Epidermodysplasia verruciformis, or EV, is a rare genodermatosis defined by a lifelong susceptibility to infection by β-genus human papillomaviruses. Patients develop disseminated flat, wart-like lesions beginning in childhood, and a substantial fraction go on to develop cutaneous squamous cell carcinoma, particularly on sun-exposed skin. Genetic work over the past two decades has traced the condition to loss-of-function variants in the TMC6 and TMC8 genes and to disruption of the CIB1–EVER1–EVER2 complex, painting a picture in which keratinocytes—the principal cells of the epidermis—lack an intrinsic defense that normally keeps β-HPV in check. When persistent viral infection combines with ultraviolet light damage, malignant transformation becomes far more likely, making EV a natural experiment in virus-driven skin carcinogenesis.

The cGAS–STING pathway sits at the crossroads of viral detection and cancer biology. The enzyme cyclic GMP-AMP synthase, or cGAS, patrols the cytoplasm for double-stranded DNA that should not be there—whether from an invading virus or from the host genome itself, leaked through damaged nuclear membranes or packaged into micronuclei generated by chromosomal instability. Upon binding such DNA, cGAS produces a cyclic dinucleotide second messenger that activates STING on the endoplasmic reticulum, triggering TBK1–IRF3 signaling and the production of type I interferons. Because cytosolic DNA can arise both from viral infection and from genomic stress, the pathway links the two great themes of EV biology: chronic papillomavirus presence and the DNA damage that ultraviolet exposure inflicts on genetically susceptible skin.

To determine whether this pathway is expressed differently in EV-associated tumors, the team constructed tissue microarrays from 47 EV-associated cutaneous squamous cell carcinomas and 83 non-EV tumors from immunocompetent patients. The specimens came from the dermatopathology archive of the university’s medical school, and EV diagnoses rested on a characteristic clinical history of childhood-onset disseminated flat warts together with compatible histopathology. Thirteen EV patients contributed 46 of the linked tumors, meaning some patients supplied multiple cancers—a feature the investigators explicitly accounted for in their statistics using generalized estimating equations with patient identity as the clustering variable. Previous molecular work had confirmed β-HPV infection in nine of the thirteen patients. Two dermatopathologists independently reviewed all cases, and up to four tumor cores per sample were arrayed for staining.

The quantification itself was rigorously digital. Immunostained slides were scanned on an Aperio ScanScope and analyzed in QuPath, with image analysis performed blinded to whether a tumor came from an EV or non-EV patient and restricted to manually delineated tumor regions. Tumor cells were classified as negative, weak, moderate or strong using fixed optical-density thresholds applied uniformly across all slides, and an H-score—combining intensity and percentage of positive cells on a 0 to 300 scale—was calculated for each core, with the tumor-level value defined as the median across valid cores. STING was detected cytoplasmically, p53 nuclearly, and BCL2 cytoplasmically, all within identical staining batches for both groups to eliminate batch effects.

The headline result was unambiguous: STING expression was substantially higher in EV-associated tumors. The median H-score reached 192.4 in EV cancers compared with 149.5 in non-EV cancers, and after adjustment for histological grade the difference was estimated at 37.2 H-score units, a highly significant gap. Intriguingly, the size of that gap depended on how differentiated the tumors were. A formal interaction test found the EV-versus-non-EV difference varied by histological grade, with the largest estimated difference—72.2 units—appearing in well-differentiated grade 1 tumors, significantly exceeding the differences seen in grades 2 and 3. A substantial difference was also seen in carcinoma in situ. Whether this grade dependence reflects biology at early stages of malignant progression or changes as tumors dedifferentiate remains an open question.

p53 told a parallel story. The protein accumulated to much higher levels in EV tumors—median H-score 97.7 versus 36.3—and EV tumors were more than three and a half times as likely to fall in the highest tertile of p53 expression after cluster-aware ordinal regression. The association persisted after adjustment for grade and other covariates. BCL2, by contrast, was barely expressed in either group and showed no independent relationship with EV status, a result consistent with earlier immunohistochemical surveys of non-melanoma skin cancer. Notably, STING and p53 levels were positively correlated across the cohort, and the correlation survived adjustment for grade, EV status and patient clustering, hinting at convergent stress signaling within these tumors.

