Prospective Identification of Elevated Circulating CDCP1 in Patients Years before Onset of Lung Cancer

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Integrating plasma protein-centric multi-omics to identify potential therapeutic targets for pancreatic cancer

BACKGROUND

Deciphering the role of plasma proteins in pancreatic cancer (PC) susceptibility can aid in identifying novel targets for diagnosis and treatment.

METHODS

We examined the relationship between genetically determined levels of plasma proteins and PC through a systemic proteome-wide Mendelian randomization (MR) analysis utilizing cis-pQTLs from multiple centers. Rigorous sensitivity analyses, colocalization, reverse MR, replications with varying instrumental variable selections and additional datasets, as well as subsequent meta-analysis, were utilized to confirm the robustness of significant findings. The causative effect of corresponding protein-coding genes' expression and their expression pattern in single-cell types were then investigated. Enrichment analysis, between-protein interaction and causation, knock-out mice models, and mediation analysis with established PC risk factors were applied to indicate the pathogenetic pathways. These candidate targets were ultimately prioritized upon druggability and potential side effects predicted by a phenome-wide MR.

RESULTS

Twenty-one PC-related circulating proteins were identified in the exploratory phase with no evidence for horizontal pleiotropy or reverse causation. Of these, 11 were confirmed in a meta-analysis integrating external validations. The causality at a transcription level was repeated for neutrophil elastase, hydroxyacylglutathione hydrolase, lipase member N, protein disulfide-isomerase A5, xyloside xylosyltransferase 1. The carbohydrate sulfotransferase 11 and histo-blood group ABO system transferase exhibited high-support genetic colocalization evidence and were found to affect PC carcinogenesis partially through modulating body mass index and type 2 diabetes, respectively. Approved drugs have been established for eight candidate targets, which could potentially be repurposed for PC therapies. The phenome-wide investigation revealed 12 proteins associated with 51 non-PC traits, and interference on protein disulfide-isomerase A5 and cystatin-D would increase the risk of other malignancies.

CONCLUSIONS

By employing comprehensive methodologies, this study demonstrated a genetic predisposition linking 21 circulating proteins to PC risk. Our findings shed new light on the PC etiology and highlighted potential targets as priorities for future efforts in early diagnosis and therapeutic strategies of PC.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study

Rationale: Quantitative interstitial abnormalities (QIAs) are early measures of lung injury automatically detected on chest computed tomography scans. QIAs are associated with impaired respiratory health and share features with advanced lung diseases, but their biological underpinnings are not well understood. Objectives: To identify novel protein biomarkers of QIAs using high-throughput plasma proteomic panels within two multicenter cohorts. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Disease]) and 2,925 participants in a younger population cohort (CARDIA [Coronary Artery Disease Risk in Young Adults]) using the SomaLogic SomaScan assays. We measured QIAs using a local density histogram method. We assessed the associations between proteomic biomarker concentrations and QIAs using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg false discovery rate-corrected P ⩽ 0.05). Measurements and Main Results: In total, 852 proteins were significantly associated with QIAs in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including biological processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; cellular components in extracellular regions; and molecular functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIAs in an older, smoking population with a higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

The CD318/CD6 axis limits type 1 diabetes islet autoantigen-specific human T cell activation

CD6 is a glycoprotein expressed on CD4 and CD8 T cells involved in immunoregulation. CD318 has been identified as a CD6 ligand. The role of CD318 in T cell immunity is restricted as it has only been investigated in a few mice autoimmune models but not in human diseases. CD318 expression was thought to be limited to mesenchymal-epithelial cells and, therefore, contribute to CD6-mediated T cell activation in the CD318-expressing tissue rather than through interaction with antigen-presenting cells. Here, we report CD318 expression in a subpopulation of CD318+ myeloid dendritic (mDC), whereas the other peripheral blood populations were CD318 negative. However, CD318 can be induced by activation: a subset of monocytes treated with LPS and IFNγ and in vitro monocyte derived DCs were CD318+. We also showed that recombinant CD318 inhibited T cell function. Strikingly, CD318+ DCs suppressed the proliferation of autoreactive T cells specific for GAD65, a well-known targeted self-antigen in Type 1 Diabetes (T1D). Our study provides new insight into the role of the CD318/CD6 axis in the immunopathogenesis of inflammation, suggesting a novel immunoregulatory role of CD318 in T cell-mediated autoimmune diseases and identifying a potential novel immune checkpoint inhibitor as a target for intervention in T1D which is an unmet therapeutic need.

