Lung cancer signatures in plasma based on proteome profiling of mouse tumor models

Platelet factor 4(PF4) and its multiple roles in diseases

Platelet factor 4 (PF4) combines with heparin to form an antigen that could produce IgG antibodies and participate in heparin-induced thrombocytopenia (HIT). PF4 has attracted wide attention due to its role in novel coronavirus vaccine-19 (COVID-9)-induced immune thrombotic thrombocytopenia (VITT) and cognitive impairments. The electrostatic interaction between PF4 and negatively charged molecules is vital in the progression of VITT, which is similar to HIT. Emerging evidence suggests its multiple roles in hematopoietic and angiogenic inhibition, platelet coagulation interference, host inflammatory response promotion, vascular inhibition, and antitumor properties. The emerging pharmacological effects of PF4 may help deepen the exploration of its mechanism, thus accelerating the development of targeted therapies. However, due to its pleiotropic properties, the development of drugs targeting PF4 is at an early stage and faces many challenges. Herein, we discussed the characteristics and biological functions of PF4, summarized PF4-mediated signaling pathways, and discussed its multiple roles in diseases to inform novel approaches for successful clinical translational research.

Characterizing the secretome of EGFR mutant lung adenocarcinoma

Background

Lung cancer is the leading cause of cancer related death worldwide, mainly due to the late stage of disease at the time of diagnosis. Non-invasive biomarkers are needed to supplement existing screening methods to enable earlier detection and increased patient survival. This is critical to EGFR-driven lung adenocarcinoma as it commonly occurs in individuals who have never smoked and do not qualify for current screening protocols.

Methods

In this study, we performed mass spectrometry analysis of the secretome of cultured lung cells representing different stages of mutant EGFR driven transformation, from normal to fully malignant. Identified secreted proteins specific to the malignant state were validated using orthogonal methods and their clinical activity assessed in lung adenocarcinoma patient cohorts.

Results

We quantified 1020 secreted proteins, which were compared for differential expression between stages of transformation. We validated differentially expressed proteins at the transcriptional level in clinical tumor specimens, association with patient survival, and absolute concentration to yield three biomarker candidates: MDK, GDF15, and SPINT2. These candidates were validated using ELISA and increased levels were associated with poor patient survival specifically in EGFR mutant lung adenocarcinoma patients.

Conclusions

Our study provides insight into changes in secreted proteins during EGFR driven lung adenocarcinoma transformation that may play a role in the processes that promote tumor progression. The specific candidates identified can harnessed for biomarker use to identify high risk individuals for early detection screening programs and disease management for this molecular subgroup of lung adenocarcinoma patients.

Integrative Multianalytical Model Based on Novel Plasma Protein Biomarkers for Distinguishing Lung Adenocarcinoma and Benign Pulmonary Nodules

Given the pressing clinical problem of making a decision in diagnosis for subjects with pulmonary nodules, we aimed to discover novel plasma protein biomarkers for lung adenocarcinoma (LUAD) and benign pulmonary nodules (BPNs) and then develop an integrative multianalytical model to guide the clinical management of LUAD and BPN patients. Through label-free quantitative plasma proteomic analysis (data are available via ProteomeXchange with identifier PXD046731), 12 differentially expressed proteins (DEPs) in LUAD and BPN were screened. The diagnostic abilities of DEPs were validated in two independent validation cohorts. The results showed that the levels of three candidate proteins (PRDX2, PON1, and APOC3) were lower in the plasma of LUAD than in BPN. The three candidate proteins were combined with three promising computed tomography indicators (spiculation, vascular notch sign, and lobulation) and three traditional markers (CEA, CA125, and CYFRA21-1) to construct an integrative multianalytical model, which was effective in distinguishing LUAD from BPN, with an AUC of 0.904, a sensitivity of 81.44%, and a specificity of 90.14%. Moreover, the model possessed impressive diagnostic performance between early LUADs and BPNs, with the AUC, sensitivity, specificity, and accuracy of 0.868, 65.63%, 90.14%, and 82.52%, respectively. This model may be a useful auxiliary diagnostic tool for LUAD and BPN by achieving a better balance of sensitivity and specificity.

High-fat diet induces C-reactive protein secretion, promoting lung adenocarcinoma via immune microenvironment modulation

To understand the effects of a high-fat diet (HFD) on lung cancer progression and biomarkers, we here used an inducible mutant epidermal growth factor receptor (EGFR)-driven lung cancer transgenic mouse model fed a regular diet (RD) or HFD. The HFD lung cancer (LC-HFD) group exhibited significant tumor formation and deterioration, such as higher EGFR activity and proliferation marker expression, compared with the RD lung cancer (LC-RD) group. Transcriptomic analysis of the lung tissues revealed that the significantly changed genes in the LC-HFD group were highly enriched in immune-related signaling pathways, suggesting that an HFD alters the immune microenvironment to promote tumor growth. Cytokine and adipokine arrays combined with a comprehensive analysis using meta-database software indicated upregulation of C-reactive protein (CRP) in the LC-HFD group, which presented with increased lung cancer proliferation and metastasis; this was confirmed experimentally. Our results imply that an HFD can turn the tumor growth environment into an immune-related pro-tumorigenic microenvironment and demonstrate that CRP has a role in promoting lung cancer development in this microenvironment.