The authors are careful about what the staining can and cannot show. Total STING immunohistochemistry measures protein abundance, not pathway activation; recent experimental work has shown that the E6 protein of HPV-8, a β-papillomavirus classically associated with EV, reduces STING phosphorylation and blunts downstream interferon signaling in keratinocytes even though cGAS is recruited to micronuclei. Likewise, p53 staining cannot distinguish wild-type p53 stabilized by cellular stress from accumulation of mutant protein, and archival material was not available for TP53 sequencing. Experimental evidence cuts both ways: p53 activation can increase STING expression, while mutant p53 can disrupt STING–TBK1–IRF3 signaling. The São Paulo findings therefore define an altered protein-expression phenotype whose functional meaning—heightened antiviral alarm, senescence-associated stress, or both—awaits phospho-STING, phospho-TBK1, phospho-IRF3 and interferon-stimulated-gene readouts in matched tumor, precursor and non-neoplastic EV skin samples.

The study’s limitations are those of a retrospective, single-institution series: only thirteen unique EV patients, no individual cumulative ultraviolet exposure data, β-HPV typing available for only part of the cohort, and no molecular HPV assessment of the non-EV tumors. Yet the signal survived multivariable adjustment for age, sex, grade and anatomical region, as well as sensitivity analyses that aggregated to the patient level, restricted to tumors with multiple valid cores, and overlapped the age distributions of the two groups. As a next step, the researchers argue that comprehensive viral typing, viral-load measurement and TP53 sequencing should accompany functional dissection of the cGAS–STING axis, and that pharmacological modulation of the pathway should wait until functional differences are firmly established. If the elevated STING phenotype proves to reflect intact or even amplified DNA-sensing, EV-associated skin cancers could become an informative model for how the immune system perceives virus-driven malignancy—and, ultimately, a testing ground for therapies that deliberately turn the alarm up.

Subject of Research: Differential STING and p53 protein expression in epidermodysplasia verruciformis-associated versus non-EV cutaneous squamous cell carcinoma

Article Title: Differential STING and p53 expression in epidermodysplasia verruciformis-associated versus non-EV cutaneous squamous cell carcinomas

Article References: Fróes, L. A. R., de Oliveira, W. R. P., Stahlschmidt, P., da Cruz Silva, L. L., Pereira, N. V., & Sotto, M. N. (2026). Differential STING and p53 expression in epidermodysplasia verruciformis-associated versus non-EV cutaneous squamous cell carcinomas. Medical Oncology, 43(10), Article 286. https://doi.org/10.1007/s12032-026-03417-0

Image Credits: AI Generated

DOI: 10.1007/s12032-026-03417-0

Keywords: epidermodysplasia verruciformis, cutaneous squamous cell carcinoma, STING, cGAS, p53, BCL2, betapapillomavirus, immunohistochemistry, tissue microarray, innate immunity, skin cancer, HPV

The researchers, led by Aaron Ge and colleagues at the National Cancer Institute’s Division of Cancer Epidemiology and Genetics, set out to solve a problem that has nagged the digital pathology community for years. Tissue microarrays are assembled by punching small cylindrical cores out of donor tissue blocks and arranging them in a precise grid on a recipient paraffin block. In practice, however, the assembly process, repeated sectioning, and high-resolution scanning introduce misalignments, missing cores, folds, and other artifacts. Any software that blindly assumes a perfect grid risks extracting the wrong tissue or mislabeling samples, errors that can silently corrupt downstream epidemiologic analyses.

Conventional de-arraying solutions have typically been desktop applications that must be downloaded, installed, and maintained, often on specific operating systems. They frequently offer limited interactivity, leaving users unable to correct the automated guesses the software makes when a core is shifted, absent, or duplicated. The NCI team argues that this rigidity has real consequences for large-scale studies, such as cancer epidemiology cohorts, where thousands of TMA slides must be processed reliably and where a single misassigned core can compromise the statistical integrity of a biomarker analysis.

