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.

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 (R² > 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. Significance Chemical and biomedical sciences depend heavily on interaction assays, particularly those providing structural insights. Here, we show interferometric, free-solution, label-free studies report conformation and hydration changes, and present a new way for interpreting these methods. Intrinsic property changes are the mechanism allowing for unprecedented sensitivities (picomolar to femtomolar) in complex milieu, even when individual binding partners are undetectable. We establish that the existing theory for label-free assay methods such as surface plasmon resonance (SPR) is not applicable and propose a model for the free-solution response function (FreeSRF), validated and highly predictive when combined with quality structural data and reliable calculations of solvent-addressable surface area. The model allows for interpretation of solution-phase, label-free interactions and could facilitate obtaining structural information from a simple mix-and-read assay.

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 K_d 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 K_d values for aptamers to ampicillin, tetracycline and norepinephrine. All measurements produced R² values >0.95 and an excellent signal to noise ratio at target concentrations that enable true K_d 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.

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.