthermore, quantitative immunohistochemical analysis of the inflammatory tumor microenvironment showed that an increased percentage of M2 macrophages is associated with lower overall survival in MET-amplified EACs.

Our results provide valuable insights into possible new therapeutic approaches for MET-amplified EACs for further research.

The proteomic heterogeneity of gastric adenocarcinoma (GC) has been extensively investigated at the bulk tissue level, which can only provide an average molecular state. In this study, we collected an in-depth quantitative proteomic dataset of tissues and interstitial fluids (ISFs) from both poorly and non-poorly differentiated GC and presented a comprehensive analysis from several perspectives. Comparison of proteomes between ISFs and tissues revealed that ISF exhibited higher abundances of proteins associated with blood microparticles, protein-lipid complexes, immunoglobulin complexes, and high-density lipoprotein particles. Also, consistent and inconsistent protein abundance changes between them were revealed by a correlation analysis. Interestingly, a more pronounced difference between tumors and normal adjacent tissues was found at the ISF level, which accurately reflected tissue properties compared to those of bulk tissue. Two ISF-derived biomarker candidates, calsyntenin-1 (CLSTN1) and prosaposin (PSAP), were identified by distinguishing patients with different differentiation statuses and were further validated in serum samples. Additionally, the silencing of CLSTN1 and PSAP was demonstrated to suppress cell proliferation, migration, and invasion in poorly differentiated gastric cancer cell lines. In summary, the ISF proteome offers a new perspective on tumor biology. This study provides a valuable resource that significantly enhances the understanding of GC and may ultimately benefit clinical practice.

Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Chromosomal instability (CIN) is involved in about 70% of colorectal cancers (CRCs) and is associated with poor prognosis and drug resistance. From a clinical perspective, a better knowledge of these tumour's biology will help to guide therapeutic strategies more effectively.

We used high-density chromosomal microarray analysis to evaluate CIN level of patient-derived organoids (PDOs) and their original mCRC tissues. We integrated the RNA-seq and mass spectrometry-based proteomics data from PDOs in a functional interaction network to identify the significantly dysregulated processes in CIN. This was followed by a proteome-wGII Pearson correlation analysis and an in silico validation of main findings using functional genomic databases and patient-tissues datasets to prioritize the high-confidence CIN features.

By applying the weighted Genome Instability Index (wGII) to identify CIN, we classified PDOs and demonstrated a good correlation with tissues. Multi-omics analysis showed that our organoids recapitulated genomic, transcriptomic and proteomic CIN features of independent tissues cohorts. Thanks to proteotranscriptomics, we uncovered significant associations between mitochondrial metabolism and epithelial-mesenchymal transition in CIN CRC PDOs. Correlating PDOs wGII with protein abundance, we identified a subset of proteins significantly correlated with CIN. Co-localisation analysis in PDOs strengthened the putative role of IPO7 and YAP, and, through in silico analysis, we found that some of the targets give significant dependencies in cell lines with CIN compatible status.

We first demonstrated that PDO models are a faithful reflection of CIN tissues at the genetic and phenotypic level. Our new findings prioritize a subset of genes and molecular processes putatively required to cope with the burden on cellular fitness imposed by CIN and associated with disease aggressiveness.

In this study, we generated label-free data-independent acquisition (DIA)-based liquid chromatography (LC)-mass spectrometry (MS) proteomics data from 261 renal cell carcinomas (RCC) and 195 normal adjacent tissues (NAT). The RCC tumors included 48 non-clear cell renal cell carcinomas (non-ccRCC) and 213 ccRCC. A total of 219,740 peptides and 11,943 protein groups were identified with 9,787 protein groups per sample on average. We adopted a comprehensive approach to select representative samples with different mutation sites, considering histopathological, immune, methylation, and non-negative matrix factorization (NMF)-based subtypes, along with clinical characteristics (gender, grade, and stage) to capture the complexity and diversity of ccRCC tumors. We used machine learning identified 55 protein signatures that distinguish RCC tumors from NATs. Furthermore, 39 protein signatures that differentiate different RCC tumor subtypes were also identified. Our findings offer an extensive perspective of the proteomic landscape in RCC, illuminating specific proteins that serve to distinguish RCC tumors from NATs and among various RCC tumor subtypes.

