Mirra Chung

Novel receptor interaction and repression domains in the orphan receptor SHP.

SHP (short heterodimer partner) is a novel orphan receptor that lacks a conventional DNA binding domain and interacts with other members of the nuclear hormone receptor superfamily. We have characterized the SHP sequences required for interaction with other superfamily members, and have defined an SHP repressor domain. In the mammalian two-hybrid system, a fusion of full-length SHP to the GAL4 DNA binding domain shows 9-cis-retinoic acid-dependent interaction with a VP16-retinoid X receptor alpha (RXR alpha) fusion. By deletion analysis, sequences required for this RXR interaction map to the central portion of SHP (amino acids 92 to 148). The same region is required for interaction with RXR in vitro and in the yeast two-hybrid system, and results from the yeast system suggest that the same SHP sequences are required for interaction with other members of the nuclear hormone receptor superfamily such as thyroid hormone receptor and retinoic acid receptor. In mammalian cells, a GAL4-SHP fusion protein shows about 10-fold-decreased transcriptional activation relative to GAL4 alone, and fusion of SHP to the C terminus of a GAL4-VP16 fusion to generate a triple chimera also results in a strong decrease in transactivation activity. Sequences required for this repressor function were mapped to the C terminus of SHP. This region is distinct from that required for corepressor interaction by other members of the nuclear hormone receptor superfamily, and SHP did not interact with N-CoR in either the yeast or mammalian two-hybrid system. Together, these results identify novel receptor interaction and repressor domains in SHP and suggest two distinct mechanisms for inhibition of receptor signaling pathways by SHP.

Mice with a Targeted Deletion of the Type 2 Deiodinase Are Insulin Resistant and Susceptible to Diet Induced Obesity

Background The type 2 iodothyronine deiodinase (D2) converts the pro-hormone thyroxine into T3 within target tissues. D2 is essential for a full thermogenic response of brown adipose tissue (BAT), and mice with a disrupted Dio2 gene (D2KO) have an impaired response to cold. BAT is also activated by overfeeding. Methodology/Principal Findings After 6-weeks of HFD feeding D2KO mice gained 5.6% more body weight and had 28% more adipose tissue. Oxygen consumption (V02) was not different between genotypes, but D2KO mice had an increased respiratory exchange ratio (RER), suggesting preferential use of carbohydrates. Consistent with this, serum free fatty acids and β-hydroxybutyrate were lower in D2KO mice on a HFD, while hepatic triglycerides were increased and glycogen content decreased. Neither genotype showed glucose intolerance, but D2KO mice had significantly higher insulin levels during GTT independent of diet. Accordingly, during ITT testing D2KO mice had a significantly reduced glucose uptake, consistent with insulin resistance. Gene expression levels in liver, muscle, and brown and white adipose tissue showed no differences that could account for the increased weight gain in D2KO mice. However, D2KO mice have higher PEPCK mRNA in liver suggesting increased gluconeogenesis, which could also contribute to their apparent insulin resistance. Conclusions/Significance We conclude that the loss of the Dio2 gene has significant metabolic consequences. D2KO mice gain more weight on a HFD, suggesting a role for D2 in protection from diet-induced obesity. Further, D2KO mice appear to have a greater reliance on carbohydrates as a fuel source, and limited ability to mobilize and to burn fat. This results in increased fat storage in adipose tissue, hepatic steatosis, and depletion of liver glycogen in spite of increased gluconeogenesis. D2KO mice are also less responsive to insulin, independent of diet-induced obesity.

