If cable TV systems had a channel called The Cancer Network, doctors would be wise to tune in.
But there’s no such channel. So for now, they’ll just have to read articles in scientific journals that publish papers on the science of networks. Scientists in the new field of systems biology have made a lot of progress in understanding the networks of molecular interactions inside cancer cells. Some of the recent results are deserving of prime time.
In decades past, scientists have not possessed effective mathematical tools for teasing out all the causes and effects underlying malignancies and metastasis. But new methods are now available for describing how nodes are linked in all sorts of complex systems. And just as the math has become available to analyze cellular networks, huge databases of genetic data have been accumulating thanks to new gene-reading technology. So the tools provided by network math can begin to pry valuable secrets out of cancer’s networks.
Cancer cells house very complex networks. In some networks molecules are the nodes, linked to each other by participating in mutual chemical reactions. In genetic networks, the genes that encode the blueprints for molecules are the nodes, linked when a molecule encoded by one gene influences the activity of another gene. Understanding such networks helps scientists predict how cancerous cells will behave — how they would respond to anticancer drugs, for instance.
Network studies have already begun to identify common molecular features shared by various versions of cancer. One recent study identified 15 molecular signatures common to all or most of 12 cancer types. Seven of the 15 were found in the activity of genes that code for proteins; three emerged from mining data on chemical modifications to DNA known as methylation. Three more turned up in the activity of genes coding for small RNA molecules. Two showed up in data on protein molecule activity.
Examining these molecular hallmarks of cancer could suggest new strategies for prevention or treatment. In particular, whether certain DNA sites are more or less methylated appears to be linked to another molecular feature known to be important in some forms of breast cancer.
“Investigating the mechanisms behind these methylation signatures is a particularly promising area for further research,” Wei-Yi Cheng and colleagues at Columbia University reported in a recent paper.
Another clue to effective treatment strategies comes from Hungarian researchers at Semmelweis University in Budapest. Their analysis found key differences in protein networks of cancer cells in their initial stages compared with late-stage tumor cells or cells that had spread to invade other tissues. It appears that protein networks in young cancer cells are flexible, adapting easily to changing circumstances, while mature cancer cells are more rigid.
“While rigid network segments preserve the result of a past adaptive process …, flexible network segments are capable of plastic adaptation to present or future challenges of the environment,” Dávid M. Gyurkó and collaborators write in a paper to appear in Seminars in Cancer Biology.
Scientists don’t know a lot about the changes in molecular networks as tumors grow, as detailed data are typically available only for the original healthy tissue and the fully developed tumors, Gyurkó and colleagues point out. So understanding the step-by-step changes in network structure as cells develop tumors and spread throughout the body should be a crucial goal for future systems biology research.
Still, it’s already clear that effective treatment of cancer in its early stages might not work for more advanced cases, and vice versa.
“We propose that the large structural changes of molecular networks during cancer development require a rather different targeting strategy in early and late phases of carcinogenesis,” the Hungarian researchers write.
In the early stages of cancer, a “central hit” strategy that damages key parts of a protein network may be the best way to disable the malignant cells. In later stages, a “network influence” approach may be more likely to succeed, by shifting the rogue network back to business as usual.
Viewing cancer from this changing-network perspective may help explain many failures in the quest for cures. Most testing for anticancer drug candidates is based on counteracting cancer cells in their early stages, the Hungarian scientists note. But when the disease is detected in humans seeking treatment, it has usually reached a later stage in which the cells have evolved a more rigid network.
“The application of early stage–optimized anticancer drugs to late-stage patients may be a reason of many failures in anticancer therapies,” the Hungarian scientists write.
These insights illustrate the value of a systems biology approach to understanding cancer’s molecular networks and how they evolve — and to bringing the complexities of cancer under medical control. No doubt further network-based studies will reveal many more important quirks of cancer for doctors to exploit. Stay tuned.
Follow Tom Siegfried on Twitter at @tom_siegfried.
D.M. Gyurko et al. Adaptation and learning of molecular networks as a description of cancer development at the systems-level: Potential use in anti-cancer therapies. arXiv.org. Posted June 14, 2013. [Go to]
W.-Y. Cheng et al. Multi-cancer molecular signatures and their interrelationships. arXiv.org. Posted June 11, 2013. [Go to]
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