Indolent, Potentially Inconsequential Lung Cancers in the Pittsburgh Lung Screening Study

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.

The Contrasting Seasonality Patterns of Some Cancer-Types and Herpes Zoster Can Be Explained by a Binary Classification of Immunological Reactions

A binary classification of the pathogenic immune reactions as anti-inflammatory high-Treg reactions or pro-inflammatory low-Treg reactions explains both the relatively low incidence rate of several types of cancer, and the relatively high incidence rate of herpes zoster cases diagnosed in the summer compared to cases diagnosed in the winter (in regions with temperate climate). This binary model also elucidates the longer survival of cancer patients diagnosed during the summer compared to these diagnosed in the winter. The three key elements of this explanation are: (a) the effect of sunlight on Treg production; (b) the evolvement of cancer from a low-Treg condition at early stage, to a high-Treg condition at advanced stage, and (c) the evolvement of herpes zoster from a high-Treg condition at pre-exudative stage to a low-Treg condition at acute exudative stage. A significant proportion of indolent tumors at the time of diagnosis (>20%) is a prerequisite for a beneficial effect of sunlight on cancer incidence rate and prognosis. This prerequisite restricts the beneficial effect of diagnosis during summer to certain types of cancer. Clinical implication: the prognosis of early stage tumors may be improved by a course of corticosteroid (or other immunosuppressant) treatment.

Diagnostic value of platelet-to-lymphocyte ratio in patients with solitary pulmonary nodules

Introduction

Nodules detected in the lung parenchyma should be considered as malignant until proven otherwise, and the necessary tests should be performed for diagnosis.

Aim

To calculate the preoperative platelet-to-lymphocyte ratio (PLR) in patients with malignant lung nodules and to investigate the diagnostic value of this ratio in determining the histopathology of the nodule.

Material and methods

Ninety-one patients who were operated on for a malignant nodule in the lung between September 2010 and September 2020 were included in the study. The PLR was calculated by dividing the absolute platelet count by the absolute lymphocyte count. These values were compared with the histopathological diagnoses of the resected tumor tissue. Patients with primary lung malignancy were classified as group 1 (n = 54), and lung metastases of other organs were classified as group 2 (n = 37).

Results

The mean PLR was 127.27 ±46.82 in the first group and 183.56 ±93.49 in the second group. There was a statistically significant difference in PLR values between the two groups, and PLR was higher in group 2. There was no statistically significant difference between the two groups in terms of lymph node positivity, nodule size and SuvMax values. A moderately strong, significant and same-sided correlation was observed between nodule size and SuvMax values in the first group of patients (r = 0.48, p = 0.001)

Conclusions

PLR values less than 89.41 indicate that the histopathological result may be a lung-derived malignancy. However, in cases where the PLR is detected above 165.6, it would be appropriate to interpret another previously detected malignancy as metastasis to the lung.

Circulating Exosome Cargoes Contain Functionally Diverse Cancer Biomarkers: From Biogenesis and Function to Purification and Potential Translational Utility

Although diagnostic and therapeutic treatments of cancer have tremendously improved over the past two decades, the indolent nature of its symptoms has made early detection challenging. Thus, inter-disciplinary (genomic, transcriptomic, proteomic, and lipidomic) research efforts have been focused on the non-invasive identification of unique "silver bullet" cancer biomarkers for the design of ultra-sensitive molecular diagnostic assays. Circulating tumor biomarkers, such as CTCs and ctDNAs, which are released by tumors in the circulation, have already demonstrated their clinical utility for the non-invasive detection of certain solid tumors. Considering that exosomes are actively produced by all cells, including tumor cells, and can be found in the circulation, they have been extensively assessed for their potential as a source of circulating cell-specific biomarkers. Exosomes are particularly appealing because they represent a stable and encapsulated reservoir of active biological compounds that may be useful for the non-invasive detection of cancer. T biogenesis of these extracellular vesicles is profoundly altered during carcinogenesis, but because they harbor unique or uniquely combined surface proteins, cancer biomarker studies have been focused on their purification from biofluids, for the analysis of their RNA, DNA, protein, and lipid cargoes. In this review, we evaluate the biogenesis of normal and cancer exosomes, provide extensive information on the state of the art, the current purification methods, and the technologies employed for genomic, transcriptomic, proteomic, and lipidomic evaluation of their cargoes. Our thorough examination of the literature highlights the current limitations and promising future of exosomes as a liquid biopsy for the identification of circulating tumor biomarkers.