Plasma proteome of growing tumors

Early detection of cancer is vital for the best chance of successful treatment, but half of all cancers are diagnosed at an advanced stage. A simple and reliable blood screening test applied routinely would therefore address a major unmet medical need. To gain insight into the value of protein biomarkers in early detection and stratification of cancer we determined the time course of changes in the plasma proteome of mice carrying transplanted human lung, breast, colon, or ovarian tumors. For protein measurements we used an aptamer-based assay which simultaneously measures ~ 5000 proteins. Along with tumor lineage-specific biomarkers, we also found 15 markers shared among all cancer types that included the energy metabolism enzymes glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phophate isomerase and dihydrolipoyl dehydrogenase as well as several important biomarkers for maintaining protein, lipid, nucleotide, or carbohydrate balance such as tryptophanyl t-RNA synthetase and nucleoside diphosphate kinase. Using significantly altered proteins in the tumor bearing mice, we developed models to stratify tumor types and to estimate the minimum detectable tumor volume. Finally, we identified significantly enriched common and unique biological pathways among the eight tumor cell lines tested.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

A Comprehensive Search of Non-Canonical Proteins in Non-Small Cell Lung Cancer and Their Impact on the Immune Response

There is substantial interest in mining neoantigens for cancer applications. Non-canonical proteins resulting from frameshift mutations have been identified as neoantigens in cancer. We investigated the landscape of non-canonical proteins in non-small cell lung cancer (NSCLC) and their induced immune response in the form of autoantibodies. A database of cryptoproteins was computationally constructed and comprised all alternate open reading frames (altORFs) and ORFs identified in pseudogenes, noncoding RNAs, and untranslated regions of mRNAs that did not align with known canonical proteins. Proteomic profiles of seventeen lung adenocarcinoma (LUAD) cell lines were searched to evaluate the occurrence of cryptoproteins. To assess the immunogenicity, immunoglobulin (Ig)-bound cryptoproteins in plasmas were profiled by mass spectrometry. The specimen set consisted of plasmas from 30 newly diagnosed NSCLC cases, pre-diagnostic plasmas from 51 NSCLC cases, and 102 control plasmas. An analysis of LUAD cell lines identified 420 cryptoproteins. Plasma Ig-bound analyses revealed 90 cryptoproteins uniquely found in cases and 14 cryptoproteins that had a fold-change >2 compared to controls. In pre-diagnostic samples, 17 Ig-bound cryptoproteins yielded an odds ratio ≥2. Eight Ig-bound cryptoproteins were elevated in both pre-diagnostic and newly diagnosed cases compared to controls. Cryptoproteins represent a class of neoantigens that induce an autoantibody response in NSCLC.

The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

Plasma Based Protein Signatures Associated with Small Cell Lung Cancer

Small-cell-lung cancer (SCLC) is associated with overexpression of oncogenes including Myc family genes and YAP1 and inactivation of tumor suppressor genes. We performed in-depth proteomic profiling of plasmas collected from 15 individuals with newly diagnosed early stage SCLC and from 15 individuals before the diagnosis of SCLC and compared findings with plasma proteomic profiles of 30 matched controls to determine the occurrence of signatures that reflect disease pathogenesis. A total of 272 proteins were elevated (area under the receiver operating characteristic curve (AUC) ≥ 0.60) among newly diagnosed cases compared to matched controls of which 31 proteins were also elevated (AUC ≥ 0.60) in case plasmas collected within one year prior to diagnosis. Ingenuity Pathway analyses of SCLC-associated proteins revealed enrichment of signatures of oncogenic MYC and YAP1. Intersection of proteins elevated in case plasmas with proteomic profiles of conditioned medium from 17 SCLC cell lines yielded 52 overlapping proteins characterized by YAP1-associated signatures of cytoskeletal re-arrangement and epithelial-to-mesenchymal transition. Among samples collected more than one year prior to diagnosis there was a predominance of inflammatory markers. Our integrated analyses identified novel circulating protein features in early stage SCLC associated with oncogenic drivers.

Computational Modeling of C-Terminal Tails to Predict the Calcium-Dependent Secretion of Endoplasmic Reticulum Resident Proteins

The lumen of the endoplasmic reticulum (ER) has resident proteins that are critical to perform the various tasks of the ER such as protein maturation and lipid metabolism. These ER resident proteins typically have a carboxy-terminal ER retention/retrieval sequence (ERS). The canonical ERS that promotes ER retrieval is Lys-Asp-Glu-Leu (KDEL) and when an ER resident protein moves from the ER to the Golgi, KDEL receptors (KDELRs) in the Golgi recognize the ERS and return the protein to the ER lumen. Depletion of ER calcium leads to the mass departure of ER resident proteins in a process termed exodosis, which is regulated by KDELRs. Here, by combining computational prediction with machine learning-based models and experimental validation, we identify carboxy tail sequences of ER resident proteins divergent from the canonical “KDEL” ERS. Using molecular modeling and simulations, we demonstrated that two representative non-canonical ERS can stably bind to the KDELR. Collectively, we developed a method to predict whether a carboxy-terminal sequence acts as a putative ERS that would undergo secretion in response to ER calcium depletion and interacts with the KDELRs. The interaction between the ERS and the KDELR extends beyond the final four carboxy terminal residues of the ERS. Identification of proteins that undergo exodosis will further our understanding of changes in ER proteostasis under physiological and pathological conditions where ER calcium is depleted.

Small-cell lung cancer

Small-cell lung cancer (SCLC) represents about 15% of all lung cancers and is marked by an exceptionally high proliferative rate, strong predilection for early metastasis and poor prognosis. SCLC is strongly associated with exposure to tobacco carcinogens. Most patients have metastatic disease at diagnosis, with only one-third having earlier-stage disease that is amenable to potentially curative multimodality therapy. Genomic profiling of SCLC reveals extensive chromosomal rearrangements and a high mutation burden, almost always including functional inactivation of the tumour suppressor genes TP53 and RB1. Analyses of both human SCLC and murine models have defined subtypes of disease based on the relative expression of dominant transcriptional regulators and have also revealed substantial intratumoural heterogeneity. Aspects of this heterogeneity have been implicated in tumour evolution, metastasis and acquired therapeutic resistance. Although clinical progress in SCLC treatment has been notoriously slow, a better understanding of the biology of disease has uncovered novel vulnerabilities that might be amenable to targeted therapeutic approaches. The recent introduction of immune checkpoint blockade into the treatment of patients with SCLC is offering new hope, with a small subset of patients deriving prolonged benefit. Strategies to direct targeted therapies to those patients who are most likely to respond and to extend the durable benefit of effective antitumour immunity to a greater fraction of patients are urgently needed and are now being actively explored.

