Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival

Corbin A. Rayfield, Fillan Grady, Gustavo De Leon, Russell Rockne, Eduardo Carrasco, Pamela Jackson, Mayur Vora, Sandra K. Johnston, Andrea Hawkins-Daarud, Kamala R. Clark-Swanson, Scott Whitmire, Mauricio E. Gamez, Alyx B Porter Umphrey, Leland S Hu, Luis Gonzalez-Cuyar, Bernard Bendok, Sujay Vora, Kristin Swanson

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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-one 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.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalJCO clinical cancer informatics
Volume2
DOIs
StatePublished - Dec 1 2018

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Glioblastoma
Survival
Growth
Neoplasms
Radiotherapy
Therapeutics
Recurrence
Adjuvant Chemotherapy
Standard of Care
Databases
Phenotype
Population

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Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival. / Rayfield, Corbin A.; Grady, Fillan; De Leon, Gustavo; Rockne, Russell; Carrasco, Eduardo; Jackson, Pamela; Vora, Mayur; Johnston, Sandra K.; Hawkins-Daarud, Andrea; Clark-Swanson, Kamala R.; Whitmire, Scott; Gamez, Mauricio E.; Porter Umphrey, Alyx B; Hu, Leland S; Gonzalez-Cuyar, Luis; Bendok, Bernard; Vora, Sujay; Swanson, Kristin.

In: JCO clinical cancer informatics, Vol. 2, 01.12.2018, p. 1-14.

Research output: Contribution to journalArticle

Rayfield, CA, Grady, F, De Leon, G, Rockne, R, Carrasco, E, Jackson, P, Vora, M, Johnston, SK, Hawkins-Daarud, A, Clark-Swanson, KR, Whitmire, S, Gamez, ME, Porter Umphrey, AB, Hu, LS, Gonzalez-Cuyar, L, Bendok, B, Vora, S & Swanson, K 2018, 'Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival', JCO clinical cancer informatics, vol. 2, pp. 1-14. https://doi.org/10.1200/CCI.17.00080
Rayfield, Corbin A. ; Grady, Fillan ; De Leon, Gustavo ; Rockne, Russell ; Carrasco, Eduardo ; Jackson, Pamela ; Vora, Mayur ; Johnston, Sandra K. ; Hawkins-Daarud, Andrea ; Clark-Swanson, Kamala R. ; Whitmire, Scott ; Gamez, Mauricio E. ; Porter Umphrey, Alyx B ; Hu, Leland S ; Gonzalez-Cuyar, Luis ; Bendok, Bernard ; Vora, Sujay ; Swanson, Kristin. / Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival. In: JCO clinical cancer informatics. 2018 ; Vol. 2. pp. 1-14.
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T1 - Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival

AU - Rayfield, Corbin A.

AU - Grady, Fillan

AU - De Leon, Gustavo

AU - Rockne, Russell

AU - Carrasco, Eduardo

AU - Jackson, Pamela

AU - Vora, Mayur

AU - Johnston, Sandra K.

AU - Hawkins-Daarud, Andrea

AU - Clark-Swanson, Kamala R.

AU - Whitmire, Scott

AU - Gamez, Mauricio E.

AU - Porter Umphrey, Alyx B

AU - Hu, Leland S

AU - Gonzalez-Cuyar, Luis

AU - Bendok, Bernard

AU - Vora, Sujay

AU - Swanson, Kristin

PY - 2018/12/1

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N2 - 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-one 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.

AB - 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-one 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.

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