Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

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

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

Phosphoprotein dynamics of interacting T cells and tumor cells by HySic

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

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

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

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.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

Extracellular vesicles released by cancer-associated fibroblast-induced myeloid-derived suppressor cells inhibit T-cell function

Myeloid cells are known to play a crucial role in creating a tumor-promoting and immune suppressive microenvironment. Our previous study demonstrated that primary human monocytes can be polarized into immunosuppressive myeloid-derived suppressor cells (MDSCs) by cancer-associated fibroblasts (CAFs) in a 3D co-culture system. However, the molecular mechanisms underlying the immunosuppressive function of MDSCs, especially CAF-induced MDSCs, remain poorly understood. Using mass spectrometry-based proteomics, we compared cell surface protein changes among monocytes, in vitro differentiated CAF-induced MDSCs, M1/M2 macrophages, and dendritic cells, and identified an extracellular vesicle (EV)-mediated secretory phenotype of MDSCs. Functional assays using an MDSC/T-cell co-culture system revealed that blocking EV generation in CAF-induced MDSCs reversed their ability to suppress T-cell proliferation, while EVs isolated from CAF-induced MDSCs directly inhibited T-cell function. Furthermore, we identified fructose bisphosphatase 1 (FBP1) as a cargo protein that is highly enriched in EVs isolated from CAF-induced MDSCs, and pharmacological inhibition of FBP1 partially reversed the suppressive phenotype of MDSCs. Our findings provide valuable insights into the cell surface proteome of different monocyte-derived myeloid subsets and uncover a novel mechanism underlying the interplay between CAFs and myeloid cells in shaping a tumor-permissive microenvironment.

Multiplexed Quantitative Proteomics Reveals Proteomic Alterations in Two Rodent Traumatic Brain Injury Models

In many cases of traumatic brain injury (TBI), conspicuous abnormalities, such as scalp wounds and intracranial hemorrhages, abate over time. However, many unnoticeable symptoms, including cognitive, emotional, and behavioral dysfunction, often last from several weeks to years after trauma, even for mild injuries. Moreover, the cause of such persistence of symptoms has not been examined extensively. Recent studies have implicated the dysregulation of the molecular system in the injured brain, necessitating an in-depth analysis of the proteome and signaling pathways that mediate the consequences of TBI. Thus, in this study, the brain proteomes of two TBI models were examined by quantitative proteomics during the recovery period to determine the molecular mechanisms of TBI. Our results show that the proteomes in both TBI models undergo distinct changes. A bioinformatics analysis demonstrated robust activation and inhibition of signaling pathways and core proteins that mediate biological processes after brain injury. These findings can help determine the molecular mechanisms that underlie the persistent effects of TBI and identify novel targets for drug interventions.

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.

Clinical, Genomic, and Transcriptomic Featurses of Lung Adenocarcinoma With Uncommon EGFR Mutation

PURPOSE

The purpose of this study is to identify the clinical, genomic, and transcriptomic features of patients with lung adenocarcinoma (LUAD) harboring uncommon epidermal growth factor receptor (EGFR) mutations (UCM) compared with common EGFR mutations (CM).

MATERIALS AND METHODS

In this multicenter retrospective cohort study, clinicopathological data were collected from 1047 consecutive patients who underwent complete surgical resection for LUAD, as well as EGFR mutation analysis, between 2005 and 2012 at 4 institutions. Differences in postoperative overall survival (OS) and recurrence-free survival (RFS) according to EGFR mutation status were evaluated. For the genomic and transcriptomic analyses, 5 cohorts from public databases were evaluated.

RESULTS

Of 466 eligible patients, 415 (89.1%) and 51 (10.9%) had CM and UCM, respectively. The 5-year OS and RFS rates in the CM/UCM groups were 86.8%/77.0% and 74.8%/59.0%, respectively. OS and RFS were significantly shorter in the UCM than CM group (both P < .01). Multivariable analysis of OS showed that UCM was an independent prognostic factor (hazard ratio 1.72, 95% confidential interval 1.01-2.93). According to the genomic analysis, tumors with UCM had a significantly higher tumor mutation burden and TP53 mutation frequency. Transcriptomic analysis showed that the T-cell-inflamed gene signature, a biomarker of the treatment for immunotherapy, was significantly associated with tumors with UCM.

CONCLUSION

UCM were associated with a poor prognosis in patients with surgically resected EGFR-mutated LUAD. Tumors with UCM had unique genomic and transcriptomic features suggestive of a tumor microenvironment responsive to immunotherapy.

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.

Automated Immunoprecipitation Workflow for Comprehensive Acetylome Analysis

Immunoprecipitation is one of the most effective methods for enrichment of lysine-acetylated peptides for comprehensive acetylome analysis using mass spectrometry. Manual acetyl peptide enrichment method using non-conjugated antibodies and agarose beads has been developed and applied in various studies. However, it is time-consuming and can introduce contaminants and variability that leads to potential sample loss and decreased sensitivity and robustness of the analysis. Here we describe a fast, automated enrichment protocol that enables reproducible and comprehensive acetylome analysis using a magnetic bead-based immunoprecipitation reagent.

