Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data

Is There a Link between Chronic Obstructive Pulmonary Disease and Lung Adenocarcinoma? A Clinico-Pathological and Molecular Study

Chronic Obstructive Pulmonary Disease (COPD) and lung cancer are strictly related. To date, it is unknown if COPD-associated cancers are different from the tumors of non-COPD patients. The main goal of the study was to compare the morphological/molecular profiles of lung adenocarcinoma (LUAD) samples of COPD, non-COPD/smokers and non-COPD/non-smokers, and to investigate if a genetic instability also characterized non-pathological areas. This study included 110 patients undergoing surgery for a LUAD, divided into three groups: COPD/smoker LUAD (38), non-COPD/smoker LUAD (54) and non-COPD/non-smoker LUAD (18). The tissue samples were systemically evaluated by pathologists and analyzed using a 30-gene Next Generation Sequencing (NGS) panel. In a subset of patients, tissues taken far from the neoplasia were also included. The non-COPD/smoker LUAD were characterized by a higher proliferative index (p = 0.001), while the non-COPD/non-smoker LUAD showed higher percentages of lepidic pattern (p = 0.008), lower necrosis, higher fibrosis, and a significantly lower mutation rate in the KRAS and PIK3CA genes. Interestingly, the same gene mutations were found in pathological and normal areas exclusively in the COPD/smokers and non-COPD/smokers. COPD/smoker LUAD seem to be similar to non-COPD/smoker LUAD, particularly for the genetic background. A less aggressive cancer phenotype was confirmed in non-COPD/non-smokers. The genetic alterations detected in normal lungs from smokers with and without COPD reinforce the importance of screening to detect early neoplastic lesions.

Genomic Profiling With Large-Scale Next-Generation Sequencing Panels Distinguishes Separate Primary Lung Adenocarcinomas From Intrapulmonary Metastases

The distinction between different separate primary lung cancers (SPLCs) and intrapulmonary metastases (IPMs) is a challenging but clinically significant issue. Histopathology-based classification is the current practice; however, it is subjective and affected by interobserver variability. Recently, next-generation sequencing (NGS) panels have been used in lung cancer diagnostics. This study aimed to investigate the value of large-scale NGS panels for distinguishing between SPLCs and IPMs. A total of 32 patients with 69 lung adenocarcinomas were included. Comprehensive histopathologic assessments of multiple pulmonary adenocarcinomas were performed independently by 3 pathologists. The consensus of histopathologic classification was determined by a majority vote. Genomic analysis was performed using an amplicon-based large-scale NGS panel, targeting single-nucleotide variants and short insertions and deletions in 409 genes. Tumor pairs were classified as SPLCs or IPMs according to a predefined molecular classification algorithm. Using NGS and our molecular classification algorithm, 97.6% of the tumor pairs can be unambiguously classified as SPLCs or IPMs. The molecular classification was predictive of postoperative clinical outcomes in terms of overall survival (P = .015) and recurrence-free interval (P = .0012). There was a moderate interobserver agreement regarding histopathologic classification (κ = 0.524 at the tumor pair level). The concordance between histopathologic and molecular classification was 100% in cases where pathologists reached a complete agreement but only 53.3% where they did not. This study showed that large-scale NGS panels are a powerful modality that can help distinguish SPLCs from IPMs in patients with multiple lung adenocarcinomas and objectively provide accurate risk stratification.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.