The impact of circulating protein levels identified by affinity proteomics on short-term, overall breast cancer risk

OBJECTIVE

Current breast cancer risk prediction scores and algorithms can potentially be further improved by including molecular markers. To this end, we studied the association of circulating plasma proteins using Proximity Extension Assay (PEA) with incident breast cancer risk.

SUBJECTS

In this study, we included 1577 women participating in the prospective KARMA mammographic screening cohort.

RESULTS

In a targeted panel of 164 proteins, we found 8 candidates nominally significantly associated with short-term breast cancer risk (P < 0.05). Similarly, in an exploratory panel consisting of 2204 proteins, 115 were found nominally significantly associated (P < 0.05). However, none of the identified protein levels remained significant after adjustment for multiple testing. This lack of statistically significant findings was not due to limited power, but attributable to the small effect sizes observed even for nominally significant proteins. Similarly, adding plasma protein levels to established risk factors did not improve breast cancer risk prediction accuracy.

CONCLUSIONS

Our results indicate that the levels of the studied plasma proteins captured by the PEA method are unlikely to offer additional benefits for risk prediction of short-term overall breast cancer risk but could provide interesting insights into the biological basis of breast cancer in the future.

Tasks and Experiences of the Prospective, Longitudinal, Multicenter MoMar (Molecular Markers) Study for the Early Detection of Mesothelioma in Individuals Formerly Exposed to Asbestos Using Liquid Biopsies

Mesothelioma is an aggressive cancer, strongly associated with prior exposure to asbestos. Commonly, tumors are detected at late stages of the disease. Detection at early stages might be meaningful, because therapies might be more effective when the tumor burden is relatively low and the tumor has not spread to distant sites. Circulating biomarkers in blood might be a promising tool to improve the early detection of mesothelioma, but for screening in asymptomatic subjects, candidate biomarkers need to be validated in appropriate studies. This study was conducted to assess the performance of biomarkers in liquid biopsies to detect mesothelioma at early stages. Over a period of 10 years, 2769 volunteers formerly exposed to asbestos were annually examined and liquid biopsies were collected. A follow-up was completed 17 months after the last blood collection. The article provides a detailed overview of our lessons learned and experiences of conducting a prospective, longitudinal, multicenter study. The existing cohort of individuals at risk is highly suitable for the validation of blood-based biomarkers for the early detection of mesothelioma as well as lung cancer.

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools

BACKGROUND

We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test.

METHODS

We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided.

RESULTS

The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model.

CONCLUSION

Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.

Ascites-derived CDCP1+ extracellular vesicles subcluster as a novel biomarker and therapeutic target for ovarian cancer

Introduction

Ovarian cancer (OVCA) is one of the most prevalent malignant tumors of the female reproductive system, and its diagnosis is typically accompanied by the production of ascites. Although liquid biopsy has been widely implemented recently, the diagnosis or prognosis of OVCA based on liquid biopsy remains the primary emphasis.

Methods

In this study, using proximity barcoding assay, a technique for analyzing the surface proteins on single extracellular vesicles (EVs). For validation, serum and ascites samples from patients with epithelial ovarian cancer (EOC) were collected, and their levels of CDCP1 was determined by enzyme-linked immunosorbent assay. Tissue chips were prepared to analyze the relationship between different expression levels of CDCP1 and the prognosis of ovarian cancer patients.

Results

We discovered that the CUB domain-containing protein 1+ (CDCP1+) EVs subcluster was higher in the ascites of OVCA patients compared to benign ascites. At the same time, the level of CDCP1 was considerably elevated in the ascites of OVCA patients. The overall survival and disease-free survival of the group with high CDCP1 expression in EOC were significantly lower than those of the group with low expression. In addition, the receiver operating characteristic curve demonstrates that EVs-derived CDCP1 was a biomarker of early response in OVCA ascites.

