Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

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.

Lung cancer volume doubling time by computed tomography: A systematic review and meta-analysis

AIM

Lung cancer growth rate influences screening strategies and treatment decisions. This review aims to provide an overview of primary lung cancer growth rate, quantified by volume doubling time (VDT) through computed tomography (CT) measurement.

METHODS

Using PRISMA-DTA guideline, PubMed, EMBASE, and Web of Science were searched until March 2024 for studies reporting CT-measured VDT of pathologically confirmed primary lung cancer before intervention. Summary data were extracted from published reports by two independent researchers. Primary outcomes were pooled mean VDT of lung cancer by nodule type and histology, distribution of indolent lung cancer (defined as VDT>400 days or negative), and correlated factors.

RESULTS

Thirty-three studies were eligible, comprising 3959 patients with primary lung cancer (mean age range:57.6-77.0 years; 60.0 % men). The pooled mean VDT for solid, part-solid, and nonsolid lung cancer were 207, 536, and 669 days, respectively (p < 0.001). When stratified by histology within solid lung cancer, the pooled mean VDT of adenocarcinoma, squamous cell carcinoma, small cell lung cancer, and others were 223, 140, 73, and 178 days, respectively (p < 0.001). Indolent lung cancer was observed in 34.9 % of lung cancer, predominantly in adenocarcinoma (68.9 %). Adenocarcinoma was associated with slower growth, whereas factors such as tumor size, solidity, TNM staging, and smoking history were positively associated with growth rates.

CONCLUSIONS

Pooled mean VDT of solid lung cancer was approximately 207 days, demonstrating significant variability in histology yet remaining under the 400-day referral threshold. Key predictors of growth rate include histology, size, solidity, and smoking history, essential for tailoring early intervention strategies.

TRIAL REGISTRATION NUMBER

CRD42023408069.

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Chemoproteogenomic stratification of the missense variant cysteinome

Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics-a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues-with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.

A brief guide to analyzing expression quantitative trait loci

Molecular quantitative trait locus (molQTL) mapping has emerged as an important approach for elucidating the functional consequences of genetic variants and unraveling the causal mechanisms underlying diseases or complex traits. However, the variety of analysis tools and sophisticated methodologies available for molQTL studies can be overwhelming for researchers with limited computational expertise. Here, we provide a brief guideline with a curated list of methods and software tools for analyzing expression quantitative trait loci (eQTL), the most widely studied type of molQTL.

Proteogenomic analysis of air-pollution-associated lung cancer reveals prevention and therapeutic opportunities

Air pollution significantly impacts lung cancer progression, but there is a lack of a comprehensive molecular characterization of clinical samples associated with air pollution. Here, we performed a proteogenomic analysis of lung adenocarcinoma (LUAD) in 169 female never-smokers from the Xuanwei area (XWLC cohort), where coal smoke is the primary contributor to the high lung cancer incidence. Genomic mutation analysis revealed XWLC as a distinct subtype of LUAD separate from cases associated with smoking or endogenous factors. Mutational signature analysis suggested that Benzo[a]pyrene (BaP) is the major risk factor in XWLC. The BaP-induced mutation hotspot, EGFR-G719X, was present in 20% of XWLC which endowed XWLC with elevated MAPK pathway activations and worse outcomes compared to common EGFR mutations. Multi-omics clustering of XWLC identified four clinically relevant subtypes. These subgroups exhibited distinct features in biological processes, genetic alterations, metabolism demands, immune landscape, and radiomic features. Finally, MAD1 and TPRN were identified as novel potential therapeutic targets in XWLC. Our study provides a valuable resource for researchers and clinicians to explore prevention and treatment strategies for air-pollution-associated lung cancers.

zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation

Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.

Proteomic basis for pancreatic acinar cell carcinoma and pancreatoblastoma as similar yet distinct entities

Acinar cell carcinoma (ACC) and pancreatoblastoma (PBL) are rare pancreatic malignancies with acinar differentiation. Proteogenomic profiling of ACC and PBL revealed distinct protein expression patterns compared to pancreatic ductal adenocarcinoma (PDAC) and benign pancreas. ACC and PBL exhibited similarities, with enrichment in proteins related to RNA processing, chromosome organization, and the mitoribosome, while PDACs overexpressed proteins associated with actin-based processes, extracellular matrix, and immune-active stroma. Pathway activity differences in metabolic adaptation, epithelial-to-mesenchymal transition, and DNA repair were characterized between these diseases. PBL showed upregulation of Wnt-CTNNB1 and IGF2 pathways. Seventeen ACC-specific proteins suggested connections to metabolic diseases with mitochondrial dysfunction, while 34 PBL-specific proteins marked this pediatric cancer with an embryonic stem cell phenotype and alterations in chromosomal proteins and the cell cycle. This study provides novel insights into the proteomic landscapes of ACC and PBL, offering potential targets for diagnostic and therapeutic development.

Lung cancer screening in never smokers

PURPOSE OF REVIEW

Low-dose computed tomography (LDCT) lung cancer screening has been established in smokers, but its role in never smokers remains unclear. The differences in lung cancer biology between smokers and nonsmokers highlight the importance of a discriminated approach. This overview focuses on the emerging data and implementation challenges for LDCT screening in nonsmokers.

RECENT FINDINGS

The first LDCT screening study in nonsmokers enriched with risk factors demonstrated a lung cancer detection rate double that of the phase 3 trials in smokers. The relative risk of lung cancer detected by LDCT has also been found to be similar amongst female never smokers and male ever smokers in Asia. Majority of lung cancers detected through LDCT screening are stage 0/1, leading to concerns of overdiagnosis. Risk prediction models to enhance individual selection and nodule management could be useful to enhance the utility of LDCT screening in never smokers.

SUMMARY

With appropriate risk stratification, LDCT screening in never smokers may attain similar efficacy as compared to smokers. A global effort is needed to generate evidence surrounding optimal screening strategies, as well as health and economic benefits to determine the suitability of widespread implementation.

