Abstract
Solid tumors are multiscale, open, complex, dynamic systems: complex because they have many interacting components, dynamic because both the components and their interactions can change with time, and open because the tumor freely communicates with surrounding and even distant host tissue. Thus, it is not surprising that striking intratumoral variations are commonly observed in clinical imaging such as MRI and CT and that several recent studies found striking regional variations in the molecular properties of cancer cells from the same tumor. Interestingly, this spatial heterogeneity in molecular properties of tumor cells is typically ascribed to branching clonal evolution due to accumulating mutations while macroscopic variations observed in, for example, clinical MRI scans are usually viewed as functions of blood flow. The clinical significance of spatial heterogeneity has not been fully determined but there is a general consensus that the varying intratumoral landscape along with patient factors such as age, morbidity and lifestyle, contributes significantly to the often unpredictable response of individual patients within a disease cohort treated with the same standard-of-care therapy.
Here we investigate the potential link between macroscopic tumor heterogeneity observed by clinical imaging and spatial variations in the observed molecular properties of cancer cells. We build on techniques developed in landscape ecology to link regional variations in the distribution of species with local environmental conditions that define their habitat. That is, we view each region of the tumor as a local ecosystem consisting of environmental conditions such as access to nutrients, oxygen, and means of waste clearance related to blood flow and the local population of tumor cells that both adapt to these conditions and, to some extent, change them through, for example, production of angiogenic factors. Furthermore, interactions among neighboring habitats can produce broader regional dynamics so that the internal diversity of tumors is the net result of complex multiscale somatic Darwinian interactions.
Methods in landscape ecology harness Darwinian dynamics to link the environmental properties of a given region to the local populations which are assumed to represent maximally fit phenotypes within those conditions. Consider a common task of a landscape ecologist: defining the spatial distribution of species in a large region, e.g., in a satellite image. Clearly the most accurate approach requires a meter by meter survey of the multiple square kilometers in the region of interest. However, this is both impractical and potentially destructive. Instead, landscape ecology breaks the task into component parts relying on the Darwinian interdependence of environmental properties and fitness of specific species’ phenotypic and genotypic properties. First, the satellite map is carefully analyzed to define the number and distribution of habitats. Then the species distribution in a representative sampling of each habitat is empirically determined. Ultimately, this permits sufficient bridging of spatial scales to accurately predict spatial distribution of plant and animal species within large regions.
Currently, identifying intratumoral subpopulations requires detailed histological and molecular studies that are expensive and time consuming. Furthermore, this method is subject to sampling bias, is invasive for vital organs such as the brain, and inherently destructive precluding repeated assessments for monitoring post-treatment response and proteogenomic evolution. In contrast, modern cross-sectional imaging can interrogate the entire tumor noninvasively, allowing repeated analysis without disrupting the region of interest. In particular, magnetic resonance imaging (MRI) provides exceptional spatial resolution and generates signals that are unique to the molecular constituents of tissue. Here we propose that MRI scans may be the equivalent of satellite images in landscape ecology and, with appropriate application of Darwinian first principles and sophisticated image analytic methods, can be used to estimate regional variations in the molecular properties of cancer cells.
We have initially examined this technique in glioblastoma, a malignant brain neoplasm which is morphologically complex and notorious for a fast progression from diagnosis to recurrence and death, making a suitable subject of noninvasive, rapidly repeated assessment of intratumoral evolution. Quantitative imaging analysis of routine clinical MRIs from glioblastoma has identified macroscopic morphologic characteristics which correlate with proteogenomics and prognosis. The key to the accurate detection and forecasting of intratumoral evolution using quantitative imaging analysis is likely to be in the understanding of the synergistic interactions between observable intratumoral subregions and the resulting tumor behavior.