Discussion

Our findings identified a CDCP1+ EVs subcluster in the ascites of OVCA patients as a possible biomarker for EOC prevention.

Plasma protein biomarkers for early prediction of lung cancer

BACKGROUND

Individual plasma proteins have been identified as minimally invasive biomarkers for lung cancer diagnosis with potential utility in early detection. Plasma proteomes provide insight into contributing biological factors; we investigated their potential for future lung cancer prediction.

METHODS

The Olink® Explore-3072 platform quantitated 2941 proteins in 496 Liverpool Lung Project plasma samples, including 131 cases taken 1-10 years prior to diagnosis, 237 controls, and 90 subjects at multiple times. 1112 proteins significantly associated with haemolysis were excluded. Feature selection with bootstrapping identified differentially expressed proteins, subsequently modelled for lung cancer prediction and validated in UK Biobank data.

FINDINGS

For samples 1-3 years pre-diagnosis, 240 proteins were significantly different in cases; for 1-5 year samples, 117 of these and 150 further proteins were identified, mapping to significantly different pathways. Four machine learning algorithms gave median AUCs of 0.76-0.90 and 0.73-0.83 for the 1-3 year and 1-5 year proteins respectively. External validation gave AUCs of 0.75 (1-3 year) and 0.69 (1-5 year), with AUC 0.7 up to 12 years prior to diagnosis. The models were independent of age, smoking duration, cancer histology and the presence of COPD.

INTERPRETATION

The plasma proteome provides biomarkers which may be used to identify those at greatest risk of lung cancer. The proteins and the pathways are different when lung cancer is more imminent, indicating that both biomarkers of inherent risk and biomarkers associated with presence of early lung cancer may be identified.

FUNDING

Janssen Pharmaceuticals Research Collaboration Award; Roy Castle Lung Cancer Foundation.

The blood proteome of imminent lung cancer diagnosis

Identification of risk biomarkers may enhance early detection of smoking-related lung cancer. We measured between 392 and 1,162 proteins in blood samples drawn at most three years before diagnosis in 731 smoking-matched case-control sets nested within six prospective cohorts from the US, Europe, Singapore, and Australia. We identify 36 proteins with independently reproducible associations with risk of imminent lung cancer diagnosis (all p < 4 × 10-5). These include a few markers (e.g. CA-125/MUC-16 and CEACAM5/CEA) that have previously been reported in studies using pre-diagnostic blood samples for lung cancer. The 36 proteins include several growth factors (e.g. HGF, IGFBP-1, IGFP-2), tumor necrosis factor-receptors (e.g. TNFRSF6B, TNFRSF13B), and chemokines and cytokines (e.g. CXL17, GDF-15, SCF). The odds ratio per standard deviation range from 1.31 for IGFBP-1 (95% CI: 1.17-1.47) to 2.43 for CEACAM5 (95% CI: 2.04-2.89). We map the 36 proteins to the hallmarks of cancer and find that activation of invasion and metastasis, proliferative signaling, tumor-promoting inflammation, and angiogenesis are most frequently implicated.

Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis

BACKGROUND

To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis.

METHODS

We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only.

FINDINGS

There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10-1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61-0.66), compared with 0.62 (95% CI: 0.59-0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: -0.003 to 0.035).

INTERPRETATION

Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information.

FUNDING

No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry.

Prenatal Exposure to Metabolism-Disrupting Chemicals, Cord Blood Transcriptome Perturbations, and Birth Weight in a Belgian Birth Cohort

