Biology subjects

Nicholson, M. D.

Publications and source records attributed to Nicholson, M. D..

6 recordsLinked to original sources

bioRxiv2026

Chromosome 7 gain establishes a just-right WNT signaling state during colorectal tumorigenesis

Aneuploidy is a hallmark of colorectal cancer, yet the functional consequences of recurrent chromosome gains and losses remain largely unknown. Leveraging advances in human colon organoid culture and chromosome copy number engineering, we here identify the gain of chromosome 7 as an adaptive event following APC loss-of-function mutation. Trisomy 7 confers a cell-intrinsic fitness advantage to APC-mutant colon organoids, while selective removal of the additional chromosome 7 copy from patient-derived colon cancer organoids severely impairs colorectal cancer organoid growth. By comparing transcriptomes of isogenic disomy 7 and trisomy 7 organoid lines, we found that chromosome 7 gain promotes organoid fitness through attenuation of excessive WNT-driven stemness caused by bi-allelic APC mutation. The gain of chromosome 7 thus establishes a just-right WNT signaling state. This effect of chromosome 7 gain is conserved from early adenoma-like APC-mutant organoids to advanced colorectal cancer organoids carrying additional oncogenic driver mutations. Moreover, consistent with our organoid data, gain of chromosome 7 is particularly common in tumors of colorectal cancer patients whose APC genotypes confer relatively high WNT activity. Overall, this work demonstrates that chromosome-specific aneuploidy can act as an adaptive mechanism to optimize, rather than maximize, oncogenic signaling during tumor evolution.

cancer biology↗

bioRxiv2025

Hyper-mutational processes provide a head-start for non-optimal cancer driver mutations explaining atypical KRAS variants

In hypermutant cancers, both the mutation rate per cell division and the genomic locations of new mutations are vastly altered. Such cancers often display atypical driver gene variants, which may provide sub-optimal oncogenic advantages. Why such weaker cancer drivers prevail in these tumours is unclear. Here, we examine the null hypothesis that aberrant mutational processes alone can account for the detection of weaker, rather than canonical, driver mutations. Using a mathematical modelling analysis, we find that simply increasing the mutation rate per-division can lead to the detection of weaker drivers, an effect which is further compounded by the biassing of mutations towards weaker driver sequence contexts. Focusing on POLE-mutant (DNA polymerase epsilon proofreading-deficient) colorectal cancers and KRAS driver mutations, we quantify the mutational bias towards non-canonical KRAS drivers in both POLE-mutant and non-hypermutant colorectal cancers. The observed bias, coupled with the increased mutation rate of POLE-mutant cancers, is sufficient to explain the enrichment of atypical KRAS drivers in POLE-mutant cancers with plausible selection parameters. Thus, within our model, differential selection between these cancer types need not be invoked to understand the observed variation in driver mutations.

cancer biology↗

bioRxiv2025

Genetic background sets the trajectory of cancer evolution

Human cancers are heterogeneous. Their genomes evolve from genetically diverse germlines in complex and dynamic environments, including exposure to potential carcinogens. This heterogeneity of humans, our environmental exposures, and subsequent tumours makes it challenging to understand the extent to which cancer evolution is predictable. Addressing this limitation, we re-ran early tumour evolution hundreds of times in diverse, inbred mouse strains, capturing genetic variation comparable to and beyond that found in human populations. The sex, environment, and carcinogenic exposures were all controlled and tumours comprehensively profiled with whole genome and transcriptome sequencing. Within a strain, there was a high degree of consistency in the mutational landscape, a limited range of driver mutations, and all strains converged on the acquisition of a MAPK activating mutation with similar transcriptional disruption of that pathway. Despite these similarities in the phenotypic state of tumours, different strains took markedly divergent paths to reach that state. This included pronounced biases in the precise driver mutations, the strain specific occurrence of whole genome duplication, and differences in subclonal selection that reflected both cancer susceptibility and tumour growth rate. These results show that interactions between the germline genome and the environment are highly deterministic for the trajectory of tumour genome evolution, and even modest genetic divergence can substantially alter selection pressures during cancer development, influencing both cancer risk and the biology of the tumour that develops.

genomics↗

bioRxiv2022

Strand-resolved mutagenicity of DNA damage and repair

DNA base damage is a major source of oncogenic mutations1. Such damage can produce strand-phased mutation patterns and multiallelic variation through the process of lesion segregation2. Here, we exploited these properties to reveal how strand-asymmetric processes, such as replication and transcription, shape DNA damage and repair. Despite distinct mechanisms of leading and lagging strand replication3,4, we observe identical fidelity and damage tolerance for both strands. For small DNA adducts, our results support a model in which the same translesion polymerase is recruited on-the-fly to both replication strands, starkly contrasting the strand asymmetric tolerance of bulky adducts5. We find that DNA damage tolerance is also common during transcription, where RNA-polymerases frequently bypass lesions without triggering repair. At multiple genomic scales, we show the pattern of DNA damage induced mutations is largely shaped by the influence of DNA accessibility on repair efficiency, rather than gradients of DNA damage. Finally, we reveal specific genomic conditions that can corrupt the fidelity of nucleotide excision repair and actively drive oncogenic mutagenesis. These results provide insight into how strand-asymmetric mechanisms underlie the formation, tolerance, and repair of DNA damage, thereby shaping cancer genome evolution.

genomics↗

bioRxiv2019

Phenotypic delay: mechanistic models and their implications for the evolution of resistance to antibiotics

Phenotypic delay - the time delay between genetic mutation and expression of the corresponding phenotype - is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbruck fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbruck distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data. Author SummaryUnderstanding precisely how some bacteria survive exposure to antibiotics is a major research focus. Specific mutations in the bacterial genome are known to provide protection. However, it remains unclear how much time passes between a bacterium acquiring the genetic change and being able to tolerate antibiotics - termed the phenotypic delay - and what controls this delay. Here, using computer simulations and mathematical arguments we discuss three biologically plausible mechanisms of phenotypic delay. We investigate how each mechanism would affect the outcome of laboratory experiments often used to study the evolution of antibiotic resistance, and we highlight how the delay might be detected in such experiments. We also show that the existence of the delay could explain an observed discrepancy in the measurement of mutation rates, and demonstrate that one of our models provides a superior fit to experimental data. Our work exposes how molecular details at the intracellular level can have a direct effect on evolution at the population level.

evolutionary biology↗

bioRxiv2018

Competing paths over fitness valleys in growing populations

Investigating the emergence of a particular cell type is a recurring theme in models of growing cellular populations. The evolution of resistance to therapy is a classic example. Common questions are: when does the cell type first occur, and via which sequence of steps is it most likely to emerge? For growing populations, these questions can be formulated in a general framework of branching processes spreading through a graph from a root to a target vertex. Cells have a particular fitness value on each vertex and can transition along edges at specific rates. Vertices represents cell states, say genotypes or physical locations, while possible transitions are acquiring a mutation or cell migration. We focus on the setting where cells at the root vertex have the highest fitness and transition rates are small. Simple formulas are derived for the time to reach the target vertex and for the probability that it is reached along a given path in the graph. We demonstrate our results on several scenarios relevant to the emergence of drug resistance, including: the orderings of resistance-conferring mutations in bacteria and the impact of imperfect drug penetration in cancer.

evolutionary biology↗