Vacuolar proteomic analysis reveals tonoplast transporters for accumulation of citric acid and sugar in citrus fruit

Vacuole largely dictates the fruit taste and flavor, as most of the sugars and organic acids are stored in the vacuoles of the fruit. However, difficulties associated with vacuole separation severely hinder identification and characterization of vacuolar proteins in fruit species. In this study, we established an effective approach for separating vacuoles and successfully purified vacuolar protein from six types of citrus fruit with varying patterns of sugar and organic acid contents. By using label-free LC-MS/MS proteomic analysis, 1443 core proteins were found to be associated with the essential functions of vacuole in citrus fruit. Correlation analysis of metabolite concentration with proteomic data revealed a transporter system for the accumulation of organic acid and soluble sugars in citrus. Furthermore, we characterized the physiological roles of selected key tonoplast transporters, ABCG15, Dict2.1, TMT2, and STP7 in the accumulation of citric acid and sugars. These findings provide a novel perspective and practical solution for investigating the transporters underlying the formation of citrus taste and flavor.

Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.

Investigative needle core biopsies for multi-omics in Glioblastoma

Glioblastoma (GBM) is a primary brain cancer with an abysmal prognosis and few effective therapies. The ability to investigate the tumor microenvironment before and during treatment would greatly enhance both understanding of disease response and progression, as well as the delivery and impact of therapeutics. Stereotactic biopsies are a routine surgical procedure performed primarily for diagnostic histopathologic purposes. The role of investigative biopsies - tissue sampling for the purpose of understanding tumor microenvironmental responses to treatment using integrated multi-modal molecular analyses ('Multi-omics") has yet to be defined. Secondly, it is unknown whether comparatively small tissue samples from brain biopsies can yield sufficient information with such methods. Here we adapt stereotactic needle core biopsy tissue in two separate patients. In the first patient with recurrent GBM we performed highly resolved multi-omics analysis methods including single cell RNA sequencing, spatial-transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics from biopsy tissue that was obtained from a single procedure. In a second patient we analyzed multi-regional core biopsies to decipher spatial and genomic variance. We also investigated the utility of stereotactic biopsies as a method for generating patient derived xenograft models in a separate patient cohort. Dataset integration across modalities showed good correspondence between spatial modalities, highlighted immune cell associated metabolic pathways and revealed poor correlation between RNA expression and the tumor MHC Class I immunopeptidome. In conclusion, stereotactic needle biopsy cores are of sufficient quality to generate multi-omics data, provide data rich insight into a patient's disease process and tumor immune microenvironment and can be of value in evaluating treatment responses.

One sentence summary

Integrative multi-omics analysis of stereotactic needle core biopsies in glioblastoma.

Using long-read CAGE sequencing to profile cryptic-promoter-derived transcripts and their contribution to the immunopeptidome

Recent studies have shown that the noncoding genome can produce unannotated proteins as antigens that induce immune response. One major source of this activity is the aberrant epigenetic reactivation of transposable elements (TEs). In tumors, TEs often provide cryptic or alternate promoters, which can generate transcripts that encode tumor-specific unannotated proteins. Thus, TE-derived transcripts (TE transcripts) have the potential to produce tumor-specific, but recurrent, antigens shared among many tumors. Identification of TE-derived tumor antigens holds the promise to improve cancer immunotherapy approaches; however, current genomics and computational tools are not optimized for their detection. Here we combined CAGE technology with full-length long-read transcriptome sequencing (long-read CAGE, or LRCAGE) and developed a suite of computational tools to significantly improve immunopeptidome detection by incorporating TE and other tumor transcripts into the proteome database. By applying our methods to human lung cancer cell line H1299 data, we show that long-read technology significantly improves mapping of promoters with low mappability scores and that LRCAGE guarantees accurate construction of uncharacterized 5' transcript structure. Augmenting a reference proteome database with newly characterized transcripts enabled us to detect noncanonical antigens from HLA-pulldown LC-MS/MS data. Lastly, we show that epigenetic treatment increased the number of noncanonical antigens, particularly those encoded by TE transcripts, which might expand the pool of targetable antigens for cancers with low mutational burden.

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.

HER2 and HLA-A*02 dual CAR-T cells utilize LOH in a NOT logic gate to address on-target off-tumor toxicity

BACKGROUND

One of the major challenges in chimeric antigen receptor (CAR)-T cell therapy for solid tumors is the potential for on-target off-tumor toxicity due to the expression of CAR tumor antigens in essential tissues and organs. Here, we describe a dual CAR NOT gate incorporating an inhibitory CAR (iCAR) recognizing HLA-A*02 ("A2") that enables effective treatment with a potent HER2 activating CAR (aCAR) in the context of A2 loss of heterozygosity (LOH).

