Jons, William

Abstract Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.

Abstract Purpose: Benign breast disease (BBD) is viewed as a non-obligate precursor stage in the development of breast cancer (BC). Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks has been routinely used in BC research. However, RNA-seq of heterogeneous bulk samples represents an averaged expression profile across all cell mixtures. Downstream analyses, such as biomarker discovery, are likely confounded by differences in cell proportions. There is a lack of research in optimizing strategies for cell type deconvolution especially for FFPE samples. We evaluated and optimized strategies for cell type deconvolution using existing single cell RNA-seq (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) breast tissue references, and applied these to a cohort of BBD FFPE samples to assess risk of subsequent BC. Methods: 7 scRNA-seq and 3 snRNA-seq public samples from women’s breast tissue were normalized using SCTransform, then integrated using Harmony to form a complete reference set. Bioinformatics evaluations examined the performance of existing statistical and deep learning-based deconvolution methods. Computational simulations were conducted to evaluate those methods on three scenarios: 1) baseline; 2) incomplete reference set; 3) FFPE artifacts. FFPE artifacts were simulated based on a generalized additive model using 7 in-house paired FFPE-fresh frozen samples. Finally, we applied the best performing method to our BBD cohort of 62 samples. Differential expression analysis adjusting for cell proportions was performed to identify transcriptomic aberrations in BBD cases. Results: snRNA-seq was more sensitive in capturing adipocytes, while scRNA-seq was more sensitive in detecting lymphocytes in breast tissue. The median number of detectable genes per cell was higher for scRNA-seq than snRNA-seq before integration (1264 vs 558). Marker gene-based regression methods (e.g. CIBERSORT) displayed higher variance and lower bias compared to methods directly using scRNA-seq data (e.g. SCDC). Incomplete reference data led to increased bias and little effect on variance. FFPE artifacts had a higher impact on performance compared to incomplete reference data. Overall, Scaden, a deep learning-based method, was the top performing method across all simulation scenarios and was especially robust to FFPE artifacts. 59 differential expressed genes (DEGs) were identified with fold change > 1.5 and p value < 0.01. DEGs involved in BC, e.g. S100A8, WNT4 and HIST1H3H, would be left out if cell proportions were not properly adjusted. Conclusions: We constructed the first complete single cell reference data for women’s breast tissue and benchmarked existing cell type deconvolution methods. Compared to analyses not adjusting for cell proportions, we observed substantial differences in perturbed genes and pathways associated with BC risk for BBD patients. Citation Format: Yuanhang Liu, Chen Wang, Stacey J. Winham, Aditya Bhagwate, Robert A. Vierkant, Julie M. Cunningham, Bryan M. McCauley, William A. Jons, Derek C. Radisky, Amy C. Degnim. Deconvolution of cell type composition in FFPE tissue: Application to benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4291.

Abstract Primary sclerosis cholangitis (PSC) predisposes individuals to liver failure, but it is challenging for radiologists examining radiologic images to predict which patients with PSC will ultimately develop liver failure. Motivated by algebraic topology, a topological data analysis - inspired framework was adopted in the study of the imaging pattern between the “Early Decompensation” and “Not Early” groups. The results demonstrate that the proposed methodology discriminates “Early Decompensation” and “Not Early” groups. Our study is the first attempt to provide a topological representation-based method into early hepatic decompensation and not early groups.