Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement.

Rationale: Despite significant advances in precision treatments and immunotherapy, lung cancer is the most common cause of cancer death worldwide. To reduce incidence and improve survival rates, a deeper understanding of lung premalignancy and the multistep process of tumorigenesis is essential, allowing timely and effective intervention before cancer development. Objectives: To summarize existing information, identify knowledge gaps, formulate research questions, prioritize potential research topics, and propose strategies for future investigations into the premalignant progression in the lung. Methods: An international multidisciplinary team of basic, translational, and clinical scientists reviewed available data to develop and refine research questions pertaining to the transformation of premalignant lung lesions to advanced lung cancer. Results: This research statement identifies significant gaps in knowledge and proposes potential research questions aimed at expanding our understanding of the mechanisms underlying the progression of premalignant lung lesions to lung cancer in an effort to explore potential innovative modalities to intercept lung cancer at its nascent stages. Conclusions: The identified gaps in knowledge about the biological mechanisms of premalignant progression in the lung, together with ongoing challenges in screening, detection, and early intervention, highlight the critical need to prioritize research in this domain. Such focused investigations are essential to devise effective preventive strategies that may ultimately decrease lung cancer incidence and improve patient outcomes.

Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.

The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Risk stratification of indeterminate pulmonary nodules.

PURPOSE OF REVIEW: Lung cancer remains the leading cause of cancer-related death in the United States, with poor overall 5-year survival. Early detection and diagnosis are key to survival as demonstrated in lung cancer screening trials. However, with increasing implementation of screening guidelines and use of computed tomography, there has been a sharp rise in the incidence of indeterminate pulmonary nodules (IPNs). Risk stratification of IPNs, particularly those in the intermediate-risk category, remains challenging in clinical practice. Individual risk factors, imaging characteristics, biomarkers, and prediction models are currently used to assist in risk stratifying patients, but such strategies remain suboptimal. This review focuses on established risk stratification methods, current areas of research, and future directions. RECENT FINDINGS: The multitude of yearly incidental and screening-detected IPNs, its management-related healthcare costs, and risk of invasive procedures provides a strong rationale for risk stratification efforts. The development of new molecular and imaging biomarkers to discriminate benign from malignant lung nodules shows great promise. Yet, risk stratification methods need integration into the diagnostic workflow and await validation in prospective, biomarker-driven clinical trials. SUMMARY: Novel biomarkers and new imaging analysis, including radiomics and deep-learning methods, have been developed to optimize the risk stratification of IPNs. While promising, additional validation and clinical studies are needed before they can be part of routine clinical practice.

Unlabeled lysophosphatidic acid receptor binding in free solution as determined by a compensated interferometric reader.

Native interactions between lysophospholipids (LPs) and their cognate LP receptors are difficult to measure because of lipophilicity and/or the adhesive properties of lipids, which contribute to high levels of nonspecific binding in cell membrane preparations. Here, we report development of a free-solution assay (FSA) where label-free LPs bind to their cognate G protein-coupled receptors (GPCRs), combined with a recently reported compensated interferometric reader (CIR) to quantify native binding interactions between receptors and ligands. As a test case, the binding parameters between lysophosphatidic acid (LPA) receptor 1 (LPA1; one of six cognate LPA GPCRs) and LPA were determined. FSA-CIR detected specific binding through the simultaneous real-time comparison of bound versus unbound species by measuring the change in the solution dipole moment produced by binding-induced conformational and/or hydration changes. FSA-CIR identified KD values for chemically distinct LPA species binding to human LPA1 and required only a few nanograms of protein: 1-oleoyl (18:1; KD = 2.08 ± 1.32 nM), 1-linoleoyl (18:2; KD = 2.83 ± 1.64 nM), 1-arachidonoyl (20:4; KD = 2.59 ± 0.481 nM), and 1-palmitoyl (16:0; KD = 1.69 ± 0.1 nM) LPA. These KD values compared favorably to those obtained using the previous generation back-scattering interferometry system, a chip-based technique with low-throughput and temperature sensitivity. In conclusion, FSA-CIR offers a new increased-throughput approach to assess quantitatively label-free lipid ligand-receptor binding, including nonactivating antagonist binding, under near-native conditions.

Quantification of Opioids in Urine Using an Aptamer-Based Free-Solution Assay.

