Cannarile, M. A. | bioRxiv Science

bioRxiv2026

Performance of the 10X Genomics Flex Single-Cell Sequencing Assay and its Application to Overcome Challenges in Clinical Trial Samples

BackgroundSingle-cell RNA sequencing (scRNA-seq) has become essential for understanding disease biology, yet its application in clinical trials is often limited by logistical challenges associated with the handling of biospecimens. Newly developed protocols aim to address these limitations by enabling profiling of fixed tissue. These new solutions need to be benchmarked against well-established protocols to assess their performance and suitability for clinical research. ResultsWe systematically compared different scRNA-seq protocols in a set of samples commonly analysed in clinical trials. The 10X Genomics GEM-X Flex Gene Expression assay (GEM-X Flex), in combination with the "chop-fix" preprocessing protocol, demonstrated superior performance to the standard GEM-X Universal Gene Expression solution when applied to both primary tumor tissue fragments and FFPE blocks. Moreover, the quality of the data obtained from GEM-X Flex applied to FFPE blocks outperformed that of single-nuclei RNA sequencing (snRNA-seq) from frozen biopsies, more robustly capturing the biological signals associated with the mechanism of action of a drug evaluated in an internal clinical trial. ConclusionsGEM-X Flex generates reliable, comprehensive transcriptomic data from both fixed tissue and clinical biopsies. By overcoming some of the limitations of fresh and frozen tissue analysis, this protocol offers a robust solution for the broad implementation of scRNA-seq in clinical trials.

genomics↗

bioRxiv2023

Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+ T cells in the tumor microenvironment and predicts clinical outcome in early- and late-phase clinical trials

BackgroundThe immune status of a patients tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, patients immune status is mostly classified based on CD8+ tumor-infiltrating lymphocytes. An unmet need exists for comparable and reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making and the understanding of how to overcome resistance mechanisms. MethodsWe systematically analyzed the CD8 immunophenotype of 2023 patients from 14 phase I-III clinical trials using immunohistochemistry (IHC) and additionally profiled gene expression by RNA-sequencing (RNA-seq). CD8 immunophenotypes were classified by pathologists into CD8-desert, CD8-excluded or CD8-inflamed tumors using CD8 IHC staining in epithelial and stromal areas of the tumor. Using regularized logistic regression, we developed an RNA-seq-based classifier as a surrogate to the IHC-based spatial classification of CD8+ tumor-infiltrating lymphocytes in the TME. ResultsThe CD8 immunophenotype and associated gene expression patterns varied across indications as well as across primary and metastatic lesions. Melanoma and kidney cancers were among the strongest inflamed indications, while CD8-desert phenotypes were most abundant in liver metastases across all tumor types. A good correspondence between the transcriptome and the IHC-based evaluation enabled us to develop a 92-gene classifier that accurately predicted the IHC-based CD8 immunophenotype in primary and metastatic samples (area under the curve (AUC) inflamed = 0.846; excluded = 0.712; desert = 0.855). The newly developed classifier was prognostic in The Cancer Genome Atlas (TCGA) data and predictive in lung cancer: patients with predicted CD8-inflamed tumors showed prolonged overall survival (OS) versus patients with CD8-desert tumors (hazard ratio [HR] 0.88; 95% confidence interval [CI]: 0.80-0.97) across TCGA, and longer OS upon immune checkpoint inhibitor administration (phase III OAK study) in non-small-cell lung cancer (HR 0.75; 95% CI: 0.58-0.97). ConclusionsWe provide a new precision immunophenotyping tool based on gene expression that reflects the spatial infiltration patterns of CD8+ lymphocytes in tumors. The classifier enables multiplex analyses and is easy to apply for retrospective, reverse translation approaches as well as for prospective patient enrichment to optimize the response to cancer immunotherapy. HIGHLIGHTSO_ST_ABSWhat is already known on this topicC_ST_ABST-cell infiltration, most commonly classified based on CD8+ T cell immunohistochemistry (IHC) staining, and various tumor microenvironment (TME)-specific resistance mechanisms, can impact response rates to cancer immunotherapy. What this study addsOur data provide new insights into the impact of tumor excision location and indication on the immune composition of the TME. We developed a transcriptome-based classifier that could accurately predict different spatial CD8+ T-cell infiltration patterns in the TME. We demonstrate the prognostic and predictive value of the classifier across independent patient cohorts (phase I to phase III trials). How this study might affect research, practice or policyOur new RNA-based tool provides a surrogate read-out for spatial IHC-based CD8 infiltration patterns, is easy to use and broadly applicable for both retrospective and prospective patient enrichment to enhance the effectiveness of cancer immunotherapy.

cancer biology↗