bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 2698: Spatial gene expression profiling in breast cancer
Authors: Spears Melanie , Talebian Vida , Liao Linda , Hopkins Megan , Jain Drashti , Quintayo Mary Anne , Bayani Jane , Cheung Alison , Yaffe Martin , Bartlett John M. 
Published: July 2021

Abstract
Over the last decade breast cancer survival has improved, largely due to the therapies offered to patients with the disease. However, despite the advances in diagnosis and treatment of breast cancer, it is still remains the second leading cause of death from cancer in women. Breast cancer is a heterogeneous disease, this in part, explains why the majority of current therapeutic approaches for cancer work best when multiple agents are combined. The interaction between immune and tumor cells is critical in the development and progression of breast cancer. Here we present in situ transcriptomic profiling, using the NanoString Digital Spatial Profiler (DSP) cancer transcriptomic atlas (CTA) assay, of a cohort of breast cancer lumpectomies to reveal the extent of heterogeneity in pathologically defined unifocal and multifocal cancers. In this study, lumpectomies were processed as whole mounts with serial blocks reviewed. Tissue cores were taken from at least three different regions through the lumpectomy for tissue microarray (TMA) construction, focusing on morphologic/histological differences in addition to the spatial orientation of the sampled region within the lumpectomy. In situ quantification of 1800 tumor and immune genes across 60 patients revealed heterogeneity of tumor and immune genes in most patients. Expression of genes such as HER2, ER and AKT were enriched in the tumor compartment. Whereas, genes such as COLA1, CD68 and CD3 were enriched in the immune compartment. Using a SpatialDecon algorithm for mixed cell deconvolution on the immune areas heterogeneity of the tumor infiltrate at a cell type levels was observed. Fibroblasts and macrophages were prevalent in all samples while immune dense areas also contained B-cells and T-cells. While there are a number of clinically validated transcriptional assays available for breast cancer, we have demonstrated that the immune microenvironment needs to be considered to develop rational stratification of patients to currently available targeted therapies.
Citation Format: Melanie Spears, Vida Talebian, Linda Liao, Megan Hopkins, Drashti Jain, Mary Anne Quintayo, Jane Bayani, Alison Cheung, Martin Yaffe, John M. Bartlett. Spatial gene expression profiling in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2698.
Abstract 2699: Transcriptome analysis identifies molecular markers of tumor-associated high endothelial venules that predict breast cancer survival
Authors: Sawada Junko , Hiraoka Nobuyoshi , Fournier-Goss Ashley , Komatsu Masanobu 
Published: July 2021

Abstract
High endothelial venules (HEVs) are specialized post-capillary venules that recruit naïve lymphocytes to the lymph nodes and are essential for the development of adaptive immunity. HEVs can develop in tumors, and these specialized tumor blood vessels are thought to be important for recruiting naïve T cells and B cells into tumors and locally enhance anti-tumor immunity by promoting tertiary lymphoid structure formation. At present, the genes involved in the development and function of tumor-associated HEVs remain largely unknown. By a comparative transcriptome analysis in human breast cancer, we identified the expression of genes that represents the molecular signature of tumor-associated HEVs distinct from the rest of the tumor vasculature. Tumor vessels highly expressing these genes were associated with the accumulation of T and B lymphocytes and the occurrence of tertiary lymphoid structures, which is known to predict therapeutic response to immune checkpoint inhibitor therapies. Moreover, the Cancer Genome Atlas (TCGA) data show that the high expression of these genes is associated with a significant survival benefit in advanced breast cancer. The molecular signature of HEVs identified here may be useful for guiding immunotherapies and provide a new direction for investigating tumor-associated HEVs and their clinical significance.
Citation Format: Junko Sawada, Nobuyoshi Hiraoka, Ashley Fournier-Goss, Masanobu Komatsu. Transcriptome analysis identifies molecular markers of tumor-associated high endothelial venules that predict breast cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2699.
Abstract 274: Comparison of proteomics identification pipelines for lymphocyte characterization
Authors: McCown Michaela A. , Allen Carolyn , Machado Daniel D. , Payne Samuel H. 
Published: July 2021

