Transformation into cancer cells will inherit these kinetics that determine initial cell and tumor population progression dynamics.

Subject to genetic mutation and epigenetic alterations, cancer cell kinetics can change, and favorable alterations that increase cellular fitness will manifest themselves and accelerate tumor progression.

We explore orthogonal cell traits, including cell migration to facilitate invasion, spontaneous cell death due to genetic drift after accumulation of irreversible deleterious mutations, symmetric cancer stem cell division that increases the cancer stem cell pool, and telomere length and erosion as a mitotic counter for inherited non-stem cancer cell proliferation potential.

An intrinsic difference in tumor initiation and propagation potential led to a classification of cancer stem cells (CSC) and non-stem cancer cells (CC) in the majority of hematologic and solid tumors [12,32—34].

Interestingly, short telomeres indicative of short-lived cancer cells with limited proliferation capacity have indeed been observed in malignant tumor population [26,36], lending further support that limiting the number of CC progeny promotes parental CSC expansion and tumor growth.

Indeed, a variety of individual human derived malignant cancer cell lines can each be carefully divided into sub-clones that form fast-growing tumors or stable disease for many months before initiating rapid growth [42—44].

These traits are comparable to physiologic stem cells, and cancer cells with such properties have been termed cancer stem cells causing a long and active discussion about the cell of origin of tumor [5,11,12].

Inferior cancer cell trait combinations could, at least in part, explain the increasing observations of pathologic but non-advancing lesions [18,19].

Abnormally increased or decreased telomerase activity [24] in cancer stem cells lengthens or shortens telomeric DNA that defines the number of cell divisions for non-stem cancer cell progeny [25,26].

We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCl-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer.

As an independent validation of our results, we used growth inhibition data from the NCI-6O collection [18] to determine whether the drug candidates we identified are better at slowing growth in lung cancer cell lines.

For all our NCI-6O analyses we used the nine lung cancer cell lines in which over 100 Connectivity Map drugs were tested (see Methods).

23 significant drugs inhibit growth in a majority of lung cancer cell lines.

This validation supported our method: drug candidates identified by CMapBatch were significantly more likely to slow growth in nine lung cancer cell lines than other CMap drugs.

5A, purple and green nodes), indicating they may have a similar mode of action and may inhibit growth in lung cancer cell lines.

To study the joint effect of drug heterogeneity, growth rate, and evolution of resistance, we analyze a multi-type stochastic branching process describing growth of cancer cells in multiple compartments with different drug concentrations and limited migration between compartments.

Cancer cells can migrate from one spatial compartment to another.

In contrast, the present mathematical framework takes into account realistic concentration-dependent response for any initial population of cancer cells distributed over multiple metastatic compartments, and allows us to calculate the risk of acquiring resistance as well as to ascertain the timing of relapse.

Moreover, our model can be readily extended to incorporate specific migration schemes of cancer cells , e. g., along with a more realistically connected vasculature [53].

Among efforts to understand the rapid acquisition of resistance by cancer cells , particular attention has been paid to the preexisting resistance arising prior to treatment [14—17].

Moreover, a most recent overview of clinical and pharmacological data concerning distribution of many anticancer drugs in human solid tumors highlights the likely importance of insufficient and/ or heterogeneous exposure of cancer cells to effective drug levels in tumor resistance [29].

By activating tissue invasion and metastases [36] , cancer cells are able to escape from the primary site and disseminate to distant parts of the body, causing life-threatening health problems [37, 38].

In contrast to conventional chemotherapy agents that have cytotoxic effects, most molecularly targeted cancer therapies have cytostatic effects on cancer cells [75].

Previous mathematical models of glioma progression have primarily focused on the growth or migration of cancerous cells from a tumor core [37—41].

Conversely, computational models of insulin signaling exist [42, 43], but have only been applied to other applications, including articular cartilage [44], ovarian cancer [45] , and human skeletal muscle [46], and exclude molecules of interest for brain cancer cells [44, 47].

Fig 1A highlights intracellular insulin signaling pathways present in brain cancer cells .

However, since the HIFloc effects are ubiquitous in all cells, alterations in HIFloc and production of IGFBP2 would be difficult to target in cancerous cells only.

Brain cancer cells use the same pathways to develop into a cancerous phenotype [20].

Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions.

Over the course of tumor growth, cancer cells interact with normal cells via processes that are difficult to understand by experiment alone.

Growth and persistence of a tumor is influenced not only by the intrinsic proliferative capacity of the cancer cells , but also by the complex ecosystem of cells, signaling molecules and vascula-ture surrounding the tumor, which collectively comprise the tumor microenvironment (TME) [1,2] An important feature of the TME is the important role played by non-tumor cells, including both immune cells and stromal cells, in promoting tumor proliferation by contributing to immune evasion, induction of angiogenesis, and other hallmarks of cancer [3,4].

For example, hypoxic pockets in tumors can promote the survival of cancer cells during chemotherapy [26] , and local gradients of key chemokines regulate chemotaxis of tumor cells and other cells in the TME to drive processes including tissue reorganization and invasion [27].

We incorporated mechanisms for macrophage chemotaxis, macrophage functional polarization to M1 or M2 states, macrophage-mediated tumor killing, and tumor necrosis-mediated activation of macrophages via release of soluble factors, along with mechanisms for oxygen uptake by cells, oxygen delivery via vasculature, angiogenesis mediated by release of pro-angiogenic factors, and hypoxia-mediated cancer cell death.

We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line.

We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line.

The activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) in the MCF7 breast cancer cell line exemplifies the impact of such transient or sustained signalling on cell fate [3, 4].

We focused on four particular time series datasets: human aortic smooth muscle cells (AoSMC) treated with FGF2 and with IL-lfi (9 time points from 0 to 360 min; 3 replicates per treatment; IL-lfi will be referred to as Ile hereafter), as well as human MCF7 breast cancer cells treated with EGF and HRG (16 time points from 0 to 480 min; 3 replicates per treatment).

Infra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology.

The Cancer Stem Cell (CSC) hypothesis, the idea that a small population of tumour cells have the capacity to seed and grow the tumour, and intra-tumour heterogeneity, the diversity of the cancer cell population Within the tumour of an individual patient, have long been considered the basis of potential prognostic indicators in oncology.

This calls into question the notion that CSCs only ever occupy a small proportion of the tumour, and paint a picture of cancer cells as malleable entities capable of generating considerable heterogeneity.