The opioid epidemic continues in the United States. Many have been impacted by this epidemic, including neonates who exhibit Neonatal Abstinence Syndrome (NAS). Opioid diagnosis and NAS can be negatively impacted by limited testing options outside the hospital, due to poor assay performance, false-negatives, rapid drug clearance rates, and difficulty in obtaining enough specimen for testing. Here we report a small volume urine assay for oxycodone, hydrocodone, fentanyl, noroxycodone, norhydrocodone, and norfentanyl with excellent LODs and LOQs. The free-solution assay (FSA), coupled with high affinity DNA aptamer probes and a compensated interferometric reader (CIR), represents a potential solution for quantifying opioids rapidly, at high sensitivity, and noninvasively on small sample volumes. The mix-and-read test is 5- to 275-fold and 50- to 1250-fold more sensitive than LC-MS/MS and immunoassays, respectively. Using FSA, oxycodone, hydrocodone, fentanyl, and their urinary metabolites were quantified using 10 µL of urine at 28-81 pg/mL, with >95% specificity and excellent accuracy in ∼1 h. The assay sensitivity, small sample size requirement, and speed could enable opioid screening, particularly for neonates, and points to the potential for pharmacokinetic tracking.

Origin and prediction of free-solution interaction studies performed label-free.

Interaction/reaction assays have led to significant scientific discoveries in the biochemical, medical, and chemical disciplines. Several fundamental driving forces form the basis of intermolecular and intramolecular interactions in chemical and biochemical systems (London dispersion, hydrogen bonding, hydrophobic, and electrostatic), and in the past three decades the sophistication and power of techniques to interrogate these processes has developed at an unprecedented rate. In particular, label-free methods have flourished, such as NMR, mass spectrometry (MS), surface plasmon resonance (SPR), biolayer interferometry (BLI), and backscattering interferometry (BSI), which can facilitate assays without altering the participating components. The shortcoming of most refractive index (RI)-based label-free methods such as BLI and SPR is the requirement to tether one of the interaction entities to a sensor surface. This is not the case for BSI. Here, our hypothesis is that the signal origin for free-solution, label-free determinations can be attributed to conformation and hydration-induced changes in the solution RI. We propose a model for the free-solution response function (FreeSRF) and show that, when quality bound and unbound structural data are available, FreeSRF correlates well with the experiment (R(2)> 0.99, Spearman rank correlation coefficients >0.9) and the model is predictive within ∼15% of the experimental binding signal. It is also demonstrated that a simple mass-weighted dη/dC response function is the incorrect equation to determine that the change in RI is produced by binding or folding event in free solution.

Longitudinal pixel averaging for improved compensation in backscattering interferometry.

Longitudinal averaging of the interference pattern in a compensated backscattering interferometer provides improved compensation for temperature induced refractive index perturbations. Fringe pattern likeness between two discrete detection regions of an off-the-shelf microfluidic chip illuminated by an inexpensive diode laser scales with interrogation length. Averaging the intensity distribution along a 2.75 mm length of the channel results in a 750-fold reduction in sensitivity to temperature and a baseline noise level of 3×10-8 refractive index units (RIU). These observations enable nanoliter-volume interferometric measurements at a level of 10-7 RIU in the presence of a 2°C temperature variation without the need for temperature control.

Taylor Dispersion Analysis of Polysaccharides Using Backscattering Interferometry.

Taylor dispersion analysis (TDA) allows the determination of the molecular diffusion coefficient (D) or the hydrodynamic radius (Rh) of a solute from the peak broadening of a plug of solute in a laminar Poiseuille flow. The main limitation plaguing the broader applicability of TDA is the lack of a sensitive detection modality. UV absorption is typically used with TDA but is only suitable for UV-absorbing or derivatized compounds. In this work, we present a development of the TDA method for non-UV absorbing compounds by using a universal detector based on refractive index (RI) sensing with backscattering interferometry (BSI). BSI was interfaced to a capillary electrophoresis-UV instrument using a polyimide coated fused silica capillary and an in-house designed flow-cell assembly. Polysaccharides were selected to demonstrate the application of TDA-BSI for size characterization. Under the conditions of validity of TDA, D and Rh average values and the entire Rh distributions were obtained from the (poly)saccharide taylorgrams, including non-UV absorbing polymers.

Characterizing aptamer small molecule interactions with backscattering interferometry.

