Previously we showed that the activator protein-1 site and the runt domain binding site in the collagenase-3 promoter act cooperatively in response to parathyroid hormone (PTH) in the rat osteoblastic osteosarcoma cell line, UMR 106-01. Our results of the expression pattern of core binding factor alpha1 (Cbfa1), which binds to the runt domain site, indicated that there is no change in the levels of Cbfa1 protein or RNA under either control conditions or after PTH treatment. The importance of posttranslational modification of Cbfa1 in the signaling pathway for PTH-induced collagenase-3 promoter activity was analyzed. PTH stimulation of collagenase-3 promoter activity was completely abrogated by protein kinase A (PKA) inhibition. To determine the role of PKA activity with respect to Cbfa1 activation (in addition to its known activity of phosphorylating cAMP-response element-binding protein to enhance c-fos promoter activity), we utilized the heterologous Gal4 transcription system. PTH stimulated the transactivation of activation domain-3 in Cbfa1 through the PKA site. In vitro phosphorylation studies indicated that the PKA site in the wild type activation domain-3 is a substrate for phosphorylation by PKA. Thus, we demonstrate that PTH induces a PKA-dependent transactivation of Cbfa1, and this transactivation is required for collagenase-3 promoter activity in UMR cells.

Adipose tissue has been suggested to contribute to the pathogenesis of various disease states, including prostate cancer. We investigated the association of cytokines and growth factors secreted by periprostatic adipose tissue with pathological features of aggressive prostate cancer. Periprostatic adipose tissue was harvested from patients undergoing radical prostatectomy and cultured for 24 hours to generate conditioned medium or snap frozen immediately for functional signaling profiling. Multiplex analysis of the periprostatic adipose tissue conditioned medium was used to detect cytokine levels and compared to patient matched serum from 7 patients. Interleukin-6 in serum and periprostatic adipose tissue conditioned medium was further analyzed by enzyme-linked immunosorbent assay and correlated with clinical variables, such as age, body mass index and Gleason score, in 45 patients. Interleukin-6 expression in periprostatic adipose tissue was determined by immunohistochemistry. Reverse phase protein microarray technology was used to analyze cell signaling networks in periprostatic adipose tissue. Interleukin-6 in periprostatic adipose tissue conditioned medium was approximately 375 times greater than that in patient matched serum and levels correlated with pathological grade. This finding was further extended by cell signaling analysis of periprostatic adipose tissue, which showed greater phosphorylation on Stat3 with high grade tumors (any component of Gleason score 4 or 5). Higher Gleason score correlated with high levels of conditioned medium derived interleukin-6. Moreover, cell signaling analysis of periprostatic adipose tissue identified activated signaling molecules, including STAT3, that correlated with Gleason score. Since STAT3 is interleukin-6 regulated, these findings suggest that periprostatic adipose tissue may have a role in modulating prostate cancer aggressiveness by serving as a source of interleukin-6. Also, we found low numbers of inflammatory cells in the fat, suggesting that adipocytes are the major secretors of interleukin-6.

Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types. Small-cell lung cancer (SCLC) is an extremely aggressive disease with poor prognosis. Despite significant advances in treatments of other cancer types, therapeutic strategies for SCLC have remained unchanged for decades. We hypothesize that distinct SCLC subtypes with differential drug sensitivities may be responsible for poor treatment outcomes. To this end, we applied a computational pipeline to identify and characterize SCLC subtypes. We found four subtypes, including one (termed “NEv2”) that had not previously been reported. Across a broad panel of drugs, we show that NEv2 is more resistant than other SCLC subtypes, suggesting that this subtype may be partly responsible for poor treatment outcomes. Importantly, we validate the existence of NEv2 cells in both human and mouse tumors. Reprogramming the identity of NEv2 cells into other subtypes may sensitize these cells to existing treatments. However, deciphering global mechanisms that regulate different subtypes is generally unfeasible. To circumvent this, we developed BooleaBayes, a modeling approach that only infers local regulatory mechanisms near stable cell subtypes. Using BooleaBayes, we found master regulators and master destabilizers for each subtype. These findings predict targets that may destabilize a particular subtype, including NEv2, and lead to successful therapy, by either knocking out master regulators or turning on master destabilizers.

A method is presented to model cell proliferation from tracked automated time-lapse imaging of cell populations. The resulting fractional proliferation graphs permit visualization of dynamic changes in the dividing and nondividing subsets of the population. We present an integrated method that uses extended time-lapse automated imaging to quantify the dynamics of cell proliferation. Cell counts are fit with a quiescence-growth model that estimates rates of cell division, entry into quiescence and death. The model is constrained with rates extracted experimentally from the behavior of tracked single cells over time. We visualize the output of the analysis in fractional proliferation graphs, which deconvolve dynamic proliferative responses to perturbations into the relative contributions of dividing, quiescent (nondividing) and dead cells. The method reveals that the response of 'oncogene-addicted' human cancer cells to tyrosine kinase inhibitors is a composite of altered rates of division, death and entry into quiescence, a finding that challenges the notion that such cells simply die in response to oncogene-targeted therapy.

Conventional metrics for assessing antiproliferative drug effect have time-dependent biases that may skew results. The drug-induced proliferation rate can be used as an alternative metric for accurate and reproducible assessment of drug performance. In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current metrics of antiproliferative small molecule effect suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.