The work, published in BMC Genomics, addresses a practical bottleneck rather than a single biological question. Single-cell sequencing can distinguish rare cell populations, characterize states that blur together in bulk tissue and reveal how tumor cells interact with immune and stromal components. Yet those benefits depend on obtaining viable or otherwise processable cells with minimal degradation. Clinical trial specimens often travel between hospitals, central laboratories and analytical sites, and delays or differences in preservation can impose major logistical constraints. A workflow compatible with fixed tissue could therefore make longitudinal molecular analysis more feasible and preserve samples that might otherwise be unsuitable for single-cell studies.

To evaluate the approach systematically, the team compared fixed-tissue and nuclei-based RNA-sequencing workflows in solid-tumor samples collected and preserved using procedures commonly encountered in clinical trials. The comparison included both frozen tumor tissue and formalin-fixed and paraffin-embedded, or FFPE, blocks. Rather than testing an isolated laboratory protocol, the study placed the GEM-X Flex assay against the established GEM-X Universal Gene Expression solution under conditions representative of real-world trial specimens. This design allowed the investigators to assess both technical data quality and the extent to which biologically relevant signals could be recovered.

Technical performance was evaluated through several related measures, including the number of genes and unique molecular identifiers, or UMIs, detected in each cell. UMIs are molecular tags that help quantify captured RNA, making their recovery an important indicator of transcriptomic sensitivity. The GEM-X Flex workflow combined with chop-fix preprocessing produced higher gene and UMI detection than the standard GEM-X Universal Gene Expression solution in both frozen and FFPE samples. It also yielded a slight reduction in ambient RNA background, the unwanted signal from RNA released outside cells that can dilute or distort cell-specific profiles. Together, these results indicate that the newer workflow did not merely recover a different number of cells; it preserved more molecular information and modestly improved the cleanliness of the resulting data.

The preparation stage is central to that improvement. Chop-fix mechanically processes tissue into small, manageable fragments before fixation, helping researchers create a suspension from which individual nuclei can be isolated and profiled. Nuclear RNA sequencing avoids the need to keep whole cells intact, an advantage for solid tumors and clinical specimens that may be fixed, stored or shipped under conditions incompatible with fresh-tissue workflows. The study’s results suggest that carefully combining tissue homogenization with the Flex assay can recover a richer transcriptomic profile than the established fixed-sample solution, while still retaining the practical advantages of nuclei-based analysis.

Beyond aggregate sequencing metrics, the investigators examined whether the workflows recovered specific biological signals and distinguished cell states that conventional approaches left less clear. The chop-fix and GEM-X Flex combination improved the capture of relevant biological signals and resolved cellular states that were less distinct with the comparison method. This matters because a higher number of detected genes does not by itself guarantee a more informative cell atlas. Useful single-cell data must preserve differences among tumor, immune and stromal populations, including subtle states that may be linked to treatment response, resistance or disease progression. The benchmark therefore suggests that the protocol improved both the amount and the biological interpretability of the data.

The samples were part of the infrastructure surrounding clinical trial NCT04338685, registered on ClinicalTrials.gov. The study was approved by the applicable independent ethics committees and regulatory authorities and conducted in accordance with the Declaration of Helsinki, ICH Good Clinical Practice E6(R1), the European Union Clinical Trials Directive and local requirements. Written informed consent was obtained from participants before eligibility assessment. The research was funded by F. Hoffmann-La Roche, and the authors disclosed affiliations with Roche entities, including employment or company shareholdings for several contributors. The use of trial-linked material illustrates the practical setting the workflow was designed to support: molecular analysis of specimens collected for therapeutic research rather than specimens prepared specifically for a sequencing laboratory.

