Caenorhabditis elegans Cyclin D/CDK4 and Cyclin E/CDK2 Induce Distinct Cell Cycle Re-Entry Programs in Differentiated Muscle Cells

Cell proliferation and differentiation are regulated in a highly coordinated and inverse manner during development and tissue homeostasis. Terminal differentiation usually coincides with cell cycle exit and is thought to engage stable transcriptional repression of cell cycle genes. Here, we examine the robustness of the post-mitotic state, using Caenorhabditis elegans muscle cells as a model. We found that expression of a G1 Cyclin and CDK initiates cell cycle re-entry in muscle cells without interfering with the differentiated state. Cyclin D/CDK4 (CYD-1/CDK-4) expression was sufficient to induce DNA synthesis in muscle cells, in contrast to Cyclin E/CDK2 (CYE-1/CDK-2), which triggered mitotic events. Tissue-specific gene-expression profiling and single molecule FISH experiments revealed that Cyclin D and E kinases activate an extensive and overlapping set of cell cycle genes in muscle, yet failed to induce some key activators of G1/S progression. Surprisingly, CYD-1/CDK-4 also induced an additional set of genes primarily associated with growth and metabolism, which were not activated by CYE-1/CDK-2. Moreover, CYD-1/CDK-4 expression also down-regulated a large number of genes enriched for catabolic functions. These results highlight distinct functions for the two G1 Cyclin/CDK complexes and reveal a previously unknown activity of Cyclin D/CDK-4 in regulating metabolic gene expression. Furthermore, our data demonstrate that many cell cycle genes can still be transcriptionally induced in post-mitotic muscle cells, while maintenance of the post-mitotic state might depend on stable repression of a limited number of critical cell cycle regulators

Rb and FZR1/Cdh1 determine CDK4/6-cyclin D requirement in C. elegans and human cancer cells

Cyclin-dependent kinases 4 and 6 (CDK4/6) in complex with D-type cyclins promote cell cycle entry. Most human cancers contain overactive CDK4/6-cyclin D, and CDK4/6-specific inhibitors are promising anti-cancer therapeutics. Here, we investigate the critical functions of CDK4/6-cyclin D kinases, starting from an unbiased screen in the nematode Caenorhabditis elegans. We found that simultaneous mutation of lin-35, a retinoblastoma (Rb)-related gene, and fzr-1, an orthologue to the APC/C co-activator Cdh1, completely eliminates the essential requirement of CDK4/6-cyclin D (CDK-4/CYD-1) in C. elegans. CDK-4/CYD-1 phosphorylates specific residues in the LIN-35 Rb spacer domain and FZR-1 amino terminus, resembling inactivating phosphorylations of the human proteins. In human breast cancer cells, simultaneous knockdown of Rb and FZR1 synergistically bypasses cell division arrest induced by the CDK4/6-specific inhibitor PD-0332991. Our data identify FZR1 as a candidate CDK4/6-cyclin D substrate and point to an APC/CFZR1 activity as an important determinant in response to CDK4/6-inhibitors

Rb and FZR1/Cdh1 determine CDK4/6-cyclin D requirement in C. elegans and human cancer cells

Cyclin-dependent kinases 4 and 6 (CDK4/6) in complex with D-type cyclins promote cell cycle entry. Most human cancers contain overactive CDK4/6-cyclin D, and CDK4/6-specific inhibitors are promising anti-cancer therapeutics. Here, we investigate the critical functions of CDK4/6-cyclin D kinases, starting from an unbiased screen in the nematode Caenorhabditis elegans. We found that simultaneous mutation of lin-35, a retinoblastoma (Rb)-related gene, and fzr-1, an orthologue to the APC/C co-activator Cdh1, completely eliminates the essential requirement of CDK4/6-cyclin D (CDK-4/CYD-1) in C. elegans. CDK-4/CYD-1 phosphorylates specific residues in the LIN-35 Rb spacer domain and FZR-1 amino terminus, resembling inactivating phosphorylations of the human proteins. In human breast cancer cells, simultaneous knockdown of Rb and FZR1 synergistically bypasses cell division arrest induced by the CDK4/6-specific inhibitor PD-0332991. Our data identify FZR1 as a candidate CDK4/6-cyclin D substrate and point to an APC/C(FZR1) activity as an important determinant in response to CDK4/6-inhibitors

Comparison of the Cancer Gene Targeting and Biochemical Selectivities of All Targeted Kinase Inhibitors Approved for Clinical Use

<div><p>The anti-proliferative activities of all twenty-five targeted kinase inhibitor drugs that are in clinical use were measured in two large assay panels: (1) a panel of proliferation assays of forty-four human cancer cell lines from diverse tumour tissue origins; and (2) a panel of more than 300 kinase enzyme activity assays. This study provides a head-on comparison of all kinase inhibitor drugs in use (status Nov. 2013), and for six of these drugs, the first kinome profiling data in the public domain. Correlation of drug activities with cancer gene mutations revealed novel drug sensitivity markers, suggesting that cancers dependent on mutant <i>CTNNB1</i> will respond to trametinib and other MEK inhibitors, and cancers dependent on <i>SMAD4</i> to small molecule EGFR inhibitor drugs. Comparison of cellular targeting efficacies reveals the most targeted inhibitors for EGFR, ABL1 and BRAF(V600E)-driven cell growth, and demonstrates that the best targeted agents combine high biochemical potency with good selectivity. For ABL1 inhibitors, we computationally deduce optimized kinase profiles for use in a next generation of drugs. Our study shows the power of combining biochemical and cellular profiling data in the evaluation of kinase inhibitor drug action.</p></div

