Jacob G. Scott

Aggressive treatment is appropriate for glioblastoma multiforme patients 70 years old or older: a retrospective review of 206 cases *

Elderly patients have largely been excluded from randomized trials for glioblastoma multiforme (GBM). We reviewed the results of treatment approaches, which included surgery, chemotherapy, and radiation in this group of patients. Patients were treated during the period 1979-2007 and were 70 years of age and older with histologically confirmed GBM. Overall survival (OS) was the primary endpoint of this retrospective study. Two hundred six patients 70 years of age and older were identified. Median age was 75 years (range 70-90). Median OS time was 4.5 months. Univariate analysis showed that OS was significantly impacted by KPS score (1.8 months for KPS ≤ 50 to 17.2 months for KPS ≥ 90, P < .001), age at diagnosis (5.1 months for age 70-79 versus 3.1 months for age ≥ 80, P < .001), and extent of disease (worse for bilateral disease [P = .003], multifocal disease [P = .005], and multicentric disease [P = .02]). On multivariate analysis, higher KPS score (P = .006), surgical resection (any surgery beyond biopsy) (P < .001), radiation therapy (P < .001), and chemotherapy (P < .001) were all found to be independently associated with improved OS. In this study of newly diagnosed glioblastoma patients over the age of 70 years, aggressive treatment with radiation, chemotherapy, and surgery is associated with OS.

Recursive partitioning analysis of prognostic factors for glioblastoma patients aged 70 years or older.

The most‐used prognostic scheme for malignant gliomas included only patients aged 18 to 70 years. The purpose of this study was to develop a prognostic model for patients ≥70 years of age with newly diagnosed glioblastoma.

Effectiveness of Radiotherapy for Elderly Patients With Glioblastoma

PURPOSE Radiotherapy plays a central role in the definitive treatment of glioblastoma. However, the optimal management of elderly patients with glioblastoma remains controversial, as the relative benefit in this patient population is unclear. To better understand the role that radiation plays in the treatment of glioblastoma in the elderly, we analyzed factors influencing patient survival using a large population-based registry. METHODS AND MATERIALS A total of 2,836 patients more than 70 years of age diagnosed with glioblastoma between 1993 and 2005 were identified from the Surveillance, Epidemiology, and End Results (SEER) registry. Demographic and clinical variables used in the analysis included gender, ethnicity, tumor size, age at diagnosis, surgery, and radiotherapy. Cancer-specific survival and overall survival were evaluated using the Kaplan-Meier method. Univariate and multivariate analysis were performed using Cox regression. RESULTS Radiotherapy was administered in 64% of these patients, and surgery was performed in 68%. Among 2,836 patients, 46% received surgery and radiotherapy, 22% underwent surgery only, 18% underwent radiotherapy only, and 14% did not undergo either treatment. The median survival for patients who underwent surgery and radiotherapy was 8 months. The median survival for patients who underwent radiotherapy only was 4 months, and for patients who underwent surgery only was 3 months. Those who received neither surgery nor radiotherapy had a median survival of 2 months (p<0.001). Multivariate analysis showed that radiotherapy significantly improved cancer-specific survival (hazard ratio [HR], 0.43, 95% confidence interval [CI] 0.38-0.49) after adjusting for surgery, tumor size, gender, ethnicity, and age at diagnosis. Other factors associated with Cancer-specific survival included surgery, tumor size, age at diagnosis, and ethnicity. Analysis using overall survival as the endpoint yielded very similar results. CONCLUSIONS Elderly patients with glioblastoma who underwent radiotherapy had improved cancer-specific survival and overall survival compared to patients who did not receive radiotherapy.

A genome-based model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study

BACKGROUND Despite its common use in cancer treatment, radiotherapy has not yet entered the era of precision medicine, and there have been no approaches to adjust dose based on biological differences between or within tumours. We aimed to assess whether a patient-specific molecular signature of radiation sensitivity could be used to identify the optimum radiotherapy dose. METHODS We used the gene-expression-based radiation-sensitivity index and the linear quadratic model to derive the genomic-adjusted radiation dose (GARD). A high GARD value predicts for high therapeutic effect for radiotherapy; which we postulate would relate to clinical outcome. Using data from the prospective, observational Total Cancer Care (TCC) protocol, we calculated GARD for primary tumours from 20 disease sites treated using standard radiotherapy doses for each disease type. We also used multivariable Cox modelling to assess whether GARD was independently associated with clinical outcome in five clinical cohorts: Erasmus Breast Cancer Cohort (n=263); Karolinska Breast Cancer Cohort (n=77); Moffitt Lung Cancer Cohort (n=60); Moffitt Pancreas Cancer Cohort (n=40); and The Cancer Genome Atlas Glioblastoma Patient Cohort (n=98). FINDINGS We calculated GARD for 8271 tissue samples from the TCC cohort. There was a wide range of GARD values (range 1·66-172·4) across the TCC cohort despite assignment of uniform radiotherapy doses within disease types. Median GARD values were lowest for gliomas and sarcomas and highest for cervical cancer and oropharyngeal head and neck cancer. There was a wide range of GARD values within tumour type groups. GARD independently predicted clinical outcome in breast cancer, lung cancer, glioblastoma, and pancreatic cancer. In the Erasmus Breast Cancer Cohort, 5-year distant-metastasis-free survival was longer in patients with high GARD values than in those with low GARD values (hazard ratio 2·11, 95% 1·13-3·94, p=0·018). INTERPRETATION A GARD-based clinical model could allow the individualisation of radiotherapy dose to tumour radiosensitivity and could provide a framework to design genomically-guided clinical trials in radiation oncology. FUNDING None.