Multiple primary lung cancer: Updates and perspectives

Management of multiple primary lung cancer (MPLC) remains challenging, partly due to its increasing incidence, especially with the significant rise in cases of multiple lung nodules caused by low-dose computed tomography screening. Moreover, the indefinite pathogenesis, diagnostic criteria, and treatment selection add to the complexity. In recent years, there have been continuous efforts to dissect the molecular characteristics of MPLC and explore new diagnostic approaches as well as treatment modalities, which will be reviewed here, with a focus on newly emerging evidence and future perspectives, hope to provide new insights into the management of MPLC and serve as inspiration for future research related to MPLC.

PrPC controls epithelial-to-mesenchymal transition in EGFR-mutated NSCLC: implications for TKI resistance and patient follow-up

Patients with EGFR-mutated non-small cell lung cancer (NSCLC) benefit from treatment with tyrosine kinase inhibitors (TKI) targeting EGFR. Despite improvements in patient care, especially with the 3rd generation TKI osimertinib, disease relapse is observed in all patients. Among the various processes involved in TKI resistance, epithelial-to-mesenchymal transition (EMT) is far from being fully characterized. We hypothesized that the cellular prion protein PrPC could be involved in EMT and EGFR-TKI resistance in NSCLC. Using 5 independent lung adenocarcinoma datasets, including our own cohort, we document that the expression of the PRNP gene encoding PrPC is associated with EMT. By manipulating the levels of PrPC in different EGFR-mutated NSCLC cell lines, we firmly establish that the expression of PrPC is mandatory for cells to maintain or acquire a mesenchymal phenotype. Mechanistically, we show that PrPC operates through an ILK-RBPJ cascade, which also controls the expression of EGFR. Our data further demonstrate that PrPC levels are elevated in EGFR-mutated versus wild-type tumours or upon EGFR activation in vitro. In addition, we provide evidence that PRNP levels increase with TKI resistance and that reducing PRNP expression sensitizes cells to osimertinib. Finally, we found that plasma PrPC levels are increased in EGFR-mutated NSCLC patients from 2 independent cohorts and that their longitudinal evolution mirrors that of disease. Altogether, these findings define PrPC as a candidate driver of EMT-dependent resistance to EGFR-TKI in NSCLC. They further suggest that monitoring plasma PrPC levels may represent a valuable non-invasive strategy for patient follow-up and warrant considering PrPC-targeted therapies for EGFR-mutated NSCLC patients with TKI failure.

Integrative proteogenomic profiling of high-risk prostate cancer samples from Chinese patients indicates metabolic vulnerabilities and diagnostic biomarkers

Prostate cancer (PCa) exhibits significant geoethnic disparities as reflected by distinct variations in the cancer genome and disease progression. Here, we perform a comprehensive proteogenomic characterization of localized high-risk PCa utilizing paired tumors and nearby tissues from 125 Chinese male patients, with the primary objectives of identifying potential biomarkers, unraveling critical oncogenic events and delineating molecular subtypes with poor prognosis. Our integrated analysis highlights the utility of GOLM1 as a noninvasive serum biomarker. Phosphoproteomics analysis reveals the crucial role of Ser331 phosphorylation on FOXA1 in regulating FOXA1-AR-dependent cistrome. Notably, our proteomic profiling identifies three distinct subtypes, with metabolic immune-desert tumors (S-III) emerging as a particularly aggressive subtype linked to poor prognosis and BCAT2 catabolism-driven PCa progression. In summary, our study provides a comprehensive resource detailing the unique proteomic and phosphoproteomic characteristics of PCa molecular pathogenesis and offering valuable insights for the development of diagnostic and therapeutic strategies.

Global burden of lung cancer in women of childbearing age attributable to ambient particulate matter pollution: 1990-2021

BACKGROUND

This study aimed to evaluate the global burden of lung cancer due to ambient particulate matter (PM) pollution in women of childbearing age from 1990 to 2021.

METHODS

This was a secondary analysis utilizing data from the Global Burden of Disease (GBD) 2021, with a focus on the temporal trends of the lung cancer burden attributable to ambient PM2.5 among women of childbearing age.

RESULTS

In 2021, the global mortality and disability-adjusted life years (DALYs) number of lung cancer burden attributable to ambient PM2.5 among women of childbearing age were approximately 5205 and 247,211, respectively. The rate of lung cancer attributable to ambient PM2.5 among women of childbearing age increased between 1990 and 2021, with the age-standardized mortality rate (ASMR) increasing from 0.22 (95% uncertainty interval [UI]; 0.13 to 0.33) to 0.25 (95% UI; 0.14 to 0.37; average annual percent change [AAPC] = 0.40) and the age-standardized DALYs rate (ASDR) increasing from 10.39 (95% UI; 5.96 to 15.72) to 12.06 (95% UI; 6.83 to 17.51; AAPC = 0.41). The middle sociodemographic index (SDI) region, East Asia, and China had the heaviest burden, while the high SDI region showed the highest decrease. ASMR and ASDR exhibited an inverted U-shaped relationship with the SDI.

CONCLUSIONS

From 1990 to 2021, the lung cancer burden attributable to ambient PM2.5 among women of childbearing age exhibited an increasing trend. Furthermore, increasing attention should be paid to the middle SDI region, East Asia, and China, as ambient PM pollution remains a critical target for intervention.

