bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 4262: Genomic profiling of uveal melanoma patients using the GENIE database
Authors: Tashtoush Shaden , Al-Akhras Ashaar , Ghazou Alina , Syaj Sebawe 
Published: April 2023

Abstract
Uveal melanoma (UM) is the most common primary adult intraocular tumor. Frequently mutated genes in UM include BAP1, EIF1AX, GNA11, GNAQ CHEK2, and SF3B1. This study aims to investigate the genomic characteristics of UM patients using a large cohort of clinicogenomic data. We assessed the frequency of genetic mutations in UM patients obtained from the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) cohort v12.1. We excluded patients with samples from a local recurrence, not otherwise specified tumor samples, and data not collected for sampling localization from our cohort. A total of 296 UM patients were included in this study, 164 (55.4%) of which were male and 131 (44.6%) were female. The sample mean age was 59.5 (SD: 14.1), and most patients were of white ethnicity (83.8%). Of 243 patients with known vital status, 32.1% (n = 78) of them were alive, while 67.9% (n = 165) of them were deceased. Sequencing samples were collected from the primary tumor in 141 patients, and from a metastatic nodule in 146 patients. We found that GNAQ and GNA11 were mutated in 43.9% and 43.6% of patients, respectively. BAP1 was mutated in 35.1%, SF3B1 in 22.3%, and EIF1AX in 13.2% of patients. No patients had GNAQ and GNA11 mutations concurrently. All patients had a median mutation count of 3, with no differences between the two genders. No significant differences were found in the frequency of mutations in regard to gender. GNAQ and GNA11 mutations were identified to be the most frequent mutations among UM patients, with a mutual exclusivity relationship between the two genes. New therapeutic modalities should consider the comprehensive genomic profiling of UM patients to improve their overall survival rate and quality of life.
Citation Format: Shaden Tashtoush, Ashaar Al-Akhras, Alina Ghazou, Sebawe Syaj. Genomic profiling of uveal melanoma patients using the GENIE database. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4262.
Abstract 4278: Transcriptome-driven combination design: A computational approach
Authors: Gavazzi Christina , Grushko Mikhail G. , Goldstein Jeremy M. , Samizadeh Mahta , ElSeht Zakary Y. , Arline Katherine 
Published: April 2023

Abstract
While rational drug combination design seeks to improve patient outcomes by leveraging biological synergy, outcomes from combination trials often fail. Only 12.4% of phase 1 trials eventually achieve regulatory approval. In vitro evidence has provided the rationale for the drug combinations in 36.6% of phase 1 combination trials, but none of those trials progressed past phase 3 (from a study by Paller CJ et al., 2019). A review of DrugCombDB, a publicly available database of experimental synergy results, supports the idea that such outcomes are mirrored in preclinical data. Analysis of the similarity of synergy scores within indication-specific subsets of cell lines from data in a 2022 paper by Douglass, et al. show that in both breast and lung cancer, combinations predicted to be synergistic in at least one cell line from that indication failed to be synergistic in the majority of indication cell lines more than half of the time.
SHEPHERD’s Gene Cluster Voting Algorithm (GCVA) was adapted to improve upon current drug combination design and anticipate transcriptome changes as the result of drug treatment. GCVA relates drug sensitivity to the transcriptome by calculating a list of biomarkers that contribute to sensitivity and resistance for specific drugs, enabling the prediction of drug sensitivity on patient transcriptomic data captured via bulk RNAseq.
Via this method, 15,137 publically available synergy data points in the Douglass paper were compared to predictions by our computational approach. For all synergy metrics contained within the dataset (ZIP, Bliss, Loewe, and HSA), the synergistic predictions made by application of GCVA have significantly higher synergy scores compared to the antagonistic predictions made by GCVA (p &lt; .001). These test results support the potential for the application of GCVA to intelligent combination design with the ultimate goal of deployment on a patient-by-patient basis.In addition, analysis of GCVA’s drug efficacy predictions for the cell line DU145 showed that treatment with 9 kinase inhibitors consistently potentiated the predicted efficacy of between 5 and 11 additional drugs. Dasatinib, temsirolimus, and cabozantinib were newly predicted effective after treatment with each and every kinase inhibitor. This data suggests an avenue for the design of intelligent drug combinations based on anticipated transcriptomic changes that may improve on existing synergy prediction methods.
Citation Format: Christina Gavazzi, Mikhail G. Grushko, Jeremy M. Goldstein, Mahta Samizadeh, Zakary Y. ElSeht, Katherine Arline. Transcriptome-driven combination design: A computational approach. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4278.
Abstract 4280: Automatic identification of cancer cell-cell interfaces at the pixel level
Authors: Zhou Jie , Martinek Jan , Liu Zichao , Foroughi Pour Ali , Wu Te-Chia , Sivajothi Santhosh , Robson Paul , Palucka Karolina , Chuang Jeffrey Hsu-Min 
Published: April 2023

