Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis.

BACKGROUND: Identification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment. METHODS: Using a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification. RESULTS: The lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR). CONCLUSIONS: These results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

Co-culture device for in vitro high throughput analysis of cancer associated fibroblast and cancer cell interactions.

Introduction Cancers in general, and specifically lung cancer, continue to have low patient survival rates when the patient is at an advanced stage when diagnosed. It appears that the local environment, especially fibroblasts and their signaling molecules, tends to induce metastasis, increase cancer cell resistance to treatment, and aid in tumor growth rates. Since 3-D models quickly become too complex and/or expensive, and therefore rarely leave the lab they are developed in, it is interesting to develop a 2-D model that more closely mimics the clustered tumor formation and bulk interaction with a surrounding fibroblast environment. Methods In the present study, we utilize an off-the-shelf stereolithography 3-D printer, standard use well plates, magnets, and metallic beads to create a customizable 2-D co-culture system capable of being analyzed quantitatively with staining and qualitatively with standard fluorescent/brightfield microscopy to determine cancer-fibroblast interactions while also being able to test chemotherapeutic drugs in a high-throughput manner with standard 96-well plates. Results Comparisons from monoculture and co-culture growth rates shows that the presence of fibroblasts allows for significantly increased growth rates for H460 cancer. Additionally, viability of cancer cells can be quantified with simple cell staining methods and morphology and cell-cell interactions can be observed and studied. Discussion The high throughput model demonstrates that boundary condition changes can be observed between cancer cells and fibroblasts based upon the different chemotherapeutics that have been administered.