Abstract P2-08-07: Predicting DCIS prognosis using infrared and raman spectroscopy of breast calcifications and soft-tissue microstructure
Authors: Nallala Jayakrupakar , Calabrese Doriana , Gosling Sarah , Hall Allison , Pinder Sarah , Bouybayoune Ihssane , King Lorraine , Marks Jeffrey , Lips Esther , Lynch Thomas , Pinto Donna , Wesseling Jelle , Hwang Shelley , Rogers Keith , Stone Nick ,   on behalf of the Grand Challenge PRECISION consortium
Published: March 2022

Abstract
Introduction: Ductal carcinoma in situ (DCIS) is a potential precursor of invasive breast cancer. The uncertain trajectory of DCIS, to progress to invasive disease or to remain in situ, currently drives treatment, despite lack of proven benefit. Therefore, understanding the molecular features of the DCIS trajectory may prevent overtreatment of this disease. Breast calcifications are a common feature in DCIS and are seen mammographically in over 80% of cases. Calcifications have been largely characterised based only on x-ray morphology; their chemical composition, their association with the surrounding soft tissue and role in DCIS and invasive breast cancer biology is largely unexplored. In this regard, a bio-photonic approach, based on infrared (IR) and Raman spectroscopy in combination with machine learning, was used to study DCIS by probing the chemical composition of calcifications and the surrounding soft tissue in breast lesions. The main aim of the work is to identify molecular compositional changes in calcifications and in soft tissue that potentially accompany or drive the progression of DCIS to invasive breast cancer, or indicates a stable DCIS phenotype. Methods: Serial tissue sections from 303 patients with (i) ‘pure DCIS’ (DCIS without recurrence) (n=158), (ii) ‘DCIS with invasive recurrence’ (DCIS from a patient who subsequently was known to develop invasive disease) (n=123) and (iii) ‘DCIS plus invasive cancer contemporaneously’ (n=22), were measured using mid-IR imaging and Raman mapping. The same calcifications and soft tissue regions from specific DCIS ducts were targeted across the techniques on the serial sections. Spectral images were analysed using cluster analysis followed by unsupervised and supervised machine learning classification models to identify spectral features associated with the progression of DCIS to invasive breast cancer. Results: Segmentation of IR and Raman spectral images based on cluster analysis identified important histopathological features including calcifications, epithelium, necrotic areas, connective tissue and stroma based on the spectral heterogeneity. Based on analysis of Raman calcification data from 145 patients with (i) ‘pure DCIS’ (n=90) and (ii) ‘DCIS with invasive recurrence’ (n=55), an area under the receiver operating characteristic (AUROC) mean value of 85% was obtained in distinguish pure DCIS from DCIS that later recurred as invasive cancer. The calcification features appeared to indicate pathology specific changes in phosphate and carbonate content and appearance of magnesium whitlockite. Similar analysis of the surrounding soft tissue spectral features showed an AUROC mean value of 76%, which showed changes in protein secondary structure and content, particularly in the necrotic regions surrounding calcifications. In addition, classification models are being developed and refined from the IR spectral data, the initial results of which have shown an AUROC value of only 54% from the same patients’ data. Perspectives: It is anticipated that the current novel approaches allowing label-free measurement of calcifications and soft tissue will provide important cues in understanding DCIS prognosis and could be a promising way forward in determining DCIS management. Current and future efforts include identification of specific discriminatory spectral features for molecular and pathological correlation. Acknowledgments: This work was supported by Cancer Research UK and by KWF Kankerbestrijding (ref. C38317/A24043).
Citation Format: Jayakrupakar Nallala, Doriana Calabrese, Sarah Gosling, Allison Hall, Sarah Pinder, Ihssane Bouybayoune, Lorraine King, Jeffrey Marks, Esther Lips, Thomas Lynch, Donna Pinto, Jelle Wesseling, Shelley Hwang, Keith Rogers, Nick Stone, on behalf of the Grand Challenge PRECISION consortium. Predicting DCIS prognosis using infrared and raman spectroscopy of breast calcifications and soft-tissue microstructure [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-08-07.