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To evaluate the clinical impact of molecular tumor profiling (MTP) with targeted sequencing panel tests, pediatric patients with extracranial solid tumors were enrolled in a prospective observational cohort study at 12 institutions. In the 345-patient analytical population, median age at diagnosis was 12 years (range 0-27.5); 298 patients (86%) had 1 or more alterations with potential for impact on care. Genomic alterations with diagnostic, prognostic or therapeutic significance were present in 61, 16 and 65% of patients, respectively. After return of the results, impact on care included 17 patients with a clarified diagnostic classification and 240 patients with an MTP result that could be used to select molecularly targeted therapy matched to identified alterations (MTT). Of the 29 patients who received MTT, 24% had an objective response or experienced durable clinical benefit; all but 1 of these patients received targeted therapy matched to a gene fusion. Of the diagnostic variants identified in 209 patients, 77% were gene fusions. MTP with targeted panel tests that includes fusion detection has a substantial clinical impact for young patients with solid tumors.

Copy number variation segments.

Representative images of RPMI 8226 cells used for calculation of aggresome formation in response to selinexor and carfilzomib treatment.

Supplementary Figure S1. Sequencing of HGSOC reveals conserved mutational signatures and TP53 mutations. Supplementary Figure S2. Distribution of mutations and mutational signatures in HGSOC. Supplementary Figure S3. Genomic instability is a core feature of ovarian cancer that frequently involves DNA-damage repair genes. Supplementary Figure S4. Annotated dendrograms of HGSOC metastatic trajectories. Supplementary Figure S5. Ex vivo model of metastasis to the fallopian tube. Supplementary Figure S6. In vitro model of HGSOC-fallopian tube adhesion.

Changes in mean tumor volume of all groups of mice treated with selinexor and carfilzomib, alone and in combination.

As in Figure 3E of the main text, MM1.S and RPMI 8226 cells were treated for 24 hours with caspase 9 inhibitor (LEHD; 50 µM), pan-caspase inhibitor, Q-VD-OPH, (VD; 25 µM), or caspase 10 inhibitor (AEVD; 25 µM) alone. Relative cell viability was determined using MTT assay.

Exportin1 (XPO1; also known as chromosome maintenance region 1, or CRM1) controls nucleo-cytoplasmic transport of most tumor suppressors and is overexpressed in many cancers, including multiple myeloma, functionally impairing tumor suppressive function via target mislocalization. Selective inhibitor of nuclear export (SINE) compounds block XPO1-mediated nuclear escape by disrupting cargo protein binding, leading to retention of tumor suppressors, induction of cancer cell death, and sensitization to other drugs. Combined treatment with the clinical stage SINE compound selinexor and the irreversible proteasome inhibitor (PI) carfilzomib induced synergistic cell death of myeloma cell lines and primary plasma cells derived from relapsing/refractory myeloma patients and completely impaired the growth of myeloma cell line–derived tumors in mice. Investigating the details of SINE/PI-induced cell death revealed (i) reduced Bcl-2 expression and cleavage and inactivation of Akt, two prosurvival regulators of apoptosis and autophagy; (ii) intracellular membrane-associated aggregation of active caspases, which depended on caspase-10 protease activity; and (iii) novel association of caspase-10 and autophagy-associated proteins p62 and LC3 II, which may prime activation of the caspase cascade. Overall, our findings provide novel mechanistic rationale behind the potent cell death induced by combining selinexor with carfilzomib and support their use in the treatment of relapsed/refractory myeloma and potentially other cancers. Mol Cancer Ther; 15(1); 60–71. ©2015 AACR.

MM1.S cells were left untreated or treated with Selinexor and Carfilzomib (CFZ; 50 nm each) overnight. Cells were then processed as described for immunofluorescence microscopy using anti-Fas mouse monoclonal antibody and anti-p62 rabbit polyclonal antibody and appropriate fluorophore-conjugated secondary antibodies. Cell surface Fas does not co-localize with p62-containing aggregates, supporting the notion that caspases and p62 localize to intracellular membranes.

Plasma cells from patients that have failed on treatment regimens that have included proteasome inhibitors, immunomodulatory drugs, and DNA damaging agents were purified from fresh bone marrow aspirates and treated with selinexor and carfilzomib.

Additional details of antibodies, reagents, and methods used in this manuscript.

Western blot analysis of markers for autophagy and apoptosis in MM1.S cells treated for the indicated time with pan-caspase inhibitor.