METHODS

A CAR-T cell screen was conducted to identify inhibitory domains derived from natural immune receptors (iDomains) to be used in a NOT gate, to kill A2- HER2+ lung cancer cell lines but spare A2+ HER2+ lung cancer cell-lines with high specificity. The extensive analysis of lead candidates included T-cell activation and killing, assays of reversibility and durability in sequential challenges, target cell specificity in mixed 3D spheroids and 2D cultures, and the characterization of CAR expression level and cell-trafficking.

RESULTS

A leukocyte immunoglobulin-like receptor B1 (LIR1) iDomain iCAR was identified as most effective in regulating the cytotoxicity of a second generation HER2 aCAR. Target transfer experiments demonstrated that the 'on' and 'off' cell state of the LIR1 NOT gate CAR-T cell is both durable and reversible. Protection required iCAR signaling and was associated with reduced aCAR and iCAR surface expression. iCAR regulation was sufficient to generate high target specificity in a 3D adjacent spheroid assay designed to model the interface between clonal A2 LOH foci and normal tissue. However, we observed significant bystander killing of A2+ cells in admix culture through aCAR dependent and independent mechanisms. LIR1 NOT gate CAR-T cells conferred protection against H1703-A2+ tumors and high efficacy against H1703-A2- tumors in-vivo. We observed that the iCAR is inactive in A2+ donors due to cis-binding, but Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) knockout of HLA-A fully restored iCAR activity.

CONCLUSIONS

We have preclinically validated an iCAR NOT gate technology broadly applicable for targeting HER2 expression in the context of A2 LOH. This approach is designed to prevent off tumor toxicity while allowing highly potent antitumor activity.

The landscape of actionable genomic alterations in lung adenocarcinomas in India

Lung adenocarcinoma (LUAD), the most prevalent form of non-small cell lung cancer (NSCLC), remains a leading cause of cancer-related death globally, including in India, with a 5-year survival rate below 10%. Despite these grim statistics, recent advances in the use of next-generation sequencing (NGS) for identifying genetic alterations and the emergence of targeted therapies have opened new possibilities for personalized treatment based on distinct molecular signatures. To understand the molecular pattern of NSCLC, a retrospective study was conducted with 53 Indian LUAD patient samples, using a targeted NGS panel of 46 cancer-relevant oncogenes to identify clinically relevant variants. Pathogenic or likely pathogenic variants were detected in 94% of the 53 cases. Non-synonymous mutations, rearrangements, copy number alterations, insertions, and deletions of functional relevance were observed in 31 out of 46 genes. The most frequently mutated genes included TP53 (52.8%) and EGFR (50.9%), followed by RET, PIK3CA and ERBB2; some patients had multiple alterations in the same gene. Gender-based enrichment analysis indicated that ALK and IDH2 alterations were more prevalent in females, while TP53 and PTEN were more common in males. No significant correlation was found between mutations and other clinicopathological attributes, such as age, stage, and subtype. A higher prevalence of EGFR, RET, PIK3CA, ERBB2 and ALK mutations were observed compared to previous LUAD genetic studies coupled with a lower frequency of KRAS mutations. Clinically actionable variants were annotated using OncoKB and categorized into the four therapeutic levels based on their clinical evidence. Seventy-nine percent of cases had at least one clinically actionable alteration. Most patients (39.6%) had the highest level of actionability (Level 1) wherein an FDA-approved drug is available specifically for the observed mutation in lung cancer patients. EGFR Exon19 in-frame deletions and EGFR L858R were the most frequent among targetable variants (20.7%). These findings emphasize the importance of a selective NGS panel in enabling personalized medicine approaches by identifying actionable molecular alterations and informing the choice of targeted therapy for more effective treatment options in Indian NSCLC patients.

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.

Proteomic Analysis of Fractionated Eimeria tenella Sporulated Oocysts Reveals Involvement in Oocyst Wall Formation

Eimeria tenella is the most pathogenic intracellular protozoan parasite of the Eimeria species. Eimeria oocyst wall biogenesis appears to play a central role in oocyst transmission. Proteome profiling offers insights into the mechanisms governing the molecular basis of oocyst wall formation and identifies targets for blocking parasite transmission. Tandem mass tags (TMT)-labeled quantitative proteomics was used to analyze the oocyst wall and sporocysts of E. tenella. A combined total of 2865 E. tenella proteins were identified in the oocyst wall and sporocyst fractions; among these, 401 DEPs were identified, of which 211 were upregulated and 190 were downregulated. The 211 up-regulated DEPs were involved in various biological processes, including DNA replication, fatty acid metabolism and biosynthesis, glutathione metabolism, and propanoate metabolism. Among these proteins, several are of interest for their likely role in oocyst wall formation, including two tyrosine-rich gametocyte proteins (EtGAM56, EtSWP1) and two cysteine-rich proteins (EtOWP2, EtOWP6). Concurrently, 96 uncharacterized proteins may also participate in oocyst wall formation. The present study significantly expands our knowledge of the proteome of the oocyst wall of E. tenella, thereby providing a theoretical basis for further understanding of the biosynthesis and resilience of the E. tenella oocyst wall.

