However, we have only seen marginal improvements in the treatment and prognosis of brain tumors over the last decade. Towards identifying the pathogenesis of brain tumors and targetable mechanisms, traditional “reductionist” approaches have generated a tremendous amount of insight and identified various mechanisms that contribute to tumor maintenance, progression, and drug resistance. The survival rate following resection and whole-brain radiation therapy ranges from three to six months and the recurrence rate is as high as 76% in patients receiving a unimodal treatment. For BM patients, prognosis is similarly poor. The prognosis of GBM patients who receive the standard of care (SOC) remains dismal, with a median survival time of approximately 15 months, a five-year survival rate of less than 10%, and a recurrence rate of ~90%. ![]() The current standard of care for GBM includes a combination of surgical resection, adjuvant radiotherapy, and chemotherapy (temozolomide, TMZ). In particular, complete resection of a GBM tumor can be extremely difficult to achieve due to the highly invasive nature of GBM cells that tend to spread to the surrounding brain tissue. Glioblastoma (GBM) and brain metastasis (BM) are the most commonly diagnosed malignant brain tumors and are among the most difficult tumor types to treat. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.īrain tumors contribute to tens of thousands of deaths per year, with an estimated 17,760 deaths in 2019 in the U.S.A. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood–brain barrier hinder progress. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Unfortunately, this prognosis has not improved for several decades. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10% the recurrence rate is nearly 90%. ![]() Finally, we provide a practical example in the context of analyzing an individual glioblastoma (GBM) patient at various stages of disease progression.īrain tumors are among the most lethal tumors. Here, we synthesize and review various methodologies that can be integrated into a framework designed to achieve a personalized precision medicine approach for treating brain tumors. A systems-level understanding of disease characteristics can facilitate precise patient stratification into clinically meaningful subtypes and inform on potential druggable targets that can enhance treatment. A systems biology approach is needed to develop a multiscale understanding of the mechanistic drivers of disease etiology and progression to realize this vision. To overcome these challenges, a personalized precision medicine approach that considers the uniqueness of an individual patient’s tumor and its cellular composition is required. ![]() Pronounced differences across individuals (interpatient variability) and cell–cell heterogeneity within a tumor (intratumoral heterogeneity) severely hinder effective brain tumor treatment.
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