Quantitative Radiomics Analysis for Assessment of Breast Cancer Risk
The annual mammogram remains the mainstay for early detection of breast cancer. Although changes to the standard annual schedule coincide with individual risk, so-called low-risk women represent the majority of newly diagnosed breast cancer patients.
Our researchers seek to overcome the limitations in this risk assessment tool by implementing density and texture analysis of the mammographic image. This method provides a novel approach to risk assessment.
Through comparison of digital mammographic images in breast cancer patients and cancer-free individuals, quantitative radiomic analysis captured texture and density features from a standard region of interest behind the nipple.
Researchers identified several texture-based features that discriminated between cancers, by subtype, and controls.
Of these, researchers identified 23 highly inter-correlated features that researchers removed from further analysis. The remaining 19 features were evaluated for discrimination between cancers and controls in 4 settings: all cancers versus all controls, as well as by each of the subtypes versus controls.
Our research has demonstrated that mammographic features appear to discriminate between cancers and controls in subtype-specific fashion. These data suggest opportunity to develop mammographic signatures of risk to guide screening.
Primary prevention of breast cancer
Repurposing existing low-toxicity, low-cost drugs represents an efficient strategy for primary prevention of breast cancer.
Previous studies on statins and aspirin as potential chemoprevention agents have provided mixed results on whether these drugs reduce breast cancer risk. However, these studies did not consider potential mechanisms of resistance to statin and the need to select women most likely to benefit.
A recent trial of statins in breast cancer patients showed an inverse relationship between response and the degree of activation of the cholesterol biosynthesis pathway, the target of statin therapy, with tumors having high baseline activation less likely to respond. In mice xenografts, our researchers found that statin treatment results in upregulation of the cholesterol pathway, consistent with its known feedback regulation. This upregulation was associated with progression along the pre-neoplastic spectrum.
Our preclinical data suggests that aspirin can abrogate the statin-induced homeostatic upregulation of the cholesterol pathway leading to sensitization of statin-resistant cells.
Thus, dual therapies must target both the cholesterol pathway and the feedback loops.
We hypothesize that a majority of women at risk for BC are inherently resistant to statin chemoprevention, partly due to activation of cholesterol pathway within the breast parenchyma.
We further hypothesize that intrinsic gene signatures indicative of cholesterol pathway activation determine sensitivity to statin-based chemoprevention.
In some women, additional mechanisms are activated that result in resistance to statins and identification of these mechanisms will allow for development of novel drug combinations to treat this subpopulation.
We will test these hypotheses by conducting a randomized phase II trial testing the efficacy of statin vs statin + aspirin in reversing cytological atypia in the breast of at risk women.
Our research team will also validate the reported cholesterol activation signature and establish threshold values predictive of response.
This will also allow characterization of genomic changes that could inform on additional opportunities for novel chemoprevention strategies in women who are resistant to statin based chemoprevention.