Project description:Novel cancer therapies are associated with survival patterns that differ from established therapies, which may include survival curves that plateau after a certain follow-up time point. A fraction of the patient population is then considered statistically cured and subject to the same mortality experience as the cancer-free general population. Mixture cure models have been developed to account for this characteristic. As compared to standard survival analysis, mixture cure models can often lead to profoundly different estimates of long-term survival, required for health economic evaluations. This tutorial is designed as a practical introduction to mixture cure models. Step-by-step instructions are provided for the entire implementation workflow, i.e., from gathering and combining data from different sources to fitting models using maximum likelihood estimation and model results interpretation. Two mixture cure models were developed to illustrate (1) an "uninformed" approach where the cure fraction is estimated from trial data and (2) an "informed" approach where the cure fraction is obtained from an external source (e.g., real-world data) used as an input to the model. These models were implemented in the statistical software R, with the freely available code on GitHub. The cure fraction can be estimated as an output from ("uninformed" approach) or used as an input to ("informed" approach) a mixture cure model. Mixture cure models suggest presumed estimates of long-term survival proportions, especially in instances where some fraction of patients is expected to be statistically cured. While this type of model may initially seem complex, it is straightforward to use and interpret. Mixture cure models have the potential to improve the accuracy of survival estimates for treatments associated with statistical cure, and the present tutorial outlines the interpretation and implementation of mixture cure models in R. This type of model will likely become more widely used in health economic analyses as novel cancer therapies enter the market.

Project description:Glioblastoma (GBM) is a highly aggressive type of glioma with poor prognosis. However, a small number of patients live much longer than the median survival. A better understanding of these long-term survivors (LTS) may provide important insight into the biology of GBM. We identified 7 patients with GBM treated at Memorial Sloan-Kettering Cancer Center (MSKCC) with survival greater than 48 months. We characterized the transcriptome of each patient and determined rates of MGMT promoter methylation and IDH1 and IDH2 mutational status. We identified LTS in two independent cohorts (TCGA and REMBRANDT) and analyzed the transcriptomal characteristics of these LTS. The median overall survival of our cohort was 62.5 months. LTS were distributed between the proneural (n=2), neural (n=2), classical (n=2) and mesenchymal (n=1) subtypes. Similarly, LTS in the TCGA and REMBRANDT cohorts demonstrated diverse transcriptomal subclassification identity. The majority of the MSKCC LTS (71%) were found to have methylation of the MGMT promoter. None of the patients had an IDH1 or IDH2 mutations, and IDH mutation occurred in a minority of the TCGA LTS as well. A set of 42 genes was found to be differentially expressed in the MSKCC and TCGA LTS. While IDH mutant proneural tumors impart a better prognosis in the short-term, survival beyond 4 years does not require IDH mutation and is not dictated by a single transcriptional subclass. In contrast, MGMT methylation continues to have strong prognostic value for survival beyond 4 years. These findings have substantial impact for understanding GBM biology and progression. All tumors (n = 7) were obtained following surgical resection at the MSKCC as part of routine clinical care, and snap frozen. Tumors were obtained in accordance with Institutional Review Board policies at the MSKCC. Each sample was examined histologically by 3 independent neuropathologists and confirmed to be World Health Organization (WHO) grade IV glioma (GBM). Before analysis, tumors were sectioned and microdissected. Genomic DNA or RNA was extracted using the DNeasy kit (Qiagen) or RNeasy Lipid Tissue Mini Kit (Qiagen) as per the manufacturer’s instructions. Expression analysis of tumors was performed using the Affymetrics U133 2.0 microarray (Affymetrix). Affymetrix CEL files were imported into the Partek Genomics Suite (Partek). The TCGA gene expression data (HT-HG-U133A) was accessed from the TCGA data repositories (http://cancergenome.nih.gov, date of download 12/2013).

