B-368 Non-invasive Longitudinal Analysis of Cancer Signal to Monitor Disease Progression and Responses to Treatment

Abstract

Abstract Background Multi-cancer early detection products are poised to change cancer treatment and survival. However, current applications lack the ability to translate analytical findings to clinical outcomes. Quantitatively tracking cancer signals is the next frontier for early cancer detection and data-driven intervention. Harbinger Health has pioneered a platform that combines novel epigenomic insights with artificial intelligence to detect cancer and monitor signal over time to track disease progression and/or response to treatment. Methods We designed a comprehensive enrichment panel (17.8 Mb) which provides simultaneous methylation and mutational state for cancer and tissue-of-origin informative regions as well as a panel of tumor suppressor genes and oncogenic drivers. We also defined the most significant methylation motifs (MMs) that contribute to pan-cancer signal and developed an algorithm to estimate the amount of tumor-derived reads in a cell-free DNA (cfDNA) sample. To verify our methodology, we performed whole exome sequencing (WES) for a cohort of 46 patients with matched FFPE and cfDNA and compared variant allele frequency of somatic mutations to tumor content estimates from MMs. We used this tumor content (%TC) estimate to quantify signal over time and monitor dynamic changes in the tumor. Simultaneously, we tracked orthogonal mutations across treatment. To clinically assess our quantitative analysis, we sourced longitudinal samples of 21 cancer patients over the course of treatment with four timepoints, averaging 38 days apart. Timepoint 0 (TP0) was before any treatment, while TP1, TP2, and TP3 were taken before subsequent treatment. For most timepoints, we had endpoint RECIST data which identified n = 8 samples as progressive disease (PD), n = 1 as stable disease (SD), n = 9 as partial response (PR), and n = 3 as complete response (CR). We also sourced 21 non-cancer subjects with two timepoints, 60 days apart. All samples across these timepoints were captured with the 17.8 Mb panel and analysis was done to assess longitudinal tracking—predict prognosis, identify minimal residual disease (MRD), and/or detect disease progression. Results We confirmed that our %TC estimates derived using MMs were highly correlated to estimates derived using WES. We therefore utilized our methylation data to estimate %TC for all timepoints across treatment. The %TC ranged from 0.02% to 60%, with Stage IV disease showing the greatest tumor burden, as expected. Notably, %TC showed dynamic fold-changes during treatment, with differential levels between response groups and non-cancers. In a small subset that included three Stage IV prostate cancer patients with high initial %TC, our biomarkers were able to identify one patient that was reported PR but showed an increase of 18.9% tumor content, indicating disease progression. This patient was deceased at last follow-up. [Additional data to be reported.] Conclusion Our analytical approach of identifying %TC based on Harbinger Health’s proprietary enrichment panel was successful in quantitatively monitoring cancer patients through treatment and disease progression. Our data can provide valuable insights to help clinicians make more informed decisions (e.g. increased monitoring or interventions) about patient care. Further development of techniques enriching specifically for these MMs are in progress.