Course Philosophy

The aims of the 3-week, intensive Cancer Systems Biology Course are to:

  1. Provide participants with an understanding of what Systems Biology is, and how it can enrich current trends in cancer biology and biomedical research, so that they may recognize the different kinds of questions that each approach can answer.
  2. Allow participants to sample, through lectures and hands-on activities, current research topics in Cancer Systems Biology.
  3. Provide participants not simply with exposure to “pre-packaged” data-analysis methods, but with sufficient grounding in mathematical, computational and statistical fundamentals, so that they can use methods appropriately, responsibly and confidently, knowing their purposes, capabilities and limits.
  4. Help participants develop a deep understanding of mathematical, computational and statistical models—how they are developed, used, and in some cases, validated—in biomedical research.
  5. Expand participants’ scientific vocabulary and concept inventory, so that they may profitably interact in the future with (and potentially collaborate with) researchers with complementary training, e.g., collaborations between Mathematicians, Physicists, Engineers, Computer Scientists and Cancer Biologists.
  6. Provide opportunities for community-building and mentoring among course participants and faculty.

All course materials (lectures, training datasets, software etc.) will be made available on-line to maximize educational outreach.

Background

Cancer is a collection of more than 200 diseases that share similar “hallmarks” of uncontrolled cell growth [1]. The basic causes of cancer come from genetic and epigenetic abnormalities [2-4] that enable cells to escape the powerful feedback control mechanisms that prevent overgrowth in normal tissues. Genetic and epigenetic changes in cancer cells alter molecular pathways that in turn modify cell behaviors so that feedback controls can either be ignored or, paradoxically, exploited. These changes influence how cancer cells communicate with each other and with their microenvironment, but these influences are not fixed because as the tumor grows, the microenvironment changes, the surrounding tissue reacts, and the host immune system floods in. Nothing is static, complex actions and reactions are triggered, and with time, a small group of cells that started out growing slightly abnormally, develops into a large, heterogeneous, mass of overgrowth that may metastasize and threaten life. It is this complexity (i.e. the dynamic ability to change and evolve) that makes cancer very difficult to treat.

Therapies targeted at a particular network often achieve only short-lived successes and they do not work for every patient.  Cancer cells respond to targeted therapy by compensatory changes in networks [5], by selecting for resistant clones, or evolution of mutations that negate the therapy [6].  Indeed, therapy resistance is what ultimately leads to disease mortality.  If we had a better, more insightful understanding of the strategies of cooperation among multiple signaling networks, or a better understanding of how tissues normally suppress the outgrowth of abnormal cells, we might be better positioned to develop smarter, long-lasting therapies [7, 8].

Systems Biology is a catch-all name for a re-alignment of biological research toward the pursuit of understanding things like complexity, dynamics, emergence, self-organization, robustness and design. Systems Biology seeks to understand relationships between the design of biological systems and the complex tasks they perform. In other words, Systems Biology takes a reverse engineering approach in which performance objectives are inferred from knowledge of how a system is built and strives to explain what the components of the system are needed for as opposed to what do they do. This is not possible from simple correlative studies of large experimental datasets. Instead, to achieve this, Systems Biology integrates mathematical, computational, and statistical modeling with experimental data at the genomic, cellular and multicellular scales. Modeling is not the final goal, but rather is a tool to increase understanding of the system and to develop testable hypotheses.

A major goal of Systems Biology is to identify systematic ways of going back and forth between models and data, i.e. ways of generating and testing hypotheses that are not simply based on the intuitive impressions that one gets from the visual inspection of data, or the output of a few basic statistical tests.  The need for such systematization is underscored by the fact that many biological and biomedical researchers now routinely make tens of thousands of measurements of potentially independent quantities in a single experiment, a situation in which old fashioned approaches to data interpretation are inefficient, to say the least.

Impact of Systems Biology on Cancer Research: The first successful applications of Systems Biology to Cancer involved methods for analyzing large and complex datasets (e.g., big data methods) to tease out relationships that are often non-intuitive between ‘omics data and disease phenotype. For example, network theory has been used to analyze RNA-interference datasets by mapping high confidence hit sets to multi-node pathways to enable functional genomic studies [9]. In the context of pancreatic cancer, network analyses of mutation data from the clinic reveals a core set of signaling pathways in which patient-specific mutations occur, which leads to the hypothesis that targeting the signaling outcomes of these pathways would improve response to therapy [10]. Network analyses have also successfully predicted drivers of metastatic breast cancer by combining data on copy-number variation and gene expression [11]. By combining proteomic and transcriptional data in glioblastoma, EGFRvIII-specific signaling networks have been defined and used to identify and validate novel chemotherapy agents [12]. Integrative transcriptome analyses have been used to identify three subclasses of liver cancer (hepatocellular carcinoma, HCC) associated with the aberrant activation of different signaling pathways, and have led to the identification of a new mechanism of Wnt pathway activation in one of the subclasses [13]. Network analyses combining gene expression data with genome-wide association studies led to the discovery of biomarkers in breast [14] and prostate [15] cancers. Recently, a statistical mechanics framework has been used to integrate biophysical and genomic data to construct network models in cancer and identify mutation-induced rewiring of networks in normal tissues [16]. Network analyses has also revealed that multiple interactions among signaling pathways can create hidden feedback loops that lead to drug resistance, an important new understanding that has underscored how pathway-specific drug combinations might provide more effective treatment response [5].

Bridging Individual Training Gaps. Widespread integration of Systems and Cancer Biology is hampered by a lack of training. Cancer researchers typically do not receive sufficient training in mathematical, statistical and computational tools, and physicists and mathematicians – those from non-biomedical fields who have a theoretical and computational knowledge base – lack sufficient training in cancer biology. Our course seeks to bridge these gaps in training and use classroom and wet and dry laboratory experiences to provide a high-level introduction to Systems Biology and its application to cancer relevant problems.