Fixed-tissue single-cell sequencing could widen the questions that clinical investigators can ask, but the study does not make archival samples interchangeable with newly collected tissue. Formalin fixation, paraffin embedding, storage history and mechanical processing can all influence molecular recovery. The benchmark’s importance lies in demonstrating that an established single-cell platform can perform well with an adapted preparation method, not that all fixed specimens will yield equivalent results. Researchers would still need to validate sample quality, examine platform-specific quality-control metrics and interpret differences in sensitivity when comparing fixed-tissue datasets with fresh or frozen alternatives. Such controls are especially important when subtle cell states are central to a trial’s biological hypothesis.

Overall, the results position the 10X Genomics GEM-X Flex Gene Expression assay with chop-fix preprocessing as a robust option for transcriptomic analysis of both frozen tissue and FFPE clinical biopsies. Its main advantage is not a claim of viral discovery or a dramatic single benchmark result, but the conversion of a persistent workflow problem into a testable technical solution. By increasing gene and UMI detection, modestly lowering ambient RNA background and improving the representation of distinct cellular states, the approach could make single-cell analysis more compatible with the way clinical-trial specimens are actually collected and preserved. If adopted carefully, fixed-tissue profiling may give researchers a more practical route from patient biopsy to a high-resolution view of tumor biology, while keeping molecular analysis closer to the clinical evidence gathered during treatment.

Subject of Research: Performance of fixed-tissue single-cell RNA sequencing for clinical-trial tumor samples

Article Title: Performance of the 10X Genomics flex single-cell sequencing assay and its application to overcome challenges in clinical trial samples

Article References: Antoniolli, M., Servera, L. A., Paetzold, K., Schmeing, S., Yong, C., Nassiri, S., Hüsser, T., Cannarile, M. A., Bacac, M., Yángüez, E., & Dettling, S. (2026). Performance of the 10X Genomics flex single-cell sequencing assay and its application to overcome challenges in clinical trial samples. BMC Genomics. https://doi.org/10.1186/s12864-026-13476-2

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13476-2

Keywords: single-cell RNA sequencing, 10X Genomics, GEM-X Flex, fixed tissue, FFPE, clinical trials, tumor microenvironment, RNA sequencing, chop-fix protocol, solid tumors, Performance, Genomics

H3 G34-mutant diffuse hemispheric glioma is a defined molecular subtype of high-grade glioma that arises in the cerebral hemispheres of children and young adults. Like its better-known adult counterpart, glioblastoma, it carries a poor prognosis, and clinicians have long searched for molecular signals that might predict which patients will benefit from temozolomide, the oral chemotherapy drug that is a cornerstone of adult glioma treatment. In adult glioblastoma, methylation of the MGMT promoter, which silences the DNA repair enzyme O6-methylguanine DNA methyltransferase, is a well-established predictor of temozolomide benefit. Whether the same logic applies to pediatric H3 G34-mutant tumors has been an open and clinically urgent question.

That question appeared to move closer to an answer when Tlais and colleagues published a study in Acta Neuropathologica in March 2026 examining clinical, imaging and molecular prognostic factors in this tumor type, including MGMT expression and temozolomide response. The study attracted wide attention because it suggested that MGMT expression might carry prognostic and predictive weight in a disease where treatment options remain desperately limited. For families facing this diagnosis, the prospect of a simple molecular test that could indicate whether an aggressive chemotherapy drug is worth its toxicity was understandably compelling.

Shen and Sheng, however, were not convinced that the data could bear that weight. Rather than generating new experiments, the two researchers did what modern science increasingly allows: they went back to the raw material. Their letter reports a reanalysis of publicly available RNA sequencing feature-count data for twenty samples classified as H3 G34-mutant diffuse hemispheric glioma, obtained from the St. Jude Cloud platform under dataset accessions SJC-DS-1001, SJC-DS-1007 and SJC-DS-1014, together with the supplementary data published alongside the original Tlais study. No new data were generated; the entire exercise was a forensic examination of existing numbers.