Aptamers are segments of single-strand DNA or RNA used in a wide array of applications, including sensors, therapeutics, and cellular process regulators. Aptamers can bind many target species, including proteins, peptides, and small molecules (SM) with high affinity and specificity. They are advantageous because they can be identified in vitro by SELEX, produced rapidly and relatively economically using oligonucleotide synthesis. The use of aptamers as SM probes has experienced a recent rebirth, and because of their unique properties they represent an attractive alternative to antibodies. Current assay methodology for characterizing small molecule-aptamer binding is limited by either mass sensitivity, as in biolayer interferometry (BLI) and surface plasmon resonance (SPR), or the need for using a fluorophore, as in thermophoresis. Here we report that backscattering interferometry (BSI), a label-free and free-solution sensing technique, can be used to effectively characterize SM-aptamer interactions, providing Kd values on microliter sample quantities and at low nanomolar sensitivity. To demonstrate this capability we measured the aptamer affinity for three previously reported small molecules; bisphenol A, tenofovir, and epirubicin showing BSI provided values consistent with those published previously. We then quantified the Kd values for aptamers to ampicillin, tetracycline and norepinephrine. All measurements produced R(2) values >0.95 and an excellent signal to noise ratio at target concentrations that enable true Kd values to be obtained. No immobilization or labeling chemistry was needed, expediting the assay which is also insensitive to the large relative mass difference between the interacting molecules.

Assessing the transportability of radiomic models for lung cancer diagnosis: commercial vs. open-source feature extractors.

Background: Radiomics has shown promise in improving malignancy risk stratification of indeterminate pulmonary nodules (IPNs) with many platforms available, but with no head-to-head comparisons. This study aimed to evaluate transportability of radiomic models across platforms by comparing performances of a commercial radiomic feature extractor (HealthMyne) with an open-source extractor (PyRadiomics) on diagnosis of lung cancer in IPNs. Methods: A commercial radiomic feature extractor was used to segment IPNs from computed tomography (CT) scans, and a previously validated radiomic model based on commercial features was used as baseline (ComRad). Using same segmentation masks, PyRadiomics, an open-source feature extractor was used to build three open-source radiomic models (OpenRad) using different methods: de novo open-source model derived using least absolute shrinkage and selection operator (LASSO) for feature selection, selecting open-source features matched to ComRad features based upon Imaging Biomarker Standardization Initiative (IBSI) nomenclature, and selecting open-source features most highly correlated to ComRad features. Radiomic models were trained on an internal cohort (n=161) and externally validated on 3 cohorts (n=278). We added Mayo clinical risk score to OpenRad and ComRad models, creating integrated clinical radiomic (ClinRad) models. All models were compared using area under the curve (AUC) and evaluated for clinical improvement using bias-corrected clinical net reclassification indices (cNRIs). Results: ComRad AUC was 0.76 [95% confidence interval (CI): 0.71-0.82], and OpenRad AUC was 0.75 (95% CI: 0.69-0.81) for LASSO model, 0.74 (95% CI: 0.68-0.79) for Spearman's correlation, and 0.71 (95% CI: 0.65-0.77) for IBSI. Mayo scores were added to OpenRad LASSO model, which performed best, forming open-source ClinRad model with AUC of 0.80 (95% CI: 0.74-0.86), identical to commercial ClinRad's AUC. Both ClinRad models showed clinical improvement compared to Mayo alone, with commercial ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.07 (95% CI: 0.00-0.13) for malignant, and open-source ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.06 (95% CI: 0.00-0.12) for malignant. Conclusions: Transportability of radiomic models across platforms directly does not conserve performance, but radiomic platforms can provide equivalent results when building de novo models allowing for flexibility in feature selection to maximize prediction accuracy.

The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort.

