Abstract
Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment, and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant – which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison for predicting risk of recurrence. Importantly, the approach provides a histologic basis to the prediction that identifies chemical and morphologic features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.
Highlights
Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers
We first classified the tissue into different cell types following previous protocols[32] and compared the average IR absorption spectra (.500 pixels) of epithelium and two types of stroma to discern differences between recurrent cases and non-recurrent controls
The spectral features from each cell type were separately handled in our data analysis pipeline (Figure 2) since cell types greatly differ in morphology, chemistry, and function
Summary
Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. We hypothesized that the chemical properties of the tissue, and especially of the stroma, may add additional information that can improve prediction To test this hypothesis in the most pressing area of contemporary PCa need, we employed a recurrence-enriched and mid-grade dominant cohort[38] where recurrent cases and non-recurrent controls were matched for age at prostatectomy, race, Gleason score, and pathologic (pTNM) stage. This cohort provides the most challenging and demanding subset for modern prostate pathology and facilitates discovering novel predictors of cancer recurrence which are independent of the conventional clinical parameters (age, race, Gleason score, and pathologic stage). It is notable that the design of the study addresses a pressing need, utilizes new ideas to examine cancer by focusing on both the tumor and microenvironment and our approach is entirely compatible with other tests – whether MRIbased non-invasive assays or digital pathology[44,45] on the same tissue specimens
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