The central concern raised in the correspondence is captured in its subtitle: technical covariates. In large-scale gene expression analysis, the measurements scientists care about, such as differences in MGMT expression between groups of tumors, can be contaminated by variables that have nothing to do with biology. Batch effects, the systematic differences introduced when samples are processed, sequenced or analyzed in different runs, are among the most notorious offenders in high-throughput genomics. A decade of methodological literature, including a widely cited 2010 review by Leek and colleagues in Nature Reviews Genetics, has documented how these artifacts can masquerade as biological discoveries if they are not explicitly identified and corrected.

Another technical hazard is RNA quality, particularly in tissue preserved as formalin-fixed paraffin-embedded material, the standard archival format in pathology departments worldwide. Chemical modification and fragmentation of RNA during fixation and storage mean that FFPE-derived samples can yield degraded genetic material whose measured expression profiles differ systematically from those of fresh-frozen tissue. A 2007 study by von Ahlfen and colleagues in PLoS ONE documented these determinants of RNA quality in FFPE samples, and the issue remains a routine consideration in any expression study that mixes preservation methods. If the samples in a comparison differ in how they were preserved or processed, apparent differences in gene expression may reflect the chemistry of preservation rather than the biology of the tumor.

Shen and Sheng’s reanalysis, conducted with standard differential expression tooling of the kind exemplified by the DESeq2 package described by Love, Huber and Anders in Genome Biology in 2014, led them to conclude that such technical covariates genuinely complicate the MGMT expression findings in this dataset. Their argument is not that MGMT is irrelevant in these tumors, but that the evidence base is not yet clean enough to support clinical decision-making. In their framing, the covariates should be addressed before clinical use, a deliberately measured phrase that nonetheless carries significant practical consequences for a field eager to translate molecular findings into treatment guidance.

The stakes of this debate extend well beyond a single gene and a single tumor subtype. Temozolomide is not a benign drug; it causes myelosuppression and other toxicities, and exposing a child to chemotherapy without a reliable expectation of benefit is a real cost. Conversely, withholding a potentially effective therapy because a flawed analysis suggested no benefit would be equally harmful. Biomarker-driven treatment decisions demand that the underlying measurements be robust, and the correspondence is a reminder that the path from a genomic dataset to a clinical test runs through unglamorous but essential quality control.

The episode also illustrates the changing sociology of scientific correction. Reanalyses of public data, once rare and often resented, have become a routine mechanism of accountability in genomics, where repositories such as St. Jude Cloud make raw feature counts available to any researcher with the skills to interrogate them. Shen performed the data reanalysis and drafted the manuscript, while Sheng contributed clinical context and critical revision; both authors reviewed and approved the final version and take responsibility for its integrity. The work received no dedicated funding, and the authors declare no conflicts of interest, details that matter when the subject is a contested biomarker with commercial and clinical implications.

For now, the practical message for clinicians and researchers is one of disciplined patience. The correspondence, published on 14 October 2026 in volume 152 of Acta Neuropathologica as article number 55, does not close the door on MGMT as a biomarker in pediatric H3 G34-mutant diffuse hemispheric glioma; it insists that the door not be opened prematurely. Resolving the question will require analyses that explicitly model and correct for batch effects, preservation-related RNA degradation and other technical covariates, ideally across larger and more uniformly processed cohorts. Until then, the fate of children with this rare tumor should, the authors argue, be decided by evidence that can survive scrutiny, not by expression patterns that may be artifacts of the laboratory rather than signatures of the disease.

Subject of Research: Reanalysis of MGMT gene expression data in pediatric H3 G34-mutant diffuse hemispheric glioma highlighting technical covariates affecting clinical biomarker use

Article Title: MGMT expression in pediatric H3 G34-mutant diffuse hemispheric glioma: technical covariates should be addressed before clinical use

Article References: Shen, F., & Sheng, H. (2026). MGMT expression in pediatric H3 G34-mutant diffuse hemispheric glioma: technical covariates should be addressed before clinical use. Acta Neuropathologica, 152(1), Article 55. https://doi.org/10.1007/s00401-026-03106-2