Prenatal exposure to metabolism-disrupting chemicals (MDCs) has been linked to birth weight, but the molecular mechanisms remain largely unknown. In this study, we investigated gene expressions and biological pathways underlying the associations between MDCs and birth weight, using microarray transcriptomics, in a Belgian birth cohort. Whole cord blood measurements of dichlorodiphenyldichloroethylene (p,p'-DDE), polychlorinated biphenyls 153 (PCB-153), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and transcriptome profiling were conducted in 192 mother-child pairs. A workflow including a transcriptome-wide association study, pathway enrichment analysis with a meet-in-the-middle approach, and mediation analysis was performed to characterize the biological pathways and intermediate gene expressions of the MDC-birth weight relationship. Among 26,170 transcriptomic features, we successfully annotated five overlapping metabolism-related gene expressions associated with both an MDC and birth weight, comprising BCAT2, IVD, SLC25a16, HAS3, and MBOAT2. We found 11 overlapping pathways, and they are mostly related to genetic information processing. We found no evidence of any significant mediating effect. In conclusion, this exploratory study provides insights into transcriptome perturbations that may be involved in MDC-induced altered birth weight.

Plasma Proteomics Unveil Novel Immune Signatures and Biomarkers upon SARS-CoV-2 Infection

Several elements have an impact on COVID-19, including comorbidities, age and sex. To determine the protein profile changes in peripheral blood caused by a SARS-CoV-2 infection, a proximity extension assay was used to quantify 1387 proteins in plasma samples among 28 Finnish patients with COVID-19 with and without comorbidities and their controls. Key immune signatures, including CD4 and CD28, were changed in patients with comorbidities. Importantly, several unreported elevated proteins in patients with COVID-19, such as RBP2 and BST2, which show anti-microbial activity, along with proteins involved in extracellular matrix remodeling, including MATN2 and COL6A3, were identified. RNF41 was downregulated in patients compared to healthy controls. Our study demonstrates that SARS-CoV-2 infection causes distinct plasma protein changes in the presence of comorbidities despite the interpatient heterogeneity, and several novel potential biomarkers associated with a SARS-CoV-2 infection alone and in the presence of comorbidities were identified. Protein changes linked to the generation of SARS-CoV-2-specific antibodies, long-term effects and potential association with post-COVID-19 condition were revealed. Further study to characterize the identified plasma protein changes from larger cohorts with more diverse ethnicities of patients with COVID-19 combined with functional studies will facilitate the identification of novel diagnostic, prognostic biomarkers and potential therapeutic targets for patients with COVID-19.

Chemical fingerprints and implicated cancer risks of Polycyclic aromatic hydrocarbons (PAHs) from fine particulate matter deposited in human lungs

Exposure to fine particles (PM_2.5) and associated PAHs are frequently linked with lung cancer, which makes the understanding of their occurrence and health risk in human lungs urgently important. Using the ultrasonic treatment and sequencing centrifugation (USC) extraction method coupled with gas chromatography-tandem mass spectrometry (GC - MS/MS) analysis, we revealed the molecular fingerprints of PM-accumulated PAHs in human lungs from a cohort of 68 patients with lung cancer in a typical air-polluted region, China. Sixteen priority PAHs can be grouped by concentrations as ∼ 1 × 10⁴ ng/g (ANT/BkF/ACE/DBA/BgP/PHN/PYR), 2-5 × 10³ ng/g (BaP/FLE/NaP/BbF), and ∼ 1 × 10³ ng/g (IND/Acy/CHR/FLT/BaA). The sum concentration of 16 PAHs was approximately equaled to 13% of those in atmospheric PM_2.5, suggesting significant pulmonary leaching of PAHs deposited in lungs. Low- and high-molecular weight PAHs accounted for ∼ 41.8% and ∼ 45.1% of the total PAHs, respectively, which indicated that atmospheric PM_2.5, tobacco and cooking smoke were likely to be important sources of pulmonary PAHs. The evident increasing concentrations of NaP and FLE in pulmonary PM were significantly correlated with smoking history among smokers. The implicated carcinogenic potency of PM-accumulated PAHs among the participants aged 70-80 was 17 times that among participants aged 40-50 on the basis of BaP equivalent concentration (BaPeq) evaluation. The particulate enrichment factor (EF_P), the PAH content in pulmonary PM relative to the bulk lung tissue, was equaled to 54 ∼ 835 and averaged at 436. The high value of EF_P suggested that PAHs were essentially accumulated in pulmonary PM and exhibited a pattern of "hotspot" distribution in the lungs, which would likely increase the risk of monoclonal tumorigenesis. The chemical characteristics of PM-accumulated PAHs in human lungs together with their implicated lung cancer risks could provide significant information for understanding health effects of particulate pollution in the human body.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

