Discriminant Analysis of Lung Cancer Using Nonlinear Clustering of Copy Numbers

Lung cancer clustering by identification of similarities and discrepancies of DNA copy numbers using maximal information coefficient

Lung cancer is the second most diagnosed cancer and the first cause of cancer related death for men and women in the United States. Early detection is essential as patient survival is not optimal and recurrence rate is high. Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with the cancer. In this research, the similarities between cancer and paired peripheral blood samples are identified using maximal information coefficient (MIC) and the spatial locations with substantially high MIC scores in each chromosome are used for clustering analysis. The results showed that a sizable reduction of feature set can be obtained using only a subset of locations with high MIC values. The clustering performance was evaluated using both true rate and normalized mutual information (NMI). Clustering results using the reduced feature set outperformed the performance of clustering using entire feature set in several chromosomes that are highly associated with lung cancer with several identified oncogenes.

Chromosomal Copy Number Variation Predicts EGFR-TKI Response and Prognosis for Patients with Non-Small Cell Lung Cancer

Purpose

Chromosomal abnormalities represent genomic signatures linked to cancer prognosis and responses to chemotherapy, immunotherapy, and drug resistance. This study aimed to investigate the impact of chromosome copy number variants (CNVs) on the efficacy of tyrosine kinase inhibitors (TKIs) in EGFR-mutated non-small cell lung cancer (NSCLC) patients, as well as its prognostic implications for progression-free survival (PFS) and overall survival (OS) in EGFR wild-type patients.

Methods

A total of 110 patients with advanced NSCLC were enrolled in this study and categorized into EGFR-mutated and wild-type groups. Utilizing next-generation sequencing (NGS) technology, we assessed 24 genes and chromosome CNVs associated with lung cancer pathways in patients' tissue samples.

Results

Within the EGFR-mutated group, patients with a gain in Chr 1p13.3-p13.1 exhibited poor TKI responses, a high relapse rate, and shortened PFS (P = 0.002). Conversely, EGFR-mutated patients with a gain in 14q31.1-q31.3 demonstrated favorable TKI responses and relatively extended PFS (P = 0.005). Among EGFR wild-type patients, the presence of 7q31.1-q31.31 CNV emerged as an independent factor influencing both PFS and OS (P = 0.013, P = 0.004). Notably, patients with a gain in 7q31.1-q31.31 exhibited prolonged PFS and OS. Additionally, independent prognostic significance for OS in EGFR wild-type patients was observed for CNVs in 9q21.31-q22.2 and 11p11.11-q12.1 regions (P = 0.001). Patients with gains in these regions experienced extended OS, while losses were predictive of poorer outcomes.

Conclusion

Our results suggested that chromosomal copy number variation is a practical indicator for predicting the response of EGFR-targeted therapy and prognosis for NSCLC patients.

Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.

Weighted Mutual Information for Aggregated Kernel Clustering

Background: A common task in machine learning is clustering data into different groups based on similarities. Clustering methods can be divided in two groups: linear and nonlinear. A commonly used linear clustering method is K-means. Its extension, kernel K-means, is a non-linear technique that utilizes a kernel function to project the data to a higher dimensional space. The projected data will then be clustered in different groups. Different kernels do not perform similarly when they are applied to different datasets. Methods: A kernel function might be relevant for one application but perform poorly to project data for another application. In turn choosing the right kernel for an arbitrary dataset is a challenging task. To address this challenge, a potential approach is aggregating the clustering results to obtain an impartial clustering result regardless of the selected kernel function. To this end, the main challenge is how to aggregate the clustering results. A potential solution is to combine the clustering results using a weight function. In this work, we introduce Weighted Mutual Information (WMI) for calculating the weights for different clustering methods based on their performance to combine the results. The performance of each method is evaluated using a training set with known labels. Results: We applied the proposed Weighted Mutual Information to four data sets that cannot be linearly separated. We also tested the method in different noise conditions. Conclusions: Our results show that the proposed Weighted Mutual Information method is impartial, does not rely on a single kernel, and performs better than each individual kernel specially in high noise.