Estimation of Personal Symptom Networks Using the Ising Model for Adult Survivors of Childhood Cancer: A Simulation Study with Real-World Data Application.

Purpose: Childhood cancer survivors experience interconnected symptoms, patterns of which can be elucidated by network analysis. However, current symptom networks are constructed based on the average survivors without considering individual heterogeneities. We propose to evaluate personal symptom network estimation using the Ising model with covariates through simulations and estimate personal symptom network for adult childhood cancer survivors. Patients and Methods: We adopted the Ising model with covariates to construct networks by employing logistic regressions for estimating associations between binary symptoms. Simulation experiments assessed the robustness of this method in constructing personal symptom network. Real-world data illustration included 1708 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study (SJLIFE), a retrospective cohort study with prospective follow-up to characterize the etiology and late effects for childhood cancer survivors. Patients' baseline symptoms in 10 domains (cardiac, pulmonary, sensation, nausea, movement, pain, memory, fatigue, anxiety, depression) and individual characteristics (age, sex, race/ethnicity, attained education, personal income, and marital status) were self-reported using survey. Treatment variables (any chemo or radiation therapy) were obtained from medical records. Personal symptom network of 10 domains was estimated using the Ising model, incorporating individual characteristics and treatment data. Results: Simulations confirmed the robustness of the Ising model with covariates in constructing personal symptom networks. Real-world data analysis identified age, sex, race/ethnicity, education, marital status, and treatment (any chemo and radiation therapy) as major factors influencing symptom co-occurrence. Older childhood cancer survivors showed stronger cardiac-fatigue associations. Survivors of racial/ethnic minorities had stronger pain-fatigue associations. Female survivors with above-college education demonstrated stronger pain-anxiety associations. Unmarried survivors who received radiation had stronger association between movement and memory problems. Conclusion: The Ising model with covariates accurately estimates personal symptom networks. Individual heterogeneities exist in symptom co-occurrence patterns for childhood cancer survivors. The estimated personal symptom network offers insights into interconnected symptom experiences.

Accelerating pediatric Hodgkin lymphoma research: the Hodgkin Lymphoma Data Collaboration (NODAL).

Data commons have proven to be an indispensable avenue for advancing pediatric cancer research by serving as unified information technology platforms that, when coupled with data standards, facilitate data sharing. The Pediatric Cancer Data Commons, the flagship project of Data for the Common Good (D4CG), collaborates with disease-based consortia to facilitate development of clinical data standards, harmonization and pooling of clinical data from disparate sources, establishment of governance structure, and sharing of clinical data. In the interest of international collaboration, researchers developed the Hodgkin Lymphoma Data Collaboration and forged a relationship with the Pediatric Cancer Data Commons to establish a data commons for pediatric Hodgkin lymphoma. Herein, we describe the progress made in the formation of Hodgkin Lymphoma Data Collaboration and foundational goals to advance pediatric Hodgkin lymphoma research.

Longitudinal clinical data improve survival prediction after hematopoietic cell transplantation using machine learning.

ABSTRACT: Serial prognostic evaluation after allogeneic hematopoietic cell transplantation (allo-HCT) might help identify patients at high risk of lethal organ dysfunction. Current prediction algorithms based on models that do not incorporate changes to patients' clinical condition after allo-HCT have limited predictive ability. We developed and validated a robust risk-prediction algorithm to predict short- and long-term survival after allo-HCT in pediatric patients that includes baseline biological variables and changes in the patients' clinical status after allo-HCT. The model was developed using clinical data from children and young adults treated at a single academic quaternary-care referral center. The model was created using a randomly split training data set (70% of the cohort), internally validated (remaining 30% of the cohort) and then externally validated on patient data from another tertiary-care referral center. Repeated clinical measurements performed from 30 days before allo-HCT to 30 days afterwards were extracted from the electronic medical record and incorporated into the model to predict survival at 100 days, 1 year, and 2 years after allo-HCT. Naïve-Bayes machine learning models incorporating longitudinal data were significantly better than models constructed from baseline variables alone at predicting whether patients would be alive or deceased at the given time points. This proof-of-concept study demonstrates that unlike traditional prognostic tools that use fixed variables for risk assessment, incorporating dynamic variability using clinical and laboratory data improves the prediction of mortality in patients undergoing allo-HCT.

Factors influencing parents' choice of palliative treatment goals for children with relapsed or refractory neuroblastoma: A multi-site longitudinal survey study.

BACKGROUND: Many parents of children with advanced cancer report curative goals and continue intensive therapies that can compound symptoms and suffering. Factors that influence parents to choose palliation as the primary treatment goal are not well understood. The objective of this study was to examine experiences impacting parents' report of palliative goals adjusted for time. The authors hypothesized that awareness of poor prognosis, recall of oncologists' prognostic disclosure, intensive treatments, and burdensome symptoms and suffering would influence palliative goal-setting. METHODS: The authors collected prospective, longitudinal surveys from parents of children with relapsed/refractory neuroblastoma at nine pediatric cancer centers across the United States, beginning at relapse and continuing every 3 months for 18 months or until death. Hypothesized covariates were examined for possible associations with parental report of palliative goals. Generalized linear mixed models were used to evaluate factors associated with parents' report of palliative goals at different time points. RESULTS: A total of 96 parents completed surveys. Parents were more likely to report a primary goal of palliation when they recalled communication about prognosis by their child's oncologist (odds ratio [OR], 52.48; p = .010). Treatment intensity and previous ineffective therapeutic regimens were not associated with parents' report of palliative goals adjusted for time. A parent who reported new suffering for their child was less likely to report palliative goals (OR, 0.13; p = .008). CONCLUSIONS: Parents of children with poor prognosis cancer may not report palliative goals spontaneously in the setting of treatment-related suffering. Prognostic communication, however, does influence palliative goal-setting. Evidence-based interventions are needed to encourage timely, person-centered prognostic disclosure in the setting of advanced pediatric cancer. PLAIN LANGUAGE SUMMARY: Many parents of children with poor-prognosis cancer continue to pursue curative treatments that may worsen symptoms and suffering. Little is known about which factors influence parents to choose palliative care as their child's main treatment goal. To explore this question, we asked parents of children with advanced neuroblastoma across the United States to complete multiple surveys over time. We found that the intensity of treatment, number of treatments, and suffering from treatment did not influence parents to choose palliative goals. However, when parents remembered their child's oncologist talking about prognosis, they were more likely to choose palliative goals of care.