Supplementary Figure 2 from Comparison of Primary Neuroblastoma Tumors and Derivative Early-Passage Cell Lines Using Genome-Wide Single Nucleotide Polymorphism Array Analysis

Supplementary Figure 1 from Comparison of Primary Neuroblastoma Tumors and Derivative Early-Passage Cell Lines Using Genome-Wide Single Nucleotide Polymorphism Array Analysis

The clinical application of complex molecular classifiers as diagnostic or prognostic tools has been limited by the time and cost needed to apply them to patients. Using an existing 50-gene expression signature known to separate two molecular subtypes of the pediatric cancer rhabdomyosarcoma, we show that an exhaustive iterative search algorithm can distill this complex classifier down to two or three features with equal discrimination. We validated the two-gene signatures using three separate and distinct datasets, including one that uses degraded RNA extracted from formalin-fixed, paraffin-embedded material. Finally, to show the generalizability of our algorithm, we applied it to a lung cancer dataset to find minimal gene signatures that can distinguish survival. Our approach can easily be generalized and coupled to existing technical platforms to facilitate the discovery of simplified signatures that are ready for routine clinical use. Cancer Res; 73(18); 5625–32. ©2013 AACR.

Supplementary Figure Legends 1-2, Tables 1-2 from Comparison of Primary Neuroblastoma Tumors and Derivative Early-Passage Cell Lines Using Genome-Wide Single Nucleotide Polymorphism Array Analysis

PDF file - 714K, Supplementary Figure 1 Two-gene combinations from the 50-gene F1/F2 classifier with >98% classification efficiency in the Williamson data set. Supplementary Figure 2 Thirteen two-gene combinations with >99% classification efficiency, identified from exhaustive analysis of top 2000 genes. Supplementary Figure 3 Gene dyads with classification efficiency >95% identified through exhaustive search of all possible combinations. Supplementary Figure 4 Detection of gene expression is comparable for fresh-frozen and paraformaldehyde-fixed material with nCounter. Supplementary Figure 5 Gene expression detected in each case with the Williamson F1/F2 metagene nCounter Code set. Supplementary Table 1 Four-gene combinations with ≥ 99% accuracy. Supplementary Table 2 Dyad classifiers from T-test ranking of Williamson data set with ≥ 98% accuracy, applied to Davicioni data. Supplementary Table 3 T-test dyad classifier genes found in F1/F2 metagenes (L) and F1/F2 metagene probes excluded from dyad pairs (R). Supplementary Table 4 Top-scoring classifiers from exhaustive search of 1.5 billion gene pairs from Williamson data set, also applied to Davicioni and Exon data. Supplementary Table 5 Tumor sample characteristics. Supplementary Table 6 Genes in nCounter set. Supplementary Table 7 PCA Scores by tumor sample for nCounter Data. Supplementary Table 8 CA Loadings for nCounter Data. Supplementary Table 9 Top-scoring nCounter dyad classifiers. Supplementary Table 10 Top gene combinations - Lung cancer data

Germ cell tumours (GCTs) are a heterogeneous group of rare neoplasms that present in different anatomical sites and across a wide spectrum of patient ages from birth through to adulthood. Once these strata are applied, cohort numbers become modest, hindering inferences regarding management and therapeutic advances. Moreover, patients with GCTs are treated by different medical professionals including paediatric oncologists, neuro-oncologists, medical oncologists, neurosurgeons, gynaecological oncologists, surgeons, and urologists. Silos of care have thus formed, further hampering knowledge dissemination between specialists. Dedicated biobank specimen collection is therefore critical to foster continuous growth in our understanding of similarities and differences by age, gender, and site, particularly for rare cancers such as GCTs. Here, the Malignant Germ Cell International Consortium provides a framework to create a sustainable, global research infrastructure that facilitates acquisition of tissue and liquid biopsies together with matched clinical data sets that reflect the diversity of GCTs. Such an effort would create an invaluable repository of clinical and biological data which can underpin international collaborations that span professional boundaries, translate into clinical practice, and ultimately impact patient outcomes., St Baldrick's Foundation

In this article, we will discuss the genesis, evolution, and progress of the INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT), which aims to foster international research and collaboration focused on pediatric soft tissue sarcoma. We will begin by highlighting the current state of clinical research for pediatric soft tissue sarcomas, including rhabdomyosarcoma and non-rhabdomyosarcoma soft tissue sarcoma. We will then explore challenges and research priorities, describe the development of INSTRuCT, and discuss how the consortium aims to address key research priorities.

