CDK4/6 Dependence of Cyclin D1–Driven Parathyroid Neoplasia in Transgenic Mice

The protein product of the cyclin D1 oncogene functions by activating partner cyclin-dependent kinases (cdk)4 or cdk6 to phosphorylate, thereby inactivating, the retinoblastoma protein pRB. Nonclassical, cdk-independent, functions of cyclin D1 have been described but their role in cyclin D1-driven neoplasia, with attendant implications for recently approved cdk4/6 chemotherapeutic inhibitors, requires further examination. We investigated whether cyclin D1’s role in parathyroid tumorigenesis in vivo is effected primarily through kinase-dependent or kinase-independent mechanisms. Using a mouse model of cyclin D1–driven parathyroid tumorigenesis (PTH-D1), we generated new transgenic lines harboring a mutant cyclin D1 (KE) that is unable to activate its partner kinases. While this kinase-dead KE mutant effectively drove mammary tumorigenesis in an analogous model, parathyroid-overexpressed cyclin D1 KE mice did not develop the characteristic biochemical hyperparathyroidism or parathyroid hypercellularity of PTH-D1 mice. These results strongly suggest that in parathyroid cells, cyclin D1 drives tumorigenesis predominantly through cdk-dependent mechanisms, in marked contrast with the cdk-independence of cyclin D1–driven mouse mammary cancer. These findings highlight crucial tissue-specific mechanistic differences in cyclin D1–driven tumorigenesis, suggest that parathyroid/endocrine cells may be more tumorigenically vulnerable to acquired genetic perturbations in cdk-mediated proliferative control than other tissues, and carry important considerations for therapeutic intervention.

RB1 Deletion in Retinoblastoma Protein Pathway-Disrupted Cells Results in DNA Damage and Cancer Progression

ABSTRACT Proliferative control in cancer cells is frequently disrupted by mutations in the retinoblastoma protein (RB) pathway. Intriguingly, RB1 mutations can arise late in tumorigenesis in cancer cells whose RB pathway is already compromised by another mutation. In this study, we present evidence for increased DNA damage and instability in cancer cells with RB pathway defects when RB1 mutations are induced. We generated isogenic RB1 mutant genotypes with CRISPR/Cas9 in a number of cell lines. Cells with even one mutant copy of RB1 have increased basal levels of DNA damage and increased mitotic errors. Elevated levels of reactive oxygen species as well as impaired homologous recombination repair underlie this DNA damage. When xenografted into immunocompromised mice, RB1 mutant cells exhibit an elevated propensity to seed new tumors in recipient lungs. This study offers evidence that late-arising RB1 mutations can facilitate genome instability and cancer progression that are beyond the preexisting proliferative control deficit.

RB1 deletion in RB-pathway disrupted cells results in DNA damage and cancer progression

SummaryProliferative control in cancer cells is frequently disrupted by mutations in the RB-pathway. Intriguingly, RB1 mutations can arise late in tumorigenesis in cancer cells whose RB-pathway is already compromised by another mutation. In this study, we present evidence for increased DNA damage and instability in CDKN2A silenced cancer cells when RB1 mutations are induced. We generated isogenic RB1 mutant genotypes with CRISPR in a number of cell lines. Cells with even one mutant copy of RB1 have increased basal levels of DNA damage and increased mitotic errors. Elevated levels of reactive oxygen species as well as impaired homologous recombination repair underlie this DNA damage. When xenografted into immune compromised mice RB1 mutant cells exhibit an elevated propensity to seed new tumors in recipient lungs. This study offers evidence that late arising RB1 mutations can facilitate genome instability and cancer progression that are beyond the pre-existing proliferative control deficit.

A mechanism for the proliferative control of tissue mechanics in the absence of growth

During the development of a multicellular organism, cells coordinate their activities to generate mechanical forces, which in turn drives tissue deformation and eventually defines the shape of the adult tissue. Broadly speaking, it is recognized that mechanical forces can be generated through differential growth and the activity of the cytoskeleton. Based on quantitative analyses of live imaging of the Drosophila dorsal thorax, we suggest a novel mechanism that can generate contractile forces within the plane of an epithelia - via cell proliferation in the absence of growth. Utilizing force inference techniques, we demonstrate that it is not the gradient of junction tension but the divergence of junction-tension associated stresses that induces the area constriction of the proliferating tissue. Using the vertex model simulations, we show that the local averaged stresses can be roughly elevated by a fold of p 2 per cell division without growth. Moreover, this mechanism is robust to disordered cell shapes and the division anisotropy, but can be dominated by growth. In competition with growth, we identify the parameter regime where this mechanism is effective and suggest experiments to test this new mechanism.

Guest Editorial—Special Collection Topic: Statistical Systems Theory in Cancer Modeling, Diagnosis, and Therapy

Aims and Scope: Cancer is a systems disease involving mutations and altered regulation. This supplement treats cancer research as it pertains to 3 systems issues of an inherently statistical nature: regulatory modeling and information processing, diagnostic classification, and therapeutic intervention and control. Topics of interest include (but are not limited to) multiscale modeling, gene/protein transcriptional regulation, dynamical systems, pharmacokinetic/pharmacodynamic modeling, compensatory regulation, feedback, apoptotic and proliferative control, copy number-expression interaction, integration of different feature types, error estimation, and reproducibility. We are especially interested in how the above issues relate to the extremely high-dimensional data sets and small- to moderate-sized data sets typically involved in cancer research, for instance, their effect on statistical power, inference accuracy, and multiple comparisons.