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Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival

Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival Purpose: Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. Patients and Methods: From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. Results: There was a significant survival difference between the clusters (P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis (P = .027) and faster growth before treatment (P = .003) but demonstrated a better response to adjuvant chemotherapy (P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). Conclusion: Dichotomizing the heterogeneity of GBMs into two populations-ne faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JCO Clinical Cancer Informatics Wolters Kluwer Health

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Publisher
Wolters Kluwer Health
Copyright
(C) 2018 by Lippincott Williams & Wilkins, Inc.
ISSN
2473-4276
DOI
10.1200/CCI.17.00080
Publisher site
See Article on Publisher Site

Abstract

Purpose: Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. Patients and Methods: From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. Results: There was a significant survival difference between the clusters (P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis (P = .027) and faster growth before treatment (P = .003) but demonstrated a better response to adjuvant chemotherapy (P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). Conclusion: Dichotomizing the heterogeneity of GBMs into two populations-ne faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.

Journal

JCO Clinical Cancer InformaticsWolters Kluwer Health

Published: Mar 1, 2018

References