Selective Targeting of <i>CTNNB1-</i>, <i>KRAS-</i> or <i>MYC-</i>Driven Cell Growth by Combinations of Existing Drugs

<div><p>The aim of combination drug treatment in cancer therapy is to improve response rate and to decrease the probability of the development of drug resistance. Preferably, drug combinations are synergistic rather than additive, and, ideally, drug combinations work synergistically only in cancer cells and not in non-malignant cells. We have developed a workflow to identify such targeted synergies, and applied this approach to selectively inhibit the proliferation of cell lines with mutations in genes that are difficult to modulate with small molecules. The approach is based on curve shift analysis, which we demonstrate is a more robust method of determining synergy than combination matrix screening with Bliss-scoring. We show that the MEK inhibitor trametinib is more synergistic in combination with the BRAF inhibitor dabrafenib than with vemurafenib, another BRAF inhibitor. In addition, we show that the combination of MEK and BRAF inhibitors is synergistic in <i>BRAF</i>-mutant melanoma cells, and additive or antagonistic in, respectively, <i>BRAF</i>-wild type melanoma cells and non-malignant fibroblasts. This combination exemplifies that synergistic action of drugs can depend on cancer genotype. Next, we used curve shift analysis to identify new drug combinations that specifically inhibit cancer cell proliferation driven by difficult-to-drug cancer genes. Combination studies were performed with compounds that as single agents showed preference for inhibition of cancer cells with mutations in either the <i>CTNNB1</i> gene (coding for β-catenin), <i>KRAS</i>, or cancer cells expressing increased copy numbers of <i>MYC</i>. We demonstrate that the Wnt-pathway inhibitor ICG-001 and trametinib acted synergistically in Wnt-pathway-mutant cell lines. The ERBB2 inhibitor TAK-165 was synergistic with trametinib in <i>KRAS</i>-mutant cell lines. The EGFR/ERBB2 inhibitor neratinib acted synergistically with the spindle poison docetaxel and with the Aurora kinase inhibitor GSK-1070916 in cell lines with <i>MYC</i> amplification. Our approach can therefore efficiently discover novel drug combinations that selectively target cancer genes.</p></div

2D structures of the kinase inhibitors profiled in this study.

<p>All are kinase inhibitors that were approved for clinical use at Nov. 2013.</p

Anova analysis reveals novel drug response markers.

<p>A: the MEK inhibitor trametinib and B: the EGFR inhibitor afatinib. The volcano plots show the average IC₅₀ shift between mutant and non-mutant cell lines (x-axis) and the significance from the Anova test (y-axis). Significance was corrected for multiple-testing and all associations above the threshold level (dotted line) are coloured green. Areas of circles are proportional with the number of cell lines carrying mutations.</p

Biochemical profiling of marketed kinase inhibitors.

<p>A: Hierarchical clustering of inhibitory profiles of all kinase drugs in a panel of more than 300 biochemical kinase assays (%-inhibition at 1 μM inhibitor concentration). Trametinib, everolimus and temsirolimus show only minor inhibition, as mTOR and MEK kinase assays are not included in the panel. B: Potent biochemical IC₅₀s on the biological target correlate with more potent cellular IC₅₀s. C: Biochemical selectivity leads to a more selective response in the cell panel. Biochemical selectivity was quantified by selectivity entropy <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092146#pone.0092146-Uitdehaag2" target="_blank">[33]</a> and the selectivity of targeting cell growth was expressed by the average IC₅₀ in the cell panel. Non-oncology drugs fasudil and tofacitinib were deleted from the analysis because of lack of response. Open circles: the mTOR and MEK inhibitors everolimus, temsirolimus and trametinib, respectively.</p

Cellular profiling of marketed kinase inhibitors.

<p>A: Tissue origin of cell lines in the Oncolines panel. B: Frequency of cancer gene changes in the cell panel, <i>i.e.</i>, mutations, translocations and copy number changes in the COSMIC Cell Line Project <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092146#pone.0092146-Garnett1" target="_blank">[4]</a>. C: Hierarchical clustering of profiling data of marketed kinase inhibitor drugs in the 44-cell line panel. Unscaled ¹⁰logIC₅₀s were used. Doxorubicin_123 is a triplicate profiling for control. Non-kinase inhibitors are coloured red. D: Kinase inhibitors have a greater selectivity in the cell panel than classic cytotoxic agents (5-fluorouracil, cisplatin, vincristine, doxorubicin, etoposide, docetaxel and bortezomib).</p

Comparison of the targeting efficacy of marketed inhibitors.

<p>Each circle represents a marketed kinase inhibitor and its targeted cell growth inhibition. A: Cell lines that overexpress <i>EGFR</i>. B: Cell lines containing the <i>BRAF(V600E)</i> mutation. C: Cell lines containing aberrant <i>ABL1</i> signalling. Compounds in the upper left corner of the plots have superior targeting. Statistically relevant associations after correction for multiple testing are coloured blue. D: Quantitative comparison of inhibitor targeting by standardization of IC₅₀ shifts between sensitive and non-sensitive cell lines.</p