Deep-scale, mass spectrometry-based proteomic studies by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) program involves tissue lysis using urea buffer before data acquisition via mass spectrometry for quantitative global proteomic and phosphoproteomic analysis. This is described in a 2018 protocol1. Here we report an update to this initial protocol by implementing a sonication step into urea-based tissue lysis. Similar to the initial CPTAC protocol, we identified >12,000 proteins and >25,000 phosphopeptides in a tandem mass tag (TMT) set containing both nonsonicated and sonicated tumor tissues from patient-derived xenograft mouse models. An improvement in the detection of membrane-bound and DNA-binding proteins was observed by including the sonication. We also offer recommendations for optimal sonication conditions such as the buffer composition, timing of sonication cycle, instrumentation settings and a troubleshooting section for potential users. Additionally, the protocol is equally applicable to other biological specimens.

HER2 (human epidermal growth factor receptor 2) is overexpressed in approximately 15-20% of breast cancers, leading to aggressive tumour growth and poor prognosis. Anti-HER2 therapies, such as trastuzumab and pertuzumab, have significantly improved the outcomes for patients with HER2-positive breast cancer by blocking HER2 signalling. However, intrinsic and acquired resistance remains a major clinical challenge, limiting the long-term effectiveness of these therapies. Understanding the mechanisms of resistance is essential for developing strategies to overcome it and improve the therapeutic outcomes. We generated multiple HER2-positive breast cancer cell line models resistant to trastuzumab and pertuzumab combination therapy. Using mass spectrometry-based proteomics, we conducted a comprehensive analysis to identify the mechanisms underlying resistance. Proteomic analysis identified 618 differentially expressed proteins, with a core of 83 overexpressed and 118 downregulated proteins. Through a series of advanced bioinformatics analyses, we identified significant protein alterations and signalling pathways potentially responsible for the development of resistance, revealing key alterations in the protein metabolism, mitochondrial function, and signalling pathways, such as MAPK, TNF, and TGFβ. These findings identify mitochondrial activity and detoxification processes as pivotal mechanisms underlying the resistance to anti-HER2 therapy. Additionally, we identified key proteins, including ANXA1, SLC2A1, and PPIG, which contribute to the tumour progression and resistance phenotype. Our study suggests that targeting these pathways and proteins could form the basis of novel therapeutic strategies to overcome resistance in HER2-positive breast cancer.

The effectiveness of radiotherapy in colorectal cancer (CRC) relies on its ability to induce cell death via the generation of reactive oxygen species (ROS). However, genes responsible for mitigating oxidative stress can impede radiotherapy's efficacy. In this study, we elucidate a significant association between the nucleolar protein Fibrillarin (FBL) and the oxidative stress response in CRC tumors. Our findings reveal elevated expression of FBL in colorectal cancer, which positively correlates with oxidative stress levels. Mechanistically, FBL demonstrates direct accumulation at DNA damage sites under the regulation of PARP1. Specifically, the N-terminal GAR domain of FBL is susceptible to PARylation by PARP1, enabling FBL to recognize PARylated proteins. The accumulation of damaged FBL plays a pivotal role in facilitating short-patched base excision repair by recruiting Ligase III and disassociating PCNA and FEN1. Moreover, tumors with heightened FBL expression exhibit reduced DNA damage levels but increased sensitivity to combined low-dose radiotherapy and olaparib treatment. This underscores the potential of leveraging PARP inhibitors to augment radiotherapy sensitivity in CRC cases characterized by elevated FBL expression, offering a promising therapeutic avenue.

