Biology subjects

Wettig, T.

Publications and source records attributed to Wettig, T..

3 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↗

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↗

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↗