Pharmacokinetics and safety of bendamustine in the BEABOVP regimen for the treatment of pediatric patients with Hodgkin lymphoma.

PURPOSE: The Stanford V chemotherapy regimen has been used to treat Hodgkin lymphoma (HL) patients since 2002 with excellent cure rates; however, mechlorethamine is no longer available. Bendamustine, a drug structurally similar to alkylating agents and nitrogen mustard, is being substituted for mechlorethamine in combination therapy in a frontline trial for low- and intermediate-risk pediatric HL patients, forming a new backbone of BEABOVP (bendamustine, etoposide, doxorubicin, bleomycin, vincristine, vinblastine, and prednisone). This study evaluated the pharmacokinetics and tolerability of a 180 mg/m2 dose of bendamustine every 28 days to determine factors that may explain this variability. METHODS: Bendamustine plasma concentrations were measured in 118 samples from 20 pediatric patients with low- and intermediate-risk HL who received a single-day dose of 180 mg/m2 of bendamustine. A pharmacokinetic model was fit to the data using nonlinear mixed-effects modeling. RESULTS: Bendamustine concentration vs time demonstrated a trend toward decreasing clearance with increasing age (p = 0.074) and age explained 23% of the inter-individual variability in clearance. The median (range) AUC was 12,415 (8,539, 18,642) µg hr/L and the median (range) maximum concentration was 11,708 (8034, 15,741) µg/L. Bendamustine was well tolerated with no grade 3 toxicities resulting in treatment delays of more than 7 days. CONCLUSIONS: A single-day dose of 180 mg/m2 of bendamustine every 28 days was safe and well tolerated in pediatric patients. While age accounted for 23% of inter-individual variability observed in bendamustine clearance, the differences did not affect the safety and tolerability of bendamustine in our patient population.

High-dimensional mediation analysis in survival models.

Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276, cg27042065, and cg26387355 with significant hazard ratios of 1.2497(95%CI: 1.1121, 1.4045), 1.0920(95%CI: 1.0170, 1.1726), and 1.1489(95%CI: 1.0518, 1.2550), respectively. The three methylation sites locate in the three genes which have been showed to be associated with lung cancer event or overall survival. However, the three CpG sites (cg21926276, cg27042065 and cg26387355) have not been reported, which are newly identified as the potential novel epigenetic markers linking smoking and survival of lung cancer patients. Collectively, the proposed high-dimensional mediation analysis procedure has good performance in mediator selection and indirect effect estimation.

The Human Adenovirus Type 5 E4orf4 Protein Targets Two Phosphatase Regulators of the Hippo Signaling Pathway.

UNLABELLED: When expressed alone at high levels, the human adenovirus E4orf4 protein exhibits tumor cell-specific p53-independent toxicity. A major E4orf4 target is the B55 class of PP2A regulatory subunits, and we have shown recently that binding of E4orf4 inhibits PP2A(B55) phosphatase activity in a dose-dependent fashion by preventing access of substrates (M. Z. Mui et al., PLoS Pathog 9:e1003742, 2013, http://dx.doi.org/10.1371/journal.ppat.1003742). While interaction with B55 subunits is essential for toxicity, E4orf4 mutants exist that, despite binding B55 at high levels, are defective in cell killing, suggesting that other essential targets exist. In an attempt to identify additional targets, we undertook a proteomics approach to characterize E4orf4-interacting proteins. Our findings indicated that, in addition to PP2A(B55) subunits, ASPP-PP1 complex subunits were found among the major E4orf4-binding species. Both the PP2A and ASPP-PP1 phosphatases are known to positively regulate effectors of the Hippo signaling pathway, which controls the expression of cell growth/survival genes by dephosphorylating the YAP transcriptional coactivator. We find here that expression of E4orf4 results in hyperphosphorylation of YAP, suggesting that Hippo signaling is affected by E4orf4 interactions with PP2A(B55) and/or ASPP-PP1 phosphatases. Furthermore, knockdown of YAP1 expression was seen to enhance E4orf4 killing, again consistent with a link between E4orf4 toxicity and inhibition of the Hippo pathway. This effect may in fact contribute to the cancer cell specificity of E4orf4 toxicity, as many human cancer cells rely heavily on the Hippo pathway for their enhanced proliferation. IMPORTANCE: The human adenovirus E4orf4 protein has been known for some time to induce tumor cell-specific death when expressed at high levels; thus, knowledge of its mode of action could be of importance for development of new cancer therapies. Although the B55 form of the phosphatase PP2A has long been known as an essential E4orf4 target, genetic analyses indicated that others must exist. To identify additional E4orf4 targets, we performed, for the first time, a large-scale affinity purification/mass spectrometry analysis of E4orf4 binding partners. Several additional candidates were detected, including key regulators of the Hippo signaling pathway, which enhances cell viability in many cancers, and results of preliminary studies suggested a link between inhibition of Hippo signaling and E4orf4 toxicity.