Colorectal cancer (CRC) patients with microsatellite-stable (MSS) tumors are mostly treated with chemotherapy. Clinical benefits of targeted therapies depend on mutational states and tumor location. Many tumors carry mutations in KRAS proto-oncogene, GTPase (KRAS) or B-Raf proto-oncogene, serine/threonine kinase (BRAF), rendering them more resistant to therapies. We performed whole-exome sequencing and RNA-Sequencing of 28 tumors of the Athens Comprehensive Cancer Center CRC cohort, and molecularly characterized CRC patients based on their microsatellite instability (MSI) status, single-nucleotide variations (SNVs)/copy number alterations (CNAs), and pathway/transcription factor activities at the individual patient level. Variants were classified using a computational score for integrative cancer variant annotation and prioritization. Complementing this with public multi-omics datasets, we identified activation of transforming growth factor beta (TGFβ) signaling to be more strongly activated in MSS patients, whereas Janus kinase (JAK)-signal transducer and activator of transcription (STAT) and mitogen-activated protein kinase (MAPK) molecular cascades were activated specifically in MSI tumors. We unraveled mechanisms consistently perturbed in the transcriptional and mutational circuits and identified Runt-related transcription factors (RUNX transcription factors) as putative biomarkers in CRC, given their role in the regulation of pathways involved in tumor progression and immune evasion. Assessing the immunogenicity of CRC tumors in the context of RAS/RAF mutations and MSI/MSS status revealed a critical impact that KRAS mutations have on immunogenicity, particularly in the MSS patient subgroup, with implications for diagnosis and treatment.

Triple-positive breast cancer (TPBC), defined by the co-expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), poses unique therapeutic challenges due to complex signaling interactions and resulting treatment resistance. This review summarizes key findings on the molecular mechanisms and cross-talk among ER, PR, and HER2 pathways, which drive tumor proliferation and resistance to conventional therapies. Current strategies in TPBC treatment, including endocrine and HER2-targeted therapies, are explored alongside emerging approaches such as immunotherapy and CRISPR/Cas9 gene editing. Additionally, we discuss the tumor microenvironment (TME) and its role in treatment resistance, highlighting promising avenues for intervention through combination therapies and predictive biomarkers. By addressing these interdependent pathways and optimizing therapeutic strategies, precision medicine holds significant potential for improving TPBC patient outcomes and advancing individualized cancer care.

Ampullary adenocarcinoma (AMPAC) is a rare and heterogeneous malignancy. Here we performed a comprehensive proteogenomic analysis of 198 samples from Chinese AMPAC patients and duodenum patients. Genomic data illustrate that 4q loss causes fatty acid accumulation and cell proliferation. Proteomic analysis has revealed three distinct clusters (C-FAM, C-AD, C-CC), among which the most aggressive cluster, C-AD, is associated with the poorest prognosis and is characterized by focal adhesion. Immune clustering identifies three immune clusters and reveals that immune cluster M1 (macrophage infiltration cluster) and M3 (DC cell infiltration cluster), which exhibit a higher immune score compared to cluster M2 (CD4+ T-cell infiltration cluster), are associated with a poor prognosis due to the potential secretion of IL-6 by tumor cells and its consequential influence. This study provides a comprehensive proteogenomic analysis for seeking for better understanding and potential treatment of AMPAC.

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein's contribution to the model's predictions using SHapley Additive exPlanations values. Three classifiers-LASSO, XGBoost, and LightGBM were evaluated for predictive performance along with hyperparameter tuning of each model using grid search, with LASSO achieving the highest AUC of 75% in the UK Biobank dataset and the AUCs for LightGBM and XGBoost are 69.61% and 71.42%, respectively. Using SHapley Additive exPlanations values, TFF3, LCN2, and CEACAM5 were found to be key biomarkers associated with cell adhesion and inflammation. Protein quantitative trait loci analyze studies provided further evidence for the involvement of TFF1, CEACAM5, and SELE in colorectal cancer, with possible connections to the PI3K/Akt and MAPK signaling pathways. By offering insights into colorectal cancer diagnostics and targeted therapeutics, our findings set the stage for further biomarker validation.

Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression-in addition to gene expression-has rarely been systematically investigated. This is significant as mRNA and protein levels frequently show only moderate correlation, driven by factors such as translation efficiency and protein degradation. Proteogenomic datasets from large tumor cohorts provide an opportunity to systematically analyze the effects of somatic mutations on mRNA and protein abundance and identify mutations with distinct impacts on these molecular levels.

