Ductal carcinoma in situ (DCIS) is a preinvasive lesion of the breast that makes up almost 30% of all mammographically detected malignancies [1,2]. In DCIS, as for many pre-malignant lesions, the main clinical challenge is predicting which lesions are likely to progress to invasive and metastatic disease. In the absence of reliable prognostic tools, all DCIS is treated as if it would progress, resulting in harm for those patients whose DCIS lacks potential for progression. Thus accurate and clinically actionable prognostic markers for DCIS are critically needed. Neoplasms progress through a process of random mutations and clonal expansions, leading to widespread heterogeneity both between and within neoplasms [4] that makes it challenging to predict prognosis on the basis of specific markers. Moreover, the clonal diversity within a neoplasm is overlaid upon a background of heterogeneous microenvironments, which can accelerate the evolutionary process [5,6] by imposing different selective pressures on different meta-populations in different tumor regions [7]. Genetic diversity in a population is the fuel for natural selection and is a key determinant of the rate of evolution [8,9]. The more genetic and microenvironmental diversity, the more opportunities for selection to drive clonal expansions and for the neoplasm to adapt to new selective pressures. Our solution to the problem of predicting progression given its stochastic nature is not to measure the products of somatic evolution (e.g., presence/absence of a mutation), but to measure the process of somatic evolution (e.g., genetic diversity).
The increased diagnosis and treatment of DCIS has had an unclear impact on breast cancer specific survival;thus, there has been growing concern that the current management of pre-invasive cancers results in more harm than benefit for patients. In this project, we propose to collect genomic, phenotypic, and radiographic measures of tumor cell diversity in DCIS and the tumor microenvironment, and test whether these diversity measures can identify which patients are most likely to develop metastatic disease. Deliverables from the proposal have high potential for rapid integration into clinical trials for active surveillance of DCIS and in addition, could have universal relevance for management of other solid tumors.
| Barry, Peter; Vatsiou, Alexandra; Spiteri, Inmaculada et al. (2018) The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer. Clin Cancer Res 24:4763-4770 |
| Martinez, Pierre; Mallo, Diego; Paulson, Thomas G et al. (2018) Evolution of Barrett's esophagus through space and time at single-crypt and whole-biopsy levels. Nat Commun 9:794 |
| Chowell, Diego; Napier, James; Gupta, Rohan et al. (2018) Modeling the Subclonal Evolution of Cancer Cell Populations. Cancer Res 78:830-839 |
| Shi, Bibo; Grimm, Lars J; Mazurowski, Maciej A et al. (2018) Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. J Am Coll Radiol 15:527-534 |
| Maley, Carlo C; Aktipis, Athena; Graham, Trevor A et al. (2017) Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 17:605-619 |
| Aktipis, Athena; Maley, Carlo C (2017) Cooperation and cheating as innovation: insights from cellular societies. Philos Trans R Soc Lond B Biol Sci 372: |
| Lote, H; Spiteri, I; Ermini, L et al. (2017) Carbon dating cancer: defining the chronology of metastatic progression in colorectal cancer. Ann Oncol 28:1243-1249 |
| Shi, Bibo; Grimm, Lars J; Mazurowski, Maciej A et al. (2017) Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol 24:1139-1147 |
| Andor, Noemi; Maley, Carlo C; Ji, Hanlee P (2017) Genomic Instability in Cancer: Teetering on the Limit of Tolerance. Cancer Res 77:2179-2185 |
| Fortunato, Angelo; Boddy, Amy; Mallo, Diego et al. (2017) Natural Selection in Cancer Biology: From Molecular Snowflakes to Trait Hallmarks. Cold Spring Harb Perspect Med 7: |
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