Profiles of Basal and Stimulated Receptor Signaling Networks Predict Drug Response in Breast Cancer Lines

Activity of receptor tyrosine kinase networks may serve as an effective means to classify breast cancers and predict their sensitivity to therapeutic drugs. Hybrid Biomarkers Changes in genes only tell part of the story in cancer. Cancer cells also exhibit altered signaling networks and can rewire their signaling pathways in response to either endogenous stimuli or drug therapy. Niepel et al. combined information about breast cancer clinical subtype, genetic status, receptor abundance, and signaling pathway activity to create hybrid biomarkers that predicted the effectiveness of various targeted breast cancer therapies. This approach may prove a better way to customize treatments for cancer patients because it accounts for heterogeneity in tumors with similar genetic alterations and because the multitude of genetic changes observed in cancer appear to result in a smaller set of dysregulated signaling states. Identifying factors responsible for variation in drug response is essential for the effective use of targeted therapeutics. We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with physiologically relevant ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. Despite the receptor-based classification of breast cancer subtypes, we found that the abundance and activity of signaling proteins in unstimulated cells (basal profile), as well as the activity of proteins in stimulated cells (signaling profile), varied within each subtype. Using a partial least-squares regression approach, we constructed models that significantly predicted sensitivity to 23 targeted therapeutics. For example, one model showed that the response to the growth factor receptor ligand heregulin effectively predicted the sensitivity of cells to drugs targeting the cell survival pathway mediated by PI3K (phosphoinositide 3-kinase) and Akt, whereas the abundance of Akt or the mutational status of the enzymes in the pathway did not. Thus, basal and signaling protein profiles may yield new biomarkers of drug sensitivity and enable the identification of appropriate therapies in cancers characterized by similar functional dysregulation of signaling networks.

The E3 ubiquitin ligase TEB4 mediates degradation of type 2 iodothyronine deiodinase

ABSTRACT The endoplasmic reticulum resident thyroid hormone-activating type 2 deiodinase (D2) is inactivated by ubiquitination via the hedgehog-inducible WSB-1. Ubiquitinated D2 can then be subsequently taken up by the proteasomal system or be reactivated by USP-33/20-mediated deubiquitination. Given that heterologously expressed D2 accumulates in Saccharomyces cerevisiae lacking the E3 ligase Doa10, we tested whether the human Doa10 ortholog, TEB4, plays a role in D2 ubiquitination and degradation. In a setting of transient coexpression in HEK-293 cells, TEB4 and D2 could be coimmunoprecipitated, and additional TEB4 expression decreased D2 activity by ∼50% (P < 0.05). A highly efficient TEB4 knockdown (>90% reduction in mRNA and protein levels) decreased D2 ubiquitination and increased D2 activity and protein levels by about fourfold. The other activating deiodinase, D1, or a truncated D2 molecule (Δ18-D2) that lacks a critical instability domain was not affected by TEB4 knockdown. Furthermore, TEB4 knockdown prolonged D2 activity half-life at least fourfold, even under conditions known to promote D2 ubiquitination. Neither exposure to 1 μM of the proteasomal inhibitor MG132 for 24 h nor RNA interference WSB-1 knockdown resulted in additive effects on D2 expression when combined with TEB4 knockdown. Similar results were obtained with MSTO-211 cells, which endogenously express D2, after TEB4 knockdown using a lentivirus-based transduction strategy. While TEB4 expression predominates in the hematopoietic lineage, both WSB-1 and TEB4 are coexpressed with D2 in a number of tissues and cell types, except the thyroid and brown adipose tissue, where TEB4 expression is minimal. We conclude that TEB4 interacts with and mediates loss of D2 activity, indicating that D2 ubiquitination and degradation can be tissue specific, depending on WSB-1 and TEB4 expression levels.

Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs

Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.

Analysis of growth factor signaling in genetically diverse breast cancer lines

BackgroundSoluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines.ResultsWe describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways.ConclusionsResponses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an “indirect negative regulation” by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program (http://www.lincsproject.org/) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.Understanding why some tumor cells respond to therapy and others do not is essential for advancing precision cancer care. Here, the authors perform large-scale transcriptomic profiling of breast cancer cell lines treated with anti-cancer drugs and find that certain drug classes induce cell line specific responses.

