AMP‐activated protein kinase (AMPK) is a key sensor of energy homeostasis and regulates cell metabolism, proliferation and chemotherapy/radiotherapy sensitivities. This study aimed to explore the ...relationship between the AMPK pathway‐related single nucleotide polymorphisms (SNPs) and clinical outcomes in patients with metastatic colorectal cancer (mCRC). We analyzed a total of 884 patients with mCRC enrolled in three randomized clinical trials (TRIBE, MAVERICC and FIRE‐3: where patients were treated with FOLFIRI, mFOLFOX6 or FOLFOXIRI combined with bevacizumab or cetuximab as the first‐line chemotherapy). The association between AMPK pathway‐related SNPs and clinical outcomes was analyzed across the six treatment cohorts, using a meta‐analysis approach. Our meta‐analysis showed that AMPK pathway had significant associations with progression‐free survival (PFS; p < 0.001) and overall survival (OS; p < 0.001), but not with tumor response (TR; p = 0.220): PRKAA1 rs13361707 was significantly associated with favorable PFS (log HR = −0.219, SE = 0.073, p = 0.003), as well as PRKAA1 rs10074991 (log HR = −0.215, SE = 0.073, p = 0.003), and there were suggestive associations of PRKAG1 rs1138908 with unfavorable OS (log HR = 0.170, SE = 0.083, p = 0.041), and of UBE2O rs3803739 with unfavorable PFS (log HR = 0.137, SE = 0.068, p = 0.042) and OS (log HR = 0.210, SE = 0.077, p = 0.006), although these results were not significant after false discovery rate adjustment. AMPK pathway‐related SNPs may be predictors for chemotherapy in mCRC. Upon validation, our findings would provide novel insight for selecting treatment strategies.
What's new?
Using data from three randomized clinical trials (TRIBE, MAVERICC, and FIRE‐3), this study investigates whether AMP‐activated kinase (AMPK)‐associated genomic markers are predictors for clinical outcome in patients with metastatic colorectal cancer. The authors find two single‐nucleotide polymorphisms (PRKAA1 rs13361707 and PRKAA1 rs10074991) that predicted favorable outcome after chemotherapy while two others (PRKAG1 rs1138908 and UBE2O rs3803739) predicted unfavorable outcome although the latter result did not reach significance. Targeting AMPK in addition to chemotherapy could be beneficial for some patients with metastatic colorectal cancer.
The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap ...article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?
We introduce a stochastic branching process model of diversity in recurrent tumors whose growth is driven by drug resistance. Here, an initially declining population can escape certain extinction via ...the production of mutants whose fitness is drawn at random from a mutational fitness landscape. Using a combination of analytical and computational techniques, we study the rebound growth kinetics and composition of the relapsed tumor. We find that the diversity of relapsed tumors is strongly affected by the shape of the mutational fitness distribution. Interestingly, the model exhibits a qualitative shift in behavior depending on the balance between mutation rate and initial population size. In high mutation settings, recurrence timing is a strong predictor of the diversity of the relapsed tumor, whereas in the low mutation rate regime, recurrence timing is a good predictor of tumor aggressiveness. Analysis reveals that in the high mutation regime, stochasticity in recurrence timing is driven by the random survival of small resistant populations rather than variability in production of resistance from the sensitive population, whereas the opposite is true in the low mutation rate setting. These conclusions contribute to an evolutionary understanding of the suitability of tumor size and time of recurrence as prognostic and predictive factors in cancer.
Objective: To identify clinically relevant predictive biomarkers of trastuzumab resistance.
Material and methods: MTT, FACS assays, immunoblotting and immunocytochemistry were used to phenotypically ...characterize drug responses of two cell models BT474R and SKBR3R. Student's t-test and Spearman's correlation were applied for statistic analysis.
Results: The activity of a downstream effector of the HER2 pathway phosphorylated ribosomal protein S6 (p-rpS6), was suppressed by trastuzumab in the parental cell lines yet remained unchanged in the resistant cells following treatment. The level of p-rpS6 was inversely correlated to the drug induced growth inhibition of trastuzumab-resistant cells when they are treated with selected HER2 targeting drugs.
Conclusion: p-rpS6 is a robust post-treatment indicator of HER2 pathway-targeted therapy resistance.
Cellular processes are complex and result from the interplay between multiple cell types and their environment. Existing cell biology techniques often do not allow for accurate interpretation of this ...interplay. Using a quantitative imaging-based approach, we present a high-content protocol for characterizing the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations to changes in environmental stimuli. We highlight our ability to distinguish between cell types based upon either fluorescence intensity or inherent morphology features depending on the application. This platform allows for a more comprehensive characterization of subpopulation response to perturbation while utilizing shorter time, smaller amounts of reagents, and lower likelihood of error than traditional cell biology assays. However, in some cases, cell populations may be difficult to identify and quantitate based on complex cellular features and will require additional troubleshooting; we highlight some of these circumstances in the protocol. We demonstrate this application using response to drug in a cancer model; however, it can easily be applied more broadly to other physiological processes. This protocol allows one to identify subpopulations within a co-culture system and characterize the particular response of each to external stimuli.
The increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing ...tools for quantitative analysis generally require expert knowledge.
CellPD (cell phenotype digitizer) facilitates quantitative phenotype analysis, allowing users to fit mathematical models of cell population dynamics without specialized training. CellPD requires one input (a spreadsheet) and generates multiple outputs including parameter estimation reports, high-quality plots, and minable XML files. We validated CellPD's estimates by comparing it with a previously published tool (cellGrowth) and with Microsoft Excel's built-in functions. CellPD correctly estimates the net growth rate of cell cultures and is more robust to data sparsity than cellGrowth. When we tested CellPD's usability, biologists (without training in computational modeling) ran CellPD correctly on sample data within 30 min. To demonstrate CellPD's ability to aid in the analysis of high throughput data, we created a synthetic high content screening (HCS) data set, where a simulated cell line is exposed to two hypothetical drug compounds at several doses. CellPD correctly estimates the drug-dependent birth, death, and net growth rates. Furthermore, CellPD's estimates quantify and distinguish between the cytostatic and cytotoxic effects of both drugs-analyses that cannot readily be performed with spreadsheet software such as Microsoft Excel or without specialized computational expertise and programming environments.
CellPD is an open source tool that can be used by scientists (with or without a background in computational or mathematical modeling) to quantify key aspects of cell phenotypes (such as cell cycle and death parameters). Early applications of CellPD may include drug effect quantification, functional analysis of gene knockout experiments, data quality control, minable big data generation, and integration of biological data with computational models.
Colorectal cancer (CRC) mortality is principally due to metastatic disease, with the most frequent organ of metastasis being the liver. Biochemical and mechanical factors residing in the tumor ...microenvironment are considered to play a pivotal role in metastatic growth and response to therapy. However, it is difficult to study the tumor microenvironment systematically owing to a lack of fully controlled model systems that can be investigated in rigorous detail.
We present a quantitative imaging dataset of CRC cell growth dynamics influenced by in vivo-mimicking conditions. They consist of tumor cells grown in various biochemical and biomechanical microenvironmental contexts. These contexts include varying oxygen and drug concentrations, and growth on conventional stiff plastic, softer matrices, and bioengineered acellular liver extracellular matrix. Growth rate analyses under these conditions were performed via the cell phenotype digitizer (CellPD).
Our data indicate that the growth of highly aggressive HCT116 cells is affected by oxygen, substrate stiffness, and liver extracellular matrix. In addition, hypoxia has a protective effect against oxaliplatin-induced cytotoxicity on plastic and liver extracellular matrix. This expansive dataset of CRC cell growth measurements under in situ relevant environmental perturbations provides insights into critical tumor microenvironment features contributing to metastatic seeding and tumor growth. Such insights are essential to dynamical modeling and understanding the multicellular tumor-stroma dynamics that contribute to metastatic colonization. It also establishes a benchmark dataset for training and testing data-driven dynamical models of cancer cell lines and therapeutic response in a variety of microenvironmental conditions.
Tumors cannot be understood in isolation from their microenvironment. Tumor and stromal cells change phenotype based upon biochemical and biophysical inputs from their surroundings, even as they ...interact with and remodel the microenvironment. Cancer should be investigated as an adaptive, multicellular system in a dynamical microenvironment. Computational modeling offers the potential to detangle this complex system, but the modeling platform must ideally account for tumor heterogeneity, substrate and signaling factor biotransport, cell and tissue biophysics, tissue and vascular remodeling, microvascular and interstitial flow, and links between all these sub-systems. Such a platform should leverage high-throughput experimental data, while using open data standards for reproducibility. In this chapter, we review advances by our groups in these key areas, particularly in advanced models of tissue mechanics and interstitial flow, open source simulation software, high-throughput phenotypic screening, and multicellular data standards. In the future, we expect a transformation of computational cancer biology from individual groups modeling isolated parts of cancer, to coalitions of groups combining compatible tools to simulate the 3-D multicellular systems biology of cancer tissues.
Abstract NK cells mediate the innate immune response, and HIV-infected individuals demonstrate altered NK cell phenotype and function. We find that CD4+ NK cells are susceptible to HIV infection; ...this could account for the NK cell dysfunction seen in HIV-infected individuals. CD4+ NK cells express CXCR4 and can be infected with X4-tropic viruses and some primary R5-utilizing viral isolates. Treatment with the CXCR4 ligands AMD3100 and SDF-1α partially blocks infection with X4-tropic virus, treatment with anti-CCL Igs upregulates CCR5 surface expression and enables infection with HIV-Bal. HIV infection of NK cells results in CD4 downregulation and the production of infectious virus. HIV-infected CD4+ NK cells mediate NK cell cytotoxicity, however, HIV infection is associated with decreased chemotaxis towards IL-16. Thus, HIV infection of CD4+ NK cells could account for the NK cell dysfunction observed in HIV-infected individuals. Furthermore infected NK cells could serve as a viral reservoir of HIV in vivo.
PDF