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References
Barajas RF Jr, Phillips JJ, Parvataneni R et al (2012) Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR imaging. Neuro Oncol 14:942–954
Gutman DA, Cooper LA, Hwang SN et al (2013) MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267:560–569
Lev MH, Ozsunar Y, Henson JW et al (2004) Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 25:214–221
Phillips HS, Kharbanda S, Chen R et al (2006) Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9:157–173
Verhaak RG, Hoadley KA, Purdom E et al (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17:98–110
Baker M, Brown J (2010) Islands of fear: effects of wooded patches on habitat suitability of the striped mouse in a South African grassland. Funct Ecol 24:1313–1322
Zhou M, Hall L, Goldgof D et al (2014) Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol 7:5–13
Inda MM, Bonavia R, Mukasa A et al (2010) Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma. Genes Dev 24:1731–1745
Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269:8–15
Lacroix M, Abi-Said D, Fourney DR et al (2001) A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95:190–198
Diehn M, Nardini C, Wang DS et al (2008) Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 105:5213–5218
Garzon B, Emblem KE, Mouridsen K et al (2011) Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction. Acta Radiol 52:1052–1060
Pope WB, Sayre J, Perlina A et al (2005) MR imaging correlates of survival in patients with high-grade gliomas. Am J Neuroradiol 26:2466–2474
Aum DJ, Kim DH, Beaumont TL et al (2014) Molecular and cellular heterogeneity: the hallmark of glioblastoma. Neurosurg Focus 37:E11
Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892
Gerlinger M, Swanton C (2010) How Darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br J Cancer 103:1139–1143
Gill BJ, Pisapia DJ, Malone HR et al (2014) MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma. Proc Natl Acad Sci U S A 111:12550–12555
Sottoriva A, Spiteri I, Piccirillo SG et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A 110:4009–4014
Yancovitz M, Litterman A, Yoon J et al (2012) Intra- and inter-tumor heterogeneity of BRAF(V600E) mutations in primary and metastatic melanoma. PLoS One 7:e29336
Patel AP, Tirosh I, Trombetta JJ et al (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:1396–1401
Zar JH (2009) Biostatistical analysis, 5th edn. Pearsons
Bisdas S, Kirkpatrick M, Giglio P et al (2009) Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease? Am J Neuroradiol 30:681–688
Jain R, Poisson L, Narang J et al (2013) Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 267:212–220
Jain R, Poisson L, Narang J et al (2012) Correlation of perfusion parameters with genes related to angiogenesis regulation in glioblastoma: a feasibility study. Am J Neuroradiol 33:1343–1348
Law M, Young RJ, Babb JS et al (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247:490–498
Mills SJ, Patankar TA, Haroon HA et al (2006) Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? Am J Neuroradiol 27:853–858
Pope WB, Chen JH, Dong J et al (2008) Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. Radiology 249:268–277
Swanson KR, Rockne RC, Claridge J et al (2011) Quantifying the role of angiogenesis in malignant progression of gliomas: in silico modeling integrates imaging and histology. Cancer Res 71:7366–7375
Van Meter T, Dumur C, Hafez N et al (2006) Microarray analysis of MRI-defined tissue samples in glioblastoma reveals differences in regional expression of therapeutic targets. Diagn Mol Pathol 15:195–205
Balagurunathan Y, Gu Y, Wang H et al (2014) Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol 7:72–87
Balagurunathan Y, Kumar V, Gu Y et al (2014) Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 27:805–823
Colen R, Foster I, Gatenby R et al (2014) NCI workshop report: clinical and computational requirements for correlating imaging phenotypes with genomics signatures. Transl Oncol 7:556–569
Gu Y, Kumar V, Hall LO et al (2013) Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recognit 46:692–702
Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248
Jain RK (2005) Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science 307:58–62
Wang Z, Dabrosin C, Yin X et al (2015) Broad targeting of angiogenesis for cancer prevention and therapy. Semin Cancer Biol 35(Suppl):S224–S243
Ehdaie B, Vertosick E, Spaliviero M et al (2014) The impact of repeat biopsies on infectious complications in men with prostate cancer on active surveillance. J Urol 191:660–664
Kern SE (2012) Why your new cancer biomarker may never work: recurrent patterns and remarkable diversity in biomarker failures. Cancer Res 72:6097–6101
Mirnezami R, Nicholson J, Darzi A (2012) Preparing for precision medicine. N Engl J Med 366:489–491
Gatenby RA, Gillies RJ (2008) A microenvironmental model of carcinogenesis. Nat Rev Cancer 8:56–61
Sonoda Y, Kumabe T, Watanabe M et al (2009) Long-term survivors of glioblastoma: clinical features and molecular analysis. Acta Neurochir 151:1349–1358
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66
Cancer Genome Atlas Research Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:1061–1068
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Kim, J.Y., Gatenby, R.A. (2017). Quantitative Clinical Imaging Methods for Monitoring Intratumoral Evolution. In: Kasid, U., Clarke, R. (eds) Cancer Gene Networks. Methods in Molecular Biology, vol 1513. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6539-7_6
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DOI: https://doi.org/10.1007/978-1-4939-6539-7_6
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