Structural insights and evaluation of the potential impact of missense variants on the interactions of SLIT2 with ROBO1/4 in cancer progression

The cognate interaction of ROBO1/4 with its ligand SLIT2 is known to be involved in lung cancer progression. However, the precise role of genetic variants, disrupting the molecular interactions is less understood. All cancer-associated missense variants of ROBO1/4 and SLIT2 from COSMIC were screened for their pathogenicity. Homology modelling was done in Modeller 9.17, followed by molecular simulation in GROMACS. Rigid docking was performed for the cognate partners in PatchDock with refinement in HADDOCK server. Post-docking alterations in conformational, stoichiometric, as well as structural parameters, were assessed. The disruptive variants were ranked using a weighted scoring scheme. In silico prioritisation of 825 variants revealed 379 to be potentially pathogenic out of which, about 12% of the variants, i.e. ROBO1 (14), ROBO4 (8), and SLIT2 (23) altered the cognate docking. Six variants of ROBO1 and 5 variants of ROBO4 were identified as "high disruptors" of interactions with SLIT2 wild type. Likewise, 17 and 13 variants of SLIT2 were found to be "high disruptors" of its interaction with ROBO1 and ROBO4, respectively. Our study is the first report on the impact of cancer-associated missense variants on ROBO1/4 and SLIT2 interactions that might be the drivers of lung cancer progression.

Differential Expression of TOM34, AL1A1, PADI2 and KLRBA in NNK Induced Lung Cancer in Wistar Rats and their Implications

BACKGROUND

Lung cancer is the most common cancer with a high mortality rate. The diagnosis only at advanced stages and lack of effective treatment are the main factors responsible for high mortality. Tobacco smoke is the major responsible factor for inflammation and tumor development in lungs.

OBJECTIVE

The present study was carried out to identify differentially expressed proteins and elucidate their role in carcinogenesis.

METHODS

The lung cancer was developed in Wistar rats by using NNK as carcinogen and cancer development was confirmed by histopathological examination. The 2D SDS PAGE was used to analyse total proteins and find out differentially expressed proteins in NNK treated lung tissue vis-a-vis control tissue. The findings of proteomic analysis were further validated by quantification of corresponding transcripts using Real Time PCR. Finally, Cytoscape was used to find out protein-protein interaction.

RESULTS

The histopathological examinations showed neoplasia at 9th month after NNK treatment. The proteomic analysis revealed several differentially expressed proteins, four of which were selected for further studies. (TOM34, AL1A1, PADI2 and KLRBA) that were up regulated in NNK treated lung tissue. The real time analysis showed over expression of the genes coding for the selected proteins. Thus, the proteomic and transcriptomic data corroborate each other. Further, these proteins showed interaction with the members of NF-κB family and STAT3.

CONCLUSION

We conclude that these proteins play a substantial role in the induction of lung cancer through NF-κB and STAT3 pathway. Therefore, these may have the potential to be used as therapeutic targets and for early detection of lung cancer.

Overcoming cancer therapeutic bottleneck by drug repurposing

Ever present hurdles for the discovery of new drugs for cancer therapy have necessitated the development of the alternative strategy of drug repurposing, the development of old drugs for new therapeutic purposes. This strategy with a cost-effective way offers a rare opportunity for the treatment of human neoplastic disease, facilitating rapid clinical translation. With an increased understanding of the hallmarks of cancer and the development of various data-driven approaches, drug repurposing further promotes the holistic productivity of drug discovery and reasonably focuses on target-defined antineoplastic compounds. The "treasure trove" of non-oncology drugs should not be ignored since they could target not only known but also hitherto unknown vulnerabilities of cancer. Indeed, different from targeted drugs, these old generic drugs, usually used in a multi-target strategy may bring benefit to patients. In this review, aiming to demonstrate the full potential of drug repurposing, we present various promising repurposed non-oncology drugs for clinical cancer management and classify these candidates into their proposed administration for either mono- or drug combination therapy. We also summarize approaches used for drug repurposing and discuss the main barriers to its uptake.

Subtype-specific secretomic characterization of pulmonary neuroendocrine tumor cells

Pulmonary neuroendocrine (NE) cancer, including small cell lung cancer (SCLC), is a particularly aggressive malignancy. The lineage-specific transcription factors Achaete-scute homolog 1 (ASCL1), NEUROD1 and POU2F3 have been reported to identify the different subtypes of pulmonary NE cancers. Using a large-scale mass spectrometric approach, here we perform quantitative secretome analysis in 13 cell lines that signify the different NE lung cancer subtypes. We quantify 1,626 proteins and identify IGFBP5 as a secreted marker for ASCL1^High SCLC. ASCL1 binds to the E-box elements in IGFBP5 and directly regulates its transcription. Knockdown of ASCL1 decreases IGFBP5 expression, which, in turn, leads to hyperactivation of IGF-1R signaling. Pharmacological co-targeting of ASCL1 and IGF-1R results in markedly synergistic effects in ASCL1^High SCLC in vitro and in mouse models. We expect that this secretome resource will provide the foundation for future mechanistic and biomarker discovery studies, helping to delineate the molecular underpinnings of pulmonary NE tumors.