The deubiquitinase USP9X regulates RIT1 protein abundance and oncogenic phenotypes

RIT1 is a rare and understudied oncogene in lung cancer. Despite structural similarity to other RAS GTPase proteins such as KRAS, oncogenic RIT1 activity does not appear to be tightly regulated by nucleotide exchange or hydrolysis. Instead, there is a growing understanding that the protein abundance of RIT1 is important for its regulation and function. We previously identified the deubiquitinase USP9X as a RIT1 dependency in RIT1-mutant cells. Here, we demonstrate that both wild-type and mutant forms of RIT1 are substrates of USP9X. Depletion of USP9X leads to decreased RIT1 protein stability and abundance and resensitizes cells to EGFR tyrosine kinase inhibitors. Our work expands upon the current understanding of RIT1 protein regulation and presents USP9X as a key regulator of RIT1-driven oncogenic phenotypes.

STK11/LKB1-Deficient Phenotype Rather Than Mutation Diminishes Immunotherapy Efficacy and Represents STING/Type I Interferon/CD8+ T-Cell Dysfunction in NSCLC

INTRODUCTION

Conflicting findings have been reported regarding the association between STK11/LKB1 mutations and immune checkpoint inhibitor (ICB) efficacy in NSCLC. It has been reported that tumors could exhibit impaired STK11/LKB1 function even without STK11 mutations. We hypothesized that STK11 phenotype rather than mutation may better stratify ICB outcomes.

METHODS

Selected functional STK11 events and LKB1 protein data were leveraged to establish a transcriptomics-based classifier of STK11 phenotype (STK11-deficient [-def] or -proficient [-prof]). We analyzed in-house and Genentech/Roche's data of three randomized trials of programmed cell death protein-1 or programmed death-ligand 1 (PD-L1) inhibition in NSCLC (ORIENT-11, n = 171; OAK, n = 699; POPLAR, n = 192) and The Cancer Genome Atlas-NSCLC cohort.

RESULTS

Tissue STK11 mutation did not affect ICB outcomes. However, the survival benefit of ICB versus chemotherapy were lost or reversed in STK11-def tumors (hazard ratios for death, 95% confidence interval: OAK [0.97, 0.69-1.35]; POPLAR [1.61, 0.88-2.97]; ORIENT-11 [1.07, 0.50-2.29]), while remaining in STK11-prof tumors (hazard ratios for death, 95% confidence interval: OAK [0.81, 0.66-0.99]; POPLAR [0.66, 0.46-0.95]; ORIENT-11 [0.59, 0.37-0.92]). In tumors differentially classified by phenotype and mutation status, STK11-wild-type/def tumors had significantly worse ICB outcomes than STK11-mutated (STK11-MUT)/prof tumors (p < 0.05). The deleterious impact of STK11 deficiency was independent of STK11/KRAS/KEAP1 status or PD-L1 expression. The STING/interferon-I signaling, which was previously shown to be suppressed in STK11-MUT models, was perturbed in patients with STK11-def tumors rather than those with STK11-MUT tumors. Surprisingly, whereas high CD8+ T-cell infiltration was significantly associated with prolonged survival with ICB in STK11-prof tumors (p < 0.05 for 3 trials), it predicted an opposite trend toward worse ICB outcomes in STK11-def tumors across three trials. This suggested an association between STK11 deficiency and CD8+ T-cell dysfunction, which might not be reversed by programmed cell death protein 1 or PD-L1 blockade.

CONCLUSIONS

STK11 phenotype rather than mutation status can accurately identify patients with ICB-refractory NSCLC and reflect immune suppression. It can help refine stratification algorithms for future clinical research and also provide a reliable resource aiding basic and translational studies in identifying therapeutic targets.

Lung proteomics combined with metabolomics reveals molecular characteristics of inflammation-related lung tumorigenesis induced by B(a)P and LPS

Inflammatory microenvironment may take a promoting role in lung tumorigenesis. However, the molecular characteristics underlying inflammation-related lung cancer remains unknown. In this work, the inflammation-related lung tumorigenesis mouse model was established by treated with B(a)P (1 mg/mouse, once a week for 4 weeks), followed by LPS (2.5 μg/mouse, once every 3 weeks for five times), the mice were sacrificed 30 weeks after exposure. TMT-labeled quantitative proteomics and untargeted metabolomics were used to interrogate differentially expressed proteins and metabolites in different mouse cancer tissues, followed by integrated crosstalk between proteomics and metabolomics through Spearman's correlation analysis. The result showed that compared with the control group, 103 proteins and 37 metabolites in B(a)P/LPS group were identified as significantly altered. By searching KEGG pathway database, proteomics pathways such as Leishmaniasis, Asthma and Intestinal immune network for IgA production, metabolomics pathways such as Vascular smooth muscle contraction, Linoleic acid metabolism and cGMP-PKG signaling pathway were enriched. A total of 22 pathways were enriched after conjoint analysis of the proteomic and metabolomics, and purine metabolism pathway, the unique metabolism-related pathway, which included significantly altered protein (adenylate cyclase 4, ADCY4) and metabolites (L-Glutamine, guanosine monophosphate (GMP), adenosine and guanosine) was found. Results suggested purine metabolism may contribute to the inflammation-related lung tumorigenesis, which may provide novel clues for the therapeutic strategies of inflammation-related lung cancer.

