A study conducted by Ludwig Cancer Research has revealed the significance of a gene expression ratio in tumor-associated macrophages, shedding light on cancer patients' outcomes and the intricate gene expression programs controlling human cancers in the tumor microenvironment (TME). Led by Mikaël Pittet, the researchers found that the ratio of two genes primarily expressed by macrophages, CXCL9 and SPP1, plays a critical role in determining the tumor's anti-tumor or pro-tumor state.
According to the study published in Science, patients with higher expression of the gene CXCL9 in their tumor-associated macrophages experienced better clinical outcomes, as these macrophages were found to be ready to attack cancer cells. On the other hand, higher expression of the gene SPP1 in immune cells indicated a state that supported tumor growth. The ratio of CXCL9 to SPP1 in the TME proved to be crucial in predicting tumor polarity, where a high CS ratio correlated with anti-tumor gene expression signatures across the TME, while a low ratio was associated with pro-tumor gene expression.
The researchers believe that this CS ratio could be used as an easily measurable molecular marker to predict patient prognosis and manage therapy. Moreover, the gene expression networks identified through the study highlight potential targets for developing drugs that can influence the TME to be more susceptible to treatments like immunotherapy.
Pittet and his team conducted an unbiased analysis of 52 tumors from patients with head and neck cancers to determine the variation in the TME between tumors. They discovered that CXCL9 and SPP1 expression, mutually exclusive in individual macrophages, were closely linked to prognosis, not only in head and neck cancers but also in other solid cancers. Additionally, the CShi (high CXCL9/SPP1 ratio) and CSlow (low CXCL9/SPP1 ratio) tumors displayed distinct gene expression signatures associated with anti-tumor and pro-tumor effects, respectively.
The researchers' next steps involve investigating whether the identified gene expression networks can predict patient outcomes and responses to therapies. Understanding the interactions between these gene expression axes and the CS ratio could lead to the development of precision medicine strategies for cancer therapy, aiming to benefit patients effectively.