We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across 6 cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including CDH1 and MSH3 truncations, as well as other mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins are more likely to be experimentally confirmed as functional.

This study reveals that somatic mutations can exhibit distinct impacts on mRNA and protein levels, underscoring the necessity of integrating proteogenomic data to comprehensively identify functionally significant cancer mutations. These insights provide a framework for prioritizing mutations for further functional validation and therapeutic targeting.

Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.

Proteomics has revolutionized the field of cancer biology because the use of a large number of in vivo (SILAC), in vitro (iTRAQ, ICAT, TMT, stable-isotope Dimethyl, and 18O) labeling techniques or label-free methods (spectral counting or peak intensities) coupled with mass spectrometry enables us to profile and identify dysregulated proteins in diseases such as cancer. These proteome and genome studies have led to many challenges, such as the lack of consistency or correlation between copy numbers, RNA, and protein-level data. This review covers solely mass spectrometry-based approaches used for cancer biomarker discovery. It also touches on the emerging role of oncoproteogenomics or proteogenomics in cancer biomarker discovery and how this new area is attracting the integration of genomics and proteomics areas to address some of the important questions to help impinge on the biology and pathophysiology of different malignancies to make these mass spectrometry-based studies more realistic and relevant to clinical settings.

Chromosomal Instability (CIN) encompasses approximately 65 %-70 % of colorectal cancer (CRC) patients, playing a pivotal role in tumor progression. However, controversies persist regarding the molecular characteristics and treatment strategies associated with these patients. Integrative colorectal cancer proteogenomic analysis identified DDX18 in colorectal cancer. We investigated the molecular mechanisms underlying the regulation of colorectal cancer by the R-loop binding protein DDX18 using colon cancer tissues, cell lines and patient-derived organoids. Our findings revealed that DDX18 expression positively correlates with the expression of genomic instability marker R-loops. Moreover, heightened DDX18 expression delays the completion of DNA damage repair, leading to an increase in double-strand DNA breaks, thereby promoting genomic instability. Notably, the upregulation of DDX18 enhances sensitivity to DNA-damaging. This study elucidated DDX18 beyond participating in fundamental physiological functions, may play a crucial role in the regulation of genomic stability, and also provides a powerful resource for further functional exploration of DDX18 in cancer progression and therapeutic application.

In the past, numerous investigations have delved into the influence of p27 (p27kip) on the prognosis and clinicopathological characteristics of colorectal cancer (CRC), yielding conclusions that are not universally statistically significant, thus rendering the discourse rather contentious.

We collected available articles published before August 2024 and extracted data to analyze the association between the expression of p27 and the prognosis and clinicopathological features of CRC. In addition, we used Gene Expression Profiling Interactive Analysis (GEPIA), University of Alabama at Birmingham's Cancer Data Analysis Portal (UALCAN), and the Human Protein Atlas (HPA) to validate our results.

Through an extensive examination of four prominent databases, a total of 21 original articles encompassing a cohort of 3,378 patients were identified. The findings indicated that a low expression of p27 could lead to shorter overall survival (OS) [hazard ratio (HR) = 0.44, 95% confidence interval (95%CI) = 0.31-0.61, Z = 4.89, p = 0.000] and disease-free survival (DFS) (HR = 0.40, 95%CI = 0.28-0.59, Z = 4.75, p = 0.000). In addition, a low expression of p27 predisposed tumors to the right colon [odds ratio (OR) = 0.61, 95%CI = 0.46-0.82, Z = 3.32, p = 0.001] and limited tumor differentiation (OR = 0.56, 95%CI = 0.41-0.77, Z = 3.62, p = 0.000), but had no effect on TNM staging (OR = 0.80, 95%CI = 0.52-1.22, Z = 1.05, p = 0.295), lymph node metastasis (OR = 0.90, 95%CI = 0.25-3.28, Z = 0.16, p = 0.876), and tumor size (OR = 0.94, 95%CI = 0.54-1.65, Z = 0.21, p = 0.835). The results from GEPIA and UALCAN showed that p27 had no effect on TNM staging, lymph node metastasis, DFS, and OS; moreover, there was no expression difference between tumor tissues and normal tissues. The findings from the HPA indicated that there was lower expression of p27 in tumor tissues compared with normal tissues.

