In recent years, tyrosine kinase inhibitors (TKIs) that target epidermal growth factor receptors (EGFR) have become first-line treatment for cases of lung cancer. However, despite their success, EGFR TKIs do not display the same efficacy across different patients – even in those who have EGFR-sensitizing mutations.
A team of researchers, however, may have found a solution to this TKI trouble by using the features of the tumor-microenvironment to predict the result of targeted therapies in select patients. The researchers used deep learning analysis of H&E-stained biopsies to develop a model that can predict how patients with EGFR-mutant metastatic lung cancer will respond to therapies. Analysis of these tumor tissue images allowed the team to identify interactions between tumor and stroma cells, which in turn allowed them to accurately predict how patients would respond.
The team found that higher tumor–tumor interactions were linked with a greater overall benefit to patients, whereas more tumor–stroma interactions showed less benefit – suggesting that the latter interplay is involved in people’s resistance to targeted therapies
In light of these findings, the paper established that patients with EGFR-mutant lung cancer and higher amounts of tumor–stroma interactions have better likelihood at responding to immunotherapy, which could have further implications for treatment plans and treatment development. In addition to this, the authors correlated signatures drawn from pathology images with established lung adenocarcinoma subtypes. This relationship between groups that are unlikely to benefit from treatment and faster-growing cancers echoes similar findings in recent work that observed significant instances of high-grade subtypes in patients with cancers resistant to EGFR TKI (2).
That said, the authors state that the study’s methodology sample size was relatively small. Furthermore, the prediction model only offered insights on intertumor rather than intratumor heterogeneity. Further research is needed to establish how these differ and what effects intratumour heterogeneity has on clinical outcomes, including studies involving larger datasets and patients without sensitizing EGFR mutations.
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References
- S Wang et al., “Features of tumor-microenvironment images predict targeted therapy survival benefit in patients with EGFR-mutant lung cancer,” J Clin Invest, 133, 2023 (2023). PMID: 36647832.
- Yu X, et al. “Adenocarcinoma of high-grade patterns associated with distinct outcome of first-line chemotherapy or egfr-tkis in patients of relapsed lung cancer,” Cancer Manag Res, 13, 3981 (2021) PMID: 34040439.