Abstract
Deciphering cell-cell interactions is critical for understanding the role of the tumor immune microenvironment. Although multiplex imaging methods are increasingly used for spatial characterization of cell communities and tissue morphologies, quantification of the interface between contacting cells has been mostly unexplored. Here we design an unsupervised pixel-level clustering method that accurately detects the contacting interface between cells. First, we observed pixel-level clusters with cell type specific phenotypes including tumor, immune infiltrates and extracellular structures in two independent melanoma datasets using cytometry time-of-flight (CyTOF) imaging mass cytometry (IMC) images. More importantly, our pixel-level approach identified clusters with mixed phenotypes, which are demonstrated to be direct contacts between T cells and myeloid cells. Specifically, pixels in the interface clusters simultaneously express T cell (CD3+, CD4+ or CD8+) and myeloid cell (CD68+) phenotypic markers at comparable levels. Spatial mapping of the pixels in interface clusters shows that interface pixels are in the physical contact regions between T cells and myeloid cells. In addition, the abundance of T cell and myeloid cell interface clusters is correlated with positive response to immune checkpoint inhibitors (ICIs) (p&lt;0.05). We also identified other clusters indicating interfaces between the stroma, tumor cells, and immune infiltrates. Our work enables reliable delineation of cell interactions at high resolution, and therefore can unravel new information about how cancer cells interact with their environment.
Citation Format: Jie Zhou, Jan Martinek, Zichao Liu, Ali Foroughi Pour, Te-Chia Wu, Santhosh Sivajothi, Paul Robson, Karolina Palucka, Jeffrey Hsu-Min Chuang. Automatic identification of cancer cell-cell interfaces at the pixel level. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4280.
Abstract 4384: Does progressive disease justify pembrolizumab discontinuation: a simulation analysis
Authors: Thiagarajan Kannan , Channavazzala Madhav , Topp Brian Gregory 
Published: April 2023

Abstract
Background: With checkpoint inhibition becoming first line therapy in several cancer indications, there is a growing number of patients that are refractory to PD1 therapy. It is unclear if these patients should remain on PD1 inhibitors while new therapies are added on top or if PD1 should be discontinued when new therapies are initiated. Here we simulate a randomized controlled study comparing pembrolizumab + a checkpoint inhibitor (P+C) to a checkpoint inhibitor (C) in virtual patients that are refractory to PD1 therapy.
Methods: An IO QSP model was developed that includes intra-patient heterogeneity in tumor dynamics (target, non-target, and new metastatic lesions). The model was calibrated to individual lesion data from a large multicentre, open-label phase III study (Keynote-001) in patients with metastatic melanoma. A two year phase III study comparing P+C to C was simulated with 310 virtual patients in each group. Virtual patients were removed from the trial at the time of RECISTv1.1 progression or dropout due to AE or loss to follow-up.
Results: Virtual patients in the P+C arm displayed deeper (-11.5% vs 24.5%) and longer response (186.4 days vs 96.5 days) than virtual patients in the CPI arm. The percent of virtual patients displaying low prevalence progression (&lt; 50% of the target lesions with PD) was 86% vs 52% for P+C vs. C. Median progression free survival was also longer in the P+C arm (3.8 vs 2.1 months). Difference in progression free survival were driven mainly by virtual patients that displayed oligoprogression during the pembrolizumab monotherapy run-in period.
Conclusions: Simulations presented here show that virtual patients that continued pembrolizumab treatment beyond RECISTv1.1 progression (as part of a combination) did better than virtual patients that discontinue pembrolizumab (and switch to a new therapy).
Citation Format: Kannan Thiagarajan, Madhav Channavazzala, Brian Gregory Topp. Does progressive disease justify pembrolizumab discontinuation: a simulation analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4384.
Abstract 455: Development of an antibody-drug conjugate panel targeting high grade serous ovarian cancer
Authors: Fu Ruisi , Lou Hantao , Wang Ning , Yan Dongnan , Tresserras-Segura Clara , Lu Haonan , Aboagye Eric 
Published: April 2023

