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From parts to mechanisms: research heuristics for addressing heterogeneity in cancer genetics

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Abstract

A major approach to cancer research in the late twentieth century was to search for genes that, when altered, initiated the development of a cell into a cancerous state (oncogenes) or failed to stop this development (tumor suppressor genes). But as researchers acquired the capacity to sequence tumors and incorporated the resulting data into databases, it became apparent that for many tumors no genes were frequently altered and that the genes altered in different tumors in the same tissue type were often distinct. To address this heterogeneity problem, many researchers looked to a higher level of organization—to mechanisms in which gene products (proteins) participated. They proposed to reduce heterogeneity by recognizing that multiple gene alterations affect the same mechanism and that it is the altered mechanism that is responsible for the cell developing one or more hallmarks of cancer. I examine how mechanisms figure in this research and focus on two heuristics researchers use to integrate proteins into mechanisms, one focusing on pathways and one focusing on clusters in networks.

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Notes

  1. I will speak mostly of alterations, not mutations, since many studies consider other forms of genetic change, such as altered copy number or chromosomal inversions. I will use mutations when the study is focused specifically on mutations.

  2. For discussion of the opposition between what has been dubbed the somatic mutation theory and the tissue organization field theory, see Bertolaso (2016), Plutynski (2018) and (Green in press).

  3. For historical reviews of the discovery of the Ras genes and the development of the oncogene framework, see Morange (1993, 1997, 2001) and Malumbres and Barbacid (2003).

  4. Cancer researchers now generally refer to those genes that play a causal role in cancer as drivers (Greenman et al. 2007; Stratton et al. 2009).

  5. Other papers of the same period reached similar conclusions: Thomas et al. (2007), Annunziata et al. (2007) and Keats et al. (2007).

  6. Ideker pithily captures the problem posed by heterogeneity: “heterogeneity by definition means that recurrent patterns are not observed for most mutations. To make matters worse, patients afflicted by such unique patterns of mutations have been labeled ‘N-of-1 s,’ to capture the idea that they cannot be joined together with any other individuals to be analyzed and treated as a larger cohort (i.e., of size N > 1). Patients enduring this desultory fate stand alone, without a friend even in disease” (Ideker 2016).

  7. An additional motivation for the pan-cancer initiative was that by combining data across cancer types, studies would have increased statistical power and be better able to identify infrequently occurring driver mutations. See Tamborero et al. (2013) for some of the new discoveries resulting from this effort.

  8. The endeavor to collect data and genetically characterize various cancers is being continued by the International Cancer Genome Consortium (ICGC), which started in 2008 (the same year as TCGA). ICGC is collaborating with TCGA in the Pan-Cancer Analysis of Whole Genomes.

  9. Yet a further source of heterogeneity is found if one compares cells within the same tissue sample (Fisher et al. 2013).

  10. For the distinction between production and control mechanisms and its relevance in the case of cancer, see Bechtel (2018).

  11. Until recently, successful synthetic lethal experiments were limited to yeast as RNAi based methods for inhibiting proteins had too many off-target effects. Recently, CRISPR technology has proven effective in identifying synthetic lethal pairs in mammalian cells and offers promise for contributing to the development of new therapeutic approaches that target genes that are synthetically lethal in particular types of cancer (Shen et al. 2017; Du et al. 2017).

  12. In a ten year update, Hanahan and Weinberg (2011) added two emerging hallmarks:

    reprogramming of energy metabolism and evading immune destruction.

  13. The Atlas of Cancer Signalling Network provides a more recent, online (https://acsn.curie.fr/), representation of pathways involved in cell regulation that are affected in cancer (Kuperstein et al. 2015). To date it includes separate networks for cell cycle, DNA repair, apoptosis, epithelial-to-mesenchymal transition and motility, and survival that are integrated into a cohesive whole. As with Google Maps, one can zoom into look at relations of individual genes in detail. One can also click on them for further information. In addition, it is possible to locate mutations in various cancers on the map to assess how they affect cell signaling.

  14. For a detailed analysis of the construction of GO, see Leonelli (2016).

  15. There are several additional network diffusion algorithms that researchers have applied to cancer data such as HotNet (Vandin et al. 2011, 2012) and HotNet2 (Leiserson et al. 2015).

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Acknowledgements

I thank Sara Green, Anya Plutynski, Ingo Brigandt, and two anonymous reviewers for this journal for valuable comments and suggestions on earlier versions of this paper. I also thank Trey Ideker for allowing me to participate in his laboratory meetings in which strategies for using networks to identify mechanisms are often discussed.

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  1. Department of Philosophy, University of California, San Diego, 92093-0119, USA

    William Bechtel

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Bechtel, W. From parts to mechanisms: research heuristics for addressing heterogeneity in cancer genetics. HPLS 41, 27 (2019). https://doi.org/10.1007/s40656-019-0266-x

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