Proteogenomic characterization identifies clinical subgroups in EGFR and ALK wild-type never-smoker lung adenocarcinoma

Patients with lung adenocarcinoma who have never smoked (NSLA) and lack key driver mutations, such as those in the EGFR and ALK genes, face limited options for targeted therapies. They also tend to have poorer outcomes with immune checkpoint inhibitors than lung cancer patients who have a history of smoking. The proteogenomic profile of nonsmoking lung adenocarcinoma patients without these oncogenic driver mutations is poorly understood, which complicates the precise molecular classification of these cancers and highlights a significant area of unmet clinical need. This study analyzed the genome, transcriptome, and LC‒MS/MS-TMT-driven proteome data of tumors obtained from 99 Korean never-smoker lung adenocarcinoma patients. NSLA tumors without EGFR or ALK driver oncogenes were classified into four proteogenomic subgroups: proliferation, angiogenesis, immune, and metabolism subgroups. These 4 molecular subgroups were strongly associated with distinct clinical outcomes. The proliferation and angiogenesis subtypes were associated with a poorer prognosis, while the immune subtype was associated with the most favorable outcome, which was validated in an external lung cancer dataset. Genomic-wide impacts were analyzed, and significant correlations were found between copy number alterations and both the transcriptome and proteome for several genes, with enrichment in the ERBB, neurotrophin, insulin, and MAPK signaling pathways. Proteogenomic analyses suggested several targetable genes and proteins, including CDKs and ATR, as potential therapeutic targets in the proliferation subgroup. Upregulated cytokines, such as CCL5 and CXCL13, in the immune subgroup may serve as potential targets for combination immunotherapy. Our comprehensive proteogenomic analysis revealed the molecular subtypes of EGFR- and ALK-wild-type NSLA with significant unmet clinical needs.

Risk Factors and Preventive Measures for Lung Cancer in the European Union

BACKGROUND

Lung cancer is worldwide one of the most common types of cancer with still very high mortality rates. The aim of this study was to identify and demonstrate correlations between lung cancer mortality rates and potential influencing factors in EU countries.

METHODS

This retrospective study investigated the connections between the mortality rates in the EU countries (n = 28) and potential influencing factors. The significant factors from the correlation analysis were identified using a stepwise multiple regression analysis.

RESULTS

The most important factors for both genders are the incidence of lung cancer, the price of tobacco, and the number of doctors per 100,000 inhabitants.

CONCLUSION

Lung cancer is a significant global health challenge. The study identified potential strategies for reducing the mortality rate from lung cancer. These strategies include an increase in the number of physicians, enhanced accessibility to cutting-edge antineoplastic medications, and state-funded coverage of the associated costs. It would be beneficial for politicians to consider implementing LDCT screening for the early detection of the disease. The implementation of uniform healthcare system optimization across the EU, combined with improvements in socio-economic conditions, has the potential to mitigate the risk of developing lung cancer.

CacyBP promotes the development of lung adenocarcinoma by regulating OTUD5

Lung cancer is the most common and lethal malignancy, with lung adenocarcinoma accounting for approximately 40% of all cases. Despite some progress in understanding the pathogenesis of this disease and developing new therapeutic approaches, the current treatments for lung adenocarcinoma remain ineffective due to factors such as high tumour heterogeneity and drug resistance. Therefore, there is an urgent need to identify novel therapeutic targets. Calcyclin-binding protein (CacyBP) can regulate a variety of physiological processes by binding to different proteins, but its function in lung adenocarcinoma is unknown. Here, we show that CacyBP is highly expressed in lung adenocarcinoma tissues, and high CacyBP expression correlates with poorer patient survival. Moreover, overexpression of CacyBP promoted the proliferation, migration and invasion of lung adenocarcinoma cell lines. Further mechanistic studies revealed that CacyBP interacts with the tumour suppressor ovarian tumour (OTU) deubiquitinase 5 (OTUD5), enhances the ubiquitination and proteasomal degradation of OTUD5 and regulates tumourigenesis via OTUD5. In conclusion, our study reveals a novel mechanism by which CacyBP promotes tumourigenesis by increasing the ubiquitination level and proteasome-dependent degradation of OTUD5, providing a potential target for the treatment of lung adenocarcinoma.

Small cell lung cancer: emerging subtypes, signaling pathways, and therapeutic vulnerabilities

Small cell lung cancer (SCLC) is a recalcitrant cancer characterized by early metastasis, rapid tumor growth and poor prognosis. In recent decades, the epidemiology, initiation and mutation characteristics of SCLC, as well as abnormal signaling pathways contributing to its progression, have been widely studied. Despite extensive investigation, fewer drugs have been approved for SCLC. Recent advancements in multi-omics studies have revealed diverse classifications of SCLC that are featured by distinct characteristics and therapeutic vulnerabilities. With the accumulation of SCLC samples, different subtypes of SCLC and specific treatments for these subtypes were further explored. The identification of different molecular subtypes has opened up novel avenues for the treatment of SCLC; however, the inconsistent and uncertain classification of SCLC has hindered the translation from basic research to clinical applications. Therefore, a comprehensives review is essential to conclude these emerging subtypes and related drugs targeting specific therapeutic vulnerabilities within abnormal signaling pathways. In this current review, we summarized the epidemiology, risk factors, mutation characteristics of and classification, related molecular pathways and treatments for SCLC. We hope that this review will facilitate the translation of molecular subtyping of SCLC from theory to clinical application.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl β-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 μg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

The genomic landscape of lung cancer in never-smokers from the Women's Health Initiative

Over 200,000 individuals are diagnosed with lung cancer in the United States every year, with a growing proportion of cases, especially lung adenocarcinoma, occurring in individuals who have never smoked. Women over the age of 50 comprise the largest affected demographic. To understand the genomic drivers of lung adenocarcinoma and therapeutic response in this population, we performed whole genome and/or whole exome sequencing on 73 matched lung tumor/normal pairs from postmenopausal women who participated in the Women's Health Initiative. Somatic copy number alterations showed little variation by smoking status, suggesting that aneuploidy may be a general characteristic of lung cancer regardless of smoke exposure. Similarly, clock-like and APOBEC mutation signatures were prevalent but did not differ in tumors from smokers and never-smokers. However, mutations in both EGFR and KRAS showed unique allelic differences determined by smoking status that are known to alter tumor response to targeted therapy. Mutations in the MYC-network member MGA were more prevalent in tumors from smokers. Fusion events in ALK, RET, and ROS1 were absent, likely due to age-related differences in fusion prevalence. Our work underscores the profound effect of smoking status, age, and sex on the tumor mutational landscape and identifies areas of unmet medical need.