Abstract
Antibody drug conjugate (ADC) is a novel class of therapeutic agent which recently showed great success in both solid tumour and blood cancer. ADCs deliver potent cytotoxic payloads to tumour via cancer specific monoclonal antibodies. A few ADCs are currently evaluated in clinical trials for high grade serous ovarian cancer (HGSOC). However, most of them target FOLR1-alpha, a conventional HGSOC target which also expresses in vital organs including lungs. Thus, it is desirable to discover more specific antigen targets, and these new targets will be the foundation for the next-generation ADC for HGSOC.We propose that ovarian tumour specificity of an ADC target could contributed by three factors: 1. Lineage (e.g. fallopian tube marker FOLR1); 2. DNA amplification (e.g. ERBB2); 3. Other carcinogenesis process including epigenetic reprogramming. Then, by combining the GTEX, TCGA dataset with our own WES, long-reads RNA-sequencing and Mass spectrometry-based proteomics cohort, an array of differentially expressed genes in HGSOC tumours are identified. Out of over 50 candidates, two most promising targets are chosen for monoclonal antibodies (mAbs) production and subsequent ADC development. First, the affinity and specificity of the mAbs were confirmed by ELISA, flow cytometry and immunofluorescence. The cellular internalisation characteristics of these antibodies are confirmed by a fluorescence-based assay. Then, ADCs for these two targets are produced by conjugating monomethyl auristatin E payload to the mAbs via a cleavable linker. Finally, both ADCs show highly target-specific and sub-nanomolar cytotoxicity in model ovarian cancer cell lines. In conclusion, we identify a panel of HGSOC-specific ADC targets and develop two ADCs candidates for further pre-clinical and clinical investigations.
Citation Format: Ruisi Fu, Hantao Lou, Ning Wang, Dongnan Yan, Clara Tresserras-Segura, Haonan Lu, Eric Aboagye. Development of an antibody-drug conjugate panel targeting high grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 455.
Abstract 4551: Reconstructing the metastatic tumor microenvironment of high grade serous ovarian cancer: understanding the contribution of malignant cells
Authors: Malacrida Beatrice , Elorbany Samar , Maniati Eleni , Laforets Florian , Manchanda Ranjit , Balkwill Frances R. 
Published: April 2023

Abstract
Following a ‘deconstruction’ of the metastatic tumor microenvironment (TME) of high grade serous ovarian cancer (HGSOC), we developed 3-dimensional (3D) human multicellular in vitro models using patient-derived primary cells and HGSOC malignant cell lines. In this study, we used these 3D in vitro human models to study how malignant cells influence the surrounding TME. Using patient-derived primary adipocytes, fibroblasts and mesothelial cells isolated from macroscopically normal omentum (the primary site of metastasis in HGSOC) from women undergoing gynaeco-oncology surgery, we built multicellular models combining those cells with different high grade serous malignant cell lines and monocytes isolated from healthy individuals. We found that peripheral blood monocytes were able to differentiate and polarize to macrophages in our pentacultures (i.e., five different cell types) with phenotypes also found in HGSOC omental metastases. In addition, the cells in our pentaculture models self-rearranged in ways that were dependent on the HGSOC malignant cell lines used in the culture. Macrophage phenotype and cytokine secretion from the multicellular cultures was also influenced by the different malignant HGSOC cell lines used in the model. RNAseq analysis of the pentacultures showed that the cultures clustered according to the malignant cell line used. In addition, we were able to identify some gene expression clusters in the macrophages and fibroblasts of the pentacultures that matched those found by single cell RNAseq analysis of human HGSOC biopsies. These human 3D in vitro multicellular models allow us to define the contribution of each cell type to tumor growth and spread, and also provide us a semi-high throughput platform to investigate antibody or drug targets.
Citation Format: Beatrice Malacrida, Samar Elorbany, Eleni Maniati, Florian Laforets, Ranjit Manchanda, Frances R. Balkwill. Reconstructing the metastatic tumor microenvironment of high grade serous ovarian cancer: understanding the contribution of malignant cells. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4551.
Abstract 4556: Matrix properties regulate 3D tumor spheroid growth and cancer drug response
Authors: Libby Jaxson R. , Li Linqing , Walker Sarah R. 
Published: April 2023

Abstract
3D cancer spheroids are vital in vitro models for cancer drug discovery. The current cancer spheroid generation utilizes low-adhesion plates or forced aggregation of single cells. However, certain cancers do not form stable spheroids in these protocols, nor do pre-formed tumor spheroids recapitulate in vivo development and progression. This project aims to overcome this limitation with the use of a biomimetic hydrogel system to mimic in vivo cancer cell growth and tumor development and use it as an in vitro platform for 3D cancer drug screening. The biocompatible and tunable hydrogel permits independent control of key biochemical and biophysical cues such as matrix degradation, cell adhesion, and stiffness; leading to systematic investigation of matrix parameter contribution to regulating in vitro tumor growth and treatment response. We modified a homo-polysaccharide dextran with methacrylate groups and generated dextran macromers (Dex-MA) containing ample reactive methacrylates per polymer. Cell adhesion is mediated by thiol-terminated RGD peptide added to the methacrylates, followed by crosslinking with di-thiolated MMP-labile peptides through Michael-type addition reaction. Varying the sequence of the crosslinker furthermore gives access to a range of degradability, independent of matrix stiffness. Our experiments showed that Dex-MA supported robust growth of SK-BR-3 breast cancer cells from single cell to 3D tumor spheroids, whereas the cell line failed to form tumor aggregates in low-adhesion plates. Alteration of hydrogel stiffness further revealed stiffness dependence of tumor growth, invasion, and response to treatment, suggesting the importance of matrix properties on regulating cell behavior. This dextran-based tunable hydrogel system provides key insights into how engineered matrix properties control tumor growth, critical for screening novel therapy compounds, and may reveal mechanisms for 3D tumor development, invasion, metastasis, and drug resistance.
Citation Format: Jaxson R. Libby, Linqing Li, Sarah R. Walker. Matrix properties regulate 3D tumor spheroid growth and cancer drug response. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4556.
Abstract 4566: 3D cancer cell-fibroblast heterospheroids forming tumor-like microenvironments are valuable tools to study immune cell infiltration and as pre-clinical drug testing models
Authors: Nielsen Boye Schnack , Madsen Natasha H. , Larsen Jesper , Larsen Hjalte M. , Gad Monika , Holmstrom Kim 
Published: April 2023

