Spatial Omics Solutions for Lung Cancer

Lung cancer is a malignant tumor that originates in the airways or lung parenchyma. Based on the pathological type, lung cancer is divided into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with the latter accounting for approximately 85% of cases. Lung cancer has a complex and diverse histologic subtype and is often aggressive cancer.

Figure 1. The lung cancer tumorigenesis in the early stage of lung cancer is depicted. (Chen, W. W., et al., 2022)

Intra-tumor heterogeneity (ITH) encompasses both spatial heterogeneity (different regions of the same tumor) and temporal heterogeneity (different from in situ and secondary cancers) and can lead to poor treatment outcomes and high rates of tumor recurrence. Although many therapies, including chemotherapy, radiosurgery, targeted therapies, and immunotherapy, have been applied to the treatment of lung cancer, the 5-year survival rate for patients with early-stage lung cancer is only 50%. Therefore, accurate and comprehensive pathological staging is essential to guide lung cancer treatment studies and predict prognosis.

How the spatial-omics solution we offer can be useful in lung cancer research

CD Genomics offers a solution in lung cancer research based on spatial transcriptomics technology. This offers our customers the possibility to identify and characterize tumor stroma or tumor tissue sections in an unbiased manner. We focus on the expression patterns of lung cancer-associated risk genes in specific tumor regions and in the overall distribution, which may help you further understand in situ or non-in situ lung tumors.

Available to our clients

• Understanding the spatial location correlation between different cell types.
• Access to the spatial distribution patterns of lung cancer-related and key genes.
• Ability to gain a fundamental understanding of tumor-immune cell interactions.
• Understand the spatial localization binding patterns of multiple cell types in lung cancer pathology sections.
• Identification of prognostic cell profiles, locations, interactions, and characteristics.

General analysis flow

Sample types: fresh and frozen samples, OCT-embedded sample compatible

Technical features and advantages

• Provides access to essential and previously unavailable information about lung biology and pathophysiology.
• Spatial transcriptome gene expression profiling can be used to predict further regions of potential cancer, tumor, and angiogenesis.
• Can be used to study new levels of mechanisms of lung carcinogenesis and progression, which are critical for understanding cell fate decisions and regulation of lung cell transformation.
• Can be used to explore the mechanisms that promote lung tissue remodeling following respiratory viral infection.

Reference

1. Chen, W. W., et al., (2022). "Deciphering the Immune–Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology." Frontiers in Oncology. 11, 716042.