Dataset Information

The clinical value of patient-derived glioblastoma tumorspheres in predicting treatment response.

ABSTRACT:

Background

Advances from glioma stemlike cell (GSC) research, though increasing our knowledge of glioblastoma (GBM) biology, do not influence clinical decisions yet. We explored the translational power of GSC-enriched cultures from patient-derived tumorspheres (TS) in predicting treatment response.

Methods

The relationship between TS growth and clinical outcome was investigated in 52 GBMs treated with surgical resection followed by radiotherapy and temozolomide (TMZ). The effect on TS of radiation (6 to 60 Gy) and of TMZ (3.9 ?M to 1 mM) was related with patients' survival.

Results

Generation of TS was an independent factor for poor overall survival (OS) and poor progression-free survival (PFS) (P < .0001 and P = .0010, respectively). Growth rate and clonogenicity of TS predicted poor OS. In general, TS were highly resistant to both radiation and TMZ. Resistance to TMZ was stronger in TS with high clonogenicity and fast growth (P < .02). Shorter PFS was associated with radiation LD50 (lethal dose required to kill 50% of TS cells) >12 Gy of matched TS (P = .0484). A direct relationship was found between sensitivity of TS to TMZ and patients' survival (P = .0167 and P = .0436 for OS and PFS, respectively). Importantly, values for TMZ half-maximal inhibitory concentration <50 ?M, which are in the range of plasma levels achieved in vivo, identified cases with longer OS and PFS (P = .0020 and P = .0016, respectively).

Conclusions

Analysis of TS holds translational relevance by predicting the response of parent tumors to radiation and, particularly, to TMZ. Dissecting the clonogenic population from proliferating progeny in TS can guide therapeutic strategies to a more effective drug selection and treatment duration.

SUBMITTER: D'Alessandris QG

PROVIDER: S-EPMC5737323 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

The clinical value of patient-derived glioblastoma tumorspheres in predicting treatment response.

D'Alessandris Quintino Giorgio QG Biffoni Mauro M Martini Maurizio M Runci Daniele D Buccarelli Mariachiara M Cenci Tonia T Signore Michele M Stancato Louis L Olivi Alessandro A De Maria Ruggero R Larocca Luigi M LM Ricci-Vitiani Lucia L Pallini Roberto R

Neuro-oncology 20170801 8

<h4>Background</h4>Advances from glioma stemlike cell (GSC) research, though increasing our knowledge of glioblastoma (GBM) biology, do not influence clinical decisions yet. We explored the translational power of GSC-enriched cultures from patient-derived tumorspheres (TS) in predicting treatment response.<h4>Methods</h4>The relationship between TS growth and clinical outcome was investigated in 52 GBMs treated with surgical resection followed by radiotherapy and temozolomide (TMZ). The effect o ...[more]

PMID: 28204560

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Project description:BACKGROUND:The absolute risk reduction (ARR) in cardiovascular events from therapy is generally assumed to be proportional to baseline risk-such that high-risk patients benefit most. Yet newer analyses have proposed using randomized trial data to develop models that estimate individual treatment effects. We tested 2 hypotheses: first, that models of individual treatment effects would reveal that benefit from intensive blood pressure therapy is proportional to baseline risk; and second, that a machine learning approach designed to predict heterogeneous treatment effects-the X-learner meta-algorithm-is equivalent to a conventional logistic regression approach. METHODS AND RESULTS:We compared conventional logistic regression to the X-learner approach for prediction of 3-year cardiovascular disease event risk reduction from intensive (target systolic blood pressure <120 mm?Hg) versus standard (target <140 mm?Hg) blood pressure treatment, using individual participant data from the SPRINT (Systolic Blood Pressure Intervention Trial; N=9361) and ACCORD BP (Action to Control Cardiovascular Risk in Diabetes Blood Pressure; N=4733) trials. Each model incorporated 17 covariates, an indicator for treatment arm, and interaction terms between covariates and treatment. Logistic regression had lower C statistic for benefit than the X-learner (0.51 [95% CI, 0.49-0.53] versus 0.60 [95% CI, 0.58-0.63], respectively). Following the logistic regression's recommendation for individualized therapy produced restricted mean time until cardiovascular disease event of 1065.47 days (95% CI, 1061.04-1069.35), while following the X-learner's recommendation improved mean time until cardiovascular disease event to 1068.71 days (95% CI, 1065.42-1072.08). Calibration was worse for logistic regression; it over-estimated ARR attributable to intensive treatment (slope between predicted and observed ARR of 0.73 [95% CI, 0.30-1.14] versus 1.06 [95% CI, 0.74-1.32] for the X-learner, compared with the ideal of 1). Predicted ARRs using logistic regression were generally proportional to baseline pretreatment cardiovascular risk, whereas the X-learner observed-correctly-that individual treatment effects were often not proportional to baseline risk. CONCLUSIONS:Predictions for individual treatment effects from trial data reveal that patients may experience ARRs not simply proportional to baseline cardiovascular disease risk. Machine learning methods may improve discrimination and calibration of individualized treatment effect estimates from clinical trial data. CLINICAL TRIAL REGISTRATION:URL: https://www.clinicaltrials.gov . Unique identifiers: NCT01206062; NCT00000620.

Dataset Information

The clinical value of patient-derived glioblastoma tumorspheres in predicting treatment response.

Background

Methods

Results

Conclusions

Publications

The clinical value of patient-derived glioblastoma tumorspheres in predicting treatment response.

Similar Datasets

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