Heritable tumor cell division rate heterogeneity induces clonal dominance

Early clonal extinction in glioblastoma progression revealed by genetic barcoding

Glioblastoma progression in its early stages remains poorly understood. Here, we transfer PDGFB and genetic barcodes in mouse brain to initiate gliomagenesis and enable direct tracing of glioblastoma evolution from its earliest possible stage. Unexpectedly, we observe a high incidence of clonal extinction events and progressive divergence in clonal sizes, even after the acquisition of malignant phenotype. Computational modeling suggests these dynamics result from clonal-based cell-cell competition. Through bulk and single-cell transcriptome analyses, coupled with lineage tracing, we reveal that Myc transcriptional targets have the strongest correlation with clonal size imbalances. Moreover, we show that the downregulation of Myc expression is sufficient to drive competitive dynamics in intracranially transplanted gliomas. Our findings provide insights into glioblastoma evolution that are inaccessible using conventional retrospective approaches, highlighting the potential of combining clonal tracing and transcriptomic analyses in this field.

Clonal dominance is an adverse prognostic factor in acute myeloid leukemia treated with intensive chemotherapy

Intra-tumor heterogeneity portends poor outcome in many cancers. In AML, a higher number of drivers worsens prognosis. The Shannon Index is a robust metric of clonal heterogeneity that accounts for the number of clones, but also their relative abundance. We show that a Shannon Index can be estimated from bulk sequencing, which is correlated (ρ = 0.76) with clonal diversity from single-colony genotyping. In a discovery cohort of 292 patients with sequencing of 43 genes, a higher number of drivers (HR = 1.18, P = 0.028) and a lower Shannon Index (HR = 0.68, P = 0.048), the latter reflecting clonal dominance, are independently associated with worse OS independently of European LeukemiaNet 2017 risk. These findings are validated in an independent cohort of 1184 patients with 111-gene sequencing (number of drivers HR = 1.16, P = 1 × 10^-5, Shannon Index HR = 0.81, P = 0.007). By re-interrogating paired diagnosis/relapse exomes from 50 cytogenetically normal AMLs, we find clonal dominance at diagnosis to be correlated with the gain of a significantly higher number of mutations at relapse (P = 6 × 10^-6), hence with clonal sweeping. Our results suggest that clonal dominance at diagnosis is associated with the presence of a leukemic phenotype allowing rapid expansion of new clones and driving relapse after chemotherapy.

Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution

Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.

Identification of key modules and prognostic markers in adrenocortical carcinoma by weighted gene co-expression network analysis

Adrenocortical carcinoma (ACC) is a rare and aggressive cancer with a high relapse rate and limited treatment options. Therefore, the identification of potential prognostic markers in patients with ACC may improve early detection, survival rates and may additionally provide novel insights into the early detection of recurrence. In the present study, clinical traits and RNA-seq data of 79 patients with ACC were obtained from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis was carried out and 17 distinct co-expression modules were built to examine the association between the modules and the clinical traits. Of the 17 modules, two co-expression modules, which contained 214 and 168 genes, were significantly correlated with two clinical traits, tumor stage and vital status. Functional enrichment analysis was performed on the selected modules. The results showed that one of the modules was primarily enriched in cell division and the other module was enriched in metabolic pathways, suggesting their involvement in tumor progression. Furthermore, cyclin dependent kinase 1 (CDK1) and ubiquitin C (UBC) were identified as hub genes in both modules. Survival analysis revealed that the high expression of the hub genes significantly correlated with the poor survival rate of patients, suggesting that CDK1 and UBC have vital roles in the progression of ACC. In the present study, a co-expression gene module of ACC was provided and the prognostic genes that may serve as new diagnostic markers in the future were defined.