Abstract
Proteomics analyses can be adapted to samples as small as a single cell, however such sample reduction severely limits the number proteins identified and quantified. Multiple software pipelines exist for processing the raw data to quantitative protein tables. For very small sample amounts, the sensitivity of software algorithms can have a significant impact on the final results. We collected whole blood samples for two subjects by venous blood draws. Using antibody guided flow cytometry cell sorting, we separated B and T cell lymphocytes. For two subjects and two different blood collections, five replicates of 145 B cells or T cells were prepared for mass spectrometry for proteomics using the AutoPOTS workflow. Using the raw files from these forty runs, we perform protein quantification with a selection of common tools such as MaxQuant, ProteomeDiscoverer, and FragPipe and compare the number of identified and quantified proteins. In small sample proteomics, the primary objective is obtaining the most quantified proteins. Although each tool has advantages in terms of usability, speed, and sensitivity, our primary concern is sensitivity. At such a low sample input, we observe that the number of quantified proteins increases by 30% using the most sensitive algorithm. For identifying and quantifying proteins from small samples, the sensitivity of the software is the most important factor to consider. In addition to the identification sensitivity of each algorithm, we are exploring optimal parameters for increased protein quantification. Differences between samples appear regardless of the processing tool, showing that using a less sensitive tool will still characterize the trends but with a significant cost to the number of proteins. Using the most sensitive algorithm for small sample proteomics will greatly improve the number of proteins identified and quantified.
Citation Format: Michaela A. McCown, Carolyn Allen, Daniel D. Machado, Samuel H. Payne. Comparison of proteomics identification pipelines for lymphocyte characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 274.
Abstract 2765: The indoleamine 2,3 dioxygenase-1 pathway drives intratumoral B cell maintenance
Authors: Johnson Burles Avner , Aragaki Adam K. , Williams Donna M. , Rogers Ophelia , Luo Li , Xian Lingling , Chia Lionel , Hahn Noah M. , Desiderio Stephen , Johnson Theodore S. , McConkey David J. , Resar Linda M.S. 
Published: July 2021

Abstract
Immunotherapy has revolutionized cancer treatment by improving survival in many cancer subtypes. While tumor infiltrating B (TIL-B) cells correlate with response to immunotherapy in selected solid tumors, they portend resistance to BRAF inhibitors in BRAF mutant melanoma. Thus, the mechanisms underlying TIL-B cell function within the tumor microenvironment have remained elusive. Here, we discovered that inhibition of the indoleamine 2,3 dioxygenase-1 (IDO1) pathway with D-1-methyl-tryptophan (D-1MT) markedly decreases TIL-B cells in a preclinical model of melanoma. Single cell RNA sequencing (scRNAseq) of murine melanoma demonstrate TIL-B cells are heterogeneous: while some B cells express markers consistent with an immune stimulatory phenotype, others express markers consistent with immune inhibitory function. D-1MT decreased splenic B cells and bone marrow derived B cell precursors in tumor bearing mice, suggesting that IDO1 pathway inhibition impedes B cell maturation. In four distinct human cancer subtypes, intratumoral IDO1 expression consistently correlates with high expression of a pan-B cell gene signature. Further, analysis of scRNAseq data from a cohort of patients with melanoma revealed that most TIL-B cells express IDO1. In mice, D-1MT also decreases intratumoral myeloid derived suppressor cells (MDSCs), which are essential for maintenance of TIL-B cells. Surprisingly, we found that D-1MT enhanced anti-tumor effects of the BRAF inhibitor, vemurafenib, in a preclinical model of BRAF mutant melanoma, suggesting that targeting B cells in this setting may be beneficial. Together, our data reveal a novel paradigm for the IDO1 pathway in regulating TIL-B cells, and uncovered IDO1 as a potential targetable mechanism of resistance to BRAF inhibition in melanoma.
Citation Format: Burles Avner Johnson, Adam K. Aragaki, Donna M. Williams, Ophelia Rogers, Li Luo, Lingling Xian, Lionel Chia, Noah M. Hahn, Stephen Desiderio, Theodore S. Johnson, David J. McConkey, Linda M.S. Resar. The indoleamine 2,3 dioxygenase-1 pathway drives intratumoral B cell maintenance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2765.
Abstract 2768: A novel non-terminal tumor sampling procedure using fine needle aspiration supports biomarker discovery
Authors: Sitnikova Suzanne I. , Munnings-Tomes Sophie , Kentner Stacy R. , Mulgrew Kathy , Espana-Agusti Judit , Zhang Tianhui , Ilieva Kristina M. , Ghadially Hormas M. , Robinson Matthew J. , Wilkinson Robert W. , Dovedi Simon J. 
Published: July 2021

