L. McDaniel

Bio: L. McDaniel is an academic researcher. The author has contributed to research in topics: Medicine & Oncology. The author has an hindex of 1, co-authored 1 publications receiving 2380 citations.

A Protein Interaction Map of Drosophila melanogaster

Abstract: Drosophila melanogaster is a proven model system for many aspects of human biology. Here we present a two-hybrid-based protein-interaction map of the fly proteome. A total of 10,623 predicted transcripts were isolated and screened against standard and normalized complementary DNA libraries to produce a draft map of 7048 proteins and 20,405 interactions. A computational method of rating two-hybrid interaction confidence was developed to refine this draft map to a higher confidence map of 4679 proteins and 4780 interactions. Statistical modeling of the network showed two levels of organization: a short-range organization, presumably corresponding to multiprotein complexes, and a more global organization, presumably corresponding to intercomplex connections. The network recapitulated known pathways, extended pathways, and uncovered previously unknown pathway components. This map serves as a starting point for a systems biology modeling of multicellular organisms, including humans.

A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity

Abstract: Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy.

Abstract 5161: Exome-scale longitudinal tracking of emerging therapeutic resistance in GIST via analysis of circulating tumor DNA

Abstract: Gastrointestinal stromal tumors (GIST) are lethal tumors characterized by constitutively activating mutations to KIT or PDGFRA. Transient disease control in the first-line setting is achieved via inhibition of tyrosine kinase signaling using the KIT inhibitor imatinib. As patients progress through subsequent lines of therapy a molecularly heterogeneous disease evolves, characterized by distinct subtypes and shifting repertoires of exon-specific KIT variants which directly impact treatment outcomes. Here, we use tumor-informed exome-scale liquid biopsy to identify and track the evolution of multiple resistance mechanisms in patients receiving tyrosine kinase inhibitors (TKIs) to address the unmet need of comprehensive understanding of GIST evolution in response to therapy. Matched tumor, normal and serial plasma samples were obtained from 15 heavily pretreated metastatic GIST patients. Following baseline sample collection, all patients received systemic TKI therapy, and were monitored until disease progression. Exome-scale detection of somatic variants in cfDNA from longitudinal matched plasma samples was achieved using the NeXT Liquid BiopsyTM platform. The ImmunoID NeXT PlatformⓇ, an augmented exome/transcriptome platform and analysis pipeline which generates comprehensive tumor and immune data was used to profile paired tumor and normal samples. Longitudinal whole exome sequencing of plasma identified dynamic shifts in existing clones harboring exon-specific KIT mutations, and evolution of new KIT mutations arising prior to identification of tumor progression using standard imaging techniques. We detected a correlation between the number of damaging mutations detected in baseline ctDNA and tumor exon 11 KIT mutation status, suggesting that plasma mutation profiles may be KIT-variant dependent. ctDNA from patients with shorter overall survival (OS) was enriched for variants in the PI3K-AKT and MAPK pathway, potentially contributing to immune evasion observed in those patients. Additional associations were observed between gene copy-number changes and OS (P = .0097). Previous studies have demonstrated that immune infiltration and activity may be KIT variant specific, here we broaden those findings, identifying a significant correlation between TCRɑ clonality and variants detected only in plasma (P = .04), as well as a significant association between TCRβ diversity and OS (HR = 2.55, log rank P = .04). Comprehensive profiling of paired tumor tissue (WES and RNA-Seq) and WES of serially collected ctDNA sensitively and repeatedly identified evolving KIT mutations and other molecular alterations prior to radiologically confirmed disease progression. These findings suggest plasma-based monitoring of late-stage GIST malignancies may be useful for non-invasive disease tracking, providing treatment guidance prior to traditional approaches. Citation Format: Charles W. Abbott, Niamh Coleman, Jing Wang, Josette Northcott, Jason Pugh, Dan Norton, Fábio C. Navarro, Lee D. McDaniel, Eric Levy, Rachel Marty Pyke, John Lyle, Jason Harris, Gabor Bartha, Filip Janku, John West, Richard O. Chen, Sean Boyle. Exome-scale longitudinal tracking of emerging therapeutic resistance in GIST via analysis of circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5161.