A New Era of Integration between Multiomics and Spatio-Temporal Analysis for the Translation of EMT towards Clinical Applications in Cancer

Epithelial-mesenchymal transition (EMT) is crucial to metastasis by increasing cancer cell migration and invasion. At the cellular level, EMT-related morphological and functional changes are well established. At the molecular level, critical signaling pathways able to drive EMT have been described. Yet, the translation of EMT into efficient diagnostic methods and anti-metastatic therapies is still missing. This highlights a gap in our understanding of the precise mechanisms governing EMT. Here, we discuss evidence suggesting that overcoming this limitation requires the integration of multiple omics, a hitherto neglected strategy in the EMT field. More specifically, this work summarizes results that were independently obtained through epigenomics/transcriptomics while comprehensively reviewing the achievements of proteomics in cancer research. Additionally, we prospect gains to be obtained by applying spatio-temporal multiomics in the investigation of EMT-driven metastasis. Along with the development of more sensitive technologies, the integration of currently available omics, and a look at dynamic alterations that regulate EMT at the subcellular level will lead to a deeper understanding of this process. Further, considering the significance of EMT to cancer progression, this integrative strategy may enable the development of new and improved biomarkers and therapeutics capable of increasing the survival and quality of life of cancer patients.

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors

Lung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a total of 38 FFPE (formalin-fixed and paraffin-embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.

Ropivacaine inhibits the malignant behavior of lung cancer cells by regulating retinoblastoma-binding protein 4

Background

Ropivacaine is a local anesthetic commonly used in regional nerve blocks to manage perioperative pain during lung cancer surgery. Recently, the antitumor potential of ropivacaine has received considerable attention. Our previous study showed that ropivacaine treatment inhibits the malignant behavior of lung cancer cells in vitro. However, the potential targets of ropivacaine in lung cancer cells have not yet been fully identified. This study aimed to explore the antitumor effects and mechanisms of action of ropivacaine in lung cancer.

Methods

Lung cancer A549 cells were treated with or without 1 mM ropivacaine for 48 h. Quantitative proteomics was performed to identify the differentially expressed proteins (DEPs) triggered by ropivacaine treatment. STRING and Cytoscape were used to construct protein-protein interaction (PPI) networks and analyze the most significant hub genes. Overexpression plasmids and small interfering RNA were used to modulate the expression of key DEPs in A549 and H1299 cells. MTS, transwell assays, and flow cytometry were performed to determine whether the key DEPs were closely related to the anticancer effect of ropivacaine on the malignant behavior of A549 and H1299 cells.

Results

Quantitative proteomic analysis identified 327 DEPs (185 upregulated and 142 downregulated proteins) following ropivacaine treatment. Retinoblastoma-binding protein 4 (RBBP4) was one of the downregulated DEPs and was selected as the hub protein. TCGA database showed that RBBP4 was significantly upregulated in lung cancer and was associated with poor patient prognosis. Inhibition of RBBP4 by siRNA resulted in a significant decrease in the proliferation and invasive capacity of lung cancer cells and the induction of cell cycle arrest. Additionally, the results indicated RBBP4 knockdown enhanced antitumor effect of ropivacaine on A549 and H1299 cells. Conversely, the overexpression of RBBP4 using plasmids reversed the inhibitory effects of ropivacaine.

Conclusion

Our data suggest that ropivacaine suppresses lung cancer cell malignancy by downregulating RBBP4 protein expression, which may help clarify the mechanisms underlying the antitumor effects of ropivacaine.

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

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

Ephexin3/ARHGEF5 Together with Cell Migration Signaling Partners within the Tumor Microenvironment Define Prognostic Transcriptional Signatures in Multiple Cancer Types

