Biology subjects

Tumor Profiler Consortium,

Publications and source records attributed to Tumor Profiler Consortium,.

5 recordsLinked to original sources

bioRxiv2026

Clone-level multi-modal prediction of tumour drug response

Tumour heterogeneity presents a major challenge for precision oncology, as genetically and phenotypically distinct tumour clones may respond differently to therapy. To address this, we introduce scClone2DR, a probabilistic multi-modal framework that predicts drug responses at the level of individual tumour clones by integrating single-cell DNA and RNA sequencing with ex-vivo drug-screening data. In simulations, scClone2DR substantially outperforms alternatives in recovering true drug-effects and clonal sensitivities. Applied to 60 melanoma and 21 acute myeloid leukaemia patient samples, the method identifies heterogeneous clonal responses, yields biologically meaningful feature rankings, highlights clones that may be resistant to treatment, and improves the prediction of clinical outcomes compared to models ignoring clonal structure. These results demonstrate that modelling tumour evolution and clonal diversity is crucial for accurate drug-response prediction and provides a foundation for more effective, clone-aware precision oncology.

cancer biology↗

bioRxiv2025

RIPPLET: Mutation-Only Gene and Pathway Profiling for Precision Oncology

Clinical implementation of comprehensive genomic profiling, via whole-genome (WGS) or whole-exome sequencing (WES), is constrained by sparse mutation burdens and analytic pipelines reliant on matched transcriptomes. Currently, gene-centric analysis prevails, but overlooks the complex, multigene and pathway perturbations shaping tumor biology. We introduce RIPPLET, a DNA-only framework converting somatic variants into quantitative gene-impact scores and topology-aware pathway-perturbation profiles. By integrating tissue-specific protein-protein interaction networks with cohort-informed reweighting, RIPPLET prioritizes likely functionally relevant alterations. Applied across 33 TCGA cancer types, RIPPLET surpasses four state-of-the-art multi-omic driver-prioritization tools in recovering cancer type-specific drivers. In a cohort of metastatic cutaneous melanomas, it identifies pathway signatures that predict drug response, provide prognostic insight and distinguish immune-infiltration phenotypes without RNA data, independently validated on an in-house cohort. RIPPLET enables DNA-only inference of tumor-specific gene and pathway dysregulation, aligning with clinical sequencing workflows and offering a scalable precision-oncology strategy in transcriptome-limited settings.

bioinformatics↗

bioRxiv2024

De novo detection of somatic variants in long-read single-cell RNA sequencing data

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variants detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

cancer biology↗

bioRxiv2023

Standardization of suspension and imaging mass cytometry readouts for clinical decision making

Suspension and imaging mass cytometry are single-cell, proteomic-based methods used to characterize tissue composition and structure. Data assessing the consistency of these methods over an extended period of time are still sparse and are needed if mass cytometry-based methods are to be used clinically. Here, we present experimental and computational pipelines developed within the Tumor Profiler clinical study, an observational clinical trial assessing the relevance of cutting-edge technologies in guiding treatment decisions for advanced cancer patients. By using aliquots of frozen antibody panels, batch effects between independent experiments performed within a time frame of one year were minimized. The inclusion of well-characterized reference samples allowed us to assess and correct for batch effects. A systematic evaluation of a test tumor sample analyzed in each run showed that our batch correction approach consistently reduced signal variations. We provide an exemplary analysis of a representative patient sample including an overview of data provided to clinicians and potential treatment suggestions. This study demonstrates that standardized suspension and imaging mass cytometry measurements generate robust data that meet clinical requirements for reproducibility and provide oncologists with valuable insights on the biology of patient tumors.

cancer biology↗

bioRxiv2022

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing for personalized oncology

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 are novel. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-{beta}/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

cancer biology↗