Early Detection of Lung Cancer with Meso Tetra (4-Carboxyphenyl) Porphyrin-Labeled Sputum

Emerging Strategies in Lung Cancer Screening: Blood and Beyond

BACKGROUND

Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection.

CONTENT

This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies.

SUMMARY

Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

BACKGROUND

Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.

METHODS

Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer.

RESULTS

Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89-0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71-0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.

CONCLUSION

CyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018.

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment

Low dose computed tomography (LDCT) is the standard of care for lung cancer screening in the United States (US). LDCT has a sensitivity of 93.8% but its specificity of 73.4% leads to potentially harmful follow-up procedures in patients without lung cancer. Thus, there is a need for additional assays with high accuracy that can be used as an adjunct to LDCT to diagnose lung cancer. Sputum is a biological fluid that can be obtained non-invasively and can be dissociated to release its cellular contents, providing a snapshot of the lung environment. We obtained sputum from current and former smokers with a 30+ pack-year smoking history and who were either confirmed to have lung cancer or at high risk of developing the disease. Dissociated sputum cells were counted, viability determined, and labeled with a panel of markers to separate leukocytes from non-leukocytes. After excluding debris and dead cells, including squamous epithelial cells, we identified reproducible population signatures and confirmed the samples’ lung origin. In addition to leukocyte and epithelial-specific fluorescent antibodies, we used the highly fluorescent meso-tetra(4-carboxyphenyl) porphyrin (TCPP), known to preferentially stain cancer (associated) cells. We looked for differences in cell characteristics, population size and fluorescence intensity that could be useful in distinguishing cancer samples from high-risk samples. We present our data demonstrating the feasibility of a flow cytometry platform to analyze sputum in a high-throughput and standardized matter for the diagnosis of lung cancer.

Selective delivery of clinically approved tubulin binding agents through covalent conjugation to an active targeting moiety

Abstract:

The efficacy and tolerability of tubulin binding agents are hampered by their low specificity for cancer cells, like most clinically used anticancer agents. To improve specificity, tubulin binding agents have been covalently conjugated to agents which target cancer cells to give actively targeted drug conjugates. These conjugates are designed to increase uptake of the drug by cancer cells, while having limited uptake by normal cells thereby improving efficacy and tolerability. Approaches used include attachment to small molecules, polysaccharides, peptides, proteins and antibodies that exploit the overexpression of receptors for these substances. Antibody targeted strategies have been the most successful to date with six such examples having gained clinical approval. Many other conjugate types, especially those targeting the folate receptor, have shown promising efficacy and toxicity profiles in pre-clinical models and in early-stage clinical studies. Presented herein is a discussion of the success or otherwise of the recent strategies used to form these actively targeted conjugates.

Zr-MOFs Integrated with a Guest Capturer and a Photosensitizer for the Simultaneous Adsorption and Degradation of 4-Chlorophenol

A bifunctional metal-organic framework (MOF) was successfully designed to realize the purification of 4-chlorophenol (4-CP) under simulated sunlight irradiation. Owing to the large-size mesopores of the MOF matrix NU-1000, β-CMCD (carboxylic β-cyclodextrin) could be incorporated into the frameworks with a density of 2.4% to pre-enrich the pollutant of 4-CP. Meanwhile, the photodegradation promoter [Pd(II) meso-tetra(4-carboxyphenyl)porphine] was in situ co-assembled with the organic ligand to realize its synchronous degradation. As for the current integrator, a Langmuir model was used to explain the adsorption isotherm, and the Langmuir-Hinshelwood model exhibited a better fit to its catalytic degradation behavior. Thanks to the simultaneous presence of a capturer and a photodegradation promoter, the adsorption capacity of 4-CP reached as high as 296 mg g^-1, which was further completely detoxified within 60 min under simulated sunlight irradiation with a half-life time of only 5.98 min. Such excellent integrated decontamination properties prefigure the great promising potential of multifunctional MOFs in the field of pollution purification.