Cancer cell migration involves a repertoire of signaling proteins that lead cytoskeleton reorganization as a critical step in metastatic dissemination. RhoGEFs are multidomain effectors that integrate signaling inputs to activate the molecular switches that orchestrate actin cytoskeleton reorganization. Ephexins, a group of five RhoGEFs, play oncogenic roles in invasive and metastatic cancer, leading to a mechanistic hypothesis about their function as signaling nodes assembling functional complexes that guide cancer cell migration. To identify clinically significant Ephexin signaling partners, we applied three systematic data mining strategies, based on the screening of essential Ephexins in multiple cancer cell lines and the identification of coexpressed signaling partners in the TCGA cancer patient datasets. Based on the domain architecture of encoded proteins and gene ontology criteria, we selected Ephexin signaling partners with a role in cytoskeletal reorganization and cell migration. We focused on Ephexin3/ARHGEF5, identified as an essential gene in multiple cancer cell types. Based on significant coexpression data and coessentiality, the signaling repertoire that accompanies Ephexin3 corresponded to three groups: pan-cancer, cancer-specific and coessential. To further select the Ephexin3 signaling partners likely to be relevant in clinical settings, we first identified those whose high expression was statistical linked to shorter patient survival. The resulting Ephexin3 transcriptional signatures represent significant accumulated risk, predictive of shorter survival, in 17 cancer types, including PAAD, LUAD, LGG, OSC, AML, KIRC, THYM, BLCA, LIHC and UCEC. The signaling landscape that accompanies Ephexin3 in various cancer types included the tyrosine kinase receptor MET and the tyrosine phosphatase receptor PTPRF, the serine/threonine kinases MARK2 and PAK6, the Rho GTPases RHOD, RHOF and RAC1, and the cytoskeletal regulator DIAHP1. Our findings set the basis to further explore the role of Ephexin3/ARHGEF5 as an essential effector and signaling hub in cancer cell migration.

Unsaturated fatty acid alters the immune response in non-small cell lung adenocarcinoma through regulation of HMGB1 trafficking

Cancer cell evasion of the immune response is critical to cancer development and metastases. The ability of clinicians to kickstart the immune system to target these rogue cells is an ever-growing area of research and medicine. In this study, we delved into the relationship between lipid metabolism, High Mobility Group Box 1 protein (HMGB1), and immune regulation within non-small cell lung adenocarcinoma (NSCLC), shedding light on novel therapeutic avenues and potential personalized approaches for patients. We found that the expression of stearoyl CoA desaturase 1 (SCD1) was decreased in NSCLC tumors compared to normal tissues. This emphasized the critical role of lipid metabolism in tumor progression. Interestingly, monounsaturated fatty acid (MUFA) availability impacted the expression of programmed death receptor ligand -1 (PD-L1), a pivotal immune checkpoint target in lung cancer cells and immune cells, suggesting a novel approach to modulating the immune response. This study uncovered a complex interplay between HMGB1, SCD1, and PD-L1, influencing the immunological sensitivity of tumors. Our work underscores the importance of understanding the intricate relationships between lipid metabolism and immune modulation to develop more effective NSCLC treatments and personalized therapies. As we continue to explore these connections, we hope to contribute to the ever-evolving field of cancer research, improving patient outcomes and advancing precision medicine in NSCLC.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Mutant RB1 enhances therapeutic efficacy of PARPis in lung adenocarcinoma by triggering the cGAS/STING pathway

Poly (ADP-ribose) polymerase inhibitors (PARPis) are approved for cancer therapy according to their synthetic lethal interactions, and clinical trials have been applied in non-small cell lung cancer. However, the therapeutic efficacy of PARPis in lung adenocarcinoma (LUAD) is still unknown. We explored the effect of a mutated retinoblastoma gene (RB1) on PARPi sensitivity in LUAD. Bioinformatic screening was performed to identify PARPi-sensitive biomarkers. Here, we showed that viability of LUAD cell lines with mutated RB1 was significantly decreased by PARPis (niraparib, rucaparib, and olaparib). RB1 deficiency induced genomic instability, prompted cytosolic double-stranded DNA (dsDNA) formation, activated the cGAS/STING pathway, and upregulated downstream chemokines CCL5 and CXCL10, triggering immune cell infiltration. Xenograft experiments indicated that PARPi treatment reduced tumorigenesis in RB1-KO mice. Additionally, single-cell RNA sequencing analysis showed that malignant cells with downregulated expression of RB1 had more communications with other cell types, exhibiting activation of specific signaling such as GAS, IFN response, and antigen-presenting and cytokine activities. Our findings suggest that RB1 mutation mediates the sensitivity to PARPis through a synthetic lethal effect by triggering the cGAS/STING pathway and upregulation of immune infiltration in LUAD, which may be a potential therapeutic strategy.

Proteogenomics Reveal the Overexpression of HLA-I in Cancer

An accurate quantification of HLA class I gene expression is important in understanding the interplay with the tumor microenvironment of antitumor cytotoxic T cell activities. Because HLA-I sequences are highly variable, standard RNAseq and mass spectrometry-based quantification workflows using common genome and protein sequence references do not provide HLA-I allele specific quantifications. Here, we used personalized HLA-I nucleotide and protein reference sequences based on the subjects' HLA-I genotypes and surveyed tumor and adjacent normal samples from patients across nine cancer types. Mass spectrometry using data dependent acquisition data was validated to be sufficient to estimate HLA-A protein expression at the allele level. We found that HLA-I proteins were present in significantly higher levels in tumors compared to adjacent normal tissues from 41 to 63% of head and neck squamous cell carcinoma, uterine corpus endometrial carcinoma, and clear cell renal cell carcinoma patients, and this was driven by increased levels of HLA-I gene transcripts. Most immune cell types are universally enriched in HLA-I high tumors, while endothelial and neuronal cells showed divergent relationships with HLA-I. Pathway analysis revealed that tumor senescence and autophagy activity influence the level of HLA-I proteins in glioblastoma. Genes correlated to HLA-I protein expression are mostly the ones directly involved in HLA-I function in immune response and cell death, while glycosylation genes are exclusively co-expressed with HLA-I at the protein level.