Abstract
3D cancer cell cultures have enabled new opportunities for replacing compound testing in experimental animals and provide a basis for high content work-flows for drug testing. Here we present data obtained from 3D multicellular tumor spheroids (MCTS) composed of cancer cell lines, fibroblasts and macrophages. We cultured HT29 and SW480 CRC cell lines (as well as others) with fibroblasts and obtained heterospheroids that could be evaluated in cell viability and apoptosis assays, by flow cytometry and high content imaging (HCI). In addition, mature MCTS were embedded in paraffin and used for traditional biomarker characterization using immunohistochemistry and in situ hybridization. The cancer cell x fibroblast interactions promoted a tumor-like microenvironment that recruited monocytes isolated from primary donors. FACS analysis indicated that the infiltrating monocytes polarized into CD163-positive M2 macrophages in HT29 MCTS, and in pancreatic MIA PaCa-2 MCTS, using Pexidartinib and anti-CSF1R mAb, we found that monocyte infiltration was strongly CSF1R dependent. Preliminary data suggest that also infiltration of T cells into the heterospheroids is possible, thus making the models useful for the study of immune cell infiltration. Whole transcriptomic analysis of mono-cultures of SW480 cancer cells and fibroblast spheroids and co-cultures (MCTS) identified traditional cell-type-related transcripts as well as a group of de novo expressed genes induced by co-culturing, such as serpin peptidase inhibitor clade A1 (serpinA1) and inhibin-β A (INHBA). The NGS analysis as well as immunohistochemical staining also showed that SW480 expressed high levels of P-glycoprotein (P-Gp, MDR1) in contrast to the fibroblasts. On the other hand, the HT29 MCTS were negative for P-Gp. Histological analysis and the apoptosis assay showed that the fibroblasts were more sensitive to 5FU and cisplatin treatment than the cancer cells. We are currently testing if the P-Gp inhibitor, PSC-833, can re-sensitize the cancer cells to 5FU and cisplatin. In conclusion, we have developed 3D multicellular heterospheroids mimicking tumor-like microspheres that can be used for drug testing of traditional drug compounds, biologics and even immunotherapeutic agents.
Citation Format: Boye Schnack Nielsen, Natasha H. Madsen, Jesper Larsen, Hjalte M. Larsen, Monika Gad, Kim Holmstrom. 3D cancer cell-fibroblast heterospheroids forming tumor-like microenvironments are valuable tools to study immune cell infiltration and as pre-clinical drug testing models. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4566.
Abstract 4657: Topological data analysis reveals pan-cancer immune phenotypes with immune-related survival differences
Authors: Murgas Kevin A. , Oh Jung H. , Deasy Joseph O. , Tannenbaum Allen R. 
Published: April 2023

Abstract
Cancer immune phenotypes present a wide range of heterogeneity across cases, with individual tumors displaying unique patterns of infiltrating immune cell types. Deconvolutional methods allow for scoring of various immune cell types in bulk tumor RNA as a quantification of immune phenotype. Understanding how immune phenotype relates to clinical outcome remains limited. Here, we demonstrate an approach applying topological data analysis to investigate differences of immune phenotype in a pan-cancer cohort (TCGA; n=11,373 tumors). We first define an Immune Activation Score based on relative abundance of activator and suppressor immune cell types and find this score depends on cancer type and distinguishes overall survival outcomes. We then implement a robust Mapper-based algorithm to delineate clusters of immune phenotypes of tumor samples across pan-cancer and within cancer types. Our method identifies immune-activated and immune-suppressed phenotypes with distinct survival outcomes and molecular features.
Citation Format: Kevin A. Murgas, Jung H. Oh, Joseph O. Deasy, Allen R. Tannenbaum. Topological data analysis reveals pan-cancer immune phenotypes with immune-related survival differences. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4657.