Image Credits: AI Generated

DOI: 10.1007/s00401-026-03106-2

Keywords: MGMT, pediatric glioma, H3 G34-mutant, diffuse hemispheric glioma, temozolomide, RNA-seq, batch effects, biomarker, Acta Neuropathologica, St. Jude Cloud, FFPE, gene expression

Hemangiosarcoma is the most common splenic malignancy in dogs, arising from endothelial cells that line blood vessels. It is notorious for its genetic heterogeneity, high metastatic rate and grim prognosis. The standard of care—surgical removal of the spleen followed by chemotherapy with agents such as doxorubicin or carboplatin—produces only modest improvements in survival, because many tumors resist conventional protocols. Earlier genomic studies, including work by Gorden and colleagues in 2014 and by Wang and colleagues in 2020, had already hinted that canine hemangiosarcoma comprises multiple molecular subtypes with striking similarities to human angiosarcoma. What remained missing was a practical, cost-effective way to detect those subtypes in the formalin-fixed, paraffin-embedded tissue samples that veterinary pathologists routinely produce.

The São Paulo team, led by Heidge Fukumasu and first author Sonia Prince Fiebig, addressed that gap using the Oncomapa qPCR panel, a targeted reverse-transcription quantitative PCR assay designed for clinical molecular profiling. The retrospective study drew on diagnostic tests performed between January 2021 and July 2023 on samples fixed in buffered formalin for at least 48 hours. For each spleen, pathologists sampled at least seven distinct regions histologically—a deliberate strategy to reduce the risk of misclassification in a tumor notorious for focal variation. Two senior pathologists confirmed every diagnosis, evaluating cellular pleomorphism, necrosis, capsular invasion and mitotic counts.

Technically, the workflow was built for robustness on degraded material. RNA extracted from the FFPE specimens was reverse-transcribed and quantified in technical duplicates for 45 cancer genes plus three housekeeping controls: GAPDH, RPL13A and RPS5. RefFinder analysis, which integrates GeNorm, NormFinder, BestKeeper and the ΔΔCT method, identified GAPDH as the most stable reference gene, and all targets were normalized to the geometric mean of the three controls using the 2^-ΔΔCT method. Genes were flagged as highly or lowly expressed when their log2 fold-change exceeded ±2 relative to the cohort average—equivalent to at least fourfold deviation. Missing cycle-threshold values, a common nuisance with fragmented FFPE RNA, were imputed with Multiple Imputation by Chained Equations, and the data were Z-score normalized before clustering. Notably, DHFR was undetectable in 23 of 27 samples, a finding with direct clinical relevance because DHFR expression is associated with methotrexate resistance.

With the expression matrix in hand, the team deployed an unsupervised machine-learning pipeline. Principal component analysis showed that the first two components explained 21.5 and 15.7 percent of the variance respectively, with the first three components accounting for roughly half of the total variation. K-means clustering, validated by the elbow method on within-cluster sum of squares and by an average silhouette score of 0.17, pointed to three clusters. Ward’s hierarchical clustering with Ward.D2 linkage independently reproduced the same three-group structure, with a cophenetic correlation coefficient of 0.68 confirming a good fit between the dendrogram and the underlying distance matrix. Kruskal-Wallis tests with false discovery rate correction then isolated the genes driving the separation.

The three signatures that emerged are strikingly coherent. Cluster 1, comprising eight tumors, was marked by elevated expression of MET, MAP2K1/2, FLT3, SRC, AKT2, TP53, PDGFRA, SETD2 and NR3C1—a profile indicating activation of the MAPK and PI3K/AKT cascades, engagement of specific tyrosine kinase receptors including MET, FLT3 and PDGFRA, and stress and immune regulation mediated by NR3C1 and TP53. This aligns with prior evidence that PIK3CA pathway alterations occur in roughly 46 percent of canine hemangiosarcomas, and with in vitro work showing that the PI3K inhibitor alpelisib is active against PIK3CA-mutated canine HSA cell lines. Significantly, this group also showed low expression of the targets of conventional chemotherapy, suggesting a built-in resistance to standard protocols.