Secreted proteins present a rich resource of potential cancer biomarkers. Here, the authors use mass spectrometry to analyze secretome remodeling in pulmonary neuroendocrine lung cancer cell lines and validate potential biomarkers and therapeutic targets in vitro and in mouse models.

Mechanistic Study of TTF-1 Modulation of Cellular Sensitivity to Cisplatin

The lung lineage master regulator gene, Thyroid Transcription Factor-1 (TTF-1, also known as NKX2-1), is used as a marker by pathologists to identify lung adenocarcinomas since TTF-1 is expressed in 60 ~ 70% of lung ADs. Much research has been conducted to investigate roles of TTF-1 in lung cancer biology. But, how it modulates cellular chemosensitivity remains poorly characterized. Our study shows that TTF-1 sensitizes the KRAS-mutated A549 and NCI-H460 lung cancer cells to cisplatin, a common chemotherapy used to treat lung cancer. This chemosensitization activity does not appear to be mediated by a TTF-1-imposed alteration on nucleotide excision repair. Mechanistically, TTF-1 induced a reduction in p-AKT (S473), which in turn activated glycogen synthase kinase 3 (GSK3) and reduced β-catenin. Intriguingly, in the EGFR-mutated NCI-H1975 and HCC827 cells, TTF-1 desensitized these cells to cisplatin; concomitantly, TTF-1 conferred an increase in p-AKT. Finally, the conditioned media of TTF-1-transefected cells sensitized TTF-1- cells to cisplatin, implicating that the TTF-1-driven chemosensitization activity may be dually pronged in both intracellular and extracellular compartments. In short, this study highlights the enigmatic activities of TTF-1 in lung cancer, and calls for future research to optimally manage chemotherapy of patients with TTF-1+ lung ADs.

Syndecan 1 is a critical mediator of macropinocytosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) remains recalcitrant to all forms of cancer treatment and carries a dismal 5-year survival rate of 8%¹. Inhibition of oncogenic KRAS (hereafter KRAS*), the earliest lesion in disease development that is present in >90% of PDAC, and its signaling surrogates has yielded encouraging preclinical results with experimental agents^2-4. However, KRAS*-independent disease recurrence following genetic extinction of Kras* in mouse models anticipates the need for co-extinction strategies^5,6. Multiple oncogenic processes are initiated at the cell surface, where KRAS* physically and functionally interacts to direct signaling essential for malignant transformation and tumor maintenance. Insights into the complexity of the functional surfaceome have been technologically limited until recently, and, in the case of PDAC, the genetic control of the function and composition of the PDAC surfaceome in the context of KRAS* signaling remains largely unexplored. Here, we developed an unbiased, functional target discovery platform to query KRAS*-dependent changes of the PDAC surfaceome, which uncovered syndecan-1 (SDC1) as a protein upregulated at the cell surface by KRAS*. Cell surface localization of SDC1 is essential for disease maintenance and progression, where it regulates macropinocytosis, an essential metabolic pathway that fuels PDAC cell growth. Thus, our study forges a mechanistic link between KRAS* signaling and a targetable molecule driving nutrient salvage pathways in PDAC and validates oncogene-driven surfaceome annotation as a strategy to identify cancer-specific vulnerabilities.

Macrophage Origin, Metabolic Reprogramming and IL-1 Signaling: Promises and Pitfalls in Lung Cancer

Macrophages are tissue-resident cells that act as immune sentinels to maintain tissue integrity, preserve self-tolerance and protect against invading pathogens. Lung macrophages within the distal airways face around 8000–9000 L of air every day and for that reason are continuously exposed to a variety of inhaled particles, allergens or airborne microbes. Chronic exposure to irritant particles can prime macrophages to mediate a smoldering inflammatory response creating a mutagenic environment and favoring cancer initiation. Tumor-associated macrophages (TAMs) represent the majority of the tumor stroma and maintain intricate interactions with malignant cells within the tumor microenvironment (TME) largely influencing the outcome of cancer growth and metastasis. A number of macrophage-centered approaches have been investigated as potential cancer therapy and include strategies to limit their infiltration or exploit their antitumor effector functions. Recently, strategies aimed at targeting IL-1β signaling pathway using a blocking antibody have unexpectedly shown great promise on incident lung cancer. Here, we review the current understanding of the bridge between TAM metabolism, IL-1β signaling, and effector functions in lung adenocarcinoma and address the challenges to successfully incorporating these pathways into current anticancer regimens.

Integrative Genomic Analyses Identifies GGA2 as a Cooperative Driver of EGFR-Mediated Lung Tumorigenesis

INTRODUCTION

Targeted therapies for lung adenocarcinoma (LUAD) have improved patient outcomes; however, drug resistance remains a major problem. One strategy to achieve durable response is to develop combination-based therapies that target both mutated oncogenes and key modifiers of oncogene-driven tumorigenesis. This is based on the premise that mutated oncogenes, although necessary, are not sufficient for malignant transformation. We aimed to uncover genetic alterations that cooperate with mutant EGFR during LUAD development.

METHODS

We performed integrative genomic analyses, combining copy number, gene expression and mutational information for over 500 LUAD tumors. Co-immunoprecipitation and Western blot analysis were performed in LUAD cell lines to confirm candidate interactions while RNA interference and gene overexpression were used for in vitro and in vivo functional assessment.