Abstract
Immunotherapy is part of the standard of care for oncology however, durable objective responses remain limited to a subset of patients. As such there is a crucial need to identify biomarkers that can predict/enrich for treatment response. So far, the majority of proposed biomarkers consist of features of the tumor microenvironment (TME). However, in preclinical mouse models, the collection of tumor tissue for this type of analysis is a terminal procedure obviating the ability to directly link potential biomarkers to response/outcome following treatment. Therefore, we have developed and validated a novel non-terminal tumor sampling method to biopsy the TME in mouse models based on fine needle aspiration. We show that this technique enables repeated in-life sampling of subcutaneous flank tumors without impacting tumor growth or animal welfare. This method yields sufficient sample for flow cytometric analysis of tumor-infiltrating immune cells and we demonstrate that it is able to recapitulate results obtained with the current methodology of whole tumor analysis. We propose that this method represents a simple, fast, minimally invasive technique to enable analysis of the TME, reduction in the number of animals used for preclinical oncology studies and refinement of pharmacodynamic analysis. Additionally, its unique ability to provide longitudinal TME sampling can support investigation of biomarkers of response to treatment.
Citation Format: Suzanne I. Sitnikova, Sophie Munnings-Tomes, Stacy R. Kentner, Kathy Mulgrew, Judit Espana-Agusti, Tianhui Zhang, Kristina M. Ilieva, Hormas M. Ghadially, Matthew J. Robinson, Robert W. Wilkinson, Simon J. Dovedi. A novel non-terminal tumor sampling procedure using fine needle aspiration supports biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2768.
Abstract 2795: Estimation of cell lineage and inter-cell interactions from spatial transcriptomics data
Authors: Ru Beibei , Huang Jinlin , Zhang Yu , Aldape Kenneth , Jiang Peng 
Published: July 2021

Abstract
The spatial transcriptomics (ST) technology has enabled geographical profiling of tumor gene expression. However, each ST spot may detect mixture signals from diverse immune or malignant cells of unknown lineages, and local tissue densities may vary significantly across regions. Therefore, the decomposition of ST cell lineages remains a challenge that cannot be resolved by previous decomposition methods for fixed cell types in bulk tumors. We developed the Spatial Cell Estimator (SpaCE) to infer the cell identities and intercellular interactions for tumor ST data. SpaCE first estimates the cancer cell clonal abundance through modeling the copy number segmental variations across ST spots. Then, we utilized a constrained regression to determine the hierarchy of immune cell lineages and local tissue densities. The prediction accuracy was validated through both simulation and double-blinded pathology annotations on hematoxylin and eosin images. Based on reliable cell lineage inference, SpaCE can further reveal how intercellular interactions affect the pathway and gene activities in distinct regions to modulate the cancer progression.
Citation Format: Beibei Ru, Jinlin Huang, Yu Zhang, Kenneth Aldape, Peng Jiang. Estimation of cell lineage and inter-cell interactions from spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2795.
Abstract 2796: Pan-cancer characterization of tumor immune microenvironment
Authors: Chu Tianqing , Bei Zhang 
Published: July 2021

Abstract
Background: Anti-PD1/PDL1 therapy has been established as standard care in multiple tumor types, however, many patients will not benefit from this treatment as lack of infiltrating immune cells. Our aim was to explore the pan-cancer immune microenvironment and whether they impacted by ICI biomarkers.
Method: 7-plex mIHC was performed to assessed immune cell infiltrations were assessed. Positive PD-L1 expression was defined as ≥1% of tumor cells with membranous staining. Statistical analyses were performed using R version 3.4.4.
Result: Among 520 samples, CD8+T cell infiltration in tumor was assessed in Esophagus, Liver, Biliary Tract, Lung, Colorectum, Stomach tumors, ordered by descending median density at the center of the tumor. The median density at the periphery was almost double than the median density at the center. In addition, we have observed high TAM.M1 density in Bladder urinary Tract, Biliary Tract, Liver, with a higher or equivalent level of infiltration in stroma than in tumor. While in contrast, the TAM.M2 cell density was higher in stroma than in tumor.
Regarding to PDL1 status in colorectum, there was little difference in the degree of CD8+ T cell infiltration between stroma and tumor. In the term of MSI status, CD8+T cell infiltration was significantly higher in MSIH colorectum tumor.
We compared the proportion of above immune cells. In stroma, except lung tumor, CD8+T cells made up the largest share of immune cells, following by NK cells. In tumor, except for lung cancer, NK cells accounted for the highest proportion. Cancer with the highest proportion of NK cells is bladder/urinary tract cancer. Although the proportion of NK cells in lung cancer is also high, macrophages proportion is the highest, especially TAM.M2.
Conclusions: Together, these data outlined the immune infiltration inter-, extra-tumor, and associations with TMB, MSI status. Tumor immune microenvironment might serve as promising predictive factors for immunotherapy.
Citation Format: Tianqing Chu, Zhang Bei. Pan-cancer characterization of tumor immune microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2796.
Abstract 2803: Classification of lung cancer histology images using deep learning
Authors: YANG Yan , ZHEN Tiantian , SHI Huijuan , XIE Weidong , HAN Anjia , WANG Xunzhang 
Published: July 2021