Brief Description of Course

Our 3-week Cancer Systems Biology course will address the training pipeline problem in apply Systems Biology concepts to Cancer by providing training at the interface between Cancer Biology and Systems Biology. The first week will consist of an optional preparatory workshop to provide training in either i) mathematical and computational methods (suitable for biomedical researchers) or ii) fundamentals of cancer biology and biomedicine (suitable for researchers from non-biomedical fields). In the two weeks that follow, the course will offer, through lectures, laboratory exercises and tutorials, a high-level introduction to important concepts from Systems Biology and research topics in Cancer Biology focused around oncogenesis and regulation of growth control, genetic and non-genetic heterogeneity, and the spatiotemporal dynamics of cellular communication and signaling between tumor cells and cells in the microenvironment.

Cutting edge training. Trainees will be introduced to cutting edge approaches, including genomic and imaging-based big data acquisition techniques and post-acquisition bioinformatic methods. For example, on the experimental side, participants will be introduced to duplex sequencing, single cell sequencing, novel imaging tools and techniques for culturing engineered tumors. Trainees will also be introduced to theoretical topics such as network analysis, information science and bioinformatics, multiscale modeling and computation and methods for systematically validating model predictions.

Laboratory exercises will emphasize both how models are derived from (and tested by) data, and how the analysis of models guides the generation of hypotheses and the acquisition of data. The purpose of the course is to provide a high-level introduction to the topics.  It is not designed to be comprehensive and historical, but rather, to help trainees start down a path of further development and learning.  Over 100 contact hours of classroom and bench exercises are planned – a depth of teaching and hands-on experience similar to a typical semester course.

References

1. Hanahan, D., and Weinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell 144, 646-674.

2. Wood, L.D., Parsons, D.W., Jones, S., Lin, J., Sjoblom, T., Leary, R.J., Shen, D., Boca, S.M., Barber, T., Ptak, J., et al. (2007). The genomic landscapes of human breast and colorectal cancers. Science 318, 1108-1113. PubMed PMID: 17932254.

3. Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A., Jr., and Kinzler, K.W. (2013). Cancer genome landscapes. Science 339, 1546-1558. PubMed PMID: 23539594; PMCID: 3749880.

4. Vogelstein, B., and Kinzler, K.W. (2015). The Path to Cancer –Three Strikes and You’re Out. N Engl J Med 373, 1895-1898. PubMed PMID: 26559569.

5. Nguyen, L.K., and Kholodenko, B.N. (2016). Feedback regulation in cell signalling: Lessons for cancer therapeutics. Semin Cell Dev Biol 50, 85-94. PubMed PMID: WOS:000371257500012.

6. Bock, C., and Lengauer, T. (2012). Managing drug resistance in cancer: lessons from HIV therapy. Nat Rev Cancer 12, 494-501. PubMed PMID: WOS:000305751700016.

7. Azmi, A.S., Wang, Z., Philip, P.A., Mohammad, R.M., and Sarkar, F.H. (2010). Proof of Concept: A review on how network and systems biology approaches aid in the discovery of potent anticancer drug combinations. Mol. Cancer Ther. 9, 3137-3144.

8. Du, W., and Elemento, O. (2015). Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies. Oncogene 34, 3215-3225. PubMed PMID: WOS:000356592300001.

9. Wilson, J.L., Hemann, M.T., Fraenkel, E., and Lauffenburger, D.A. (2013). Integrated network analyses for functional genomic studies in cancer. Semin Cancer Biol 23, 213-218. PubMed PMID: WOS:000323455900002.

10. Jones, S., Zhang, X.S., Parsons, D.W., Lin, J.C.H., Leary, R.J., Angenendt, P., Mankoo, P., Carter, H., Kamiyama, H., Jimeno, A., et al. (2008). Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801-1806. PubMed PMID: WOS:000259501300029.

11. Tran, L.M., Zhang, B., Zhang, Z., Zhang, C.S., Xie, T., Lamb, J.R., Dai, H.Y., Schadt, E.E., and Zhu, J. (2011). Inferring causal genomic alterations in breast cancer using gene expression data. Bmc Syst Biol 5. PubMed PMID: WOS:000294302700001.

12. Huang, S.S.C., Clarke, D.C., Gosline, S.J.C., Labadorf, A., Chouinard, C.R., Gordon, W., Lauffenburger, D.A., and Fraenkel, E. (2013). Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling. Plos Comput Biol 9. PubMed PMID: WOS:000315708600005.

13. Hoshida, Y., Nijman, S.M.B., Kobayashi, M., Chan, J.A., Brunet, J.P., Chiang, D.Y., Villanueva, A., Newell, P., Ikeda, K., Hashimoto, M., et al. (2009). Integrative Transcriptome Analysis Reveals Common Molecular Subclasses of Human Hepatocellular Carcinoma. Cancer Res 69, 7385-7392. PubMed PMID: WOS:000269954000033.

14. Hicks, C., Asfour, R., Pannuti, A., and Miele, L. (2011). An integrative genomics approach to biomarker discovery in breast cancer. Cancer Inform 10, 185-204. PubMed PMID: 21869864; PMCID: PMC3153161.

15. Hicks, C., Koganti, T., Giri, S., Tekere, M., Ramani, R., Sitthi-Amorn, J., and Vijayakumar, S. (2014). Integrative Genomic Analysis for the Discovery of Biomarkers in Prostate Cancer. Biomarker Insights 9, 39-51.

16. Al Quraishi, M., Koytiger, G., Jenney, A., MacBeath, G., and Sorger, P.K. (2014). A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks. Nat Genet 46, 1363-1371. PubMed PMID: WOS:000345547300020.