Abstract CCR8+FoxP3+ regulatory T cells (Tregs) are a key immunosuppressive subset in solid tumors, but their accurate quantification in multiplex immunofluorescence (mIF) is hindered by slide-to-slide staining variability, dim FoxP3 signal, and background-driven CCR8 false positives. We developed a fully automated, background-aware R pipeline for robust cross-slide calling of CCR8+FoxP3+ cells from HALO single-cell data across NSCLC, CRC, GEJ, and HNSCC. Single-cell intensities for CCR8 and FoxP3 were transformed using inverse hyperbolic sine and normalized per slide using robust z-scores (rz) computed from medians and MADs, with fallbacks when FoxP3− or CCR8− reference populations were sparse. FoxP3 positivity was reassessed using a two-stage approach: (i) “soft” FoxP3 calls integrating HALO labels with high-rz outliers, and (ii) a strict rescue rule in which each cell’s FoxP3 rz was evaluated against the empirical FoxP3− distribution, requiring both a minimum rz floor and low FoxP3 background-z, preventing inflation of false positives. CCR8 calling used a spatially explicit background model: slides were partitioned into fine spatial bins, and CCR8 background medians and dispersions were estimated from local CCR8− cells, with smoothing across sparse regions. This produced a background z-score (bgᵦ) and a background-adjusted CCR8 rz (rzbg). From these, we defined a lenient background-aware CCR8 call and a strict CCR8 keeper enforcing strong evidence and artifact filters (cytoplasm completeness, CK negativity, nucleus quality). Final CCR8+FoxP3+ assignments used a FoxP3-aware rule: FoxP3+ cells were evaluated with lenient CCR8 criteria to recover dim true positives, whereas FoxP3− cells required the strict CCR8 keeper to prevent spurious double-positive inflation. Agreement analyses showed that the strict CCR8 criteria substantially reduced Halo-only artifacts while adding very few “rescued-only” events. Conversely, FoxP3 soft-rescue recovered dim nuclei primarily in low-background regions. Processing was optimized using Arrow to avoid loading full datasets into memory, enabling analysis of 10 million cells on a standard workstation. The combined FoxP3 normalization, empirical rescue, and CCR8 background-aware gating produced stable distributions across indications and improved both sensitivity and specificity for CCR8+FoxP3+ Treg quantification. Citation Format: Joao Paulo Oliveira da Costa, Alina Ainbinder, Kenneth Trieu, Henry Reinhart, Yury Sheikin,. Automated robust normalization and context-aware rescue of TReg cells from multiplex immunofluorescence abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts) ; 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86 (7 Suppl): Abstract nr 4159.
Costa et al. (Fri,) studied this question.