RESULTS

We identified frequent amplifications/deletions of chromosomal regions affecting the activity of genes specifically in the context of EGFR mutation, including amplification of the mutant EGFR allele and deletion of dual specificity phosphatase 4 (DUSP4), which have both previously been reported. In addition, we identified the novel amplification of a segment of chromosome arm 16p in mutant-EGFR tumors corresponding to increased expression of Golgi Associated, Gamma Adaptin Ear Containing, ARF Binding Protein 2 (GGA2), which functions in protein trafficking and sorting. We found that GGA2 interacts with EGFR, increases EGFR protein levels and modifies EGFR degradation after ligand stimulation. Furthermore, we show that overexpression of GGA2 enhances EGFR mediated transformation while GGA2 knockdown reduces the colony and tumor forming ability of EGFR mutant LUAD.

CONCLUSIONS

These data suggest that overexpression of GGA2 in LUAD tumors results in the accumulation of EGFR protein and increased EGFR signaling, which helps drive tumor progression. Thus, GGA2 plays a cooperative role with EGFR during LUAD development and is a potential therapeutic target for combination-based strategies in LUAD.

Analytical validation of a novel multi-analyte plasma test for lung nodule characterization

BACKGROUND

In the National Lung Screening Trial, 96.4% of nodules had benign etiology. To avoid unnecessary actions and exposure to harm, individuals with benign disease must be identified. We describe herein the analytical validation of a multi-analyte immunoassay for characterizing the risk that a lung nodule found on CT is malignant. Those at lower risk may be considered for serial surveillance to avoid unnecessary and potentially harmful procedures. While those nodules characterized at higher risk may be appropriate for more aggressive actions.

OBJECTIVE

To validate the analytical performance of multiplexed plasma protein assays used in a novel test for lung nodule characterization.

METHODS

A multiplexed immunoassay panel for the measurement of plasma proteins in current smokers who present with a lung nodule on CT scan was evaluated in a clinical testing laboratory. Assay analytical sensitivity, reproducibility, precision, and recovery of Epidermal Growth Factor Receptor (EGFR), Prosurfactant protein B (ProSB), and Tissue Inhibitor of Metalloproteinases 1 (TIMP1) from human EDTA plasma samples were evaluated across multiple runs, lots, and technicians. Interfering substances and sample pre-analytical storage conditions were evaluated for their effect on analyte recovery. The lung nodule risk score reproducibility was assessed across multiple lots.

RESULTS

The assay sensitivities were 0.10 ng/mL EGFR, 0.02 ng/mL ProSB, and 0.29 ng/mL TIMP1 with over three orders of magnitude in the assay dynamic ranges. The assays and analytes are robust to pre-analytical sample handling and the plasma can be stored for up to 4 days at 4°C either when freshy collected or thawed after long-term storage at -80°C. Total imprecision after 20 days of testing remained under 9% for all three assays. Risk score variability remained within a ± 10% risk score range.

CONCLUSIONS

The three protein assays comprising the multi-analyte plasma test for lung nodule characterization performed quite acceptably in a clinical laboratory.

Risk assessment for indeterminate pulmonary nodules using a novel, plasma-protein based biomarker assay

BACKGROUND

The increase in lung cancer screening is intensifying the need for a noninvasive test to characterize the many indeterminate pulmonary nodules (IPN) discovered. Correctly identifying non-cancerous nodules is needed to reduce overdiagnosis and overtreatment. Alternatively, early identification of malignant nodules may represent a potentially curable form of lung cancer.

OBJECTIVE

To develop and validate a plasma-based multiplexed protein assay for classifying IPN by discriminating between those with a lung cancer diagnosis established pathologically and those found to be clinically and radiographically stable for at least one year.

METHODS

Using a novel technology, we developed assays for plasma proteins associated with lung cancer into a panel for characterizing the risk that an IPN found on chest imaging is malignant. The assay panel was evaluated with a cohort of 277 samples, all from current smokers with an IPN 4-30 mm. Subjects were divided into training and test sets to identify a Support Vector Machine (SVM) model for risk classification containing those proteins and clinical factors that added discriminatory information to the Veteran's Affairs (VA) Clinical Factors Model. The algorithm was then evaluated in an independent validation cohort.

RESULTS

Among the 97 validation study subjects, 68 were grouped as having intermediate risk by the VA model of which the SVM model correctly identified 44 (65%) of these intermediate-risk samples as low (n=16) or high risk (n=28). The SVM model negative predictive value (NPV) was 94% and its sensitivity was 94%.

CONCLUSION

The performance of the novel plasma protein biomarker assay supports its use as a noninvasive risk assessment aid for characterizing IPN. The high NPV of the SVM model suggests its application as a rule-out test to increase the confidence of providers to avoid aggressive interventions for their patients for whom the VA model result is an inconclusive, intermediate risk.

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow-up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.

Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins

Importance

There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases.

Objective

To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria.

Design, Setting, and Participants

Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS).

Main Outcomes and Measures

Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity).

Results

In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model.

Conclusions and Relevance

This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine

Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.

The association between human papillomavirus presence and epidermal growth factor receptor mutations in Asian patients with non-small cell lung cancer

Background

The etiology of non-small cell lung cancer (NSCLC) in non-smoker patients remains largely unknown. It has been widely proved that human papillomavirus (HPV) participates in the development of various cancers. Epidermal growth factor receptor (EGFR) mutation patients represent a large portion of non-smokers with NSCLC. We performed this meta-analysis to determine whether HPV infection in NSCLC tissue is associated with EGFR mutations compared with HPV negative controls.

Methods

Online databases were searched up to June 30th 2017. We included studies in which HPV detection was based on polymerase chain reaction (PCR) methods. Random effects model was used in data synthesis and the relative effects were presented as odds ratio (OR) with 95% confidence intervals (CIs).