Liquid Biopsy for Biomarker Testing in Non-Small Cell Lung Cancer: A European Perspective

The development of targeted therapies has improved survival rates for patients with advanced non-small cell lung cancer (NSCLC). However, tissue biopsy is unfeasible or inadequate in many patients, limiting biomarker testing and access to targeted therapies. The increasing numbers of established and emerging biomarkers with available targeted treatments highlights the challenges associated with sequential single-gene testing and limited tissue availability. Multiplex next-generation sequencing (NGS) offers an attractive alternative and represents a logical next step, and in cases where the tumour is inaccessible, tissue biopsy yields insufficient tumour content, or when the patient’s performance status does not allow a tissue biopsy, liquid biopsy can provide valuable material for molecular diagnosis. Here, we explore the role of liquid biopsy (i.e., circulating cell-free DNA analysis) in Europe. Liquid biopsies could be used as a complementary approach to increase rates of molecular diagnosis, with the ultimate aim of improving patient access to appropriate targeted therapies. Expert opinion is also provided on potential future applications of liquid biopsy in NSCLC, including for cancer prevention, detection of early stage and minimum residual disease, monitoring of response to therapy, selection of patients for immunotherapy, and monitoring of tumour evolution to enable optimal adaptation/combination of drug therapies.

Are Incidental Minute Pulmonary Nodules Ultimately Determined to Be Metastatic Nodules in Esophageal Cancer Patients?

PURPOSE

Esophageal cancer patients may simultaneously have resectable esophageal cancer and undiagnosable incidental minute solid pulmonary nodules. While the latter is rarely metastatic, only a few studies have reported on the outcomes of such nodules after surgery. In this retrospective study, we assessed the incidence of such nodules, the probability that they are ultimately metastatic nodules, and the prognosis of patients after esophagectomy according to the metastatic status of the nodules.

METHODS

Data of 398 patients who underwent esophagectomy for resectable esophageal cancer between January 2012 and December 2016 were collected. We reviewed computed tomography (CT) images from the first visit and searched for incidental minute pulmonary nodules <10 mm in size. We followed the outcomes of these nodules and compared the characteristics of metastatic and nonmetastatic nodules. We also assessed the prognosis of patients whose minute pulmonary nodules were metastatic.

RESULTS

Among the patients who underwent esophagectomy, 149 (37.4%) had one or more minute pulmonary nodules, with a total of 285 nodules. Thirteen (4.6%) of these nodules in 12 (8.1%) patients were ultimately diagnosed as being metastatic. Thirteen (8.7%) patients experienced recurrence at a different location from where the nodules were originally identified. Characteristics of the metastatic nodules were not unique in terms of size, SUVmax, or location in the lungs. Two-year and 5-year overall survival rates of patients whose nodules were metastatic were 64.2 and 32.1%, respectively.

CONCLUSION

The rate of minute pulmonary nodules which were ultimately metastatic was 4.6%. Our findings suggest that esophagectomy followed by the identification of minute pulmonary nodules is an acceptable strategy even if the nodules cannot be diagnosed as being metastatic on the first visit CT due to their small size.