PARP4 interacts with hnRNPM to regulate splicing during lung cancer progression

BACKGROUND

The identification of cancer driver genes from sequencing data has been crucial in deepening our understanding of tumor biology and expanding targeted therapy options. However, apart from the most commonly altered genes, the mechanisms underlying the contribution of other mutations to cancer acquisition remain understudied. Leveraging on our whole-exome sequencing of the largest Asian lung adenocarcinoma (LUAD) cohort (n = 302), we now functionally assess the mechanistic role of a novel driver, PARP4.

METHODS

In vitro and in vivo tumorigenicity assays were used to study the functional effects of PARP4 loss and mutation in multiple lung cancer cell lines. Interactomics analysis by quantitative mass spectrometry was conducted to identify PARP4's interaction partners. Transcriptomic data from cell lines and patient tumors were used to investigate splicing alterations.

RESULTS

PARP4 depletion or mutation (I1039T) promotes the tumorigenicity of KRAS- or EGFR-driven lung cancer cells. Disruption of the vault complex, with which PARP4 is commonly associated, did not alter tumorigenicity, indicating that PARP4's tumor suppressive activity is mediated independently. The splicing regulator hnRNPM is a potentially novel PARP4 interaction partner, the loss of which likewise promotes tumor formation. hnRNPM loss results in splicing perturbations, with a propensity for dysregulated intronic splicing that was similarly observed in PARP4 knockdown cells and in LUAD cohort patients with PARP4 copy number loss.

CONCLUSIONS

PARP4 is a novel modulator of lung adenocarcinoma, where its tumor suppressive activity is mediated not through the vault complex-unlike conventionally thought, but in association with its novel interaction partner hnRNPM, thus suggesting a role for splicing dysregulation in LUAD tumorigenesis.

A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study

OBJECTIVE

To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance.

METHODS

The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %.

CONCLUSION

The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.

Sarcoma incidence worldwide: regional differences in histology and molecular subtypes

PURPOSE OF REVIEW

There are numerous sarcoma subtypes and vary widely in terms of epidemiology, clinical characteristics, genetic profiles, and pathophysiology. They also differ widely between ethnic groups. This review focuses on the different incidence rates of sarcomas in different regions and the potential explanations for these disparities.

RECENT FINDINGS

In an intercontinental study using national cancer registry databases from France and Taiwan, the French population had a higher risk of liposarcomas, leiomyosarcomas, and synovial sarcomas, whereas the Taiwanese population had a higher incidence of angiosarcomas and malignant peripheral nerve sheath tumors. The anatomical distribution of these sarcomas also varied between these two regions. In France, most angiosarcoma cases occurred in the extremities and trunk, whereas in Taiwan, angiosarcoma cases in the abdomen and pelvis were more common. Another international study showed that in addition to the common known TP53 and NF1 germline mutations, genes involved in centromere and telomere maintenance were also involved in sarcomagenesis. We reviewed factors related to genetics, environmental effects, chemical exposure, and radiation exposure that could explain the differences in sarcoma incidence among different geographical or ethnic regions.

SUMMARY

Our understanding of the potential cause of sarcomas with different subtypes is limited. Establishing a comprehensive global database for patients with sarcomas from all ethnic groups is essential to deepen our understanding of the potential risk factors and the pathophysiology of all sarcoma subtypes.

Serum Lipid Levels, Genetic Risk, and Lung Cancer Incidence: A Large Prospective Cohort Study

BACKGROUND

Previous studies usually focused on the separate association of metabolism or genetic factors with lung cancer risk and have largely ignored their combined effect. We aimed to examine the associations between serum lipid levels, genetic risk, and lung cancer risk.

METHODS

A total of 426,524 participants of the UK Biobank were included. The Cox proportional hazards models and restricted cubic splines were performed to assess the association between serum lipid and lung cancer risk. Polygenic risk score (PRS) was constructed to assess its joint effect and interaction with serum lipid on lung cancer risk.

RESULTS

Higher level of apolipoprotein A was significantly correlated with lower lung cancer risk. An inverse-J-shaped relationship between high-density lipoprotein (HDL) and incident lung cancer was found. Individuals with low total cholesterol, HDL, low-density lipoprotein (LDL), apolipoprotein A, and apolipoprotein B, combined with high PRS, showed significantly elevated lung cancer risks. Compared to those with low PRS and low triglycerides, participants with high PRS and elevated triglyceride levels had a notably higher risk. The interaction effect of high PRS and low LDL [relative excess risk due to the interaction (RERI): 0.25, 95% confidence interval, 0.04-0.46], as well as the interaction effect of high PRS and low apolipoprotein B (RERI: 0.28, 95% confidence interval, 0.07-0.48), were both greater than the sum of their individual effects on lung cancer risk.

CONCLUSIONS

Serum lipids were associated with lung cancer risk. LDL or apolipoprotein B interacting with genetic risk may affect lung cancer risk.

IMPACT

Our findings emphasize the need for individuals with heightened genetic risk should pay more attention to their lipid levels to reduce lung cancer risk.