Project description:BackgroundLong-term childhood cancer survivors (CCS) are at increased risk of adverse cardiovascular events; however, there is a paucity of risk-stratification tools to identify those at higher-than-normal risk.Subjects, materials, and methodsThis was a population-based study using data from the Surveillance, Epidemiology, and End Results Program (1973-2013). Long-term CCS (age at diagnosis ?19 years, survival ?5 years) were followed up over a median time period of 12.3 (5-40.9) years. Independent predictors of cardiovascular mortality (CVM) were combined into a risk score, which was developed in a derivation set (n?=?22,374), and validated in separate patient registries (n?=?6,437).ResultsIn the derivation registries, older age at diagnosis (?10 years vs. reference group of 1-5 years), male sex, non-white race, a history of lymphoma, and a history of radiation were independently associated with an increased risk of CVM among long-term CCS (p?<?.05). A risk score derived from this model (Childhood and Adolescence Cancer Survivor CardioVascular score [CHACS-CV], range: 0-8) showed good discrimination for CVM (Harrell's C-index [95% confidence interval (CI)]: 0.73 [0.68-0.78], p?<?.001) and identified a high-risk group (CHACS-CV ?6), with cumulative CVM incidence over 30 years of 6.0% (95% CI: 4.3%-8.1%) versus 2.6% (95% CI: 1.8%-3.7%), and 0.7% (95% CI: 0.5%-1.0%) in the mid- (CHACS-CV?=?4-5) and low-risk groups (CHACS-CV ?3), respectively (plog-rank?<?.001). In the validation set, the respective cumulative incidence rates were 4.7%, 3.1%, and 0.8% (plog-rank?<?.001).ConclusionWe propose a simple risk score that can be applied in everyday clinical practice to identify long-term CCS at increased cardiovascular risk, who may benefit from early cardiovascular screening, and risk-reduction strategies.Implications for practiceChildhood cancer survivors (CCS) are known to be at increased cardiovascular risk. Currently available prognostic tools focus on treatment-related adverse events and late development of congestive heart failure, but there is no prognostic model to date to estimate the risk of cardiovascular mortality among long-term CCS. A simple clinical tool is proposed for cardiovascular risk stratification of long-term CCS based on easily obtainable information from their medical history. This scoring system may be used as a first-line screening tool to assist health care providers in identifying those who may benefit from closer follow-up and enable timely deployment of preventive strategies.

Project description:Cancer-related cognitive impairment (CRCI) is a frequent side effect experienced by an increasing number of cancer survivors with a significant impact on their quality of life. Different definitions and means of evaluation have been used in available literature; hence the exact incidence of CRCI remains unknown. CRCI can be described as cognitive symptoms reported by cancer patients in self-reported questionnaires or as cognitive changes evaluated by formal neuropsychological tests. Nevertheless, association between cognitive symptoms and objectively assessed cognitive changes is relatively weak or absent. Studies have focused especially on breast cancer patients, but CRCI has been reported in multiple types of cancer, including colorectal, lung, ovarian, prostate, testicular cancer and hematological malignancies. While CRCI has been associated with various treatment modalities, including radiotherapy, chemotherapy, hormone therapy and novel systemic therapies, it has been also detected prior to cancer treatment. Therefore, the effects of cancer itself with or without the psychological distress may be involved in the pathogenesis of CRCI as a result of altered coping mechanisms after cancer diagnosis. The development of CRCI is probably multifactorial and the exact mechanisms are currently not completely understood. Possible risk factors include administered treatment, genetic predisposition, age and psychological factors such as anxiety, depression or fatigue. Multiple mechanisms are suggested to be responsible for CRCI, including direct neurotoxic injury of systemic treatment and radiation while other indirect contributing mechanisms are hypothesized. Chronic neuroinflammation mediated by active innate immune system, DNA-damage or endothelial dysfunction is hypothesized to be a central mechanism of CRCI pathogenesis. There is increasing evidence of potential plasma (e.g., damage associated molecular patterns, inflammatory components, circulating microRNAs, exosomes, short-chain fatty acids, and others), cerebrospinal fluid and radiological biomarkers of cognitive dysfunction in cancer patients. Discovery of biomarkers of cognitive impairment is crucial for early identification of cancer patients at increased risk for the development of CRCI or development of treatment strategies to lower the burden of CRCI on long-term quality of life. This review summarizes current literature on CRCI with a focus on long-term effects of different cancer treatments, possible risk factors, mechanisms and promising biomarkers.

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A sup-score test for the cure fraction in mixture models for long-term survivors.

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