Germ cell tumors (GCTs) are considered a rare disease but are the most common solid tumors in adolescents and young adults, accounting for 15% of all malignancies in this age group. The rarity of GCTs in some groups, particularly children, has impeded progress in treatment and biologic understanding. The most effective GCT research will result from the interrogation of data sets from historical and prospective trials across institutions. However, inconsistent use of terminology among groups, different sample-labeling rules, and lack of data standards have hampered researchers’ efforts in data sharing and across-study validation. To overcome the low interoperability of data and facilitate future clinical trials, we worked with the Malignant Germ Cell International Consortium (MaGIC) and developed a GCT clinical data model as a uniform standard to curate and harmonize GCT data sets. This data model will also be the standard for prospective data collection in future trials. Using the GCT data model, we developed a GCT data commons with data sets from both MaGIC and public domains as an integrated research platform. The commons supports functions, such as data query, management, sharing, visualization, and analysis of the harmonized data, as well as patient cohort discovery. This GCT data commons will facilitate future collaborative research to advance the biologic understanding and treatment of GCTs. Moreover, the framework of the GCT data model and data commons will provide insights for other rare disease research communities into developing similar collaborative research platforms.

ABSTRCT Objective Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. Materials and Methods Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. Results The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. Conclusions Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

PURPOSE The Children’s Oncology Group (COG) stratifies the treatment of patients with neuroblastoma on the basis of a combination of biomarkers that include age and tumor histology classified by age-linked International Neuroblastoma Pathology Classification (INPC) criteria. By definition, this leads to a duplication of the prognostic contribution of age. The individual histologic features underlying the INPC have prognostic strength and are incorporated in the International Neuroblastoma Risk Group classification schema. Here, we analyzed data in the International Neuroblastoma Risk Group Data Commons to validate the prognostic strength of the underlying INPC criteria and to determine whether a risk classification devoid of the confounding of age and INPC criteria will identify new prognostic subgroups. PATIENTS AND METHODS Event-free survival of patients diagnosed between 1990 and 2002 (cohort 1; n = 10,104) and between 2003 and 2016 (cohort 2; n = 8,761) was analyzed. Recursive partitioning with univariate Cox models of event-free survival (“survival tree regression”) was performed using (1) individual INPC criteria (age at diagnosis, histologic category, mitosis-karyorrhexis index (MKI), grade of differentiation) and (2) factors in (1) plus other COG-risk biomarkers (International Neuroblastoma Staging System [INSS] stage, MYCN status, ploidy). RESULTS The independent prognostic ability of age, histologic category, MKI, and grade were validated. Four histologic prognostic groups were identified (< 18 months with low v high MKI, and ≥ 18 months with differentiating v undifferentiated/poorly differentiating tumors). Compared with survival trees generated with established COG risk criteria, an additional prognostic subgroup was identified and validated when individual histologic features were analyzed in lieu of INPC. CONCLUSION Replacing INPC with individual histologic features in the COG risk classification will eliminate confounding, facilitate international harmonization of risk classification, and provide a schema for more precise prognostication and refined therapeutic approaches.

PURPOSE There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field. METHODS We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed. RESULTS A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts. CONCLUSION We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.

PURPOSE Metaiodobenzylguanidine (MIBG) scans are a radionucleotide imaging modality that undergo Curie scoring to semiquantitatively assess neuroblastoma burden, which can be used as a marker of therapy response. We hypothesized that a convolutional neural network (CNN) could be developed that uses diagnostic MIBG scans to predict response to induction chemotherapy. METHODS We analyzed MIBG scans housed in the International Neuroblastoma Risk Group Data Commons from patients enrolled in the Children's Oncology Group high-risk neuroblastoma study ANBL12P1. The primary outcome was response to upfront chemotherapy, defined as a Curie score ≤ 2 after four cycles of induction chemotherapy. We derived and validated a CNN using two-dimensional whole-body MIBG scans from diagnosis and evaluated model performance using area under the receiver operating characteristic curve (AUC). We also developed a clinical classification model to predict response on the basis of age, stage, and MYCN amplification. RESULTS Among 103 patients with high-risk neuroblastoma included in the final cohort, 67 (65%) were responders. Performance in predicting response to upfront chemotherapy was equivalent using the CNN and the clinical model. Class-activation heatmaps verified that the CNN used areas of disease within the MIBG scans to make predictions. Furthermore, integrating predictions using a geometric mean approach improved detection of responders to upfront chemotherapy (geometric mean AUC 0.73 v CNN AUC 0.63, P < .05; v clinical model AUC 0.65, P < .05). CONCLUSION We demonstrate feasibility in using machine learning of diagnostic MIBG scans to predict response to induction chemotherapy for patients with high-risk neuroblastoma. We highlight improvements when clinical risk factors are also integrated, laying the foundation for using a multimodal approach to guiding treatment decisions for patients with high-risk neuroblastoma.