Results

Finally, four eligible studies with a total of 498 patients from Asian countries were identified and included. The general EFGR mutation positive rate was 38.2% among all patients, and the HPV DNA detection rate (HPV subtype being involved: 16, 18, 33 and 58) was 35.3%. The presence of EGFR mutation was significantly higher in HPV-positive patients compared with HPV-negative controls (52% vs. 31%; OR =2.41, 95% CI: 1.21 to 4.77; P=0.012), with moderate heterogeneity among studies (I²=59%; P=0.062).

Conclusions

Our results suggest that HPV infection is associated with EGFR mutations in NSCLC, at least in Asian populations. Further efforts should be made on exploring the potential mechanism and the prognostic character of HPV/EGFR positive NSCLC patient.

Blood based biomarkers beyond genomics for lung cancer screening

While there is considerable interest at the present time in the development of so-called liquid biopsy approaches for cancer detection based notably on circulating tumor DNA, there are other types of potential biomarkers that show promise for lung cancer screening and early detection. Here we review approaches and some of the promising markers based on proteomics, metabolomics and the immune response to tumor antigens in the form of autoantibodies.

Extracellular domain shedding influences specific tumor uptake and organ distribution of the EGFR PET tracer 89Zr-imgatuzumab

Preclinical positron emission tomography (PET) imaging revealed a mismatch between in vivo epidermal growth factor receptor (EGFR) expression and EGFR antibody tracer tumor uptake. Shed EGFR ectodomain (sEGFR), which is present in cancer patient sera, can potentially bind tracer and therefore influence tracer kinetics. To optimize EGFR-PET, we examined the influence of sEGFR levels on tracer kinetics and tumor uptake of EGFR monoclonal antibody 89Zr-imgatuzumab in varying xenograft models. Human cancer cell lines A431 (EGFR overexpressing, epidermoid), A549 and H441 (both EGFR medium expressing, non-small cell lung cancer) were xenografted in mice. Xenografted mice received 10, 25 or 160 μg 89Zr-imgatuzumab, co-injected with equal doses 111In-IgG control. MicroPET scans were made 24, 72 and 144 h post injection, followed by biodistribution analysis. sEGFR levels in liver and plasma samples were determined by ELISA. 89Zr-imgatuzumab uptake in A431 tumors was highest (29.8 ± 5.4 %ID/g) in the 160 μg dose group. Contrary, highest uptake in A549 and H441 tumors was found at the lowest (10 μg) 89Zr-imgatuzumab dose. High 89Zr-imgatuzumab liver accumulation was found in A431 xenografted mice, which decreased with antibody dose increments. 89Zr-imgatuzumab liver uptake in A549 and H441 xenografted mice was low at all doses. sEGFR levels in liver and plasma of A431 bearing mice were up to 1000-fold higher than levels found in A549, H441 and non-tumor xenografted mice. 89Zr-imgatuzumab effectively visualizes EGFR-expressing tumors. High sEGFR levels can redirect 89Zr-imgatuzumab to the liver, in which case tumor visualization can be improved by increasing tracer antibody dose.

The levels of serine proteases in colon tissue interstitial fluid and serum serve as an indicator of colorectal cancer progression

The proteins in tissue interstitial fluids (TIFs) can spread into the blood and have been proposed as an ideal material to find blood biomarkers. The colon TIFs were collected from 8-, 13-, 18-, and 22-week Apc^Min/+, a typical mouse model of colorectal cancer (CRC), and wild-type mice. iTRAQ-based quantification proteomics was conducted to survey the TIF proteins whose abundance appeared to depend on tumor progression. A total of 46 proteins that exhibited consecutive changes in abundance were identified, including six serine proteases, chymotrypsin-like elastase 1 (CELA1), chymotrypsin-like elastase 2A (CEL2A), chymopasin, chymotrypsinogen B (CTRB1), trypsin 2 (TRY2), and trypsin 4 (TRY4). The observed increases in the abundance of serine proteases were supported in another quantitative evaluation of the individual colon TIFs using a multiple reaction monitor (MRM) assay. Importantly, the increases in the abundance of serine proteases were also verified in the corresponding sera. The quantitative verification of the serine proteases was further extended to the clinical sera, revealing significantly higher levels of CELA1, CEL2A, CTRL/chymopasin, and TRY2 in CRC patients. The receiver operating characteristic analysis illustrated that the combination of CELA1 and CTRL reached the best diagnostic performance, with 90.0% sensitivity and 80.0% specificity. Thus, the quantitative target analysis demonstrated that some serine proteases are indicative of CRC progression.

Reduced IL-6 levels and tumor-associated phospho-STAT3 are associated with reduced tumor development in a mouse model of lung cancer chemoprevention with myo-inositol

Several promising chemopreventive agents have for lung cancer emerged in preclinical models and in retrospective trials. These agents have been shown to modulate pathways altered in carcinogenesis and reduce markers of carcinogenesis in animal and cell culture models. Cancer-prone transgenic mice with oncogenic Kras expressed in the airway epithelium (Ccsp^Cre/+ ; Kras^LSL-G12D/+ ) were raised on diets compounded with myo-inositol. These animals form lung premalignant lesions in a stereotypical fashion over the ten weeks following weaning. Mice raised on myo-inositol containing diets showed potent reduction in the number, size, and stage of lesions as compared to those raised on control diets. myo-inositol has previously been reported to inhibit phosphoinositide 3-kinase (PI3K) signaling. However, in mice raised on myo-inositol, total PI3K signaling was largely unaffected. Proteomic and cytokine analyses revealed large reduction in IL-6 related pathways, including STAT3 phosphorylation. This effect was not due to direct inhibition of IL-6 production and autocrine signaling within the tumor cell, but rather through alteration in macrophage recruitment and in phenotype switching, with an increase in antitumoral M1 macrophages.

