Clinical Utility of Molecular Profiling in Recurrent Glioblastoma Multiforme

Introduction: Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor found in adults. GBM has limited therapeutic options. Initial tumor sampling establishes the histopathologic diagnosis, identifies prognostic and therapeutic biomarkers, and provides an opportunity for molecular profiling. By contrast, the utility of repeat tumor sampling and molecular profiling in recurrent GBM is not well established. Clinical Findings: We present a 69-year-old woman with GBM whose tumor recurred after standard treatment with temozolomide (TMZ) and concurrent radiation, followed by adjuvant TMZ. This patient had a methylated O6-methylguanine-DNA methyltransferase (MGMT) promoter, which ordinarily predicts a favorable response to TMZ. Main Diagnosis, Therapeutic Interventions, and Outcomes: Our patient’s recurrent tumor was rechallenged with TMZ based on persistent methylation of the MGMT promoter. However, her tumor was refractory to TMZ, and she floridly progressed through multiple treatments. We performed retrospective molecular profiling using next-generation sequencing (NGS) on her recurrent tumor. The NGS results showed a TMZ hypermutation signature that confers resistance to TMZ. This signature impacted our patient’s treatment plan in real time and prompted an immediate discontinuation of TMZ. Conclusions: Advances in NGS provide further insight into the molecular landscape of GBM. As NGS becomes more timely and cost-effective, molecular profiling of recurrent tumors could impact treatment decisions through either avoiding a particular treatment paradigm or identifying a potential targetable mutation. For this reason, we suggest that clinical practice routinely consider repeat biopsy and molecular profiling for recurrent GBM

Rational clinical evaluation of suspected acute coronary syndromes: The value of more information

Objective: Many meta-analyses have provided synthesised likelihood ratio data to aid clinical decision-making. However, much less has been published on how to safely combine clinical information in practice. We aimed to explore the benefits and risks of pooling clinical information during the ED assessment of suspected acute coronary syndrome. Methods: Clinical information on 1776 patients was collected within a randomised trial conducted across five South Australian EDs between July 2011 and March 2013. Bayes theorem was used to calculate patient-specific post-test probabilities using age- and gender-specific pre-test probabilities and likelihood ratios corresponding to the presence or absence of 18 clinical factors. Model performance was assessed as the presence of adverse cardiac outcomes among patients theoretically discharged at a post-test probability less than 1%. Results: Bayes theorem-based models containing high-sensitivity troponin T (hs-troponin) outperformed models excluding hs-troponin, as well as models utilising TIMI and GRACE scores. In models containing hs-troponin, a plateau in improving discharge safety was observed after the inclusion of four clinical factors. Models with fewer clinical factors better approximated the true event rate, tended to be safer and resulted in a smaller standard deviation in post-test probability estimates. Conclusions: We showed that there is a definable point where additional information becomes uninformative and may actually lead to less certainty. This evidence supports the concept that clinical decision-making in the assessment of suspected acute coronary syndrome should be focused on obtaining the least amount of information that provides the highest benefit for informing the decisions of admission or discharge.</p