Title: Synthesizing Image-derived Heterogeneity with Genomic Measurements for Assessing Disease Aggressiveness in Lower Grade Gliomas WHO Grade II low grade gliomas (LGGs) are a type of brain tumor with an average incidence of 1/100,000 person-years within the population. Approximately 4000 people are diagnosed with this disease each year. It has been observed that even after the first line of therapy (surgery, chemo-radiation), this disease inevitably recurs. Further, it eventually transforms to a higher grade (WHO Grade III/IV) over time, a process referred to as malignant transformation (MT). Even though MT is almost inevitable, the time-to-MT can be quite variable. In aggressive cases, the LGG can undergo MT quite early in disease course, while in other (less aggressive) cases, the duration to MT is much longer. This variability in time-to-MT can spell very disparate prognoses for the patient. Such uncertainty in the disease evolution of LGGs also creates significant challenges for treatment man- agement. Apart from creating uncertainty for the oncologist as how to appropriately manage the disease, this also creates an anxiety-ridden scenario for the patients and their caregivers who try to understand their individual condition. A rational strategy to understand and characterize the aggressiveness of LGGs can provide valuable insight into the appropriate approach of treatment and surveillance for these patients. In this proposal, we will address three specific aims that build a comprehensive, integrative statistical model, which incorporates novel, complex imaging predictors, in addition to standard clinical and genomic characteris- tics, for informing time-to-malignant transformation of LGGs. This tool can provide an assessment of anticipated disease course that will help the patient and physician make suitable treatment decisions as well as to design appropriate monitoring methods to track the progression and status of the tumor. Such a tool can also help ameliorate some of the uncertainty and anxiety for the patients and their caregivers who will be better equipped to understand their individual disease, in addition to enabling a more effective collaboration between patient and physician to determine the most appropriate treatment approach. In particular, we will develop novel tumor het- erogeneity objects, which efficiently capture inter- and intra-tumor variation in morphology and intensity-distribu- tion characteristics. These objects are functional in nature and lie on nonlinear spaces. This introduces a signif- icant challenge in statistical analysis such as defining association measures between the tumor heterogeneity objects and genomic covariates, or incorporating these objects as predictors in an integrative statistical model. To address these goals, we will use data on WHO Grade II LGG patients treated at the University of Texas MD Anderson Cancer Center to identify imaging, genomic and clinical characteristics of malignant transformation. This dataset is unique because, aside from imaging (radiographic) and genomic information, there is carefully curated clinical data about the disease course of every patient (such as overall survival and time-to-malignant transformation).

Public Health Relevance

The incidence of WHO Grade II, diffuse lower grade gliomas (LGG) is about 15% of all gliomas; the assessment of disease aggressiveness is a long-standing challenge for treatment decision-making and surveillance strategies in WHO Grade II gliomas because of the tremendous variability in the course of the disease. It is thus essential to develop integrative decision models that leverage existing clinical and genomic measurements along with novel, diagnostic imaging-derived predictors to empower the physicians to assess disease course and effectively manage patients: for instance, highly aggressive LGGs are often candidates for aggressive surgical resection, which requires an accurate assessment of LGG aggressiveness. Such tools will also enable the patients and their caregivers to understand their individual disease, and enable them to collaborate more effectively with their oncologist to design appropriately tailored treatment strategies.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Method to Extend Research in Time (MERIT) Award (R37)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Ossandon, Miguel
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University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Schools of Medicine
Ann Arbor
United States
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