Enhanced Proteomic Coverage in Tissue Microenvironment by Immune Cell Subtype Library-Assisted DIA-MS

Immune cells that infiltrate the tumor microenvironment (TME) play crucial roles in shaping cancer development and influencing clinical outcomes and therapeutic responses. However, obtaining a comprehensive proteomic snapshot of tumor-infiltrating immunity in clinical specimens is often hindered by small sample amounts and a low proportion of immune infiltrating cells in the TME. To enable in-depth and highly sensitive profiling of microscale tissues, we established an immune cell-enriched library-assisted strategy for data-independent acquisition mass spectrometry (DIA-MS). Firstly, six immune cell subtype-specific spectral libraries were established from sorted cluster of differentiation markers, CD8+, CD4+ T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, and macrophages in murine mesenteric lymph nodes (MLNs), covering 7815 protein groups with surface markers and immune cell-enriched proteins. The feasibility of microscale immune proteomic profiling was demonstrated on 1 μg tissue protein from the tumor of murine colorectal cancer (CRC) models using single-shot DIA; the immune cell-enriched library increased coverage to quantify 7419 proteins compared to directDIA analysis (6978 proteins). The enhancement enabled the mapping of 841 immune function-related proteins and exclusive identification of many low-abundance immune proteins, such as CD1D1, and CD244, demonstrating high sensitivity for immune landscape profiling. This approach was used to characterize the MLNs in CRC models, aiming to elucidate the mechanism underlying their involvement in cancer development within the TME. Even with a low percentage of immune cell infiltration (0.25-3%) in the tumor, our results illuminate downregulation in the adaptive immune signaling pathways (such as C-type lectin receptor signaling, and chemokine signaling), T cell receptor signaling, and Th1/Th2/Th17 cell differentiation, suggesting an immunosuppressive status in MLNs of CRC model. The DIA approach using the immune cell-enriched libraries showcased deep coverage and high sensitivity that can facilitate illumination of the immune proteomic landscape for microscale samples.

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

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

Cerebrospinal Fluid Proteome Map Reveals Molecular Signatures of Reversible Cerebral Vasoconstriction Syndrome

Reversible cerebral vasoconstriction syndrome (RCVS) is a complex neurovascular disorder characterized by repetitive thunderclap headaches and reversible cerebral vasoconstriction. The pathophysiological mechanism of this mysterious syndrome remains underexplored and there is no clinically available molecular biomarker. To provide insight into the pathogenesis of RCVS, this study reported the first landscape of dysregulated proteome of cerebrospinal fluid (CSF) in patients with RCVS (n  =  21) compared to the age- and sex-matched controls (n  =  20) using data-independent acquisition mass spectrometry. Protein-protein interaction and functional enrichment analysis were employed to construct functional protein networks using the RCVS proteome. An RCVS-CSF proteome library resource of 1054 proteins was established, which illuminated large groups of upregulated proteins enriched in the brain and blood-brain barrier (BBB). Personalized RCVS-CSF proteomic profiles from 17 RCVS patients and 20 controls reveal proteomic changes involving the complement system, adhesion molecules, and extracellular matrix, which may contribute to the disruption of BBB and dysregulation of neurovascular units. Moreover, an additional validation cohort validated a panel of biomarker candidates and a two-protein signature predicted by machine learning model to discriminate RCVS patients from controls with an area under the curve of 0.997. This study reveals the first RCVS proteome and a potential pathogenetic mechanism of BBB and neurovascular unit dysfunction. It also nominates potential biomarker candidates that are mechanistically plausible for RCVS, which may offer potential diagnostic and therapeutic opportunities beyond the clinical manifestations.

Oncogene-Driven Non-Small Cell Lung Cancers in Patients with a History of Smoking Lack Smoking-Induced Mutations

Non-small cell lung cancers (NSCLC) in nonsmokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET and fusions involving ALK and RET. In addition to occurring in nonsmokers, alterations in these "nonsmoking-related oncogenes" (NSRO) also occur in smokers. To better understand the clonal architecture and genomic landscape of NSRO-driven tumors in smokers compared with typical-smoking NSCLCs, we investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 NSCLC patients. NSRO-driven NSCLCs in smokers and nonsmokers had similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs, which was confirmed in two large NSCLC data sets from other geographic regions. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to the regulation of the cell cycle. These findings suggest that, whereas the genomic landscape is similar between NSRO-driven NSCLC in smokers and nonsmokers, smoking still affects the tumor phenotype independently of genomic alterations.

SIGNIFICANCE

Non-small cell lung cancers driven by nonsmoking-related oncogenes do not harbor genomic scars caused by smoking regardless of smoking history, indicating that the impact of smoking on these tumors is mainly nongenomic.

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

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

A retrospective, descriptive analysis identifying non‑small cell lung cancer molecular markers

Non-small-cell lung cancer (NSCLC) remains one of the leading causes of cancer mortality worldwide. The aim of the present study was to review the histologic patterns and molecular drivers of NSCLC in patients with lung cancer. The electronic health records (EHR) of all patients diagnosed with lung cancer between April 2015 and September 2022 were obtained from a tertiary care hospital and retrospectively analysed. A total of 224 patients were identified of which 192 (138 males and 54 females) were included in the final analysis. Adenocarcinoma was the most common type of lung cancer identified, and accounted for 134 patients (70%), followed by squamous cell carcinoma in 47 (24%) patients, while large cell lung cancer was noted in only 5 (3%) patients. The most common mutations were EGFR mutations and were detected in 29 (15%) patients, followed by PD-L1 expression which was present in 56 (24.7%) patients, KRAS in 16 (8.3%) patients, ALK1 in 8 (4.2%) patients and BRAF, ROS1 and MET were present in 3 (1.6%), 2 (1%) and 1 (0.5%), respectively. The findings from the present study offer important insights into the epidemiological, clinical and molecular characteristics of NSCLC. Further research is warranted to explore the clinical implications of these findings.

