Haake, S. | Search bioRxiv

bioRxiv2026

Not All Predictors Of RCC Clusters Are The Same: An individual sample predictive model to classify patients with metastatic renal cell carcinoma

Clear cell renal cell carcinoma (RCC) patients have multiple approved therapies, including anti-angiogenesis tyrosine kinase inhibitors (TKIs) and immuno-oncology therapies (IO), but lack a clinically validated biomarker. Using RNAseq data from the IMmotion 151 clinical trial (IM151)1-3, 7 RCC biologic clusters have been defined3. Several groups have attempted to predict these clusters on various RCC datasets4,5 with negative results, suggesting that the association with therapy response from IM151 could not be reproduced. We hypothesized that the specific approaches used to generate cluster predictions led to the misinterpretation of findings. Both published models used standardization (z-scores) to normalize the data within their patient cohorts, imposing an expected gene expression distribution in which [~]50% of patients have apparently higher-than-average expression, artificially impacting the proportion of cluster assignments and leading to potential misclassification. We developed a machine learning (ML) model, IRIS-RCC (Individual RNA-seq Intrinsic Subtyping for RCC), to predict treatment (TKI vs. IO) for patients using an individual-sample model using the IM151 trial (N=823) and validated the model on the JAVELIN Renal 101 trial (JR101; N=726)3,6. Our method normalizes gene expression within a given sample using ratiometric expression. This method results in different cluster assignments for individual tumors and distinct clinical correlations. An additional advantage of individual-sample predictions is that they can be readily applied in a prospective setting, where patients must be classified one at a time. IRIS-RCC is currently being validated in a prospective biomarker-driven Phase II clinical trial (OPTIC RCC).

Cancer Biology↗

bioRxiv2025

Discovery of a new fibronectin-binding surface protein of Streptococcus canis with serum opacification activity through Transposon Directed Insertion-Site Sequencing

Infective endocarditis is a rare but severe disease in humans and dogs which can be caused by Streptococcus canis. The bacterial factors that mediate endothelial adherence remain poorly defined. To understand how S. canis can adhere to and invade the endocardium, we combined a high-throughput approach called transposon directed insertion-site sequencing (TraDIS) with a physiologically relevant endothelial infection model that incorporates venous-range shear stress to identify S. canis genes required for host cell interaction. A saturated transposon library of clinical strain IMT49926 was screened in a microfluidic infection assay, enabling genome-wide selection of mutants impaired in endothelial adhesion. Comparative analysis of input and non-adherent output pools revealed several candidate genes, including a fibronectin-binding LPXTG-anchored surface protein with high homology to streptococcal serum opacity factors (SOFs). Our findings identified ScSOF as a multifunctional surface protein that plays an important role in the infection potential of S. canis. It facilitates adhesion to endothelial cells, prevents endothelial wound closure, contributes to the streptococcal surface architecture, binds fibronectin, opacifies serum, and inhibits {beta}-haemolytic activity. ScSOF represents a strong candidate for future studies into pathogenesis, immune evasion, and (potentially) vaccine or therapeutic targeting in S. canis infections. Authors summaryStreptococcus canis is a bacterium commonly found in healthy dogs and cats, but it can sometimes cause serious infections in animals and humans, including infective endocarditis, a dangerous infection of the heart lining. To cause this disease, the bacteria must first attach to and invade the cells that line blood vessels. However, very little is known about how S. canis can do this. In this study, we used a genetic screening method that includes all the genes from S. canis to identify which bacterial genes are needed for attachment to human endothelial cells under conditions that mimic real blood flow. We discovered a previously uncharacterized surface protein, which we named ScSOF, that plays several important roles during infection. ScSOF helps the bacteria bind to fibronectin, a major host tissue protein, alters the bacterial surface structure, and causes serum opacification, a known marker of virulence in related streptococci. When ScSOF was removed, the bacteria were much less able to attach to endothelial cells, cause cell damage, or interfere with the healing of endothelial wounds. Our findings show that ScSOF is a key factor that enables S. canis to interact with host tissues and may contribute to heart valve infections.

microbiology↗