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

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

Targeting the up-regulated CNOT3 reverses therapeutic resistance and metastatic progression of EGFR-mutant non-small cell lung cancer

Lung cancer is the leading cause of cancer-related mortality worldwide. CNOT3, a subunit of the CCR4-NOT complex, has recently been suggested to be overexpressed in lung cancer and involved in tumor malignancy. However, its precise role and the underlying mechanisms still need to be fully revealed. In the present study, we found in lung cancer cells the expression of CNOT3 could be regulated by EGFR signaling pathway and c-Jun, a transcription factor downstream of EGFR, transcriptionally regulated its expression. Interestingly, CNOT3 could inversely regulate the expression of c-Jun via modulating its translation. Thus, a feedback loop existed between c-Jun and CNOT3. CNOT3 reduction post EGFR blockade facilitated the drug-induced cell death, and simultaneously inhibited cell proliferation via impacting TSC1/mTOR axis. Whereas, further up-regulation of the CNOT3 expression was observed in gefitinib-resistant cells, which dampened gefitinib sensitivity. Mechanically, the elevation of CNOT3 was induced by the bypass activation of HER2/c-Jun signaling. Depleting CNOT3 in vitro and in vivo sensitized the drug-resistant cells to gefitinib treatment and inhibited metastatic progression. These results give novel insights into the role of CNOT3 in lung cancer malignancy and provide a theoretical basis for the development of therapeutic strategies to solve acquired resistance to EGFR-TKIs.

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural networks can improve the accuracy and reduce the subjectivity of LUAD histologic subtyping using hematoxylin and eosin (H&E)-stained whole slide images (WSIs). However, developing supervised models with good prediction accuracy usually requires extensive manual data annotation, which is time-consuming and labor-intensive. This work proposes three self-supervised learning (SSL) pretext tasks to reduce labeling effort. These tasks not only leverage the multi-resolution nature of the H&E WSIs but also explicitly consider the relevance to the downstream task of classifying the LUAD histologic subtypes. Two tasks involve predicting the spatial relationship between tiles cropped from lower and higher magnification WSIs. We hypothesize that these tasks induce the model to learn to distinguish different tissue structures presented in the images, thus benefiting the downstream classification. The third task involves predicting the eosin stain from the hematoxylin stain, inducing the model to learn cytoplasmic features relevant to LUAD subtypes. The effectiveness of the three proposed SSL tasks and their ensemble was demonstrated by comparison with other state-of-the-art pretraining and SSL methods using three publicly available datasets. Our work can be extended to any other cancer type where tissue architectural information is important. The model could be used to expedite and complement the process of routine pathology diagnosis tasks. The code is available at https://github.com/rina-ding/ssl_luad_classification.

Targeted Proteomic Quantitation of NRF2 Signaling and Predictive Biomarkers in HNSCC

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

Development and Validation of txSim: A Model of Advanced Lung Cancer Treatment in Australia

BACKGROUND AND OBJECTIVE

Since 2016, new therapies have transformed the standard of care for lung cancer, creating a need for up-to-date evidence for health economic modelling. We developed a discrete event simulation of advanced lung cancer treatment to provide estimates of survival outcomes and healthcare costs in the Australian setting that can be updated as new therapies are introduced.

METHODS

Treatment for advanced lung cancer was modelled under a clinician-specified treatment algorithm for Australia in 2022. Prevalence of lung cancer subpopulations was extracted from cBioPortal and the Sax Institute's 45 and Up Study, a large prospective cohort linked to cancer registrations. All costs were from the health system perspective for the year 2020. Pharmaceutical and molecular diagnostic costs were obtained from public reimbursement fees, while other healthcare costs were obtained from health system costs in the 45 and Up Study. Treatment efficacy was obtained from clinical trials and observational study data. Costs and survival were modelled over a 10-year horizon. Uncertainty intervals were generated with probabilistic sensitivity analyses. Overall survival predictions were validated against real-world studies.

RESULTS

Under the 2022 treatment algorithm, estimated mean survival and costs for advanced lung cancer 10 years post-diagnosis were 16.4 months (95% uncertainty interval [UI]: 14.7-18.1) and AU$116,069 (95% UI: $107,378-$124,933). Survival and costs were higher assuming optimal treatment utilisation rates (20.5 months, 95% UI: 19.1-22.5; $154,299, 95% UI: $146,499-$161,591). The model performed well in validation, with good agreement between predicted and observed survival in real-world studies.

CONCLUSIONS

Survival improvements for advanced lung cancer have been accompanied by growing treatment costs. The estimates reported here can be used for budget planning and economic evaluations of interventions across the spectrum of cancer control.

