Photon- and pion-nucleon interactions in a unitary and causal effective field theory based on the chiral Lagrangian

We present and apply a novel scheme for studying photon- and pion-nucleon scattering beyond the threshold region. Partial-wave amplitudes for the $\gamma\, N$ and $\pi N$ states are obtained by an analytic extrapolation of subthreshold reaction amplitudes computed in chiral perturbation theory, where the constraints set by electromagnetic-gauge invariance, causality and unitarity are used to stabilize the extrapolation. Based on the chiral Lagrangian we recover the empirical s- and p-wave amplitudes up to energies $\sqrt{s}\simeq 1300$ MeV in terms of the parameters relevant at order $Q^3$.Comment: 76 pages, 23 figures, one additional figure, Tables 4,5 and Figure 4 are corrected, a few references and comments are added. The role of higher partial waves in pion photoproduction is clarifie

Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures.

Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures

Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer

Novel Gene Expression Analyses to Accelerate Precision Pediatric Oncology Research

Cancer is the second leading cause of death in the United States. While there have been medical advances in treating cancer, the standard of care has not changed significantly in recent decades. Chemotherapy, radiation, and surgery are the clinician’s first line of defense against cancer progression, but new therapeutic strategies such as precision oncology are being developed that personalize cancer therapy to individuals. Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. Numerous challenges have arisen in the incorporation of transcriptome analysis into precision oncology workflows. One such challenge is in the necessary consideration of relative rather than absolute gene expression level, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures. This led to the identification of recurrent tumor microenvironment signatures across pediatric cancers as well as a relationship between transposable element expression and immune infiltration. I then developed the vaccinaTE software toolkit to further characterize transposable elements as potential immunotherapy targets. Using RNA-seq and mass spectrometry analysis, I found expression and MHC-bound peptides uniquely mapping to transposable element loci. This led to the creation of a novel process for prioritizing TE vaccine targets as well as a microarray technology for personalizing TE vaccine therapy. To address the need for accurate preclinical models to accelerate drug development for pediatric cancers, I then created a Bayesian hierarchical modeling framework for evaluating patient-derived xenografts. I generated a database of PDX-specific pathway expression to facilitate validation studies that attempt to target differentially expressed pathways. This thesis has sought to improve the treatment of pediatric cancers through the identification of tumor subtypes that respond to specific therapies, identify novel immunotherapy targets based on tumor microenvironment states, and use gene expression analysis to optimize preclinical validation experiments. These methods have been developed for pediatric cancers but can be modified for adult cancers as well as other diseasesfor which gene expression data is available

Novel Gene Expression Analyses to Accelerate Precision Pediatric Oncology Research

Cancer is the second leading cause of death in the United States. While there have been medical advances in treating cancer, the standard of care has not changed significantly in recent decades. Chemotherapy, radiation, and surgery are the clinician’s first line of defense against cancer progression, but new therapeutic strategies such as precision oncology are being developed that personalize cancer therapy to individuals. Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. Numerous challenges have arisen in the incorporation of transcriptome analysis into precision oncology workflows. One such challenge is in the necessary consideration of relative rather than absolute gene expression level, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures. This led to the identification of recurrent tumor microenvironment signatures across pediatric cancers as well as a relationship between transposable element expression and immune infiltration. I then developed the vaccinaTE software toolkit to further characterize transposable elements as potential immunotherapy targets. Using RNA-seq and mass spectrometry analysis, I found expression and MHC-bound peptides uniquely mapping to transposable element loci. This led to the creation of a novel process for prioritizing TE vaccine targets as well as a microarray technology for personalizing TE vaccine therapy. To address the need for accurate preclinical models to accelerate drug development for pediatric cancers, I then created a Bayesian hierarchical modeling framework for evaluating patient-derived xenografts. I generated a database of PDX-specific pathway expression to facilitate validation studies that attempt to target differentially expressed pathways. This thesis has sought to improve the treatment of pediatric cancers through the identification of tumor subtypes that respond to specific therapies, identify novel immunotherapy targets based on tumor microenvironment states, and use gene expression analysis to optimize preclinical validation experiments. These methods have been developed for pediatric cancers but can be modified for adult cancers as well as other diseasesfor which gene expression data is available

ProTECT-Prediction of T-Cell Epitopes for Cancer Therapy.

Somatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies and Peptide- and RNA-based Neoepitope Vaccines to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patient's HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of potential immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling (single nucleotide variants, short insertions and deletions, and gene fusions), peptide:MHC binding prediction, and ranking of final candidates. We demonstrate the scalability, efficiency, and utility of ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, identifying recurrent potential neoepitopes from TMPRSS2-ERG fusions, and from SNVs in SPOP. We also compare ProTECT with results from published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 min per sample (on average) when run in large batches. ProTECT is freely available at https://www.github.com/BD2KGenomics/protect