Genomic Underpinnings of Tumor Behavior in In Situ and Early Lung Adenocarcinoma

Rationale: We have a limited understanding of the molecular underpinnings of early adenocarcinoma (ADC) progression. We hypothesized that the behavior of early ADC can be predicted based on genomic determinants.Objectives: To identify genomic alterations associated with resected indolent and aggressive early lung ADCs.Methods: DNA was extracted from 21 ADCs in situ (AISs), 27 minimally invasive ADCs (MIAs), and 54 fully invasive ADCs. This DNA was subjected to deep next-generation sequencing and tested against a custom panel of 347 cancer genes.Measurements and Main Results: Sequencing data was analyzed for associations among tumor mutation burden, frequency of mutations or copy number alterations, mutation signatures, intratumor heterogeneity, pathway alterations, histology, and overall survival. We found that deleterious mutation burden was significantly greater in invasive ADC, whereas more copy number loss was observed in AIS and MIA. Intratumor heterogeneity establishes early, as in AIS. Twenty-one significantly mutated genes were shared among the groups. Mutation signature profiling did not vary significantly, although the APOBEC signature was associated with ADC and poor survival. Subclonal KRAS mutations and a gene signature consisting of PIK3CG, ATM, EPPK1, EP300, or KMT2C mutations were also associated with poor survival. Mutations of KRAS, TP53, and NF1 were found to increase in frequency from AIS and MIA to ADC. A cancer progression model revealed selective early and late drivers.Conclusions: Our results reveal several genetic driver events, clonality, and mutational signatures associated with poor outcome in early lung ADC, with potential future implications for the detection and management of ADC.

Lung Cancer Screening in Patients with COPD-A Case Report

We present two cases demonstrating the nuances that must be considered when determining if a patient could benefit from low dose computed tomography (LDCT) lung cancer screening. Our case report discusses the available literature, where it exists, on lung cancer screening with special attention to the impact of chronic obstructive pulmonary disease (COPD), and poor functional status. Patients with COPD and concurrent smoking history are at higher risk of lung cancer and may therefore benefit from lung cancer screening. However, this population is at increased risk for complications related to biopsies and lobar resections. Appropriate interventions other than surgical resection exist for COPD patients with poor pulmonary reserve. Risks and benefits of lung cancer screening are unique to each patient and require shared decision-making.

Multi-window CT based Radiomic signatures in differentiating indolent versus aggressive lung cancers in the National Lung Screening Trial: a retrospective study

Background

We retrospectively evaluated the capability of radiomic features to predict tumor growth in lung cancer screening and compared the performance of multi-window radiomic features and single window radiomic features.

Methods

One hundred fifty lung nodules among 114 screen-detected, incident lung cancer patients from the National Lung Screening Trial (NLST) were investigated. Volume double time (VDT) was calculated as the difference between continuous two scans and used to define indolent and aggressive lung cancers. Lung nodules were semi-automatically segmented using lung and mediastinal windows separately, and subtracting the mediastinal window region from the lung window region generated the difference region. 364 radiomic features were separately exacted from nodules using the lung window, the mediastinal window and the difference region. Multivariable models were conducted to identify the most predictive features in predicting tumor growth. Clinical information was also obtained from the database.

Results

Based on our definition, 26% of the cases were indolent lung cancer. The tumor growth pattern could be predicted by radiomic models constructed using features obtained in the lung window, the difference region, and by combining features obtained in both the lung window and difference regions with areas under the receiver operator characteristic (AUROCs) of 0.799, 0.819, and 0.846, respectively. The multi-window feature model showed better performance compared to single window features (P < 0.001). Incorporating clinical factors into the multi-window feature models showed improvement, yielding an accuracy of 84.67% and AUROC of 0.855 for distinguishing indolent from aggressive disease.

Conclusions

Multi-window CT based radiomics features are valuable predictors of indolent lung cancers and out performed single CT window setting. Combining clinical information improved predicting performance.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0232-6) contains supplementary material, which is available to authorized users.

Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models

Introduction

Low-dose CT (LDCT) is currently used in lung cancer screening of high-risk populations for early lung cancer diagnosis. However, 96% of individuals with detected nodules are false positives.

Methods

In order to develop an efficient early lung cancer predictor from clinical, demographic and LDCT features, we studied a total of 218 subjects with lung cancer or benign nodules. Probabilistic graphical models (PGMs) were used to integrate demographics, clinical data and LDCT features from 92 subjects (training cohort) from the Pittsburgh Lung Screening Study cohort.