Measuring Cancer Drug Sensitivity and Resistance in Cultured Cells

Measuring the potencies of small‐molecule drugs in cell lines is a critical aspect of preclinical pharmacology. Such experiments are also prototypical of high‐throughput experiments in multi‐well plates. The procedure is simple in principle, but many unrecognized factors can affect the results, potentially making data unreliable. The procedures for measuring drug response described here were developed by the NIH LINCS program to improve reproducibility. Key features include maximizing uniform cell growth during the assay period, accounting for the effects of cell density on response, and correcting sensitivity measures for differences in proliferation rates. Two related protocols are described: one involves an endpoint measure well‐suited to large‐scale studies and the second is a time‐dependent measurement that reveals changes in response over time. The methods can be adapted to other types of plate‐based experiments.

Therapeutically advantageous secondary targets of abemaciclib identified by multi-omics profiling of CDK4/6 inhibitors

The target profiles of many drugs are established early in their development and are not systematically revisited at the time of FDA approval. Thus, it is often unclear whether therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five different phenotypic and biochemical assays to compare approved inhibitors of cyclin-dependent kinases 4/6 – collectively regarded as breakthroughs in the treatment of hormone receptor-positive breast cancer. We find that transcriptional, proteomic and phenotypic changes induced by palbociclib, ribociclib, and abemaciclib differ significantly; abemaciclib in particular has advantageous activities partially overlapping those of alvocidib, an older polyselective CDK inhibitor. In cells and mice, abemaciclib inhibits kinases other than CDK4/6 including CDK2/Cyclin A/E – implicated in resistance to CDK4/6 inhibition – and CDK1/Cyclin B. The multi-faceted experimental and computational approaches described here therefore uncover under-appreciated differences in CDK4/6 inhibitor activities with potential importance in treating human patients.Three inhibitors of the cyclin-dependent kinases CDK4/6, palbociclib, ribociclib, and abemaciclib, have emerged as highly promising therapies for the treatment of breast cancer and other solid tumors. These drugs are reported to have similar mechanisms of action although recent data suggest that abemaciclib exhibits distinct single-agent activity and toxicity. We compare their mechanisms of action using biochemical assays, mRNA profiling, mass spectrometry-based phospho-proteomics, and GR-based dose-response assays. We find that abemaciclib has activities not shared by palbociclib or ribociclib including: induction of cell death (even in pRb-deficient cells), arrest in the G2 phase of the cell cycle, and reduced drug adaptation. These activities appear to arise from inhibition of CDKs other than CDK4/6 including CDK2/Cyclin A/E and CDK1/Cyclin B. We propose that inhibition of these kinases by abemaciclib overcomes known mechanisms of resistance to CDK4/6 inhibition and may be therapeutically advantageous for patients whose tumors progress on palbociclib or ribociclib.

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

Evidence that some influential biomedical results cannot be recapitulated has increased calls for data that is findable, accessible, interoperable, and reproducible (FAIR). Here, we study factors influencing the reproducibility of a prototypical cell-based assay: responsiveness of cultured cell lines to anti-cancer drugs. Such assays are important for drug development, mechanism of action studies, and patient stratification. This study involved seven research centers comprising the NIH LINCS Program Consortium, which aims to systematically characterize the responses of human cells to perturbation by gene disruption, small molecule drugs, and components of the microenvironment. We found that factors influencing the measurement of drug response vary substantially with the compound being analyzed and thus, the underlying biology. For example, substitution of a surrogate assay such as CellTiter-Glo® for direct microscopy-based cell counting is acceptable in the case of neratinib or alpelisib, but not palbociclib or etoposide. Uncovering and controlling for such context sensitivity requires systematic measurement of assay robustness in the face of biological variation, which is distinct from assay precision and sensitivity. Conversely, validating assays only over a narrow range of conditions has the potential to introduce serious systematic error in a large dataset spanning many compounds and cell lines.