Tumoral Densities of T-Cells and Mast Cells Are Associated With Recurrence in Early-Stage Lung Adenocarcinoma.

Introduction: Lung cancer is the deadliest cancer in the United States and worldwide, and lung adenocarcinoma (LUAD) is the most prevalent histologic subtype in the United States. LUAD exhibits a wide range of aggressiveness and risk of recurrence, but the biological underpinnings of this behavior are poorly understood. Past studies have focused on the biological characteristics of the tumor itself, but the ability of the immune response to contain tumor growth represents an alternative or complementary hypothesis. Emerging technologies enable us to investigate the spatial distribution of specific cell types within the tumor nest and characterize this immune response. This study aimed to investigate the association between immune cell density within the primary tumor and recurrence-free survival (RFS) in stage I and II LUAD. Methods: This study is a prospective collection with retrospective evaluation. A total of 100 patients with surgically resected LUAD and at least 5-year follow-ups, including 69 stage I and 31 stages II tumors, were enrolled. Multiplexed immunohistochemistry panels for immune markers were used for measurement. Results: Cox regression models adjusted for sex and EGFR mutation status revealed that the risk of recurrence was reduced by 50% for the unit of one interquartile range (IQR) change in the tumoral T-cell (adjusted hazard ratio per IQR increase = 0.50, 95% confidence interval: 0.27-0.93) and decreased by 64% in mast cell density (adjusted hazard ratio per IQR increase = 0.36, confidence interval: 0.15-0.84). The analyses were reported without the type I error correction for the multiple types of immune cell testing. Conclusions: Analysis of the density of immune cells within the tumor and surrounding stroma reveals an association between the density of T-cells and RFS and between mast cells and RFS in early-stage LUAD. This preliminary result is a limited study with a small sample size and a lack of an independent validation set.

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.

Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules.

Purpose: We propose a systematic methodology to quantify incidentally identified pulmonary nodules based on observed radiological traits (semantics) quantified on a point scale and a machine-learning method using these data to predict cancer status.Experimental Design: We investigated 172 patients who had low-dose CT images, with 102 and 70 patients grouped into training and validation cohorts, respectively. On the images, 24 radiological traits were systematically scored and a linear classifier was built to relate the traits to malignant status. The model was formed both with and without size descriptors to remove bias due to nodule size. The multivariate pairs formed on the training set were tested on an independent validation data set to evaluate their performance.Results: The best 4-feature set that included a size measurement (set 1), was short axis, contour, concavity, and texture, which had an area under the receiver operator characteristic curve (AUROC) of 0.88 (accuracy = 81%, sensitivity = 76.2%, specificity = 91.7%). If size measures were excluded, the four best features (set 2) were location, fissure attachment, lobulation, and spiculation, which had an AUROC of 0.83 (accuracy = 73.2%, sensitivity = 73.8%, specificity = 81.7%) in predicting malignancy in primary nodules. The validation test AUROC was 0.8 (accuracy = 74.3%, sensitivity = 66.7%, specificity = 75.6%) and 0.74 (accuracy = 71.4%, sensitivity = 61.9%, specificity = 75.5%) for sets 1 and 2, respectively.Conclusions: Radiological image traits are useful in predicting malignancy in lung nodules. These semantic traits can be used in combination with size-based measures to enhance prediction accuracy and reduce false-positives. Clin Cancer Res; 23(6); 1442-9. ©2016 AACR.

The Pursuit of Noninvasive Diagnosis of Lung Cancer.

The noninvasive diagnosis of lung cancer remains a formidable challenge. Although tissue diagnosis will remain the gold standard for the foreseeable future, questions pertaining to the risks and costs associated with invasive diagnostic procedures are of prime relevance. This review addresses new modalities for improving the noninvasive evaluation of suspicious lung nodules. Ultimately, the goal is to translate early diagnosis into early treatment. We discuss how biomarkers could assist in distinguishing benign from malignant nodules and aggressive from indolent tumors. The field of biomarkers is rapidly expanding and progressing, and efforts are well underway to apply molecular diagnostics to address the shortcomings of current lung cancer diagnostic tools.

Biomarkers of risk to develop lung cancer in the new screening era.

Low-dose computed tomography for high-risk individuals has for the first time demonstrated unequivocally that early detection save lives. The currently accepted screening strategy comes at the cost of a high rate of false positive findings while still missing a large percentage of the cases. Therefore, there is increasing interest in developing strategies to better estimate the risk of an individual to develop lung cancer, to increase the sensitivity of the screening process, to reduce screening costs and to reduce the numbers of individuals harmed by screening and follow-up interventions. New molecular biomarkers candidates show promise to improve lung cancer outcomes. This review discusses the current state of biomarker research in lung cancer screening with the primary focus on risk assessment.