Although inconsistent results were reached with the bioinformatics analysis from this meta-analysis, it was confirmed that a low expression of p27 can adversely affect the prognosis of patients with CRC and make a meaningful impact on a part of the clinicopathological features in the meta-analysis with abundant data. In the future, predicting the prognosis of patients with CRC and guiding treatment might emerge as a significant objective.

Since their discovery, corticosteroids have been widely used in the treatment of several diseases, including asthma, acute lymphoblastic leukemia, chronic obstructive pulmonary disease, and many other conditions. However, it has been noted that some patients develop undesired side effects or even fail to respond to treatment. The reasons behind this have not yet been fully elucidated. This poses a significant challenge to effective treatment that needs to be addressed urgently. Recent genomic, transcriptomic, and other omics-based approximations have begun to shed light into the genetic factors influencing interindividual variability in corticosteroid efficacy and its side effects. Here, we comprehensively revise the recent literature on corticosteroid response in various critical and chronic diseases, with a focus on omics approaches, and highlight existing knowledge gaps where further investigation is urgently needed.

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Multi-omics studies of breast ductal carcinoma (BRDC) have advanced the understanding of the disease's biology and accelerated targeted therapies. However, the temporal order of a series of biological events in the progression of BRDC is still poorly understood. A comprehensive proteogenomic analysis of 224 samples from 168 patients with malignant and benign breast diseases is carried out. Proteogenomic analysis reveals the characteristics of linear multi-step progression of BRDC, such as tumor protein P53 (TP53) mutation-associated estrogen receptor 1 (ESR1) overexpression is involved in the transition from ductal hyperplasia (DH) to ductal carcinoma in situ (DCIS). 6q21 amplification-associated nuclear receptor subfamily 3 group C member 1 (NR3C1) overexpression helps DCIS_Pure (pure DCIS, no histologic evidence of invasion) cells avoid immune destruction. The T-cell lymphoma invasion and metastasis 1, androgen receptor, and aldo-keto reductase family 1 member C1 (TIAM1-AR-AKR1C1) axis promotes cell invasion and migration in DCIS_adjIDC (DCIS regions of invasive cancers). In addition, AKR1C1 is identified as a potential therapeutic target and demonstrated the inhibitory effect of aspirin and dydrogesterone as its inhibitors on tumor cells. The integrative multi-omics analysis helps to understand the progression of BRDC and provides an opportunity to treat BRDC in different stages.

Polycystic ovary syndrome (PCOS) is a prevalent cause of menstrual irregularities and infertility in women, impacting quality of life. Despite advancements, current understanding of PCOS pathogenesis and treatment remains limited. This study uses machine learning-based data mining to identify acetylation-related genetic markers associated with PCOS, aiming to enhance diagnostic precision and therapeutic efficacy.

Advanced machine learning techniques were used to improve the precision of key gene identification and reveal their biological mechanisms. Validation on an independent dataset (GSE48301) confirmed their diagnostic value, assessed through ROC curves and nomograms for PCOS risk prediction. Molecular mechanisms of acetylation-related gene regulation in PCOS were further examined through clustering, immune-environmental, and gene network analyses.

Our analysis identified 15 key acetylation-regulated genes differentially expressed in PCOS, including SGF29, NOL6, KLF15, and INO80D, which are relevant to PCOS pathogenesis. ROC curve analyses on training and validation datasets confirmed the model's high diagnostic accuracy. Additionally, these genes were associated with immune cell infiltration, offering insights into the inflammatory aspect of PCOS.

The identified acetylation gene markers offer novel insights into the molecular mechanisms underlying PCOS and hold promise for enhancing the development of precise diagnostic and therapeutic strategies.