Predictive biomarkers of treatment response are lacking for metastatic clear
cell renal cell carcinoma (ccRCC), a tumor type that is treated with
angiogenesis inhibitors, immune checkpoint inhibitors, mTOR inhibitors and a
HIF2 inhibitor. The Angioscore, an RNA-based quantification of angiogenesis, is
arguably the best candidate to predict anti-angiogenic (AA) response. However,
the clinical adoption of transcriptomic assays faces several challenges
including standardization, time delay, and high cost. Further, ccRCC tumors are
highly heterogenous, and sampling multiple areas for sequencing is impractical.
Here we present a novel deep learning (DL) approach to predict the Angioscore
from ubiquitous histopathology slides. To overcome the lack of
interpretability, one of the biggest limitations of typical DL models, our
model produces a visual vascular network which is the basis of the model's
prediction. To test its reliability, we applied this model to multiple cohorts
including a clinical trial dataset. Our model accurately predicts the RNA-based
Angioscore on multiple independent cohorts (spearman correlations of 0.77 and
0.73). Further, the predictions help unravel meaningful biology such as
association of angiogenesis with grade, stage, and driver mutation status.
Finally, we find our model can predict response to AA therapy, in both a
real-world cohort and the IMmotion150 clinical trial. The predictive power of
our model vastly exceeds that of CD31, a marker of vasculature, and nearly
rivals the performance (c-index 0.66 vs 0.67) of the ground truth RNA-based
Angioscore at a fraction of the cost. By providing a robust yet interpretable
prediction of the Angioscore from histopathology slides alone, our approach
offers insights into angiogenesis biology and AA treatment response.

通过深度学习方法，可以从组织病理学切片中准确预测血管生成分数，为血管生成生物学和抗血管生成疗法的研究提供了新的洞察。