119 Sensitive prediction of immunotherapy response by integrating immune infiltration and neoantigen presentation score in late-stage melanoma

Abstract: Background Single-modality biomarkers such as tumor muta-tional burden (TMB) often fail to reliably predict response to immune checkpoint blockade (ICB), likely due to incomplete characterization of the complex tumor-immune interactions that influence treatment efficacy. We previously developed the composite biomarker, presentation (NEOPS TM ), which integrates neoantigen processing and presentation potency and showed it outperformed TMB and other single-modality biomarkers in predicting ICB response in melanoma. 1,2 Here, we combined NEOPS with the assessment of tumor immune infiltration, and demonstrated more accurate patient stratification for ICB response. Methods We assessed the interaction effect of 17 immune and stromal cell types on NEOPS, as measured with the Immu-noID NeXT Platform ® , using logistic regression in a retrospec-tive cohort of 45 stage III/IV melanoma patients who received anti-PD1 therapy. Next, we evaluated the impact of the result-ing immune-selected phenotype on the accuracy of NEOPS, built integrated models, and validated them in a cohort of 109 anti-PD1 treated late-stage melanoma. Results The predictive value of NEOPS was increased in patients with higher levels of naïve CD4/CD8 T cell, exhausted CD8 T cell, and CD8 T cell gene expression signatures. Correlated cell signatures were further engineered into features reflecting naïve T lymphocytes (naïve CD4/8 T) and total CD8 (exhausted and CD8 T) T cell infiltrations. Both features were shown

Ultra-sensitive, tumor-informed ctDNA profiling in patients with gastroesophageal cancer and treated with pembrolizumab and longitudinal ctDNA kinetics.

Abstract: 4040 Background: Metastatic esophagogastric cancer (mEGC) is a lethal disease with poor long-term survival. Recent studies have established anti-PD-1 therapy in combination with chemotherapy as the standard of care for first-line therapy for mEGC. KeyLargo (NCT03342937) was a single arm phase II study of pembrolizumab in combination with oxaliplatin and capecitabine in the first line treatment of patients (pts) with HER2 negative mEGC. While high response rates were noted, not all pts received benefit, emphasizing the need for better biomarkers. Paired tumor biopsies and plasma were collected for optimal biomarker testing. In this retrospective study, we employed a novel, tumor-informed ctDNA approach as a tool for longitudinal disease monitoring and dynamic tumor evolution. Methods: Thirty-six pts were enrolled between January 2018 and January 2020. Of 34 evaluable pts, 25 pts achieved a response (ORR = 74%), including 6 pts with a complete response (CR) and 19 pts with a partial response (PR). Our initial analysis includes baseline tumor samples collected from 16 pts with over 59 corresponding on-treatment (OT; up to cycle 35) plasma samples. NeXT Personal, a tumor-informed ctDNA assay, generated personalized liquid biopsy panels derived from somatic variants (SV) from tumor whole genome sequencing. Each personalized assay includes up to 1,800 SVs for sensitive minimal residual disease (MRD) detection and a constant set of 2,100 clinically actionable variants (CAV). Results: Of the 36 pts who enrolled on KeyLargo, 32 pts had baseline tumor and longitudinal plasma samples collected and stored for testing. In this initial cohort of 16 pts, MRD events dynamically varied from 5.3 to 302,000 parts per million (PPM). 16/16 (100%) pts were MRD-positive at baseline, with a limit of detection between 1.5 and 4.6 PPM. OT samples were collected every 3 cycles (9 weeks). The ratio of PPM (rPPM) between baseline and the first available OT sample (typically Cycle 4 to 7) correlated with progression free survival (PFS; p = 0.0004, logrank). rPPM was significantly reduced in pts having a best overall response of PR/CR (98% mean rPPM) versus progressive disease (25% mean rPPM, p < 0.037, U-test). Two pts demonstrated CR, each with 11/11 (100%) MRD-negative plasma samples over approximately two years. CAVs were identified in longitudinal samples with TP53 repeatedly detected in 5 patients and a PIK3CA mutation emerging in the final 3 (of 9) timepoints from one patient. Conclusions: ctDNA was present in all pts at baseline; OT PPM reductions correlated with PFS and best overall response. CAV profiling suggested a de novo PIK3CA variant arising during therapy in one patient. These findings suggest that tumor-informed plasma-based ctDNA profiling in mEGC may detect known CAVs arising during therapy, and with subsequent investigations, may inform therapeutic decisions.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Network biology: understanding the cell's functional organization

Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

Network Medicine: A Network-Based Approach to Human Disease

Abstract: Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

BioGRID: a general repository for interaction datasets

Abstract: Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.