Parwani, A. | bioRxiv Science

bioRxiv2026

Intra-slide calibration technology improves immunohistochemical harmonization within and between anatomic pathology laboratories

The reproducibility of immunohistochemistry in tumor tissue analysis across reference labs remains a persistent challenge. We tested the extent to which an intra-slide calibration technology mitigated discprepencies in inter-laboratory assays of p53 immunohistochemical (IHC) reactions in brain biopsies of glioblastoma (GB), IDH-wildtype. Intra-slide calibration technologies apply a 0-100% concentration scale incorporating primary surrogate and secondary antibodies to generate a standardized curve for DAB precipitation. IHC from GB samples was performed independently by pathology departments from two different hospital laboratories and were digitalized at 40x magnification using Aperio Image Scope software. Feature extraction, including intensity and texture parameters was performed using the EBImage package in R, followed by UMAP dimensionality reduction and DBSCAN clustering analysis. Our results show significant differences in intensity and texture clustering patterns between laboratory tissue samples and intra-slide calibration technology ruler caused by the different laboratories. Intra-slide calibration technology coupled with polynomial regression analysis improved ~90% the data harmonization. Our findings demonstrate a key role for computational pathology using intra-slide calibration technology to enable intra-laboratory consistency and inter-laboratory reproducibility. These advances strengthen the reproducibility of diagnostic assessments and support more objective, data-driven decision-making in neuro-oncology.

bioinformatics↗

bioRxiv2026

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance

Precision oncology lacks scalable methods to identify the mechanisms that mediate therapeutic resistance for individual patients. Resistance often arises from focal cellular niches that are obscured by bulk profiling and costly to resolve with multi-omics. Here, we show that deep learning (DL), applied to routine histology, can localize focal tissue regions enriched for therapeutically relevant biology. Using 3111 breast cancer H&E slides with matched bulk transcriptomics, we trained weakly-supervised DL models to infer activities of immune, metabolic, and tumor-intrinsic phenotypes implicated in therapeutic resistance (AUROC>0.80; PCC>0.64). Accurate inference of these phenotypes should identify tissue regions enriched for the corresponding biological signal. Therefore, we validated phenotype inference and spatial localization with complementary analyses. Tissue-matched multiplexed immunofluorescence showed concordance between inferred immune states and corresponding cell fractions (p=0.006-0.106). Across multi-institutional cohorts, model-derived phenotypes recovered expected relationships with therapeutic outcomes (p<0.045). Finally, in a blinded evaluation, pathologists confirmed that model-derived high-attention regions were enriched for phenotype-specific morphology (p<2.408*10-5). Because evaluated phenotypes represent diverse mechanisms of resistance across therapeutic modalities, these findings provide a foundation for resistance-directed localization using therapeutic outcomes as supervision. By directing deep profiling toward model-prioritized regions, this framework could enable scalable nomination of candidate mediators of resistance for subsequent functional validation across real-world patient populations. One sentence summaryWeakly supervised deep learning localizes focal tissue regions enriched for therapeutically relevant biology in routine histology, thus offering a scalable strategy to study therapeutic resistance across large patient populations.

biophysics↗