Cancer is a multistep process characterized by altered signal transduction, cell growth, and metabolism. To identify such processes in early carcinogenesis we use an information theoretic approach to characterize gene expression quantified as mRNA levels in primary keratinocytes ( K ) and human papillomavirus 16 (HPV16)-transformed keratinocytes (HF1 cells) from early ( E ) and late ( L ) passages and from benzo( a )pyrene-treated (BP) L cells. Our starting point is that biological signaling processes are subjected to the same quantitative laws as inanimate, nonequilibrium chemical systems. Environmental and genomic constraints thereby limit the maximal thermodynamic entropy that the biological system can reach. The procedure uncovers the changes in gene expression patterns in different networks and defines the significance of each altered network in the establishment of a particular phenotype. The development of transformed HF1 cells is shown to be represented by one major transcription pattern that is important at all times. Two minor transcription patterns are also identified, one that contributes at early times and a distinguishably different pattern that contributes at later times. All three transcription patterns defined by our analysis were validated by gene expression values and biochemical means. The major transcription pattern includes reduced transcripts participating in the apoptotic network and enhanced transcripts participating in cell cycle, glycolysis, and oxidative phosphorylation. The two minor patterns identify genes that are mainly involved in lipid or carbohydrate metabolism.

The most prevalent and aggressive type of brain cancer, namely, glioblastoma (GBM), is characterized by intra- and inter-tumor heterogeneity and strong spreading capacity, which makes treatment ineffective. A true therapeutic answer is still in its infancy despite various studies that have made significant progress toward understanding the mechanisms behind GBM recurrence and its resistance. The primary causes of GBM recurrence are attributed to the heterogeneity and diffusive nature; therefore, monitoring the tumor’s heterogeneity and spreading may offer a set of therapeutic targets that could improve the clinical management of GBM and prevent tumor relapse. Additionally, the blood–brain barrier (BBB)-related poor drug delivery that prevents effective drug concentrations within the tumor is discussed. With a primary emphasis on signaling heterogeneity, tumor infiltration, and computational modeling of GBM, this review covers typical therapeutic difficulties and factors contributing to drug resistance development and discusses potential therapeutic approaches.

Computers are organized into hardware and software. Using a theoretical approach to identify patterns in gene expression in a variety of species, organs, and cell types, we found that biological systems similarly are comprised of a relatively unchanging hardware-like gene pattern. Orthogonal patterns of software-like transcripts vary greatly, even among tumors of the same type from different individuals. Two distinguishable classes could be identified within the hardware-like component: those transcripts that are highly expressed and stable and an adaptable subset with lower expression that respond to external stimuli. Importantly, we demonstrate that this structure is conserved across organisms. Deletions of transcripts from the highly stable core are predicted to result in cell mortality. The approach provides a conceptual thermodynamic-like framework for the analysis of gene-expression levels and networks and their variations in diseased cells.

Significance: Microscopic analysis of cellular architectures within a diseased tissue often provides an independent assay relative to measurements of molecular biomarkers from that same tissue. Both methods may point to the same disease state, but the relationship between the two is often not clear. We explore a connection by investigating how growth factor-driven protein signaling depends upon the distance separating pairs of cancer cells. A thermodynamic-derived theory identifies the intercellular separation that corresponds to the steady state of the signaling. That length scale is found to be the dominant cell separation distance in bulk tissue culture. The approach is tested in one cell line and validated in another, and may provide insight into the diffusive nature of certain brain cancer phenotypes.

Significance: We demonstrate the role of free energy in determining the direction of motion in a biological process. A thermodynamics-motivated approach is used to show that free-energy changes in cell–cell signaling determine the force gradient for directed cell motion. Using isolated cell pairs and assays of secreted protein levels, we compute the free energy of the cell–cell signaling network as a function of cell–cell separation. The changes in free energy as a function of separation correspond to a potential-energy gradient that can influence cell–cell motion. Recordings of cell motion trajectories were compared with the direction of the gradient. Neutralizing the secreted proteins most involved in establishing the free-energy gradient cancels the directed motion.

DNA damage checkpoints cause cell cycle arrest, allowing DNA repair before resumption of the cell cycle. These checkpoints can be activated through several signaling pathways. Checkpoint activators include p53, checkpoint kinase 1 (CHK1), checkpoint kinase 2 and/or MAPKAP kinase 2 (MK2). Many cancer cells lack p53 activity and, therefore, depend on alternative checkpoint activators to arrest the cell cycle following DNA damage. Inhibition of these pathways is expected to specifically sensitize these p53-deficient cells to DNA damage caused by chemotherapy. Using isogenic p53-proficient and p53-deficient cancer cell lines, we show that inactivation of CHK1, but not MK2, abrogates cell cycle arrest following chemotherapy, specifically in p53-deficient cells. However, we show that CHK1 is required to maintain genome integrity and cell viability, and that p53-proficient cells are no less sensitive than p53-deficient cells to CHK1 inhibition in the presence of DNA damage. Thus, combining CHK1 inhibition with DNA damage does not lead to preferential killing of p53-deficient over p53-proficient cells, and inhibiting CHK1 does not appear to be a promising approach for potentiation of cancer chemotherapy.