Abstract
The pathologist's diagnosis on glass slides is the basis for clinical and pharmaceutical research and, more importantly, for the decision on how to treat the patient. Diagnosing pathology slides at microscopy-level is a complicated task that requires years of pathologist training. Only in recent years, computational pathology has emerged to facilitate computer-assisted diagnostics and to enable a digital workflow for pathologists. Meanwhile, deep learning architectures have great potential in pathology imaging, since the enormous success and advancement had been achieved in solving image classification tasks, which would reform the digital workflow with higher efficiency, greater capacity. In this study, we utilize various computational methods based on deep learning framework and build a stand-alone diagnostic tool to effectively classify histopathology images. The dataset of tissue slides consists of 135,000 samples from eligible patients with lung cancer in the First Affiliated Hospital of Sun Yat-sen University. The slide images were intercepted in non-overlapping 1500 × 1000 pixel windows at a magnification of ×40. These tiles above were labeled, and split into a training set, for learning the CNN parameters, and validation set, for validating the model, and test set, for evaluating its performance. Then, features were extracted from the heat map to train a slide aggregation model. The malignant classification of lung cancer by the decision support system include the subtype of adenocarcinoma and squamous carcinoma. An accuracy of 86% for non-cancer (normal) vs. malignant (i.e. adenocarcinoma or squamous carcinoma) on slide tile model, as well as an accuracy of 99% for the multi-label classification on slide aggregation model was obtained in the test set. We believe that our method provides an innovative and reliable means for making diagnostic suggestions, and could be deploy as next-generation, artificial intelligence-enhanced CAD technology in diagnostic pathology,which would empower pathologists' efficiency and accuracy to ultimately provide better patient care.
Citation Format: Yan YANG, Tiantian ZHEN, Huijuan SHI, Weidong XIE, Anjia HAN, Xunzhang WANG. Classification of lung cancer histology images using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2803.
Abstract 2807: Variations in glycolytic activity of HCC subtypes as monitored by in vivo h 13C MRI
Authors: Dou Qianhui , Grant Aaron K. , Callahan Cody , Ahmed Muneeb , Tsai Leo L. 
Published: July 2021

Abstract
Hepatocellular carcinoma (HCC) is the 4th leading cause of cancer-related death worldwide. Despite therapeutic advancements, a significant proportion of patients progress, highlighting the need to improve diagnostic approaches to monitor therapy. Radiofrequency ablation (RFA), an accepted locoregional therapy for early HCC, can stimulate more aggressive tumor biology within under-treated or distant tumors. Increased glycolytic activity is a hallmark feature of cancer, but variations in metabolism and its link to tumor aggression can impact the sensitivity of HCC to therapies affecting these pathways. Tumor metabolic phenotypes can be characterized by hyperpolarized 13C MRI (h13C MRI), which can also be used to assess treatment response. In this study, we used orthotopic rat HCC models with two different cell lines N1S1 and MCA-RH7777, and analyzed their mRNA expression of glycolysis-related genes and in vivo glycolytic activity with h13C MRI. We then partially-treated tumors with radiofrequency ablation (RFA) and used h13C MRI to measure in vivo tumor metabolism before and 3 days after RFA to compare them to histologic and phenotypical measures of disease progression.
N1S1 tumors grew only as single lesions, while MCA-RH7777 rapidly produced intra-hepatic and distal metastases. Glycolysis-related genes including HK2, HK3, PFKFB3, and MCT4 were highly expressed in N1S1, but not in MCA-RH7777, while GPT1 was highly expressed in MCA-RH7777 but not in N1S1. In vivo h13C MRI demonstrated significantly increased 13C lactate flux in N1S1 tumor compared to the background normal liver, while 13C lactate flux in MCA-RH7777 rat model did not, consistent with mRNA expression patterns.
Tumor 13C lactate flux was significantly increased and 13C alanine flux was significantly decreased following partial RFA treatment to N1S1 tumors when compared to untreated tumors, which was associated with an increase in tumor Ki67 expression. However, no change was seen on h13C MRI between the RFA and control arms in MCA-RH7777 tumors, even though growth and total tumor burden progressed significantly more following RFA. In contrast to N1S1, there was also no difference between 13C lactate flux and 13C alanine flux within MCA-RH7777 tumors and the neighboring normal hepatic parenchyma, suggesting that glycolytic activity in these tumors did not drive RFA-stimulated progression. Targeting of glycolytic pathways as a potential therapy for HCC may therefore be limited to certain subtypes, and h13C MRI may be used to select for sensitivity.
Citation Format: Qianhui Dou, Aaron K. Grant, Cody Callahan, Muneeb Ahmed, Leo L. Tsai. Variations in glycolytic activity of HCC subtypes as monitored by in vivo h 13C MRI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2807.