CUB Domain-Containing Protein 1 (CDCP1) is a rational target for the development of imaging tracers and antibody-drug conjugates for cancer detection and therapy

Rationale: An antibody-drug conjugate (ADC) is a targeted therapy consisting of a cytotoxic payload that is linked to an antibody which targets a protein enriched on malignant cells. Multiple ADCs are currently used clinically as anti-cancer agents significantly improving patient survival. Herein, we evaluated the rationale of targeting the cell surface oncoreceptor CUB domain-containing protein 1 (CDCP1) using ADCs and assessed the efficacy of CDCP1-directed ADCs against a range of malignant tumors.

Methods: CDCP1 mRNA expression was evaluated using large transcriptomic datasets of normal/tumor samples for 23 types of cancer and 15 other normal organs, and CDCP1 protein expression was examined in 34 normal tissues, >300 samples from six types of cancer, and in 49 cancer cell lines. A recombinant human/mouse chimeric anti-CDCP1 antibody (ch10D7) was labelled with ⁸⁹Zirconium or monomethyl auristatin E (MMAE) and tested in multiple pre-clinical cancer models including 36 cancer cell lines and three mouse xenograft models.

Results: Analysis of CDCP1 expression indicates elevated CDCP1 expression in the majority of the cancers and restricted expression in normal human tissues. Antibody ch10D7 demonstrates a high affinity and specificity for CDCP1 inducing cell signalling via Src accompanied by rapid internalization of ch10D7/CDCP1 complexes in cancer cells.⁸⁹Zirconium-labelled ch10D7 accumulates in CDCP1 expressing cells enabling detection of pancreatic cancer xenografts in mice by PET imaging. Cytotoxicity of MMAE-labelled ch10D7 against kidney, colorectal, lung, ovarian, pancreatic and prostate cancer cells in vitro, correlates with the level of CDCP1 on the plasma membrane. ch10D7-MMAE displays robust anti-tumor effects against mouse xenograft models of pancreatic, colorectal and ovarian cancer.

Conclusion: CDCP1 directed imaging agents will be useful for selecting cancer patients for personalized treatment with cytotoxin-loaded CDCP1 targeting agents including antibody-drug conjugates.

Considerations of biomarker application for cancer continuum in the era of precision medicine

Purpose of the review

The goal of this review is to highlight emerging biomarker research by the key phases of the cancer continuum and outline the methodological considerations for biomarker application.

Recent findings

While biomarkers have an established role in targeted therapy and to some extent, disease monitoring, their role in early detection and survivorship remains to be elucidated. With the advent of omics technology, the discovery of biomarkers has been accelerated exponentially, therefore careful consideration to ensure an unbiased study design and robust validity is crucial.

Summary

The rigor of biomarker research holds the key to the success of precision health care. The potential clinical utility and the feasibility of implementation should be central to future biomarker research study design.

Overexpression of CDCP1 is Associated with Poor Prognosis and Enhanced Immune Checkpoints Expressions in Breast Cancer

CUB-domain containing protein 1 (CDCP1) is a transmembrane protein acting as an effector of SRC family kinases, which play an oncogenic role in multiple human cancers. However, its clinical and immune correlations in breast cancer (BrCa) have not been explored. To define the expression, prognostic value, and potential molecular role of CDCP1 in BrCa, multiple public datasets, and an in-house cohort were used. Compared with paratumor tissue, CDCP1 was remarkably upregulated in the tumor tissues at both mRNA and protein levels. In the in-house cohort, CDCP1 protein expression was related to several clinicopathological parameters, including age, ER status, PR status, molecular type, and survival status. Kaplan–Meier analysis and Cox regression analysis exhibited that CDCP1 was an important prognostic biomarker in BrCa. In addition, enrichment analysis uncovered that CDCP1 was not only involved in multiple oncogenic pathways, but correlated with overexpression of immune checkpoints. Overall, we reported that increased expression of CDCP1 is a favorable prognostic factor in patients with BrCa. In addition, the correlations between CDCP1 and immune checkpoints provide a novel insight into the adjuvant treatment for immune checkpoint blockade via targeting CDCP1.

