Genetic Characterization of Blood Group Antigens for Polynesian Heritage Norfolk Island Residents.

Improvements in blood group genotyping methods have allowed large scale population-based blood group genetics studies, facilitating the discovery of rare blood group antigens. Norfolk Island, an external and isolated territory of Australia, is one example of an underrepresented segment of the broader Australian population. Our study utilized whole genome sequencing data to characterize 43 blood group systems in 108 Norfolk Island residents. Blood group genotypes and phenotypes across the 43 systems were predicted using RBCeq. Predicted frequencies were compared to data available from the 1000G project. Additional copy number variation analysis was performed, investigating deletions outside of RHCE, RHD, and MNS systems. Examination of the ABO blood group system predicted a higher distribution of group A1 (45.37%) compared to group O (35.19%) in residents of the Norfolk Island group, similar to the distribution within European populations (42.94% and 38.97%, respectively). Examination of the Kidd blood group system demonstrated an increased prevalence of variants encoding the weakened Kidd phenotype at a combined prevalence of 12.04%, which is higher than that of the European population (5.96%) but lower than other populations in 1000G. Copy number variation analysis showed deletions within the Chido/Rodgers and ABO blood group systems. This study is the first step towards understanding blood group genotype and antigen distribution on Norfolk Island.

Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study.

Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p < 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging.

ImaGene: a web-based software platform for tumor radiogenomic evaluation and reporting.

Summary: Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest, while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Relationships between tumor genotype and phenotype can be identified from these data through traditional correlation analyses and artificial intelligence (AI) models. However, the radiogenomics community lacks a unified software platform with which to conduct such analyses in a reproducible manner. To address this gap, we developed ImaGene, a web-based platform that takes tumor omics and imaging datasets as inputs, performs correlation analysis between them, and constructs AI models. ImaGene has several modifiable configuration parameters and produces a report displaying model diagnostics. To demonstrate the utility of ImaGene, we utilized data for invasive breast carcinoma (IBC) and head and neck squamous cell carcinoma (HNSCC) and identified potential associations between imaging features and nine genes (WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2, and SLC22A31) for IBC and eight genes (NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1, and MECOM) for HNSCC. ImaGene has the potential to become a standard platform for radiogenomic tumor analyses due to its ease of use, flexibility, and reproducibility, playing a central role in the establishment of an emerging radiogenomic knowledge base. Availability and implementation: www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git. Supplementary information: Supplementary data are available at https://github.com/skr1/Imagene.git.

Exome sequencing identifies novel somatic variants in African American esophageal squamous cell carcinoma.

Esophageal cancer has a strikingly low survival rate mainly due to the lack of diagnostic markers for early detection and effective therapies. In the U.S., 75% of individuals diagnosed with esophageal squamous cell carcinoma (ESCC) are of African descent. African American ESCC (AA ESCC) is particularly aggressive, and its biological underpinnings remain poorly understood. We sought to identify the genomic abnormalities by conducting whole exome sequencing of 10 pairs of matched AA esophageal squamous cell tumor and control tissues. Genomic analysis revealed diverse somatic mutations, copy number alterations (SCNAs), and potential cancer driver genes. Exome variants created two subgroups carrying either a high or low tumor mutation burden. Somatic mutational analysis based on the Catalog of Somatic Mutations in Cancer (COSMIC) detected SBS16 as the prominent signature in the high mutation rate group suggesting increased DNA damage. SBS26 was also detected, suggesting possible defects in mismatch repair and microsatellite instability. We found SCNAs in multiple chromosome segments, encoding MYC on 8q24.21, PIK3CA and SOX2 on 3q26, CCND1, SHANK2, CTTN on 11q13.3, and KRAS on 12p12. Amplifications of EGFRvIII and EGFRvIVa mutants were observed in two patients, representing a novel finding in ESCC that has potential clinical relevance. This present exome sequencing, which to our knowledge, represents the first comprehensive exome analysis exclusively in AA ESCC, and highlights novel mutated loci that might explain the aggressive nature of AA ESCC and lead to the development of diagnostic and prognostic markers as well as therapeutic targets.

Molecular Neuropathology in Practice: Clinical Profiling and Integrative Analysis of Molecular Alterations in Glioblastoma.

