Brookhart, A. | bioRxiv Science

bioRxiv2026

Systems serology of responses against tumor antigens in ovarian cancer reveal disrupted Fc-mediated immunity

High-grade serous ovarian cancer (HGSOC) represents 75% of ovarian cancer cases and 80% of deaths, with most patients relapsing despite initial treatment response. The limited effectiveness of immunotherapies in HGSOC indicates urgent need for novel therapeutic approaches. HGSOC patients produce tumor-binding autoantibodies (TBAs) with high tumor selectivity. Since effective antibody-mediated tumor cell killing requires Fc domain interactions with immune cells, we hypothesized that, although TBAs recognize tumor cells, they might still poorly elicit cell killing responses. Using a systems serology approach, we profiled TBA subclass and biophysical interactions with Fc receptors in HGSOC, comparing them to antiviral antibody responses. TBAs were consistently identified within ascites and serum and were heterogeneous in subclass composition. However, TBAs consistently lacked the capacity to bind Fc{gamma}RIIIa despite abundant interaction with Fc{gamma}RIIa and poorly elicited antibody-dependent cellular cytotoxicity, suggesting their Fc features prevent cell killing responses. Restoring Fc{gamma}RIIIa interaction may be a promising therapeutic approach in HGSOC. HighlightsO_LITBAs in ovarian carcinoma patients consistently lack interaction with Fc{gamma}RIIIa C_LIO_LIAscites- and serum-derived TBAs have heterogeneous subclass composition C_LIO_LISystems analysis shows complex serologic differences between TBAs and antiviral responses C_LIO_LIPatient-expressed TBAs demonstrate little antibody-dependent cellular cytotoxicity C_LI

cancer biology↗

bioRxiv2026

Accurate strand-specific long-read transcript isoform discovery and quantification at bulk, single-cell, and single-nucleus resolution

Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

bioinformatics↗