Investigating prostate cancer tumour–stroma interactions: clinical and biological insights from an evolutionary game

Background:Tumours are made up of a mixed population of different types of cells that include normal structures as well as ones associated with the malignancy, and there are multiple interactions between the malignant cells and the local microenvironment. These intercellular interactions, modulated by the microenvironment, effect tumour progression and represent a largely under-appreciated therapeutic target. We use observations of primary tumour biology from prostate cancer to extrapolate a mathematical model. Specifically, it has been observed that in prostate cancer three disparate cellular outcomes predominate: (i) the tumour remains well differentiated and clinically indolent – in this case the local stromal cells may act to restrain the growth of the cancer; (ii) early in its genesis the tumour acquires a highly malignant phenotype, growing rapidly and displacing the original stromal population (often referred to as small cell prostate cancer) – these less common aggressive tumours are relatively independent of the local microenvironment and (iii) the tumour co-opts the local stroma – taking on a classic stromagenic phenotype where interactions with the local microenvironment are critical to the cancer growth.Methods:We present an evolutionary game theoretical construct that models the influence of tumour–stroma interactions in driving these outcomes. We consider three characteristic and distinct cellular populations: stromal cells, tumour cells that are self-reliant in terms of microenvironmental factors and tumour cells that depend on the environment for resources, but can also co-opt stroma.Results:Using evolutionary game theory we explore a number of different scenarios that elucidate the impact of tumour–stromal interactions on the dynamics of prostate cancer growth and progression, and how different treatments in the metastatic setting can affect different types of tumours.Conclusion:The tumour microenvironment has a crucial role in selecting the traits of the tumour cells that will determine prostate cancer progression. Equally important treatments like hormone therapy affect the selection of these cancer phenotypes making it very important to understand how they impact prostate cancers somatic evolution.

The role of IDH1 mutated tumour cells in secondary glioblastomas: an evolutionary game theoretical view

Recent advances in clinical medicine have elucidated two significantly different subtypes of glioblastoma which carry very different prognoses, both defined by mutations in isocitrate dehydrogenase-1 (IDH-1). The mechanistic consequences of this mutation have not yet been fully clarified, with conflicting opinions existing in the literature; however, IDH-1 mutation may be used as a surrogate marker to distinguish between primary and secondary glioblastoma multiforme (sGBM) from malignant progression of a lower grade glioma. We develop a mathematical model of IDH-1 mutated secondary glioblastoma using evolutionary game theory to investigate the interactions between four different phenotypic populations within the tumor: autonomous growth, invasive, glycolytic, and the hybrid invasive/glycolytic cells. Our model recapitulates glioblastoma behavior well and is able to reproduce two recent experimental findings, as well as make novel predictions concerning the rate of invasive growth as a function of vascularity, and fluctuations in the proportions of phenotypic populations that a glioblastoma will experience under different microenvironmental constraints.

Unifying metastasis — integrating intravasation, circulation and end-organ colonization

Recent technological advances that have enabled the measurement of circulating tumour cells (CTCs) in patients have spurred interest in the circulatory phase of metastasis. Techniques that do not solely rely on a blood sample allow substantial biological interrogation beyond simply counting CTCs.

Invasion and proliferation kinetics in enhancing gliomas predict IDH1 mutation status

BACKGROUND Glioblastomas with a specific mutation in the isocitrate dehydrogenase 1 (IDH1) gene have a better prognosis than gliomas with wild-type IDH1. METHODS Here we compare the IDH1 mutational status in 172 contrast-enhancing glioma patients with the invasion profile generated by a patient-specific mathematical model we developed based on MR imaging. RESULTS We show that IDH1-mutated contrast-enhancing gliomas were relatively more invasive than wild-type IDH1 for all 172 contrast-enhancing gliomas as well as the subset of 158 histologically confirmed glioblastomas. The appearance of this relatively increased, model-predicted invasive profile appears to be determined more by a lower model-predicted net proliferation rate rather than an increased model-predicted dispersal rate of the glioma cells. Receiver operator curve analysis of the model-predicted MRI-based invasion profile revealed an area under the curve of 0.91, indicative of a predictive relationship. The robustness of this relationship was tested by cross-validation analysis of the invasion profile as a predictive metric for IDH1 status. CONCLUSIONS The strong correlation between IDH1 mutation status and the MRI-based invasion profile suggests that use of our tumor growth model may lead to noninvasive clinical detection of IDH1 mutation status and thus lead to better treatment planning, particularly prior to surgical resection, for contrast-enhancing gliomas.

Whole brain radiotherapy for brain metastasis.

Whole brain radiotherapy (WBRT) is a mainstay of treatment in patients with both identifiable brain metastases and prophylaxis for microscopic disease. The use of WBRT has decreased somewhat in recent years due to both advances in radiation technology, allowing for a more localized delivery of radiation, and growing concerns regarding the late toxicity profile associated with WBRT. This has prompted the development of several recent and ongoing prospective studies designed to provide Level I evidence to guide optimal treatment approaches for patients with intracranial metastases. In addition to defining the role of WBRT in patients with brain metastases, identifying methods to improve WBRT is an active area of investigation, and can be classified into two general categories: Those designed to decrease the morbidity of WBRT, primarily by reducing late toxicity, and those designed to improve the efficacy of WBRT. Both of these areas of research show diversity and promise, and it seems feasible that in the near future, the efficacy/toxicity ratio may be improved, allowing for a more diverse clinical application of WBRT.

Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.