AXL/CDCP1/SRC axis confers acquired resistance to osimertinib in lung cancer

Osimertinib, a third-generation EGFR-TKI, has nowadays been applied to non-small cell lung cancer harboring activated EGFR mutation with or without T790M, but ultimately develop resistance to this drug. Here we report a novel mechanism of acquired resistance to osimertinib and the reversal of which could improve the clinical outcomes. In osimertinib-resistant lung cancer cell lines harboring T790M mutation that we established, expression of multiple EGFR family proteins and MET was markedly reduced, whereas expression of AXL, CDCP1 and SRC was augmented along with activation of AKT. Surprisingly, AXL or CDCP1 expression was induced by osimertinib in a time-dependent manner up to 3 months. Silencing of CDCP1 or AXL restored the sensitivity to osimertinib with reduced activation of SRC and AKT. Furthermore, silencing of both CDCP1 and AXL increased the sensitivity to osimertinib. Either silencing of SRC or dasatinib, a SRC family kinase (SFK) inhibitor, suppressed AKT phosphorylation and cell growth. Increased expression of AXL and CDCP1 was observed in refractory tumor samples from patients with lung cancer treated with osimertinib. Together, this study suggests that AXL/SFK/AKT and CDCP1/SFK/AKT signaling pathways play some roles in acquired osimertinib resistance of non-small cell lung cancer.

CDCP1 Expression Is a Potential Biomarker of Poor Prognosis in Resected Stage I Non-Small-Cell Lung Cancer

Background: Although early-stage lung cancer has increased owing to the introduction of screening programs, high recurrence rate remains a critical concern. We aimed to explore biomarkers related to the prognosis of surgically resected non-small-cell lung cancer (NSCLC). Methods: In this retrospective study, we collected medical records of patients with NSCLC and matched tissue microarray blocks from surgical specimens. Semiquantitative immunohistochemistry was performed for measuring the expression level of fibroblast activation protein-alpha (FAP-α), Jagged-1 (JAG1), and CUB-domain-containing protein 1 (CDCP1). Results: A total of 453 patients who underwent complete resection between January 2011 and February 2012 were enrolled; 55.2% patients had stage I NSCLC, and 31.1% presented squamous cell carcinoma. Disease stage was a significant risk factor for recurrence and death, and age ≥ 65 years and male sex were associated with poor overall survival. FAP-a and JaG1 were not related to survivals, while CDCP1-expressing patients exhibited poor disease-free and overall survival. Moreover, CDCP1 expression in stage I NSCLC was significantly associated with recurrence. Conclusions: Old age, male sex, and high pathological stage were poor prognostic factors in patients with NSCLC who underwent surgical resection. Furthermore, CDCP1 expression could serve as a biomarker for poor prognosis in stage I NSCLC.

Contemporary Lung Cancer Screening and the Promise of Blood-Based Biomarkers

In this issue, the study by Dagnino and colleagues represents an important addition to the maturing field of blood-based biomarkers for lung cancer screening. Their comprehensive approach to analyzing circulating inflammatory proteins identified CDCP1 as a potential biomarker for distinguishing patients with or without lung cancer, a finding that was confirmed in a validation cohort. CDCP1 blood levels, when combined with smoking history, gave an AUC receiver operator characteristic of 0.75. Analysis of transcripts in peripheral blood cells suggested a Wnt/β-catenin signaling-based mechanism for CDCP1 in tumorigenesis providing biologic plausibility. CDCP1 now joins the ranks of other potential blood-based lung cancer screening biomarkers (including epithelial tumor marker proteins, tumor-associated miRNA, antitumor antibodies, and tumor-specific DNA methylation) that need validation in future clinical trials. Further exploration of how CDCP1 levels might be integrated into current lung cancer screening programs, including both detection of lung cancer, and evaluation of the need for invasive biopsies, as well as how CDCP1 performs in different racial populations, is warranted.See related article by Dagnino et al., p. 3738.