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/

Animal tissue-based quantitative comparison of dual-energy CT to SPR conversion methods using high-resolution gel dosimetry

Dual-energy computed tomography (DECT) has been shown to allow for more accurate ion therapy treatment planning by improving the estimation of tissue stopping power ratio (SPR) relative to water, among other tissue properties. In this study, we measured and compared the accuracy of SPR values derived using both dual- and single-energy CT (SECT) based on different published conversion algorithms. For this purpose, a phantom setup containing either fresh animal soft tissue samples (beef, pork) and a water reference or tissue equivalent plastic materials was designed and irradiated in a clinical proton therapy facility. Dosimetric polymer gel was positioned downstream of the samples to obtain a three-dimensional proton range distribution with high spatial resolution. The mean proton range in gel for each tissue relative to the water sample was converted to a SPR value. Additionally, the homogeneous samples were probed with a variable water column encompassed by two ionization chambers to benchmark the SPR accuracy of the gel dosimetry. The SPR values measured with both methods were consistent with a mean deviation of 0.2%, but the gel dosimetry captured range variations up to 5 mm within individual samples. Across all fresh tissue samples the SECT approach yielded significantly greater mean absolute deviations from the SPR deduced using gel range measurements, with an average difference of 1.2%, compared to just 0.3% for the most accurate DECT-based algorithm. These results show a significant advantage of DECT over SECT for stopping power prediction in a realistic setting, and for the first time allow to compare a large set of methods under the same conditions

RAS mutation prevalence among patients with metastatic colorectal cancer: a meta-analysis of real-world data

AIM: A confirmed wild-type RAS tumor status is commonly required for prescribing anti-EGFR treatment for metastatic colorectal cancer. This noninterventional, observational research project estimated RAS mutation prevalence from real-world sources. MATERIALS & METHODS: Aggregate RAS mutation data were collected from 12 sources in three regions. Each source was analyzed separately; pooled prevalence estimates were then derived from meta-analyses. RESULTS: The pooled RAS mutation prevalence from 4431 tumor samples tested for RAS mutation status was estimated to be 43.6% (95% CI: 38.8-48.5%); ranging from 33.7% (95% CI: 28.4-39.3%) to 54.1% (95% CI: 51.7-56.5%) between sources. CONCLUSION: The RAS mutation prevalence estimates varied among sources. The reasons for this are not clear and highlight the need for further research

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites

GRcalculator: an online tool for calculating and mining dose–response data

Background: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose–response curves such as IC 50, AUC, and E max, are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521–627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose–response assays and the reliability of pharmacogenomic associations (Hafner et al. 500–502, 2017). Results: We describe here an interactive website (www.grcalculator.org) for calculation, analysis, and visualization of dose–response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics. Conclusions: GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose–response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose–response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program (http://www.lincsproject.org/) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose–response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry. Electronic supplementary material The online version of this article (10.1186/s12885-017-3689-3) contains supplementary material, which is available to authorized users

Analytic philosophy for biomedical research: the imperative of applying yesterday's timeless messages to today's impasses

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined

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

Ex vivo modelling of drug efficacy in a rare metastatic urachal carcinoma

Background Ex vivo drug screening refers to the out-of-body assessment of drug efficacy in patient derived vital tumor cells. The purpose of these methods is to enable functional testing of patient specific efficacy of anti-cancer therapeutics and personalized treatment strategies. Such approaches could prove powerful especially in context of rare cancers for which demonstration of novel therapies is difficult due to the low numbers of patients. Here, we report comparison of different ex vivo drug screening methods in a metastatic urachal adenocarcinoma, a rare and aggressive non-urothelial bladder malignancy that arises from the remnant embryologic urachus in adults. Methods To compare the feasibility and results obtained with alternative ex vivo drug screening techniques, we used three different approaches; enzymatic cell viability assay of 2D cell cultures and image-based cytometry of 2D and 3D cell cultures in parallel. Vital tumor cells isolated from a biopsy obtained in context of a surgical debulking procedure were used for screening of 1160 drugs with the aim to evaluate patterns of efficacy in the urachal cancer cells. Results Dose response data from the enzymatic cell viability assay and the image-based assay of 2D cell cultures showed the best consistency. With 3D cell culture conditions, the proliferation rate of the tumor cells was slower and potency of several drugs was reduced even following growth rate normalization of the responses. MEK, mTOR, and MET inhibitors were identified as the most cytotoxic targeted drugs. Secondary validation analyses confirmed the efficacy of these drugs also with the new human urachal adenocarcinoma cell line (MISB18) established from the patient’s tumor. Conclusions All the tested ex vivo drug screening methods captured the patient’s tumor cells’ sensitivity to drugs that could be associated with the oncogenic KRASG12V mutation found in the patient’s tumor cells. Specific drug classes however resulted in differential dose response profiles dependent on the used cell culture method indicating that the choice of assay could bias results from ex vivo drug screening assays for selected drug classes

Nonheritable Cellular Variability Accelerates the Evolutionary Processes of Cancer

Heritable genetic or epigenetic changes in cells are thought to drive tumor development, metastasis, and drug resistance. This essay discusses the possibility that nonheritable phenotypic variability contributes to the evolution of cancer, suggesting new approaches to treatment