Foresight in clinical proteomics: current status, ethical considerations, and future perspectives

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

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

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

Exploring the pan-cancer landscape of posttranscriptional regulation

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

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.

Integrated transcriptomic and proteomic analyses reveal the mechanism of easy acceptance of artificial pelleted diets during food habit domestication in Largemouth bass (Micropterus salmoides)

Acceptance of artificial pelleted diets contributes to increasing the cultured areas and output of carnivorous fish. However, the mechanism of acceptance of artificial pelleted diets remains largely unknown. In this study, the easy acceptance of artificial pelleted diets (EAD) group and the not easy acceptance of artificial pelleted diets (NAD) group of Largemouth bass (Micropterus salmoides) were divided based on the ratios of stomach weight/body weight (SB) after 0.5 h feeding, which was bigger than 18% in the EAD group and ranged from 8 to 12% in the NAD group. Through transcriptome and proteome sequencing, a total of 2463 differentially expressed genes (DEGs) and 230 differentially expressed proteins (DEPs) were identified, respectively. Integrated analyses of transcriptome and proteome data revealed that 152 DEPs were matched with the corresponding DEGs (named co-DEGs-DEPs), and 54 co-DEGs-DEPs were enriched in 16 KEGG pathways, including the metabolic pathways, steroid biosynthesis, fatty acid biosynthesis, etc. Furthermore, 3 terpenoid backbone biosynthesis-related genes (Hmgcr, Hmgcs, and Fdps) in metabolic pathways, 10 steroid biosynthesis-related genes (Fdft1, Sqle, Lss, Cyp51a1, Tm7sf2, Nsdhl, Hsd17b7, Dhcr24, Sc5d, and Dhcr7), and 3 fatty acid biosynthesis-related genes (Acaca, Fasn, and Ascl) were all up-regulated in the EAD group, suggesting that the lipid metabolism pathway and steroid biosynthesis pathway play important roles in early food habit domestication in Largemouth bass. In addition, the detection results of randomly selected 15 DEGs and 15 DEPs indicated that both transcriptome and proteome results in the study were reliable. Our study provides useful information for further research on the mechanisms of food habit domestication in fish.

Dosage amplification dictates oncogenic regulation by the NKX2-1 lineage factor in lung adenocarcinoma

Amplified oncogene expression is a critical and widespread driver event in cancer, yet our understanding of how amplification-mediated elevated dosage mediates oncogenic regulation is limited. Here, we find that the most significant focal amplification event in lung adenocarcinoma (LUAD) targets a lineage super-enhancer near the NKX2-1 lineage transcription factor. The NKX2-1 super-enhancer is targeted by focal and co-amplification with NKX2-1, and activation or repression controls NKX2-1 expression. We find that NKX2-1 is a widespread dependency in LUAD cell lines, where NKX2-1 pioneers enhancer accessibility to drive a lineage addicted state in LUAD, and NKX2-1 confers persistence to EGFR inhibitors. Notably, we find that oncogenic NKX2-1 regulation requires expression above a minimum dosage threshold-NKX2-1 dosage below this threshold is insufficient for cell viability, enhancer remodeling, and TKI persistence. Our data suggest that copy-number amplification can be a gain-of-function alteration, wherein amplification elevates oncogene expression above a critical dosage required for oncogenic regulation and cancer cell survival.

Dissecting signaling regulators driving AXL-mediated bypass resistance and associated phenotypes by phosphosite perturbations

Receptor tyrosine kinase (RTK)-targeted therapies are often effective but invariably limited by drug resistance. A major mechanism of acquired resistance involves "bypass" switching to alternative pathways driven by non-targeted RTKs that restore proliferation. One such RTK is AXL whose overexpression, frequently observed in bypass resistant tumors, drives both cell survival and associated malignant phenotypes such as epithelial-to-mesenchymal (EMT) transition and migration. However, the signaling molecules and pathways eliciting these responses have remained elusive. To explore these coordinated effects, we generated a panel of mutant lung adenocarcinoma PC9 cell lines in which each AXL intracellular tyrosine residue was mutated to phenylalanine. By integrating measurements of phosphorylation signaling and other phenotypic changes associated with resistance through multivariate modeling, we mapped signaling perturbations to specific resistant phenotypes. Our results suggest that AXL signaling can be summarized into two clusters associated with progressive disease and poor clinical outcomes in lung cancer patients. These clusters displayed favorable Abl1 and SFK motifs and their phosphorylation was consistently decreased by dasatinib. High-throughput kinase specificity profiling showed that AXL likely activates the SFK cluster through FAK1 which is known to complex with Src. Moreover, the SFK cluster overlapped with a previously established focal adhesion kinase (FAK1) signature conferring EMT-mediated erlotinib resistance in lung cancer cells. Finally, we show that downstream of this kinase signaling, AXL and YAP form a positive feedback loop that sustains drug tolerant persister cells. Altogether, this work demonstrates an approach for dissecting signaling regulators by which AXL drives erlotinib resistance-associated phenotypic changes.