Minor, A. | bioRxiv Science

bioRxiv2026

AI-enabled Spatial Profiling of Circulating Tumor-Immune Ecosystems Predicts Patient Outcomes Across Cancers

Circulating tumor cells (CTCs) and immune cells form dynamic multicellular ecosystems in blood, but their spatial organization and clinical relevance have not been systematically characterized. We developed the Cell and Cluster Identification Program (CCIP), an artificial intelligence-based framework that analyzes routine multiplex immunofluorescence blood scans to segment cells, identify CTCs and five immune lineages with high accuracy, and quantify multicellular clusters and tumor-immune interactions. Applying CCIP to 2,693 blood scans from 1,399 patients, we profiled over 60 million cells (>7 million multi-cell clusters) and linked imaging-derived features to patient outcomes. Correlated with circulating-tumor DNA mutation burdens, a 14-feature image model predicted overall survival in breast cancer, outperformed clinicopathologic variables and CTC enumeration, and generalized to prostate cancer. Prognostic imaging signatures were also associated with therapy response-related progression-free survival as well as with single-cell RNA sequencing-derived immune suppression states, connecting circulating tumor-immune architecture with systemic immune dysfunction.

cancer biology↗

bioRxiv2025

A De Novo Algorithm for Allele Reconstruction from Oxford Nanopore Amplicon Reads, with Application to CYP2D6

The Oxford Nanopore Technologies sequencing platform offers a path towards bedside genomics, producing long reads that can completely cover a gene of interest, and thus detect any known or novel variant the gene contains. However, the analysis of these long reads to identify actionable genotypes remains challenging and typically requires customization depending on the target gene. Here, we describe a generic algorithm to accurately reconstruct allele sequences derived from long-reads of genomic-amplicon origin. Rather than calling variants directly from these long-reads, our method takes a "sequence-first" approach, performing an unbiased reconstruction of the underlying amplicon sequences to generate high-confidence reconstructed allele sequences. This is done without user input of the expected target gene, allowing for any source amplicon to be reconstructed. These high-confidence reconstructed allele sequences are then compared to the genomic reference sequence of the gene to infer the specific diplotype present in the sample. This approach is agnostic towards the number of genes and alleles present and readily detects novel variants. We demonstrate our approach using three independent data sets for CYP2D6, a diverse and complex gene with over 175 known alleles of clinical significance affecting drug dosing. We show how our approach can accurately recover validated CYP2D6 diplotypes from 20 Coriell samples sequenced using different primer sets, on different Oxford Nanopore Technologies flow cell versions, and to different depths. This includes inferring occurrences of copy number variation from relative abundances of each allele, a critical factor for ascribing functional effects to a diplotype. Further, we demonstrate our approachs utility for other genomic regions, including HLA.

bioinformatics↗