TY - JOUR
T1 - Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type
AU - Cai, Qi
AU - Christie, Alana
AU - Rajaram, Satwik
AU - Zhou, Qinbo
AU - Araj, Ellen
AU - Chintalapati, Suneetha
AU - Cadeddu, Jeffrey
AU - Margulis, Vitaly
AU - Pedrosa, Ivan
AU - Rakheja, Dinesh
AU - McKay, Renee M.
AU - Brugarolas, James
AU - Kapur, Payal
N1 - Publisher Copyright:
© 2019
PY - 2020/1
Y1 - 2020/1
N2 - Background: Clear cell renal cell carcinoma (ccRCC) is a particularly challenging tumor type because of its extensive phenotypic variability as well as intra-tumoral heterogeneity (ITH). Clinically, this complexity has been reduced to a handful of pathological variables such as stage, grade and necrosis, but these variables fail to capture the breadth of the disease. How different phenotypes affect patient prognosis and influence therapeutic response is poorly understood. Extensive ITH illustrates remarkable plasticity, providing a framework to study tumor evolution. While multiregional genomic analyses have shown evolution from an ancient clone that acquires metastatic competency over time, these studies have been conducted agnostic to morphological cues and phenotypic plasticity. Methods: We established a systematic ontology of ccRCC phenotypic variability by developing a multi-scale framework along three fundamental axes: tumor architecture, cytology and the microenvironment. We defined 33 parameters, which we comprehensively evaluated in 549 consecutive ccRCCs retrospectively. We systematically evaluated the impact of each parameter on patient outcomes, and assessed their contribution through multivariate analyses. We measured therapeutic impact in the context of anti-angiogenic therapies. We applied dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) algorithms to tumor architectures for the study of tumor evolution superimposing tumor size and grade vectors. Evolutionary models were refined through empirical analyses of directed evolution of tumor intravascular extensions, and metastatic competency (as determined by tumor reconstitution in a heterologous host). Findings: We discovered several novel ccRCC phenotypes, developed an integrated taxonomy, and identified features that improve current prognostic models. We identified a subset of ccRCCs refractory to anti-angiogenic therapies. We developed a model of tumor evolution, which revealed converging evolutionary trajectories into an aggressive type. Interpretation: This work serves as a paradigm for deconvoluting tumor complexity and illustrates how morphological analyses can improve our understanding of ccRCC pleiotropy. We identified several subtypes associated with aggressive biology, and differential response to targeted therapies. By analyzing patterns of spatial and temporal co-occurrence, intravascular tumor extensions and metastatic competency, we were able to identify distinct trajectories of convergent phenotypic evolution.
AB - Background: Clear cell renal cell carcinoma (ccRCC) is a particularly challenging tumor type because of its extensive phenotypic variability as well as intra-tumoral heterogeneity (ITH). Clinically, this complexity has been reduced to a handful of pathological variables such as stage, grade and necrosis, but these variables fail to capture the breadth of the disease. How different phenotypes affect patient prognosis and influence therapeutic response is poorly understood. Extensive ITH illustrates remarkable plasticity, providing a framework to study tumor evolution. While multiregional genomic analyses have shown evolution from an ancient clone that acquires metastatic competency over time, these studies have been conducted agnostic to morphological cues and phenotypic plasticity. Methods: We established a systematic ontology of ccRCC phenotypic variability by developing a multi-scale framework along three fundamental axes: tumor architecture, cytology and the microenvironment. We defined 33 parameters, which we comprehensively evaluated in 549 consecutive ccRCCs retrospectively. We systematically evaluated the impact of each parameter on patient outcomes, and assessed their contribution through multivariate analyses. We measured therapeutic impact in the context of anti-angiogenic therapies. We applied dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) algorithms to tumor architectures for the study of tumor evolution superimposing tumor size and grade vectors. Evolutionary models were refined through empirical analyses of directed evolution of tumor intravascular extensions, and metastatic competency (as determined by tumor reconstitution in a heterologous host). Findings: We discovered several novel ccRCC phenotypes, developed an integrated taxonomy, and identified features that improve current prognostic models. We identified a subset of ccRCCs refractory to anti-angiogenic therapies. We developed a model of tumor evolution, which revealed converging evolutionary trajectories into an aggressive type. Interpretation: This work serves as a paradigm for deconvoluting tumor complexity and illustrates how morphological analyses can improve our understanding of ccRCC pleiotropy. We identified several subtypes associated with aggressive biology, and differential response to targeted therapies. By analyzing patterns of spatial and temporal co-occurrence, intravascular tumor extensions and metastatic competency, we were able to identify distinct trajectories of convergent phenotypic evolution.
KW - Angiogenesis inhibitors
KW - Immune checkpoint inhibitors
KW - Morphologic heterogeneity
KW - Prognosis
KW - TKIs
KW - Tumor evolution
KW - ccRCC
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UR - http://www.scopus.com/inward/citedby.url?scp=85076524405&partnerID=8YFLogxK
U2 - 10.1016/j.ebiom.2019.10.052
DO - 10.1016/j.ebiom.2019.10.052
M3 - Article
C2 - 31859241
AN - SCOPUS:85076524405
SN - 2352-3964
VL - 51
JO - EBioMedicine
JF - EBioMedicine
M1 - 102526
ER -