Abstract A038: Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data
Authors: Manasa Iyer, Diego Mallo, Carlo C. Maley, Angelo Fortunato, Luis Cisneros, Lorraine M. King, Marc D. Ryser, Joseph Y. Lo, Allison Hall, Jeffrey R. Marks, Shelley Hwang
Journal: Cancer Research (2024)

Abstract Ductal carcinoma in situ (DCIS) is a very common non-life threatening, pre-invasive form of breast cancer constituting 25% of all new breast cancer diagnoses in the USA, and is normally treated with invasive measures. However, only 20 to 30% of DCIS cases will progress to life-threatening invasive breast cancer in their lifetime. There is a need for patient stratification to decrease treatment burden and focus resources on patients who actually require treatment. To ameliorate this issue, we aim to develop evolutionary biomarkers that predict both DCIS recurrence and its progression to invasive breast cancer. We conducted an observational longitudinal study in which two DCIS samples from each primary tumor were acquired prior to treatment—to account for intratumor heterogeneity—and followed up with (median event-free patient observation time: 106 months) to classify each patient’s outcome into its respective cohort category of [1] no DCIS recurrence, [2] DCIS recurrence, or [3] invasive ductal carcinoma (IDC) recurrence (progression). Each of the three cohort groups comprises approximately 30 patients, for which our primary data includes low-pass whole genome sequencing (lpWGS) and whole exome sequencing (WES) of the two primary DCIS samples, in addition to WES data for normal tissue control, clinical and epidemiological covariates, and time to recurrence. Preliminary estimates of copy number alterations (CNAs) generated using lpWGS data show that CNA burden, representing the proportion of altered genome, has statistically significant predictive capability for DCIS progression (Univariate Cox regression: p=0.014, HR=1.5). CNA divergence, representing the proportion of non-overlapping altered genome, has predictive capabilities of both recurrence and progression (Univariate Cox regression: Recurrence: p=0.021, HR=0.74; Progression: p=0.024, HR=0.67). In this work, our primary objective is to test if CNAs generated utilizing WES data are comparable in their predictive power to those yielded by lpWGS data for the same human DNA samples. It is not expected that the copy number alteration (CNA) estimates will be identical between the two methods, but rather that mutational burden and intratumor heterogeneity estimated using different data sources are strongly correlated and will have similar predictive capabilities. If we successfully confirm that CNAs estimated with both lpWGS and WES data have similar predictive power of DCIS progression, we will then be able to validate our evolutionary biomarkers using a different cohort for which we only possess WES data. This would also expand the applicability of our evolutionary biomarkers to a larger number of pre-existing datasets. We ultimately aim to develop strong evolutionary biomarkers that, when utilized alongside evolutionary therapies, will revolutionize the clinical management of cancer with the potential to improve both patient prognoses and quality-of-life outcomes. Citation Format: Manasa Iyer, Diego Mallo, Carlo C. Maley, Angelo Fortunato, Luis Cisneros, Lorraine M. King, Marc D. Ryser, Joseph Y. Lo, Allison Hall, Jeffrey R. Marks, Shelley Hwang. Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A038.