Today I’m proud to introduce you to a talented postdoctoral fellow in my own group at the Los Alamos National Laboratory. No, I had nothing to do with his work, which is why I can discuss it without any competing interests. Ours is the Theoretical Biology group, and what that means is that we do biology from a purely theoretical perspective: we design analytical models and analyze data from experiments. Sounds trivial, but it’s not, and it takes the joint forces of people coming from the most disparate fields to do what we do: in our group, you’ll find physicists, immunologists, biologists, statisticians, mathematicians, and then more physicists.
The particular research I want to discuss today involves looking at the genomes of cancer cells. Perhaps the most famous mutations associated with cancer are the ones found in the genes BRCA1 and BRCA2. These are germline mutations, i.e. mutations that are found in certain people from birth. Women who have these mutations in the BRCA1 and/or BRCA2 genes have a 60% higher chance of developing breast cancer during their lifetime than those who don’t (and yet 80% of breast cancers are not associated to these mutations, see this older post for more details on that).
DNA has a certain likelihood to accumulate new random mutations every time the cell divides. These are called somatic mutations, i.e. mutations that aren’t present at birth, but arise as we age. Some environmental exposures like smoking and radiation can also cause somatic mutations. Cancer tissue, as you can imagine, is riddled with somatic mutations, but, as it turns out, the mutations differ from cancer to cancer, and also depending on what exposure caused the disease. For example, certain mutational patterns occur most frequently in lung cancer caused by smoking, while others in skin cancers caused by ultraviolet light. These mutational patterns become “signatures” of a particular cancer, and the question is: can we predict the prognosis of the disease based on these signatures? Can we find specific treatments that work for specific signatures?
Some drugs are already in use that work only with cancer tissues that have specific receptors.
Ludmil Alexandrov, a postdoc in my group, used the incredible wealth of DNA sequencing data from tumor tissues to develop a mathematical framework that analyzes the different mutational patterns of each single cell genomes and explore how these signatures developed over time. As Alexandrov wrote in his recent article  in Science:
“I curated the majority of publicly available data and compiled a data set encompassing ~5 million somatic mutations from the mutational catalogs of 7042 primary cancers of 30 different classes. These data revealed the existence of 21 distinct mutational signatures in human cancer. Some were present in many cancer types, [. . .] others were confined to a single cancer class.”
Because of this work, which he developed during his graduate research at the Wellcome Trust Sanger Institute, Ludmil won the Science and SciLifeLab prize for young scientists (awarded by the American Association for the Advancement of Science and Science Magazine) and the 2015 Weintraub Award for Graduate Research. In his own words,
“In summary, my Ph.D. thesis provided a basis for deciphering mutational signatures from cancer genomics data and developed the first comprehensive census of mutational signatures in human cancer. The results reveal the diversity of mutational processes underlying the development of cancer and have far-reaching implications for understanding cancer etiology, as well as for developing cancer prevention strategies and novel targeted cancer therapies.”
Congratulations, Ludmil, well deserved!
 Alexandrov, L. (2015). Understanding the origins of human cancer Science, 350 (6265), 1175-1177 DOI: 10.1126/science.aad7363