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

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

The mutagenic forces shaping the genomic landscape of lung cancer in never smokers

Lung cancer in never smokers (LCINS) accounts for up to 25% of all lung cancers and has been associated with exposure to secondhand tobacco smoke and air pollution in observational studies. Here, we evaluate the mutagenic exposures in LCINS by examining deep whole-genome sequencing data from a large international cohort of 871 treatment-naïve LCINS recruited from 28 geographical locations within the Sherlock-Lung study. KRAS mutations were 3.8-fold more common in adenocarcinomas of never smokers from North America and Europe, while a 1.6-fold higher prevalence of EGFR and TP53 mutations was observed in adenocarcinomas from East Asia. Signature SBS40a, with unknown cause, was found in most samples and accounted for the largest proportion of single base substitutions in adenocarcinomas, being enriched in EGFR-mutated cases. Conversely, the aristolochic acid signature SBS22a was almost exclusively observed in patients from Taipei. Even though LCINS exposed to secondhand smoke had an 8.3% higher mutational burden and 5.4% shorter telomeres, passive smoking was not associated with driver mutations in cancer driver genes or the activities of individual mutational signatures. In contrast, patients from regions with high levels of air pollution were more likely to have TP53 mutations while exhibiting shorter telomeres and an increase in most types of somatic mutations, including a 3.9-fold elevation of signature SBS4 (q-value=3.1 × 10-5), previously linked mainly to tobacco smoking, and a 76% increase of clock-like signature SBS5 (q-value=5.0 × 10-5). A positive dose-response effect was observed with air pollution levels, which correlated with both a decrease in telomere length and an elevation in somatic mutations, notably attributed to signatures SBS4 and SBS5. Our results elucidate the diversity of mutational processes shaping the genomic landscape of lung cancer in never smokers.

Ribosomal Dysregulation in Metastatic Laryngeal Squamous Cell Carcinoma: Proteomic Insights and CX-5461's Therapeutic Promise

One of the main barriers to the successful treatment of laryngeal squamous cell carcinoma (LSCC) is postoperative progression, primarily due to tumor cell metastasis. To systematically investigate the molecular characteristics and potential mechanisms underlying the metastasis in laryngeal cancer, we carried out a TMT-based proteomic analysis of both cancerous and adjacent non-cancerous tissues from 10 LSCC patients with lymph node metastasis (LNM) and 10 without. A total of 5545 proteins were quantified across all samples. We identified 57 proteins that were downregulated in LSCC with LNM, which were enriched in cell adhesion pathways, and 69 upregulated proteins predominantly enriched in protein production pathways. Importantly, our data revealed a strong correlation between increased ribosomal activity and the presence of LNM, as 18 ribosomal subunit proteins were found to be upregulated, with RPS10 and RPL24 being the most significantly overexpressed. The potential of ribosomal proteins, including RPS10 and RPL24, as biomarkers for LSCC with LNM was confirmed in external validation samples (six with LNM and six without LNM) using Western blotting and immunohistochemistry. Furthermore, we have confirmed that the RNA polymerase I inhibitor CX-5461, which impedes ribosome biogenesis in LSCC, also decreases the expression of RPS10, RPL24, and RPS26. In vitro experiments have revealed that CX-5461 moderately reduces cell viability, while it significantly inhibits the invasion and migration of LSCC cells. It can enhance the expression of the epithelial marker CDH1 and suppress the expression of the mesenchymal markers CDH2, VIM, and FN at a dose that does not affect cell viability. Our study broadens the scope of the proteomic data on laryngeal cancer and suggests that ribosome targeting could be a supplementary therapeutic strategy for metastatic LSCC.

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

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

SIGNIFICANCE

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

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

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.

Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks

Although papillary thyroid cancer (PTC) has a good prognosis, its recurrence rate is high and remains a core concern in the clinic. Molecular factors contributing to different recurrence risks (RRs) remain poorly defined. Here, we perform an integrative proteogenomic and metabolomic characterization of 102 Chinese PTC patients with different RRs. Genomic profiling reveals that mutations in MUC16 and TERT promoter as well as multiple gene fusions like NCOA4-RET are enriched by the high RR. Integrative multi-omics analyses further describe the multi-dimensional characteristics of PTC, especially in metabolism pathways, and delineate dominated molecular patterns of different RRs. Moreover, the PTC patients are clustered into four subtypes (CS1: low RR and BRAF-like; CS2: high RR and metabolism type, worst prognosis; CS3: high RR and immune type, better prognosis; CS4: high RR and BRAF-like) based on the omics data. Notably, the subtypes display significant differences considering BRAF and TERT promoter mutations, metabolism and immune pathway profiles, epithelial cell compositions, and various clinical factors (especially RRs and prognosis) as well as druggable targets. This study can provide insights into the complex molecular characteristics of PTC recurrences and help promote early diagnosis and precision treatment of recurrent PTC.

Advancements in the diagnosis and treatment of sub‑centimeter lung cancer in the era of precision medicine (Review)

Lung cancer is the malignancy with the highest global mortality rate and imposes a substantial burden on society. The increasing popularity of lung cancer screening has led to increasing number of patients being diagnosed with pulmonary nodules due to their potential for malignancy, causing considerable distress in the affected population. However, the diagnosis and treatment of sub-centimeter grade pulmonary nodules remain controversial. The evolution of genetic detection technology and the development of targeted drugs have positioned the diagnosis and treatment of lung cancer in the precision medicine era, leading to a marked improvement in the survival rate of patients with lung cancer. It has been established that lung cancer driver genes serve a key role in the development and progression of sub-centimeter lung cancer. The present review aimed to consolidate the findings on genes associated with sub-centimeter lung cancer, with the intent of serving as a reference for future studies and the personalized management of sub-centimeter lung cancer through genetic testing.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Low-dose CT screening among never-smokers with or without a family history of lung cancer in Taiwan: a prospective cohort study

BACKGROUND

In Taiwan, lung cancers occur predominantly in never-smokers, of whom nearly 60% have stage IV disease at diagnosis. We aimed to assess the efficacy of low-dose CT (LDCT) screening among never-smokers, who had other risk factors for lung cancer.

