Single cell fitness landscapes induced by genetic and pharmacologic perturbations in cancer

Abstract

1Tumour fitness landscapes underpin selection in cancer, impacting etiology, evolution and 28 response to treatment. Progress in defining fitness landscapes has been impeded by a lack 29 of timeseries perturbation experiments over realistic intervals at single cell resolution. We 30 studied the nature of clonal dynamics induced by genetic and pharmacologic perturbation 31 with a quantitative fitness model developed to ascribe quantitative selective coefficients to 32 individual cancer clones, enable prediction of clone-specific growth potential, and forecast 33 competitive clonal dynamics over time. We applied the model to serial single cell genome 34 (>60,000 cells) and transcriptome (>58,000 cells) experiments ranging from 10 months to 2.5 35 years in duration. We found that genetic perturbation of TP53 in epithelial cell lines induces 36 multiple forms of copy number alteration that confer increased fitness to clonal populations 37 with measurable consequences on gene expression. In patient derived xenografts, predicted 38 selective coefficients accurately forecasted clonal competition dynamics, that were validated 39 with timeseries sampling of experimentally engineered mixtures of low and high fitness 40 clones. In cisplatin-treated patient derived xenografts, the fitness landscape was inverted in a 41 time-dependent manner, whereby a drug resistant clone emerged from a phylogenetic lineage 42 of low fitness clones, and high fitness clones were eradicated. Moreover, clonal selection 43 mediated reversible drug response early in the selection process, whereas late dynamics in 44 genomically fixed clones were associated with transcriptional plasticity on a fixed clonal 45 genotype. Together, our findings outline causal mechanisms with implication for interpreting 46 how mutations and multi-faceted drug resistance mechanisms shape the etiology and cellular 47 49 Cellular fitness underpins the tissue population dynamics of cancer progression and treatment 50 response. Yet, quantifying fitness in heterogeneous cell populations, and identifying causal 51 mechanisms shaping fitness landscapes remain as open problems, impeding progress in developing 52 effective and durable therapeutic strategies. In particular, quantitative fitness modeling of cancer 53 cells has numerous and diverse implications; attributing clonal dynamics to drift or selection, 54 identifying the determinants of clonal expansion, enabling causal inference, and forecasting growth 55 trajectories. Drug resistance and etiology are among the key unresolved areas of investigation that 56 require advanced understanding of fitness in cancer. For example, drug resistance mechanisms are 57 commonly attributed to phenotypic plasticity encoded via epigenetic changes 1, 2 , or evolutionary 58 selection of pre-existing genomic clones 3 . However, the relative contribution of these processes 59 when studied in tandem is poorly understood and requires integrated genome-transcriptome 60 investigation. Moreover, how changes in genomic architecture brought ...

TP53loss initiates chromosomal instability in high-grade serous ovarian cancer

“…Moreover, it should be noted that such aneuploidy tolerance studies utilised experimental induction of chromosome mis-segregation in cells lacking p53. However, the emergence of aneuploid clones with TP53 loss has been observed in untreated mammary epithelial and RPE-1 cells (Kok et al, 2020;Salehi et al, 2020;Soto et al, 2017). In addition, multiple cellular processes were deregulated in response to p53 inactivation in transformed murine embryonic fibroblasts, including ploidy control (Valente et al, 2020).…”

The impact of mutational processes on structural genomic plasticity in cancer cells

“…Structural variation differences between BRCA1 and BRCA2 associated tumours observed in bulk prompted us to delineate how HRD-Dup and HRD-Del mutational processes lead to accrual of alterations at the single cell level. We first investigated an isogenic in vitro system, through generating loss of function TP53 21 , TP53/BRCA1 and TP53/BRCA2 alleles from diploid 184-hTERT mammary epithelial cells 24 using CRISPR-Cas9 editing ( Supplementary Table 4, Extended Data Fig. 5, Extended Data Fig.…”

Evolutionary tracking of cancer haplotypes at single-cell resolution

“…In summary, our study shows how haplotype resolved copy number at single cell resolution can be used to infer instability rates, dissect complex structural rearrangements and identify parallel copy number events. As cohorts of patients profiled at single cell resolution become larger and high throughput methods are applied throughout different stages of disease progression 33 and across space and time 34 , we envisage that these approaches will enable accurate tracking of the evolutionary history of cancer haplotypes and high resolution characterization of intra-tumour heterogeneity across genomically unstable tumours.…”

“…184hTERT cells were cultured as previously described 34,44 in MEBM (Lonza) supplemented with the SingleQuots kit (Lonza), 5 μg/ml transferrin (Sigma-Aldrich) and 10uM isoproterenol (Sigma-Aldrich). Cells were pelleted and gently resuspended in 200ul PBS followed by 800ul 100% methanol and incubation at -20C for 30mins to fix and dehydrate cells.…”