Tumor progression is assumed to be driven by ongoing mutation accumulation and selection, but researchers at the Stanford Cancer Institute have found that some tumors may be destined to invade or metastasize from the outset — they are “born to be bad.”
SCI member Christina Curtis, PhD, MSc, assistant professor of medicine (oncology) and of genetics, is pioneering the way for her “big bang” model, which proposes that mutations that occur during the earliest steps of tumor formation may determine how a tumor will progress.
Curtis’s research focuses on computational biology, which comprises the fields of computer science and biology. Curtis describes computational biology as bringing “a quantitative understanding to the needs of biological data.” “It’s a really exciting time in the field because we are generating a deluge of high dimensional data, and quantitative tools are becoming increasingly important for uncovering new biology,” she said.
Understanding the ways that tumors change as they grow, are exposed to therapy, and what enables some cancers to spread to distant tissue sites is challenging since we only see the end products of these processes. While direct measurement of human tumor progression is not feasible, by analyzing patient genomic data at unprecedented resolution and by simulating the growth of realistically sized tumors (composed of 10s of billions of cells), Curtis found that once a tumor was established, there did not necessarily need to be ongoing selection for additional mutations. “In fact, the tumor cells might already be so highly ‘adapted’ for their environment at an early stage that the tumor may already have what it needs to grow, invade surrounding tissue and potentially even spread to distant organs. It turns the table on how we think about tumor progression.”
Curtis explained that this alternative model and the original model of tumor progression have different prevalence across tumor types and disease stages. However, the “big bang” model adds motivation for detecting cancer at the earliest possible stage. “We study the whole continuum of tumor progression from initiation to metastasis, but because of our recent findings, we are increasingly interested in earlier detection,” Curtis said.
An essential part of Curtis’s work is delineating the life history of different tumors in order to learn cancer’s evolutionary rulebook. “If we understand these rules, we might be able to anticipate what the tumor might do next and, in turn, how we need to intervene in response,” she said. “One of the biggest challenges is understanding which tumors are aggressive versus indolent. We have few biomarkers for that, but know that not all tumors behave the same. We need to be able to identify the tumors that are born to be bad — destined, for example, to metastasize.”
Curtis recently received a National Institutes of Health (NIH) Pioneer Award, which supports scientists with outstanding records of pioneering approaches to major challenges in research.
“The beauty of this award is that it allows me to test bold hypotheses using new technological and systems biology approaches,” Curtis said. “[The award] speaks to the NIH’s support for the application of computational approaches to better understand mechanisms of cancer development and progression.”