The evolving role of the pathologist in the management of lung cancer

Targeted therapies in non-small cell lung cancer and the potential role of AI interventions in cancer treatment

Non-small cell lung cancer is the most prevalent lung cancer, and almost three-fourths of patients are diagnosed in the advanced stage directly. In this stage, chemotherapy gives only a 15% 5-year survival rate. As people have varied symptoms and reactions to a specific cancer type, treatment for the tumor is likely to fall short, complicating cancer therapy. Immunotherapy is a breakthrough treatment involving drugs targeting novel immune checkpoint inhibitors like CTLA-4 and PD-1/PD-L1, along with combination therapies. In addition, the utility of engineered CAR-T and CAR-NK cells can be an effective strategy to promote the immune response against tumors. The concept of personalized cancer vaccines with the discovery of neoantigens loaded on dendritic cell vectors can also be an effective approach to cure cancer. Advances in genetic engineering tools like CRISPR/Cas9-mediated gene editing of T cells to enhance their effector function is another ray of hope. This review aims to provide an overview of recent developments in cancer immunotherapy, which can be used in first- and second-line treatments in the clinical space. Further, the intervention of artificial intelligence to detect cancer tumors at an initial stage with the help of machine learning techniques is also explored.

Quality indicators and excellence requirements for a multidisciplinary lung cancer tumor board by the Spanish Lung Cancer Group

Multidisciplinary care is needed to decide the best therapeutic approach and to provide optimal care to patients with lung cancer (LC). Multidisciplinary teams (MDTs) are optimal strategies for the management of patients with LC and have been associated with better outcomes, such as an increase in quality of life and survival. The Spanish Lung Cancer Group has promoted this review about the current situation of the existing national LC-MDTs, which also offers a set of excellence requirements and quality indicators to achieve the best care in any patient with LC. Time and sufficient resources; leadership; administrative and institutional support; and recording of activity are key factors for the success of LC-MDTs. A set of excellence requirements in terms of staff, resources and organization of the LC-MDT have been proposed. At last, a list of quality indicators has been agreed to achieve and measure the performance of current LC-MDTs.

Genome-wide copy number variation pattern analysis and a classification signature for non-small cell lung cancer

The accurate classification of non-small cell lung carcinoma (NSCLC) into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is essential for both clinical practice and lung cancer research. Although the standard WHO diagnosis of NSCLC on biopsy material is rapid and economic, more than 13% of NSCLC tumors in the USA are not further classified. The purpose of this study was to analyze the genome-wide pattern differences in copy number variations (CNVs) and to develop a CNV signature as an adjunct test for the routine histopathologic classification of NSCLCs. We investigated the genome-wide CNV differences between these two tumor types using three independent patient datasets. Approximately half of the genes examined exhibited significant differences between LUAD and LUSC tumors and the corresponding non-malignant tissues. A new classifier was developed to identify signature genes out of 20 000 genes. Thirty-three genes were identified as a CNV signature of NSCLC. Using only their CNV values, the classification model separated the LUADs from the LUSCs with an accuracy of 0.88 and 0.84, respectively, in the training and validation datasets. The same signature also classified NSCLC tumors from their corresponding non-malignant samples with an accuracy of 0.96 and 0.98, respectively. We also compared the CNV patterns of NSCLC tumors with those of histologically similar tumors arising at other sites, such as the breast, head, and neck, and four additional tumors. Of greater importance, the significant differences between these tumors may offer the possibility of identifying the origin of tumors whose origin is unknown.