PURPOSE Molecular tumor profiling is becoming a routine part of clinical cancer care, typically involving tumor-only panel testing without matched germline. We hypothesized that integrated germline sequencing could improve clinical interpretation and enhance the identification of germline variants with significant hereditary risks. MATERIALS AND METHODS Tumors from pediatric patients with high-risk, extracranial solid malignancies were sequenced with a targeted panel of cancer-associated genes. Later, germline DNA was analyzed for a subset of these genes. We performed a post hoc analysis to identify how an integrated analysis of tumor and germline data would improve clinical interpretation. RESULTS One hundred sixty participants with both tumor-only and germline sequencing reports were eligible for this analysis. Germline sequencing identified 38 pathogenic or likely pathogenic variants among 35 (22%) patients. Twenty-five (66%) of these were included in the tumor sequencing report. The remaining germline pathogenic or likely pathogenic variants were single-nucleotide variants filtered out of tumor-only analysis because of population frequency or copy-number variation masked by additional copy-number changes in the tumor. In tumor-only sequencing, 308 of 434 (71%) single-nucleotide variants reported were present in the germline, including 31% with suggested clinical utility. Finally, we provide further evidence that the variant allele fraction from tumor-only sequencing is insufficient to differentiate somatic from germline events. CONCLUSION A paired approach to analyzing tumor and germline sequencing data would be expected to improve the efficiency and accuracy of distinguishing somatic mutations and germline variants, thereby facilitating the process of variant curation and therapeutic interpretation for somatic reports, as well as the identification of variants associated with germline cancer predisposition.

The international pediatric oncology community has a long history of research collaboration. In the United States, the 2019 launch of the Children's Cancer Data Initiative puts the focus on developing a rich and robust data ecosystem for pediatric oncology. In this spirit, we present here our experience in constructing the Pediatric Cancer Data Commons (PCDC) to highlight the significance of this effort in fighting pediatric cancer and improving outcomes and to provide essential information to those creating resources in other disease areas. The University of Chicago's PCDC team has worked with the international research community since 2015 to build data commons for children's cancers. We identified six critical features of successful data commons design and implementation: (1) establish the need for a data commons, (2) develop and deploy the technical infrastructure, (3) establish and implement governance, (4) make the data commons platform easy and intuitive for researchers, (5) socialize the data commons and create working knowledge and expertise in the research community, and (6) plan for longevity and sustainability. Data commons are critical to conducting research on large patient cohorts that will ultimately lead to improved outcomes for children with cancer. There is value in connecting high-quality clinical and phenotype data to external sources of data such as genomic, proteomics, and imaging data. Next steps for the PCDC include creating an informed and invested data-sharing culture, developing sustainable methods of data collection and sharing, standardizing genetic biomarker reporting, incorporating radiologic and molecular analysis data, and building models for electronic patient consent. The methods and processes described here can be extended to any clinical area and provide a blueprint for others wishing to develop similar resources.