METHODS

The Taiwan Lung Cancer Screening in Never-Smoker Trial (TALENT) was a nationwide, multicentre, prospective cohort study done at 17 tertiary medical centres in Taiwan. Eligible individuals had negative chest radiography, were aged 55-75 years, had never smoked or had smoked fewer than 10 pack-years and stopped smoking for more than 15 years (self-report), and had one of the following risk factors: a family history of lung cancer; passive smoke exposure; a history of pulmonary tuberculosis or chronic obstructive pulmonary disorders; a cooking index of 110 or higher; or cooking without using ventilation. Eligible participants underwent LDCT at baseline, then annually for 2 years, and then every 2 years up to 6 years thereafter, with follow-up assessments at each LDCT scan (ie, total follow-up of 8 years). A positive scan was defined as a solid or part-solid nodule larger than 6 mm in mean diameter or a pure ground-glass nodule larger than 5 mm in mean diameter. Lung cancer was diagnosed through invasive procedures, such as image-guided aspiration or biopsy or surgery. Here, we report the results of 1-year follow-up after LDCT screening at baseline. The primary outcome was lung cancer detection rate. The p value for detection rates was estimated by the χ2 test. Univariate and multivariable logistic regression analyses were used to assess the association between lung cancer incidence and each risk factor. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of LDCT screening were also assessed. This study is registered with ClinicalTrials.gov, NCT02611570, and is ongoing.

FINDINGS

Between Dec 1, 2015, and July 31, 2019, 12 011 participants (8868 females) were enrolled, of whom 6009 had a family history of lung cancer. Among 12 011 LDCT scans done at baseline, 2094 (17·4%) were positive. Lung cancer was diagnosed in 318 (2·6%) of 12 011 participants (257 [2·1%] participants had invasive lung cancer and 61 [0·5%] had adenocarcinomas in situ). 317 of 318 participants had adenocarcinoma and 246 (77·4%) of 318 had stage I disease. The prevalence of invasive lung cancer was higher among participants with a family history of lung cancer (161 [2·7%] of 6009 participants) than in those without (96 [1·6%] of 6002 participants). In participants with a family history of lung cancer, the detection rate of invasive lung cancer increased significantly with age, whereas the detection rate of adenocarcinoma in situ remained stable. In multivariable analysis, female sex, a family history of lung cancer, and age older than 60 years were associated with an increased risk of lung cancer and invasive lung cancer; passive smoke exposure, cumulative exposure to cooking, cooking without ventilation, and a previous history of chronic lung diseases were not associated with lung cancer, even after stratification by family history of lung cancer. In participants with a family history of lung cancer, the higher the number of first-degree relatives affected, the higher the risk of lung cancer; participants whose mother or sibling had lung cancer were also at an increased risk. A positive LDCT scan had 92·1% sensitivity, 84·6% specificity, a PPV of 14·0%, and a NPV of 99·7% for lung cancer diagnosis.

INTERPRETATION

TALENT had a high invasive lung cancer detection rate at 1 year after baseline LDCT scan. Overdiagnosis could have occurred, especially in participants diagnosed with adenocarcinoma in situ. In individuals who do not smoke, our findings suggest that a family history of lung cancer among first-degree relatives significantly increases the risk of lung cancer as well as the rate of invasive lung cancer with increasing age. Further research on risk factors for lung cancer in this population is needed, particularly for those without a family history of lung cancer.

FUNDING

Ministry of Health and Welfare of Taiwan.

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.

Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma

Lung adenocarcinoma follows a stepwise progression from pre-invasive to invasive. However, there remains a knowledge gap regarding molecular events from pre-invasive to invasive. Here, we conduct a comprehensive proteogenomic analysis comprising whole-exon sequencing, RNA sequencing, and proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas. The deletion of chr4q12 contributes to the progression from pre-invasive to invasive adenocarcinoma by downregulating SPATA18, thus suppressing mitophagy and promoting cell invasion. Proteomics reveals diverse enriched pathways in normal lung tissues and pre-invasive and invasive adenocarcinoma. Proteomic analyses identify three proteomic subtypes, which represent different stages of tumor progression. We also illustrate the molecular characterization of four immune clusters, including endothelial cells, B cells, DCs, and immune depression subtype. In conclusion, this comprehensive proteogenomic study characterizes the molecular architecture and hallmarks from pre-invasive to invasive lung adenocarcinoma, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease.

Proteomic analysis reveals LRPAP1 as a key player in the micropapillary pattern metastasis of lung adenocarcinoma

Objectives

Lung adenocarcinomas have different prognoses depending on their histological growth patterns. Micropapillary growth within lung adenocarcinoma, particularly metastasis, is related to dismal prognostic outcome. Metastasis accounts for a major factor leading to mortality among lung cancer patients. Understanding the mechanisms underlying early stage metastasis can help develop novel treatments for improving patient survival.

Methods

Here, quantitative mass spectrometry was conducted for comparing protein expression profiles among various histological subtypes, including adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma (including acinar and micropapillary [MIP] types). To determine the mechanism of MIP-associated metastasis, we identified a protein that was highly expressed in MIP. The expression of the selected highly expressed MIP protein was verified via immunohistochemical (IHC) analysis and its function was validated by an in vitro migration assay.

Results

Proteomic data revealed that low-density lipoprotein receptor-related protein-associated protein 1 (LRPAP1) was highly expressed in MIP group, which was confirmed by IHC. The co-expressed proteins in this study, PSMD1 and HSP90AB1, have been reported to be highly expressed in different cancers and play an essential role in metastasis. We observed that LRPAP1 promoted lung cancer progression, including metastasis, invasion and proliferation in vitro and in vivo.

Conclusion

LRPAP1 is necessary for MIP-associated metastasis and is the candidate novel anti-metastasis therapeutic target.

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.

A multiomic investigation of lung adenocarcinoma molecular subtypes

BACKGROUND

Lung adenocarcinoma-an aggressive and life-threatening malignancy-is a type of non-small-cell lung cancer. Despite medical advancements, the prognosis of lung adenocarcinoma remains unfavorable, likely because of its heterogeneous nature. Furthermore, few subtype-specific treatments are available for lung adenocarcinoma. This study was conducted to explore the molecular subtypes of lung adenocarcinoma.