Molecular profiling of glioblastoma has revealed complex cytogenetic, epigenetic, and molecular abnormalities that are necessary for diagnosis, prognosis, and treatment. Our neuro-oncology group has developed a data-driven, institutional consensus guideline for efficient and optimal workup of glioblastomas based on our routine performance of molecular testing. We describe our institution's testing algorithm, assay development, and genetic findings in glioblastoma, to illustrate current practices and challenges in neuropathology related to molecular and genetic testing. We have found that coordination of test requisition, tissue handling, and incorporation of results into the final pathologic diagnosis by the neuropathologist improve patient care. Here, we present analysis of O6-methylguanine-DNA-methyltransferase promoter methylation and next-generation sequencing results of 189 patients, obtained utilizing our internal processes led by the neuropathology team. Our institutional pathway for neuropathologist-driven molecular testing has streamlined the management of glioblastoma samples for efficient return of results for incorporation of genomic data into the pathological diagnosis and optimal patient care.

Validation of a next-generation sequencing oncology panel optimized for low input DNA.

One caveat of next-generation sequencing (NGS)-based clinical oncology testing is the high amount of input DNA required. We sought to develop a focused NGS panel that could capture hotspot regions in relevant genes requiring 0.5-10 ng input DNA. The resulting Penn Precision Panel (PPP) targeted 20 genes containing clinically significant variants relevant to many cancers. One hundred twenty-three samples were analyzed, including 83 solid tumor specimens derived from FFPE. Various input quantities of DNA (0.5-10 ng) were amplified with content-specific PCR primer pools, then sequenced on a MiSeq instrument (Illumina, Inc.) via paired-end, 2 × 186 base pair reads to an average read depth of greater than 6500x. Variants were detected using an in-house analysis pipeline. Clinical sensitivity and specificity were assessed using results from our previously validated solid tumor NGS panel; sensitivity of the PPP is 96.75% (387/400 variants) and specificity is 99.9% (8427/8428 base pairs). Variant allele frequencies (VAFs) are highly concordant across both assays (r = 0.98 p < 0.0001). The PPP is a robust, clinically validated test optimized for low-yield solid tumor specimens, capturing a high percentage of clinically relevant variants found by larger commercially available NGS panels while using only 0.5-10 ng of input DNA.

Correction to: Characterization of the first fully human anti-TEM1 scFv in models of solid tumor imaging and immunotoxin-based therapy.

Tumor endothelial marker 1 (TEM1) has been identified as a novel surface marker upregulated on the blood vessels and stroma in many solid tumors. We previously isolated a novel single-chain variable fragment (scFv) 78 against TEM1 from a yeast display scFv library. Here we evaluated the potential applications of scFv78 as a tool for tumor molecular imaging, immunotoxin-based therapy and nanotherapy. Epitope mapping, three-dimensional (3D) structure docking and affinity measurements indicated that scFv78 could bind to both human and murine TEM1, with equivalent affinity, at a well-conserved conformational epitope. The rapid internalization of scFv78 and scFv78-labeled nanoparticles was triggered after specific TEM1 binding. The scFv78-saporin immunoconjugate also exerted dose-dependent cytotoxicity with high specificity to TEM1-positive cells in vitro. Finally, specific and sensitive tumor localization of scFv78 was confirmed with optical imaging in a mouse tumor model that has highly endogenous mTEM1 expression in the vasculature. Our data indicate that scFv78, the first fully human anti-TEM1 recombinant antibody, recognizes both human and mouse TEM1 and has unique and favorable features that are advantageous for the development of imaging probes or antibody-toxin conjugates for a large spectrum of human TEM1-positive solid tumors.

Characterization of the first fully human anti-TEM1 scFv in models of solid tumor imaging and immunotoxin-based therapy.

Tumor endothelial marker 1 (TEM1) has been identified as a novel surface marker upregulated on the blood vessels and stroma in many solid tumors. We previously isolated a novel single-chain variable fragment (scFv) 78 against TEM1 from a yeast display scFv library. Here, we evaluated the potential applications of scFv78 as a tool for tumor molecular imaging, immunotoxin-based therapy and nanotherapy. Epitope mapping, three-dimensional structure docking and affinity measurements indicated that scFv78 could bind to both human and murine TEM1, with equivalent affinity, at a well-conserved conformational epitope. The rapid internalization of scFv78 and scFv78-labeled nanoparticles was triggered after specific TEM1 binding. The scFv78-saporin immunoconjugate also exerted dose-dependent cytotoxicity with high specificity to TEM1-positive cells in vitro. Finally, specific and sensitive tumor localization of scFv78 was confirmed with optical imaging in a tumor mouse model that has highly endogenous mTEM1 expression in the vasculature. Our data indicated that scFv78, the first fully human anti-TEM1 recombinant antibody, recognizes both human and mouse TEM1 and has unique and favorable features that are advantageous for the development of imaging probes or antibody-toxin conjugates for a large spectrum of human TEM1-positive solid tumors.