Identification of a novel mechanism for meso-tetra (4-carboxyphenyl) porphyrin (TCPP) uptake in cancer cells

Porphyrins are used for cancer diagnostic and therapeutic applications, but the mechanism of how porphyrins accumulate in cancer cells remains elusive. Knowledge of how porphyrins enter cancer cells can aid the development of more accurate cancer diagnostics and therapeutics. To gain insight into porphyrin uptake mechanisms in cancer cells, we developed a flow cytometry assay to quantify cellular uptake of meso-tetra (4-carboxyphenyl) porphyrin (TCPP), a porphyrin that is currently being developed for cancer diagnostics. We found that TCPP enters cancer cells through clathrin-mediated endocytosis. The LDL receptor, previously implicated in the cellular uptake of other porphyrins, only contributes modestly to uptake. We report that TCPP instead binds strongly ( K D = 42 nM ) to CD320, the cellular receptor for cobalamin/transcobalamin II (Cbl/TCN2). Additionally, TCPP competes with Cbl/TCN2 for CD320 binding, suggesting that CD320 is a novel receptor for TCPP. Knockdown of CD320 inhibits TCPP uptake by up to 40% in multiple cancer cell lines, including lung, breast, and prostate cell lines, which supports our hypothesis that CD320 both binds to and transports TCPP into cancer cells. Our findings provide some novel insights into why porphyrins concentrate in cancer cells. Additionally, our study describes a novel function for the CD320 receptor which has been reported to transport only Cbl/TCN2 complexes.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Cyclodextrin as a magic switch in covalent and non-covalent anticancer drug release systems

Cancer has been a threat to human health, so its treatment is a huge challenge to the present medical field. One of commonly used methods is the controlled release of anticancer drug to reduce the dose for patients, increase the stability of drug treatment and minimize side effects. Cyclodextrin is a kind of cyclic oligosaccharide produced by amylase hydrolysis. Because cyclodextrin contains a cavity structure and active hydroxyl groups, it has a positive effect on the study of the controlled release of anticancer drugs. This article reviews the controlled release of current anticancer drugs based on cyclodextrins as a "flexible switch", and discusses the classification of different types of release systems, highlighting their role in cancer treatment. Moreover, the opportunities and challenges of cyclodextrin as a magic switch in the controlled release of anticancer drugs are discussed.

Porphin-Based Carbon Dots for "Turn Off-On" Phosphate Sensing and Cell Imaging

Porphin-based carbon dots (denoted as PCDs) are prepared through a one-step hydrothermal method by using meso-tetra (4-carboxyphenyl) porphin (TCPP), citric acid, and ethanediamine as precursor. PCDs give rise to the optimal photoluminescence at λex/λem = 375/645 nm, exhibit an excitation-independent property, excellent water solubility, and good biocompatibility, which provide red emission and avoid the autofluorescence as an efficient fluorescent imaging probe. On the other hand, when Eu3+ is added into PCDs, the carboxylate groups located on the surface of PCDs exhibit high affinity to Eu3+, resulting in the fluorescence of PCDs turning off via static quenching. In the presence of phosphate, owing to the strong coordination with Eu3+, the fluorescence of PCDs turns on. Based on this performance, a novel "turn off-on" phosphate sensing system is developed. The detection limit of this sensing system can attain 3.59 × 10-3 μmol L-1. This system has been utilized for the detection of phosphate in real samples successfully, which further demonstrates potential applications in biological diagnostic and environmental analysis.

Biomarkers in Lung Cancer

Biomarkers that focus on lung cancer risk assessment, detection, prognosis, diagnosis, and personalized treatment are in various stages of development. This article provides an overview of lung cancer biomarker development, focusing on clinical utility and highlighting 2 unmet clinical needs: selection of high-risk patients for lung cancer screening and differentiation of early lung cancer from benign pulmonary nodules. The authors highlight biomarkers under development and those lung cancer screening and nodule management biomarkers post-clinical validation. Finally, trends in lung cancer biomarker development that may improve accuracy and accelerate implementation in practice are discussed.

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.