METHODS

We performed a joint analysis of transcriptome and proteome data from East Asian patients with lung adenocarcinoma (nonsmokers, 86.5%).

RESULTS

Four novel subtypes were identified based on distinct molecular characteristics: subtypes I, II, III, and IV. In patients with subtype I lung adenocarcinoma, eukaryotic translation initiation factor 4 gamma 1 activates cell proliferation; inhibiting this factor suppresses tumor growth, and reducing its level induces autophagy. Subtype II is characterized by Kristen rat sarcoma viral oncogene homolog-activating oncogenesis; the onset age of this subtype is the lowest among all subtypes. Subtype III manifests as an advanced disease at diagnosis; it is characterized by a core serum response-related oncogenic signature, which indicates poor overall survival in Western patients with lung cancer. Subtype IV is more common in men than in women; it has astroglial characteristics. A Connectivity Map analysis revealed that the oncogenic expression patterns corresponding to subtypes I, II, III, and IV can be reversed by the inhibitors of Inhibitor of κB (IκB) kinase (eg, withaferin A), mammalian target of rapamycin (eg, everolimus), Src proto-oncogene (Src) (eg, saracatinib), and Transforming Growth Factor (TGF)-β/Smad (eg, LY-364947), respectively.

CONCLUSION

This study introduced an innovative multiomics data analysis pipeline. Using this approach, we successfully identified four molecular subtypes of lung adenocarcinoma and their candidate therapeutic agents. The newly identified subtypes can be combined with the current biomarkers to generate a comprehensive roadmap for treatment decision-making.

Comprehensive Genomic and Transcriptomic Analysis of Sclerosing Pneumocytoma

Sclerosing pneumocytoma is a rare and distinct lung neoplasm whose histogenesis and molecular alterations are the subject of ongoing research. Our recent study revealed that AKT1 internal tandem duplications (ITD), point mutations, and short indels were present in almost all tested sclerosing pneumocytomas, suggesting that AKT1 mutations are a major driving oncogenic event in this tumor. Although the pathogenic role of AKT1 point mutations is well established, the significance of AKT1 ITD in oncogenesis remains largely unexplored. We conducted comprehensive genomic and transcriptomic analyses of sclerosing pneumocytoma to address this knowledge gap. RNA-sequencing data from 23 tumors and whole-exome sequencing data from 44 tumors were used to obtain insights into their genetic and transcriptomic profiles. Our analysis revealed a high degree of genetic and transcriptomic similarity between tumors carrying AKT1 ITD and those with AKT1 point mutations. Mutational signature analysis revealed COSMIC signatures 1 and 5 as the prevailing signatures of sclerosing pneumocytoma, associated with the spontaneous deamination of 5-methylcytosine and an unknown etiology, respectively. RNA-sequencing data analysis revealed that the sclerosing pneumocytoma gene expression profile is characterized by activation of the PI3K/AKT/mTOR pathway, which exhibits significant similarity between tumors harboring AKT1 ITD and those with AKT1 point mutations. Notably, an upregulation of SOX9, a transcription factor known for its involvement in fetal lung development, was observed in sclerosing pneumocytoma. Specifically, SOX9 expression was prominent in the round cell component, whereas it was relatively lower in the surface cell component of the tumor. To the best of our knowledge, this is the first comprehensive investigation of the genomic and transcriptomic characteristics of sclerosing pneumocytoma. Results of the present study provide insights into the molecular attributes of sclerosing pneumocytoma and a basis for future studies of this enigmatic tumor.

Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy

Patients with hepatocellular carcinoma (HCC) at the same clinical stage can have extremely different prognoses, and molecular subtyping provides an opportunity for individualized precision treatment. In this study, genomic, transcriptomic, proteomic, and phosphoproteomic profiling of primary tumor tissues and paired para-tumor tissues from HCC patients (N = 160) are integrated. Proteomic profiling identifies three HCC subtypes with different clinical prognosis, which are validated in three publicly available external validation sets. A simplified panel of nine proteins associated with metabolic reprogramming is further identified as a potential subtype-specific biomarker for clinical application. Multi-omics analysis further reveals that three proteomic subtypes have significant differences in genetic alterations, microenvironment dysregulation, kinase-substrate regulatory networks, and therapeutic responses. Patient-derived cell-based drug tests (N = 26) show personalized responses for sorafenib in three proteomic subtypes, which can be predicted by a machine-learning response prediction model. Overall, this study provides a valuable resource for better understanding of HCC subtypes for precision clinical therapy.

Integrated multi-omics analysis for lung adenocarcinoma in Xuanwei, China

BACKGROUND

Xuanwei lung cancer (XWLC) is well-known for its high incidence and mortality. However, the molecular mechanism is still unclear.

METHODS

We performed a comprehensive transcriptomic, proteomic, and phosphoproteomic characterization of tumors and matched normal adjacent tissues from three XWLC patients with lung adenocarcinoma (LUAD).

RESULTS

Integrated transcriptome and proteome analysis revealed dysregulated molecules and pathways in tumors and identified enhanced metabolic-disease coupling. Non-coding RNAs were widely involved in post-transcriptional regulatory mechanisms to coordinate the progress of LUAD and partially explained the molecular differences between RNA and protein expression patterns. Phosphoproteome provided evidence support for new phosphate sites, reporting the potential roles of core kinase family members and key kinase pathways involved in metabolism, immunity, and homeostasis. In addition, by comparing with the previous LUAD researches, we emphasized the higher degree of oxidative phosphorylation in Xuanwei LUAD and pointed that VIPR1 deficiency aggravated metabolic dysfunction.

CONCLUSION

Our integrated multi-omics analysis provided a powerful resource for a systematic understanding of the molecular structure of XWLC and proposed therapeutic opportunities based on redox metabolism.