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors.

As our understanding of the driver mutations necessary for initiation and progression of cancers improves, we gain critical information on how specific molecular profiles of a tumor may predict responsiveness to therapeutic agents or provide knowledge about prognosis. At our institution a tumor genotyping program was established as part of routine clinical care, screening both hematologic and solid tumors for a wide spectrum of mutations using two next-generation sequencing (NGS) panels: a custom, 33 gene hematological malignancies panel for use with peripheral blood and bone marrow, and a commercially produced solid tumor panel for use with formalin-fixed paraffin-embedded tissue that targets 47 genes commonly mutated in cancer. Our workflow includes a pathologist review of the biopsy to ensure there is adequate amount of tumor for the assay followed by customized DNA extraction is performed on the specimen. Quality control of the specimen includes steps for quantity, quality and integrity and only after the extracted DNA passes these metrics an amplicon library is generated and sequenced. The resulting data is analyzed through an in-house bioinformatics pipeline and the variants are reviewed and interpreted for pathogenicity. Here we provide a snapshot of the utility of each panel using two clinical cases to provide insight into how a well-designed NGS workflow can contribute to optimizing clinical outcomes.

A novel approach for next-generation sequencing of circulating tumor cells.

BACKGROUND: Next-generation sequencing (NGS) of surgically resected solid tumor samples has become integral to personalized medicine approaches for cancer treatment and monitoring. Liquid biopsies, or the enrichment and characterization of circulating tumor cells (CTCs) from blood, can provide noninvasive detection of evolving tumor mutations to improve cancer patient care. However, the application of solid tumor NGS approaches to circulating tumor samples has been hampered by the low-input DNA available from rare CTCs. Moreover, whole genome amplification (WGA) approaches used to generate sufficient input DNA are often incompatible with blood collection tube preservatives used to facilitate clinical sample batching. METHODS: To address this, we have developed a novel approach combining tumor cell isolation from preserved blood with Repli-G WGA and Illumina TruSeq Amplicon Cancer Panel-based NGS. We purified cell pools ranging from 10 to 1000 cells from three different cell lines, and quantitatively demonstrate comparable quality of DNA extracted from preserved versus unpreserved samples. RESULTS: Preservation and WGA were compatible with the generation of high-quality libraries. Known point mutations and gene amplification were detected for libraries that had been prepared from amplified DNA from preserved blood. CONCLUSION: These spiking experiments provide proof of concept of a clinically applicable workflow for real-time monitoring of patient tumor using noninvasive liquid biopsies.

Understanding the limitations of next generation sequencing informatics, an approach to clinical pipeline validation using artificial data sets.

The advantages of massively parallel sequencing are quickly being realized through the adoption of comprehensive genomic panels across the spectrum of genetic testing. Despite such widespread utilization of next generation sequencing (NGS), a major bottleneck in the implementation and capitalization of this technology remains in the data processing steps, or bioinformatics. Here we describe our approach to defining the limitations of each step in the data processing pipeline by utilizing artificial amplicon data sets to simulate a wide spectrum of genomic alterations. Through this process, we identified limitations of insertion, deletion (indel), and single nucleotide variant (SNV) detection using standard approaches and described novel strategies to improve overall somatic mutation detection. Using these artificial data sets, we were able to demonstrate that NGS assays can have robust mutation detection if the data can be processed in a way that does not lead to large genomic alterations landing in the unmapped data (i.e., trash). By using these pipeline modifications and a new variant caller, AbsoluteVar, we have been able to validate SNV mutation detection to 100% sensitivity and specificity with an allele frequency as low 4% and detection of indels as large as 90 bp. Clinical validation of NGS relies on the ability for mutation detection across a wide array of genetic anomalies, and the utility of artificial data sets demonstrates a mechanism to intelligently test a vast array of mutation types.