Matt Gevaert

Patients with high-grade glioma (HGG) have an extremely poor prognosis compounded by a lack of advancement in clinical care over the past few decades. Regardless of classification, most newly diagnosed patients receive the same treatment, radiation and temozolomide (RT/TMZ). We developed a functional precision oncology test that prospectively identifies individual patient’s response to this treatment regimen. Tumor tissues isolated from patients with newly diagnosed HGG enrolled in 3D PREDICT… Show more

Patients with high-grade glioma (HGG) have an extremely poor prognosis compounded by a lack of advancement in clinical care over the past few decades. Regardless of classification, most newly diagnosed patients receive the same treatment, radiation and temozolomide (RT/TMZ). We developed a functional precision oncology test that prospectively identifies individual patient’s response to this treatment regimen. Tumor tissues isolated from patients with newly diagnosed HGG enrolled in 3D PREDICT REGISTRY were evaluated for response to chemotherapeutic agents using the 3D Predict™ Glioma test. Patients receiving RT/TMZ were followed for 2 years. Clinical outcomes including imaging, assessments, and biomarker measurements were compared to patient matched test-predicted therapy response. Median survival between test-predicted temozolomide responders and test-predicted temozolomide non-responders revealed a statistically significant increase in progression-free survival when using the test to predict response across multiple subgroups including HGG (5.8 months), glioblastoma (4.7 months), and MGMT unmethylated glioblastoma (4.7 months). Overall survival was also positively separated across the subgroups at 7.6, 5.1, and 6.3 months respectively. The strong correlation of 3D Predict Glioma test results with clinical outcomes demonstrates that this functional test is prognostic in patients treated with RT/TMZ and supports aligning clinical treatment to test-predicted response across varying HGG subgroups. Show less

Abstract
Clinical outcomes in high-grade glioma (HGG) have remained relatively unchanged over the last 3 decades with only modest increases in overall survival. Despite the validation of biomarkers to classify treatment response, most newly diagnosed (ND) patients receive the same treatment regimen. This study aimed to determine whether a prospective functional assay that provides a direct, live tumor cell-based drug response prediction specific for each patient could accurately predict… Show more

Abstract
Clinical outcomes in high-grade glioma (HGG) have remained relatively unchanged over the last 3 decades with only modest increases in overall survival. Despite the validation of biomarkers to classify treatment response, most newly diagnosed (ND) patients receive the same treatment regimen. This study aimed to determine whether a prospective functional assay that provides a direct, live tumor cell-based drug response prediction specific for each patient could accurately predict clinical drug response prior to treatment.

Results
Absent biomarker stratification, the test accurately predicted clinical response/nonresponse to temozolomide in 17/20 (85%, P = .007) ND patients within 7 days of their surgery, prior to treatment initiation. Test-predicted responders had a median overall survival post-surgery of 11.6 months compared to 5.9 months for test-predicted nonresponders (P = .0376). Case studies provided examples of the clinical utility of the assay predictions and their impact upon treatment decisions resulting in positive clinical outcomes.

Conclusion
This study both validates the developed assay analytically and clinically and provides case studies of its implementation in clinical practice. Show less

Although 70–80% of newly diagnosed ovarian cancer patients respond to first-line therapy, almost all relapse and five-year survival remains below 50%. One strategy to increase five-year survival is prolonging time to relapse by improving first-line therapy response. However, no biomarker today can accurately predict individual response to therapy. In this study, we present analytical and prospective clinical validation of a new test that utilizes primary patient tissue in 3D cell culture to… Show more

Although 70–80% of newly diagnosed ovarian cancer patients respond to first-line therapy, almost all relapse and five-year survival remains below 50%. One strategy to increase five-year survival is prolonging time to relapse by improving first-line therapy response. However, no biomarker today can accurately predict individual response to therapy. In this study, we present analytical and prospective clinical validation of a new test that utilizes primary patient tissue in 3D cell culture to make patient-specific response predictions prior to initiation of treatment in the clinic. Test results were generated within seven days of tissue receipt from newly diagnosed ovarian cancer patients obtained at standard surgical debulking or laparoscopic biopsy. Patients were followed for clinical response to chemotherapy. In a study population of 44, the 32 test-predicted Responders had a clinical response rate of 100% across both adjuvant and neoadjuvant treated populations with an overall prediction accuracy of 89% (39 of 44, p < 0.0001). The test also functioned as a prognostic readout with test-predicted Responders having a significantly increased progression-free survival compared to test-predicted Non-Responders, p = 0.01. This correlative accuracy establishes the test’s potential to benefit ovarian cancer patients through accurate prediction of patient-specific response before treatment. Show less

The scientific method—hypothesis-driven design and execution of an experiment—is great . . . except when it could kill you (or me). That’s why, for example, there are extensive legal requirements to investigate new pharmaceutical agents using proxies before testing drug toxicity in a human clinical trial. The US Food and Drug Administration requires a combination of nonliving techniques, in vitro models, and animal studies before new compounds may be administered to humans.

Although… Show more

The scientific method—hypothesis-driven design and execution of an experiment—is great . . . except when it could kill you (or me). That’s why, for example, there are extensive legal requirements to investigate new pharmaceutical agents using proxies before testing drug toxicity in a human clinical trial. The US Food and Drug Administration requires a combination of nonliving techniques, in vitro models, and animal studies before new compounds may be administered to humans.

Although pharmaceutical and regulatory industries are doing the best they can in the current paradigm, to be blunt it’s not going very well. According to recent publications, despite best efforts to predict those drug candidates’ efficacy and toxicity during preclinical testing, 88% of them fail when put to the test in humans.

A new paradigm is needed! And the biggest opportunity lies in cell culture, which typically is still done in a Petri dish (or its derivative, the multiwell plate). This ubiquitous scientific container, first described well over a hundred years ago in the late 19th century, was already commonplace when cells were first widely cultured in the mid-20th century and remains the standard of cell culture today.

The tremendous opportunity for improvement lies in the fact that cells are living organisms and can respond dynamically to local stimuli provided by and in their environment. The solution is to provide a different environment with more of the “right” physical, mechanical, and biochemical stimuli. Developments that address this challenge will affect much more than in vitro modeling of in vivo physiology. Aside from the desire to model human beings and the need to minimize the very serious consequences of the scientific method for certain kinds of questions, better in vitro systems have enormous implications as both manufacturing methods for implants (e.g., in tissue engineering and regenerative medicine) and as process steps for cell therapy. Show less