Biology subjects

Spang, R.

Publications and source records attributed to Spang, R..

7 recordsLinked to original sources

bioRxiv2026

A Scalable Framework for Pan-Cancer Tumor Evolution Analysis Enables Transfer of Progression Mechanisms Across Tumor Entities

MotivationCancer progression is driven by the stochastic accumulation of interacting genetic alterations, and tumors from different tissues can share common evolutionary patterns despite distinct anatomical origins. While simplified progression models can be applied at scale, the use of expressive models that capture complex inter-event dependencies remains challenging in pan-cancer settings due to their computational demands. ResultsWe present fastMHN, a scalable approximation to Mutual Hazard Network-based cancer progression models, that enables pan-cancer analysis of large genomic datasets with the explicit aim of enabling transfer of progression mechanisms across tumor entities. Applying fastMHN to a large clinical cohort, we identify a tumor-progression group spanning multiple tissues that is characterized by a link between STK11 mutations and poor patient survival. While the clinical relevance of STK11 mutations is well established in non-small cell lung cancer, our results suggest that a similar progression mechanism is present in molecularly defined subgroups of other cancer types. These findings illustrate how scalable pan-cancer progression modeling can facilitate cross-entity transfer of biological and potentially clinical insights. Availability and implementationThe pan-cancer classification workflow and all data are available at github.com/simon-pfahler/fastMHN-classification.

bioinformatics↗

bioRxiv2025

Harp: Platform Independent Deconvolution Tool

MotivationThe cellular composition of a solid tissue can be assessed either through the physical dissociation of the tissue followed by single-cell analysis techniques or by computational deconvolution of bulk gene expression profiles. However, both approaches are prone to significant biases. Tissue dissociation often results in disproportionate cell loss, while deconvolution is hindered by biological and technological inconsistencies between the datasets it relies on. ResultsUsing calibration datasets that include both experimentally measured and deconvolution-based cell compositions, we present a new method, Harp, which reconciles these approaches to produce more reliable deconvolution results in applications where only gene expression data is available. Both on simulated and real data, harmonizing cell reference profiles proved advantageous over competing state-of-the-art deconvolution tools, overcoming technological and biological batch effects. Availability and ImplementationR package available at https://github.com/spang-lab/harp. Code for reproducing the results of this paper is available at https://github.com/spang-lab/harplication. ContactZahra.Nozari@klinik.uni-regensburg.de

bioinformatics↗

bioRxiv2024

Modeling metastatic progression from cross-sectional cancer genomics data

Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

bioinformatics↗

bioRxiv2023

Overcoming Observation Bias for Cancer Progression Modeling

Cancers evolve by accumulating genetic alterations, such as mutations and copy number changes. The chronological order of these events is important for understanding the disease, but not directly observable from cross-sectional genomic data. Cancer progression models (CPMs), such as Mutual Hazard Networks (MHNs), reconstruct the progression dynamics of tumors by learning a network of causal interactions between genetic events from their co-occurrence patterns. However, current CPMs fail to include effects of genetic events on the observation of the tumor itself and assume that observation occurs independently of all genetic events. Since a dataset contains by definition only tumors at their moment of observation, neglecting any causal effects on this event leads to the "conditioning on a collider" bias: Events that make the tumor more likely to be observed appear anti-correlated, which results in spurious suppressive effects or masks promoting effects among genetic events. Here, we extend MHNs by modeling effects from genetic progression events on the observation event, thereby correcting for the collider bias. We derive an efficient tensor formula for the likelihood function and learn two models on somatic mutation datasets from the MSK-IMPACT study. In colon adenocarcinoma, we find a strong effect on observation by mutations in TP53, and in lung adenocarcinoma by mutations in EGFR. Compared to classical MHNs, this explains away many spurious suppressive interactions and uncovers several promoting effects. The data, code, and results are available at https://github.com/cbg-ethz/ObservationMHN.

bioinformatics↗

bioRxiv2023

Virtual Tissue Expression Analysis

MotivationBulk RNA expression data is widely accessible, whereas single-cell data is relatively scarce in comparison. However, single-cell data offers profound insights into the cellular composition of tissues and cell-type-specific gene regulation, both of which remain hidden in bulk expression analysis. ResultsHere, we present tissueResolver an algorithm designed to extract single-cell type information from bulk data, enabling us to attribute expression changes to individual cell types. The outcome is a virtual tissue that can be analyzed in a manner similar to single-cell RNA-seq data. When validated on simulated data tissueResolver outperforms competing methods. Additionally, our study demonstrates that tissueResolver reveals previously overlooked celltype specific regulatory distinctions between the activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subtypes of diffuse large B-cell lymphomas (DLBCL). Availability and ImplementationR package available at https://github.com/spang-lab/tissueResolver. Code for reproducing the results of this paper is available at https://github.com/spang-lab/tissueResolver-docs. Contactjakob.simeth@klinik.uni-regensburg.de

bioinformatics↗

bioRxiv2023

PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors

Mass spectrometry (MS)-based high-throughput proteomics data cover abundances of 1,000s of proteins and facilitate the study of co- and post-translational modifications (CTMs/PTMs) such as acetylation, ubiquitination, and phosphorylation. Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics layers or phenotypical information. Network inference methods aim for a holistic analysis of data to reveal relationships between molecular variables and to resolve underlying regulatory mechanisms. Among those, graphical models have received increased attention as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics as a graphical modeling approach to integrate proteomics data with complementary omics layers and pheno- and genotypical information. PriOmics models intensities of individual peptides and incorporates their protein affiliation as prior knowledge in order to resolve statistical relationships between proteins and CTMs/PTMs. We show in simulation studies that PriOmics improves the recovery of statistical associations compared to the state of the art and demonstrate that it can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a dataset of Diffuse Large B-Cell Lymphomas (DLBCLs) where we integrate SWATH-MS-based proteomics data with transcriptomic and phenotypic information. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/566517v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@19bcac0org.highwire.dtl.DTLVardef@11c126forg.highwire.dtl.DTLVardef@1fe6631org.highwire.dtl.DTLVardef@e73187_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2018

Modelling cancer progression using Mutual Hazard Networks

MotivationCancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap.\n\nResultsHere we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data. MHN model events by their spontaneous rate of fixation and by multiplicative effects they exert on the rates of successive events. MHN compared favourably to acyclic models in cross-validated model fit on four datasets tested. In application to the glioblastoma dataset from The Cancer Genome Atlas, MHN proposed a novel interaction in line with consecutive biopsies: IDH1 mutations are early events that promote subsequent fixation of TP53 mutations.\n\nAvailabilityImplementation and data are available at https://github.com/RudiSchill/MHN.

bioinformatics↗