BackgroundTumor-infiltrating CD8+ T cells and neoantigens are predictors of a favorable prognosis and response to immunotherapy with checkpoint inhibitors in many types of adult cancer, but little is known about their role in pediatric malignancies. Here, we analyzed the prognostic strength of T cell-inflamed gene expression and neoantigen load in high-risk neuroblastoma. We also compared transcriptional programs in T cell-inflamed and non-T cell-inflamed high-risk neuroblastomas to investigate possible mechanisms of immune exclusion.MethodsA defined T cell-inflamed gene expression signature was used to categorize high-risk neuroblastomas in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program (n=123), and the Gabriella Miller Kids First (GMKF) program (n=48) into T cell-inflamed, non-T cell-inflamed, and intermediate groups. Associations between the T cell-inflamed and non-T cell-inflamed group, MYCN amplification, and survival were analyzed by Cox proportional hazards models. Additional survival analysis was conducted after integrating neoantigen load predicted from somatic mutations. Pathways activated in non-T cell-inflamed relative to T cell-inflamed tumors were analyzed using causal network analysis.ResultsPatients with T cell-inflamed high-risk tumors showed improved overall survival compared with those with non-T cell-inflamed tumors (pMYCN amplification status, in both TARGET and GMKF cohorts. Higher neoantigen load was also associated with better event-free and overall survival (pMYCN, ASCL1, SOX11, and KMT2A transcriptional programs was inversely correlated with the T cell-inflamed signature in both cohorts.ConclusionsOur results indicate that tumors from children with high-risk neuroblastoma harboring a strong T cell-inflamed signature have a more favorable clinical outcome, and neoantigen load is a prognosis predictor, independent of T cell inflammation. Strategies to target SOX11 and other signaling pathways associated with non-T cell-inflamed tumors should be pursued as potential immune-potentiating interventions.

As the availability and sophistication of mobile health (mHealth) technology (wearables, mobile technology, and sensors) continues to increase, there is great promise that these tools will be transformative for clinical trials and drug development. This review provides an overview of the current landscape of potential measurement options, including the various types of data collected, methods/tools for collecting them, and a crosswalk of available options. The opportunities and potential drawbacks of mHealth in cancer clinical trials are discussed. Specific concerns related to data accuracy, provenance, and regulatory issues are highlighted, with suggestions for how to address these in future research. Next steps for establishing mHealth methods and tools as legitimate and accepted measures in oncology clinical trials include continuation of regulatory definition by the FDA; establishment of security standards and protocols; refinement and implementation of methods to establish and document data accuracy; and finally, creation of feedback loops wherein regulators receive updates from researchers with better and more timely data, which should decrease trial times and lessen drug development costs. Implementing mHealth technologies into cancer clinical trials has the potential to transform and propel oncology drug development and precision medicine to keep pace with the rapidly increasing developments in genomics and immunology.

Background Applied pharmacogenomics presents opportunities for improving patient care through precision medicine, particularly when paired with appropriate clinical decision support (CDS). However, a lack of patient resources for understanding pharmacogenomic test results may hinder shared decision-making and patient confidence in treatment. We sought to create a patient pharmacogenomics education and results delivery platform complementary to a CDS system to facilitate further research on the relevance of patient education to pharmacogenomics. Methods We conceptualized a model that extended the data access layer of an existing institutional CDS tool to allow for the pairing of decision supports offered to providers with patient-oriented summaries at the same level of phenotypic specificity. We built a two-part system consisting of a secure portal for patient use and an administrative dashboard for patient summary creation. The system was built in an ASP.NET and AngularJS architecture, and all data was housed in a HIPAA-compliant data center, with PHI secure in transit and at rest. Results The YourPGx Patient Portal was deployed on the institutional network in June 2019. Fifty-eight unique patient portal summaries have been written so far, which can provide over 4500 results modules to the pilot population of 544 patients. Patient behavior on the portal is being logged for further research. Conclusions To our knowledge, this is the first automated system designed and deployed to provide detailed, personalized patient pharmacogenomics education complementary to a clinical decision support system. Future work will expand upon this system to allow for telemedicine and patient notification of new or updated results.

For children with neuroblastoma, the likelihood of cure varies widely according to age at diagnosis, disease stage, and tumor biology. Treatments are tailored for children with this clinically heterogeneous malignancy on the basis of a combination of markers that are predictive of risk of relapse and death. Sequential risk-based, cooperative-group clinical trials conducted during the past 4 decades have led to improved outcome for children with neuroblastoma. Increasingly accurate risk classification and refinements in treatment stratification strategies have been achieved with the more recent discovery of robust genomic and molecular biomarkers. In this review, we discuss the history of neuroblastoma risk classification in North America and Europe and highlight efforts by the International Neuroblastoma Risk Group (INRG) Task Force to develop a consensus approach for pretreatment stratification using seven risk criteria including an image-based staging system—the INRG Staging System. We also update readers on the current Children’s Oncology Group risk classifier and outline plans for the development of a revised 2021 Children’s Oncology Group classifier that will incorporate INRG Staging System criteria to facilitate harmonization of risk-based frontline treatment strategies conducted around the globe. In addition, we discuss new approaches to establish increasingly robust, future risk classification algorithms that will further refine treatment stratification using machine learning tools and expanded data from electronic health records and the INRG Data Commons.