Cluster 2, the largest group with ten tumors, overexpressed BCL2, NOTCH1, RET and KIT. This signature points to evasion of apoptosis and enhanced cell survival, and it highlights some of the most druggable targets in the entire study. The authors propose that RET and KIT signaling could be attacked with veterinary-approved tyrosine kinase inhibitors such as toceranib and masitinib, while elevated BCL2—an anti-death protein exploited by many cancers—makes the tumors rational candidates for venetoclax, a BH3 mimetic that has already shown activity against canine hematological cancers in recent studies. Cluster 3, with nine tumors, displayed a hyperproliferative phenotype: overexpression of MYC, TOP2A, RRM2, TYMS and BRCA1 signaled runaway cell-cycle activity, DNA synthesis and DNA repair. Consistent with the molecular data, these tumors carried significantly more mitotic figures than the other groups—24.43 versus roughly 11 per high-power field, a statistically significant difference. Their gene signature also suggests vulnerabilities to existing cytotoxics: doxorubicin targets TOP2A, hydroxyurea and gemcitabine hit RRM2, and 5-fluorouracil or capecitabine exploit TYMS dependence.

The authors are careful to frame these therapeutic links as hypotheses rather than prescriptions. Because the study was retrospective, the dogs were not treated according to cluster assignment, and standardized survival data were not available, the work cannot yet demonstrate that cluster-guided therapy improves outcomes. Bulk RNA from FFPE blocks also averages the signal across heterogeneous cell populations, meaning intratumoral diversity—well documented in solid tumors—could blur the boundaries between subtypes. The cohort of 27 dogs was small and not validated against an independent external dataset, and protein-level confirmation through immunohistochemistry was not possible with the available archived material. The team argues that the natural next step is prospective validation in larger, independently annotated cohorts, following the established trajectory of translational biomarker development that transformed human oncology after landmark expression-profiling studies in breast cancer and diffuse large B-cell lymphoma.

Even with those caveats, the study carries real weight for the emerging field of veterinary precision oncology. It demonstrates that clinically actionable molecular stratification is feasible using routine FFPE tissue and a comparatively inexpensive qPCR panel, bypassing the cost and infrastructure demands of next-generation sequencing. Expression profiling captures functional pathway activity that DNA mutation panels often miss, and it can in principle track therapy resistance dynamically. Because off-label use of human drugs is already common in veterinary medicine, and because agents such as venetoclax and sorafenib are increasingly deployed against canine tumors, the distance between a molecular signature and a treatment trial is shorter than ever. Given the documented molecular parallels between canine hemangiosarcoma and human angiosarcoma, a better molecular map of the canine disease may ultimately inform the human one as well—turning one of veterinary medicine’s most feared diagnoses into a test bed for personalized cancer therapy.

Subject of Research: RNA expression profiling reveals three molecular subtypes of canine splenic hemangiosarcoma for precision oncology

Article Title: Molecular profiling by RNA expression reveals distinct subtypes of canine splenic hemangiosarcoma

Article References: Fiebig, S. P., Ferrero, A. T., Nunes, A. T., Xavier, P. L. P., Qazi, T. J., Strefezzi, R. F., Sueiro, F. A., Rodrigues, L., & Fukumasu, H. (2026). Molecular profiling by RNA expression reveals distinct subtypes of canine splenic hemangiosarcoma. Veterinary Oncology, 3(1), Article 15. https://doi.org/10.1186/s44356-026-00064-5

Image Credits: AI Generated

DOI: 10.1186/s44356-026-00064-5

Keywords: canine hemangiosarcoma, gene expression profiling, molecular subtypes, precision veterinary oncology, qPCR, FFPE, PI3K/AKT pathway, tyrosine kinase inhibitors, comparative oncology, targeted therapy, dog cancer, transcriptomics