Results

Learnt PGMs identified three variables directly (causally) linked to malignant nodules and the largest benign nodule and used them to build the Lung Cancer Causal Model (LCCM), which was validated in a separate cohort of 126 subjects. Nodule and vessel numbers and years since the subject quit smoking were sufficient to discriminate malignant from benign nodules. Comparison with existing predictors in the training and validation cohorts showed that (1) incorporating LDCT scan features greatly enhances predictive accuracy; and (2) LCCM improves cancer detection over existing methods, including the Brock parsimonious model (p<0.001). Notably, the number of surrounding vessels, a feature not previously used in predictive models, significantly improves predictive efficiency. Based on the validation cohort results, LCCM is able to identify 30% of the benign nodules without risk of misclassifying cancer nodules.

Discussion

LCCM shows promise as a lung cancer predictor as it is significantly improved over existing models. Validated in a larger, prospective study, it may help reduce unnecessary follow-up visits and procedures.

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

Computer Aided Nodule Analysis and Risk Yield (CANARY) characterization of adenocarcinoma: radiologic biopsy, risk stratification and future directions

The majority of incidentally and screen-detected lung cancers are adenocarcinomas. Optimal management of these tumors is clinically challenging due to variability in tumor histopathology and behavior. Invasive adenocarcinoma (IA) is generally aggressive while adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) may be extremely indolent. Computer Aided Nodule Analysis and Risk Yield (CANARY) is a quantitative computed tomography (CT) analysis tool that allows non-invasive assessment of tumor characteristics. This analysis may obviate the need for tissue biopsy and facilitate the risk stratification of adenocarcinoma of the lung. CANARY was developed by unsupervised machine learning techniques using CT data of histopathologically-characterized adenocarcinomas of the lung. This technique identified 9 distinct exemplars that constitute the spectrum of CT features found in adenocarcinoma of the lung. The distributions of these features in a nodule correlate with histopathology. Further automated clustering of CANARY nodules defined three distinct groups that have distinctly different post-resection disease free survival (DFS). CANARY has been validated within the NLST cohort and multiple other cohorts. Using semi-automated segmentation as input to CANARY, there is excellent repeatability and interoperator correlation of results. Confirmation and longitudinal tracking of indolent adenocarcinoma with CANARY may ultimately add decision support in nuanced cases where surgery may not be in the best interest of the patient due to competing comorbidity. Currently under investigation is CANARY's role in detecting differing driver mutations and tumor response to targeted chemotherapeutics. Combining the results from CANARY analysis with clinical information and other quantitative techniques such as analysis of the tumor-free surrounding lung may aid in building more powerful predictive models. The next step in CANARY investigation will be its prospective application, both in selecting low-risk stage 1 adenocarcinoma for active surveillance and investigation in selecting high-risk early stage adenocarcinoma for adjuvant therapy.

Vasculature surrounding a nodule: A novel lung cancer biomarker

PURPOSE

To investigate whether the vessels surrounding a nodule depicted on non-contrast, low-dose computed tomography (LDCT) can discriminate benign and malignant screen detected nodules.

MATERIALS AND METHODS

We collected a dataset consisting of LDCT scans acquired on 100 subjects from the Pittsburgh Lung Screening study (PLuSS). Fifty subjects were diagnosed with lung cancer and 50 subjects had suspicious nodules later proven benign. For the lung cancer cases, the location of the malignant nodule in the LDCT scans was known; while for the benign cases, the largest nodule in the LDCT scan was used in the analysis. A computer algorithm was developed to identify surrounding vessels and quantify the number and volume of vessels that were connected or near the nodule. A nonparametric receiver operating characteristic (ROC) analysis was performed based on a single nodule per subject to assess the discriminability of the surrounding vessels to provide a lung cancer diagnosis. Odds ratio (OR) were computed to determine the probability of a nodule being lung cancer based on the vessel features.

RESULTS

The areas under the ROC curves (AUCs) for vessel count and vessel volume were 0.722 (95% CI=0.616-0.811, p<0.01) and 0.676 (95% CI=0.565-0.772), respectively. The number of vessels attached to a nodule was significantly higher in the lung cancer group 9.7 (±9.6) compared to the non-lung cancer group 4.0 (±4.3) CONCLUSION: Our preliminary results showed that malignant nodules are often surrounded by more vessels compared to benign nodules, suggesting that the surrounding vessel characteristics could serve as lung cancer biomarker for indeterminate nodules detected during LDCT lung cancer screening using only the information collected during the initial visit.