PF4 Promotes Platelet Production and Lung Cancer Growth

Co-option of host components by solid tumors facilitates cancer progression and can occur in both local tumor microenvironments and remote locations. At present, the signals involved in long-distance communication remain insufficiently understood. Here, we identify platelet factor 4 (PF4, CXCL4) as an endocrine factor whose overexpression in tumors correlates with decreased overall patient survival. Furthermore, engineered PF4 over-production in a Kras-driven lung adenocarcinoma genetic mouse model expanded megakaryopoiesis in bone marrow, augmented platelet accumulation in lungs, and accelerated de novo adenocarcinogenesis. Additionally, anti-platelet treatment controlled mouse lung cancer progression, further suggesting that platelets can modulate the tumor microenvironment to accelerate tumor outgrowth. These findings support PF4 as a cancer-enhancing endocrine signal that controls discrete aspects of bone marrow hematopoiesis and tumor microenvironment and that should be considered as a molecular target in anticancer therapy.

[Omics technologies in diagnostics of lung adenocarcinoma]

To date lung adenocarcinoma (LAC) is the most common type of lung cancer. Numerous studies on LAC biology resulted in identification of crucial mutations in protooncogenes and activating neoplastic transformation pathways. Therapeutic approaches that significantly increase the survival rate of patients with LAC of different etiology have been developed and introduced into clinical practice. However, the main problem in the treatment of LAC is early diagnosis, taking into account both factors and mechanisms responsible in tumor initiation and progression. Identification of a wide biomarker repertoire with high specificity and reliability of detection appears to be a solution to this problem. In this context, proteins with differential expression in normal and pathological condition, suitable for detection in biological fluids are the most promising biomarkers. In this review we have analyzed literature data on studies aimed at search of LAC biomarkers. The major attention has been paid to protein biomarkers as the most promising and convenient subject of clinical diagnosis. The review also summarizes existing knowledge on posttranslational modifications, splice variants, isoforms, as well as model systems and transcriptome changes in LAC.

Role of CPS1 in Cell Growth, Metabolism and Prognosis in LKB1-Inactivated Lung Adenocarcinoma

Background

Liver kinase B1 ( LKB1 ) is a tumor suppressor in lung adenocarcinoma (LADC). We investigated the proteomic profiles of 45 LADC cell lines with and without LKB1 inactivation. Carbamoyl phosphate synthetase 1 (CPS1), the first rate-limiting mitochondrial enzyme in the urea cycle, was distinctively overexpressed in LKB1-inactivated LADC cell lines. We therefore assessed the role of CPS1 and its clinical relevance in LKB1-inactivated LADC.

Methods

Mass spectrometric profiling of proteome and metabolome and function of CPS1 were analyzed in LADC cell lines. CPS1 and LKB1 expression in tumors from 305 LADC and 160 lung squamous cell carcinoma patients was evaluated by immunohistochemistry. Kaplan-Meier and Cox regression analyses were applied to assess the association between overall survival and CPS1 and LKB1 expression. All statistical tests were two-sided.

Results

CPS1 knockdown reduced cell growth, decreased metabolite levels associated with nucleic acid biosynthesis pathway, and contributed an additive effect when combined with gemcitabine, pemetrexed, or CHK1 inhibitor AZD7762. Tissue microarray analysis revealed that CPS1 was expressed in 65.7% of LKB1-negative LADC, and only 5.0% of LKB1-positive LADC. CPS1 expression showed statistically significant association with poor overall survival in LADC (hazard ratio = 3.03, 95% confidence interval = 1.74 to 5.25, P < .001).

Conclusions

Our findings suggest functional relevance of CPS1 in LKB1-inactivated LADC and association with worse outcome of LADC. CPS1 is a promising therapeutic target in combination with other chemotherapy agents, as well as a prognostic biomarker, enabling a personalized approach to treatment of LADC.

Roles of Thyroid Transcription Factor 1 in Lung Cancer Biology

Thyroid transcription factor 1 (TTF-1 or NKX2-1) is a transcription factor of fundamental importance in driving lung maturation and morphogenesis. In the last decade, scientists began to appreciate the functional roles of TTF-1 in lung tumorigenesis. This movement was triggered by the discoveries of genetic alterations of TTF-1 in the form of gene amplification in lung cancer. Many downstream target genes of TTF-1 relevant to the lung cancer biology of TTF-1 have been documented. One of the most surprising findings was that TTF-1 may exhibit either pro- or antitumorigenic activities, an outcome with the complexity exceeding the original anticipation purely based on the fact that TTF-1 undergoes gene amplification in lung cancer. In the coming decade, we believe, we will witness additional surprises as the research exploring the cancer roles of TTF-1 progresses.

Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease

Given superior analytical features, MS proteomics is well suited for the basic investigation and clinical diagnosis of human disease. Modern MS enables detailed functional characterization of the pathogenic biochemical processes, as achieved by accurate and comprehensive quantification of proteins and their regulatory chemical modifications. Here, we describe how high-accuracy MS in combination with high-resolution chromatographic separations can be leveraged to meet these analytical requirements in a mechanism-focused manner. We review the quantification methods capable of producing accurate measurements of protein abundance and posttranslational modification stoichiometries. We then discuss how experimental design and chromatographic resolution can be leveraged to achieve comprehensive functional characterization of biochemical processes in complex biological proteomes. Finally, we describe current approaches for quantitative analysis of a common functional protein modification: reversible phosphorylation. In all, current instrumentation and methods of high-resolution chromatography and MS proteomics are poised for immediate translation into improved diagnostic strategies for pediatric and adult diseases.

