Analysis of growth factor signaling in genetically diverse breast cancer lines

Background: Soluble 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. Results: We 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. Conclusions: Responses 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/

Proteomic Profiling Across Breast Cancer Cell Lines and Models

We performed quantitative proteomics on 60 human-derived breast cancer cell line models to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics

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

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.After 6-weeks of HFD feeding D2KO mice gained 5.6% more body weight and had 28% more adipose tissue. Oxygen consumption (V0(2)) 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.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

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

Nuclear poly(ADP-ribose) activity is a therapeutic target in amyotrophic lateral sclerosis

Abstract Amyotrophic lateral sclerosis (ALS) is a devastating and fatal motor neuron disease. Diagnosis typically occurs in the fifth decade of life and the disease progresses rapidly leading to death within ~ 2–5 years of symptomatic onset. There is no cure, and the few available treatments offer only a modest extension in patient survival. A protein central to ALS is the nuclear RNA/DNA-binding protein, TDP-43. In > 95% of ALS patients, TDP-43 is cleared from the nucleus and forms phosphorylated protein aggregates in the cytoplasm of affected neurons and glia. We recently defined that poly(ADP-ribose) (PAR) activity regulates TDP-43-associated toxicity. PAR is a posttranslational modification that is attached to target proteins by PAR polymerases (PARPs). PARP-1 and PARP-2 are the major enzymes that are active in the nucleus. Here, we uncovered that the motor neurons of the ALS spinal cord were associated with elevated nuclear PAR, suggesting elevated PARP activity. Veliparib, a small-molecule inhibitor of nuclear PARP-1/2, mitigated the formation of cytoplasmic TDP-43 aggregates in mammalian cells. In primary spinal-cord cultures from rat, Veliparib also inhibited TDP-43-associated neuronal death. These studies uncover that PAR activity is misregulated in the ALS spinal cord, and a small-molecular inhibitor of PARP-1/2 activity may have therapeutic potential in the treatment of ALS and related disorders associated with abnormal TDP-43 homeostasis

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 drug response metrics that are insensitive to division number. These are based on estimating 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 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 drugs, discovery of drug response biomarkers, and identification of drugs effective on specific patient-derived tumor cells