TMA-Grid’s answer is a two-stage workflow that combines machine learning with human oversight. At its core is a convolutional neural network trained to segment tissue from background, detecting the individual cores on a scanned slide regardless of staining method or scanner idiosyncrasies. The network runs in the browser itself, powered by TensorFlow.js, which means the computational heavy lifting of tissue segmentation happens on the user’s own machine rather than on a remote server. This design choice has a welcome side effect for privacy-conscious institutions: sensitive patient slides never need to be uploaded anywhere to be processed.

Once cores are detected, the application applies an interactive grid-estimation algorithm that maps the detected tissue spots onto expected row and column positions, reconstructing the logical array layout that the pathologist originally designed. Crucially, this is not a black box. At every step, users can inspect, adjust, and override both the segmentation results and the grid assignment, adding missing cores, deleting artifacts, or nudging boundaries by hand. The developers describe this marriage of automated detection and user-driven correction as the central design principle: the machine does the tedious work, but the human retains final authority over what counts as a valid core.

The technical architecture follows what the authors call a zero-footprint approach. Nothing is installed on the user’s computer, and nothing is copied or cached to a server. The application operates on source images in place, whether they sit on a local hard drive, a remote institutional server, or cloud storage, reading them through standard web protocols. This means a researcher in Calgary, a statistician in London, and a bioinformatician in Maryland can all process slides from the same archive without ever duplicating multi-gigabyte whole-slide image files, a nontrivial benefit when studies involve hundreds of slides and limited bandwidth.

Beyond convenience, the team emphasizes adherence to FAIR principles, making the software Findable, Accessible, Interoperable, and Reusable. The application and all of its components are freely available and open source, and the authors designed its building blocks, from the segmentation model to the visualization layer built on the OpenSeadragon deep-zoom viewer, so that other groups can repurpose them in their own web-based digital pathology pipelines. In an era when reproducibility concerns shadow much of computational biology, publishing a fully open, browser-native tool with reusable components is a deliberate statement about how research software should be built and shared.

The practical implications reach well into cancer epidemiology. The work emerged from the NCI Intramural Research Program within the Division of Cancer Epidemiology and Genetics and its Episphere platform, an environment built for large-scale collaborative cancer research. Cohorts such as the Polish Breast Cancer Study and other population studies rely on tissue microarrays stained with hematoxylin and eosin or immunohistochemistry markers to screen candidate biomarkers across thousands of tumors. A reliable, interactive de-arraying tool that pathologists and technicians can use without IT support could compress what used to be a bottleneck into a routine step, accelerating the pace at which tissue-based discoveries translate into population-level insights.

The tool also reflects a broader shift in scientific software toward running where the data already live. As whole-slide imaging has exploded in scale, with single slides now routinely exceeding a gigapixel, moving files between workstations has become one of the biggest friction points in digital pathology. Zero-footprint web applications sidestep the problem entirely, and TMA-Grid demonstrates that even deep-learning-based image analysis, traditionally the domain of GPU-equipped servers, can now be pushed to the client side of the browser. The authors suggest the same pattern can generalize to other digital pathology tasks, from quality control to annotation workflows.

TMA-Grid is freely available as an open-source application, and its authors, drawn from the National Cancer Institute, the University of Calgary, Leidos Biomedical Research, and The Institute of Cancer Research in London, position it as both a practical solution for working TMA laboratories and a reusable foundation for the next generation of browser-based pathology tools. For a field whose progress increasingly depends on extracting maximum value from irreplaceable tissue collections, a tool that pairs automated intelligence with transparent human control may prove to be exactly what the pathologist ordered.

Subject of Research: Open-source browser-based tissue microarray de-arraying using deep learning for digital pathology

Article Title: TMA-Grid: an open-source, zero-footprint web application for FAIR tissue microarray de-arraying

Article References: Ge, A., Saha, M., Duggan, M. A., Lenz, P., Abubakar, M., García-Closas, M., Balasubramanian, J., Almeida, J. S., & Bhawsar, P. M. (2026). TMA-Grid: an open-source, zero-footprint web application for FAIR tissue microarray de-arraying. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06638-2

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06638-2

Keywords: tissue microarray, de-arraying, digital pathology, whole slide imaging, FAIR software, web application, TensorFlow.js, OpenSeadragon, convolutional neural network, cancer epidemiology, open source, zero-footprint