The cholesterol-binding protein NPC2 restrains recruitment of stromal macrophage-lineage cells to early-stage lung tumours

The tumour microenvironment is known to play an integral role in facilitating cancer progression at advanced stages, but its function in some pre-cancerous lesions remains elusive. We have used the ^V600EBRAF-driven mouse lung model that develop premalignant lesions to understand stroma–tumour interactions during pre-cancerous development. In this model, we have found that immature macrophage-lineage cells (IMCs) producing PDGFA, TGFβ and CC chemokines are recruited to the stroma of premalignant lung adenomas through CC chemokine receptor 1 (CCR1)-dependent mechanisms. Stromal IMCs promote proliferation and transcriptional alterations suggestive of epithelial–mesenchymal transition in isolated premalignant lung tumour cells ex vivo, and are required for the maintenance of early-stage lung tumours in vivo. Furthermore, we have found that IMC recruitment to the microenvironment is restrained by the cholesterol-binding protein, Niemann-Pick type C2 (NPC2). Studies on isolated cells ex vivo confirm that NPC2 is secreted from tumour cells and is taken up by IMCs wherein it suppresses secretion of the CCR1 ligand CC chemokine 6 (CCL6), at least in part by facilitating its lysosomal degradation. Together, these findings show that NPC2 secreted by premalignant lung tumours suppresses IMC recruitment to the microenvironment in a paracrine manner, thus identifying a novel target for the development of chemopreventive strategies in lung cancer.

The steady progress of targeted therapies, promising advances for lung cancer

Lung cancer remains one of the most complex and challenging cancers, being responsible for almost a third of all cancer deaths. This grim picture seems however to be changing, for at least a subset of lung cancers. The number of patients who can benefit from targeted therapies is steadily increasing thanks to the progress made in identifying actionable driver lesions in lung tumours. The success of the latest generation of EGFR and ALK inhibitors in the clinic not only illustrates the value of targeted therapies, but also shows how almost inevitably drug resistance develops. Therefore, more sophisticated approaches are needed to achieve long-term remissions. Although there are still significant barriers to be overcome, technological advances in early detection of relevant mutations and the opportunity to test new drugs in predictive preclinical models justify the hope that we will overcome these obstacles.

Optimizing mouse models for precision cancer prevention

As cancer has become increasingly prevalent, cancer prevention research has evolved towards placing a greater emphasis on reducing cancer deaths and minimizing the adverse consequences of having cancer. 'Precision cancer prevention' takes into account the collaboration of intrinsic and extrinsic factors in influencing cancer incidence and aggressiveness in the context of the individual, as well as recognizing that such knowledge can improve early detection and enable more accurate discrimination of cancerous lesions. However, mouse models, and particularly genetically engineered mouse (GEM) models, have yet to be fully integrated into prevention research. In this Opinion article, we discuss opportunities and challenges for precision mouse modelling, including the essential criteria of mouse models for prevention research, representative success stories and opportunities for more refined analyses in future studies.

Immunoproteasome deficiency is a feature of non-small cell lung cancer with a mesenchymal phenotype and is associated with a poor outcome

The immunoproteasome plays a key role in generation of HLA peptides for T cell-mediated immunity. Integrative genomic and proteomic analysis of non-small cell lung carcinoma (NSCLC) cell lines revealed significantly reduced expression of immunoproteasome components and their regulators associated with epithelial to mesenchymal transition. Low expression of immunoproteasome subunits in early stage NSCLC patients was associated with recurrence and metastasis. Depleted repertoire of HLA class I-bound peptides in mesenchymal cells deficient in immunoproteasome components was restored with either IFNγ or 5-aza-2'-deoxycytidine (5-aza-dC) treatment. Our findings point to a mechanism of immune evasion of cells with a mesenchymal phenotype and suggest a strategy to overcome immune evasion through induction of the immunoproteasome to increase the cellular repertoire of HLA class I-bound peptides.

Thyroid Transcription Factor 1 Reprograms Angiogenic Activities of Secretome

Through both gain- and loss-of-TTF-1 expression strategies, we show that TTF-1 positively regulates vascular endothelial growth factor (VEGF) and that the VEGF promoter element contains multiple TTF-1-responsive sequences. The major signaling receptor for VEGF, i.e VEGFR2, also appears to be under a direct and positive regulation of TTF-1. The TTF-1-dependent upregulation of VEGF was moderately sensitive to rapamycin, implicating a partial involvement of mammalian target of rapamycin (mTOR). However, hypoxia did not further increase the secreted VEGF level of the TTF-1⁺ lung cancer cells. The TTF-1-induced VEGF upregulation occurs in both compartments (exosomes and exosome-depleted media (EDM)) of the conditioned media. Surprisingly, the EDM of TTF-1⁺ lung cancer cells (designated EDM-TTF-1⁺) displayed an anti-angiogenic activity in the endothelial cell tube formation assay. Mechanistic studies suggest that the increased granulocyte-macrophage colony-stimulating factor (GM-CSF) level in the EDM-TTF-1⁺ conferred the antiangiogenic activities. In human lung cancer, the expression of TTF-1 and GM-CSF exhibits a statistically significant and positive correlation. In summary, this study provides evidence that TTF-1 may reprogram lung cancer secreted proteome into an antiangiogenic state, offering a novel basis to account for the long-standing observation of favorable prognosis associated with TTF-1⁺ lung adenocarcinomas.

Deciphering the complexity of the cancer proteome for diagnostic applications

The proteome is the most functional component encoded in the genome, yet most features of the proteome that are deregulated in cancer cannot be predicted from genomic analysis alone. These include post-translational modifications (PTMs), sub-cellular localization, networks and circuitry, formation of complexes, and functional activity, all of which could play a role or be affected as part of tumorigenesis. Thus, there is a substantial opportunity to elucidate protein alterations in cancer and to translate knowledge into diagnostics and therapeutics. The progress made in mining the cancer proteome for diagnostic applications and the path forward are herein reviewed.