BACKGROUND: Pulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical prediction models were developed to inform physician assessment of pretest probability of nodule malignancy but have not been validated in a high-risk cohort of nodules for which biopsy was ultimately performed. RESEARCH QUESTION: Do guideline-recommended prediction models sufficiently discriminate between benign and malignant nodules when applied to cases referred for biopsy by navigational bronchoscopy? STUDY DESIGN AND METHODS: We assembled a prospective cohort of 322 indeterminate pulmonary nodules in 282 patients referred to a tertiary medical center for diagnostic navigational bronchoscopy between 2017 and 2019. We calculated the probability of malignancy for each nodule using the Brock model, Mayo Clinic model, and Veterans Affairs (VA) model. On a subset of 168 patients who also had PET-CT scans before biopsy, we also calculated the probability of malignancy using the Herder model. The performance of the models was evaluated by calculating the area under the receiver operating characteristic curves (AUCs) for each model. RESULTS: The study cohort contained 185 malignant and 137 benign nodules (57% prevalence of malignancy). The malignant and benign cohorts were similar in terms of size, with a median longest diameter for benign and malignant nodules of 15 and 16 mm, respectively. The Brock model, Mayo Clinic model, and VA model showed similar performance in the entire cohort (Brock AUC, 0.70; 95% CI, 0.64-0.76; Mayo Clinic AUC, 0.70; 95% CI, 0.64-0.76; VA AUC, 0.67; 95% CI, 0.62-0.74). For 168 nodules with available PET-CT scans, the Herder model had an AUC of 0.77 (95% CI, 0.68-0.85). INTERPRETATION: Currently available clinical models provide insufficient discrimination between benign and malignant nodules in the common clinical scenario in which a patient is being referred for biopsy, especially when PET-CT scan information is not available.

2-Cyano-1-methyl-pyridinium iodide.

The cation in the title compound, C7H7N2 (+)·I(-), is planar (r.m.s. deviation for the nine fitted non-H atoms = 0.040 Å). The crystal packing is best described as undulating layers of cations and anions associated via C-H⋯I inter-actions.

2-Cyano-anilinium iodide.

The solid-state structure of the title salt, C7H7N2 (+.)I(-), consists of cation-anion sheets lying parallel to (110), with the components linked by N-H⋯I hydrogen bonds.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness.

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent (x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention. Significance: This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

Cancer-associated fibroblasts in early-stage lung adenocarcinoma correlate with tumor aggressiveness.

Lung adenocarcinoma (LUAD) is the predominant type of lung cancer in the U.S. and exhibits a broad variety of behaviors ranging from indolent to aggressive. Identification of the biological determinants of LUAD behavior at early stages can improve existing diagnostic and treatment strategies. Extracellular matrix (ECM) remodeling and cancer-associated fibroblasts play a crucial role in the regulation of cancer aggressiveness and there is a growing need to investigate their role in the determination of LUAD behavior at early stages. We analyzed tissue samples isolated from patients with LUAD at early stages and used imaging-based biomarkers to predict LUAD behavior. Single-cell RNA sequencing and histological assessment showed that aggressive LUADs are characterized by a decreased number of ADH1B+ CAFs in comparison to indolent tumors. ADH1B+ CAF enrichment is associated with distinct ECM and immune cell signatures in early-stage LUADs. Also, we found a positive correlation between the gene expression of ADH1B+ CAF markers in early-stage LUADs and better survival. We performed TCGA dataset analysis to validate our findings. Identified associations can be used for the development of the predictive model of LUAD aggressiveness and novel therapeutic approaches.

Assessing the clinical utility of biomarkers using the intervention probability curve (IPC).

BACKGROUND: Assessing the clinical utility of biomarkers is a critical step before clinical implementation. The reclassification of patients across clinically relevant subgroups is considered one of the best methods to estimate clinical utility. However, there are important limitations with this methodology. We recently proposed the intervention probability curve (IPC) which models the likelihood that a provider will choose an intervention as a continuous function of the probability, or risk, of disease. OBJECTIVE: To assess the potential impact of a new biomarker for lung cancer using the IPC. METHODS: The IPC derived from the National Lung Screening Trial was used to assess the potential clinical utility of a biomarker for suspected lung cancer. The summary statistics of the change in likelihood of intervention over the population can be interpreted as the expected clinical impact of the added biomarker. RESULTS: The IPC analysis of the novel biomarker estimated that 8% of the benign nodules could avoid an invasive procedure while the cancer nodules would largely remain unchanged (0.1%). We showed the benefits of this approach compared to traditional reclassification methods based on thresholds. CONCLUSIONS: The IPC methodology can be a valuable tool for assessing biomarkers prior to clinical implementation.

Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing.

BACKGROUND: Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer. METHODS: Adults ages 40 to 90 years with 6-30 mm IPNs were included from four sites. Serum samples were tested for histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model. RESULTS: We included 327 patients; 157 from histoplasmosis-endemic regions. The combined biomarker model including radiomics, histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79-0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67-0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules. CONCLUSIONS: Fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of histoplasmosis and cancer impact model performance when using disease related biomarkers. IMPACT: Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.