bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5008: The potential role of hsa-miR-3157-3p in prostate cancer pathogenesis
Authors: Wali Akhil , Koul Hari 
Published: June 2022

Abstract
Prostate cancer remains a major area of concern as it is the second leading cause of cancer mortalities amongst American men. Our study aimed to find the role of differentially expressed microRNAs that play a role in prostate cancer etiopathology. With the use of the National Center for Biotechnology Information (NCBI), we narrowed down a desired dataset (GSE138740). While analyzing the dataset, we found the expression pattern of hsa-miR-3157-3p (adjusted p-value 0.04797) to be significantly upregulated in low grade tumor samples (Gleason Score 6 and n=88) compared to high grade tumor samples (Gleason Score 8,9 and n=13). Furthermore, the role of hsa-miR-3157-3p in prostate cancer remains a novel finding. We have identified target genes such as E2F3, UBE2M, ABCF1, PKN3 and VANGL1 using the RNA Central website portal. Further studies are underway to elucidate the expression pattern of this miRNA and it’s target genes that would correlate with the disease progression observed among tumor samples in The Cancer Genome Atlas (TCGA) database. The Ingenuity Pathway Analysis (IPA) software generated report will be presented at the meeting to describe gene networks and pathways implicated in Prostate Cancer. Overall, these studies are designed to further validate whether this miRNA and its target genes can serve as potential biomarkers to predict the onset and progression from low to high grade prostate cancer.
Citation Format: Akhil Wali, Hari Koul. The potential role of hsa-miR-3157-3p in prostate cancer pathogenesis [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 5008.
Abstract 5019: Eliminating copy number alteration effects in the gene essentiality data from the Cancer Dependency Map project
Authors: Bhattacharya Arkajyoti , Urzúa-Traslaviña Carlos G. , van Vugt Marcel A. T. M. , Fehrmann Rudolf S. N. 
Published: June 2022

Abstract
The Cancer Dependency Map project (DepMap) aims to identify cancer vulnerabilities through the in vitro study of genetic dependencies in cancer cell lines using CRISPR/Cas9 loss-of-function screens. Cas9-mediated DNA break formation induces toxic effects that are proportional with the number of cuts. Consequently, the DepMap data displays a ‘copy number alteration effect’ (CNA effect), in which sgRNAs targeting highly amplified regions of the genome produce depletion effects. Indeed, when sgRNAs target intergenic regions of amplified regions, the observed effects are comparable to the targeting of essential genes. Hence, correcting the CNA effect is crucial to reduce false positive (and increase true negative) co-dependencies between genes. Currently, the CERES methodology is used to correct for CNA effects. By applying a consensus-independent component analysis-based (consensus ICA) algorithm on gene essentiality data after CERES correction, we observed that many CNA effects are still present in the CERES-corrected data. Subsequently, we developed a methodology that outperforms CERES in removing the CNA effect. For example, we identified a set of genes mapping to chromosome 8q of sample ACH-000542 (ovarian adenocarcinoma cell line HEYA8 having amplification at chromosome 8q) having significantly lower essentiality levels compared to the rest of the genome. Whereas the CERES algorithm did not correct this effect of the chromosome 8q amplification, it was captured by consensus ICA in a consensus estimated source and was corrected by our novel methodology. After removing all CNA effects from gene essentiality data, we observed that 12.07% of co-dependency scores reported by CERES-corrected data decreased by 0.2 (on a range of -1 to 1), and 11.34% co-dependency scores increased by 0.2. In conclusion, consensus ICA-based methodology improves the correction of the CNA effect on gene essentiality data. Application of our improved method of CNA effect correction could reduce the number of false positive targets in validating biological hypotheses leading to novel therapeutic strategies in cancer.
Citation Format: Arkajyoti Bhattacharya, Carlos G. Urzúa-Traslaviña, Marcel A. T. M. van Vugt, Rudolf S. N. Fehrmann. Eliminating copy number alteration effects in the gene essentiality data from the Cancer Dependency Map project [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 5019.
Abstract 5032: Data driven refinement of gene signatures for enrichment analysis and cell state characterization
Authors: Wenzel Alexander T. , Tamayo Pablo , Mesirov Jill P. 
Published: June 2022

Abstract
In the study of human disease, the use of gene expression data has been crucial to the functional characterization of changes in molecular pathway activity and for identifying targets for novel treatments. However, the interpretation of this data is often complicated by its high dimensionality and the difficulty of identifying biological signals within a list of differentially expressed genes. Gene Set Enrichment Analysis (GSEA) is a community standard method for identifying pathway enrichment in gene expression data by testing whether a set of genes whose expression would indicate the activity of a specific process or phenotype are coordinately up- or downregulated more than would be expected by chance. As GSEA relies on high quality gene sets with coordinately regulated member genes, we maintain the Molecular Signatures Database (MSigDB) which contains 9 collections of curated, annotated gene sets representing different biological pathways and processes. Over time, we have observed that some of the MSigDB gene sets, especially those that are manually curated or defined in a very specific biological context, may not provide a sensitive and specific enough co-regulation signature for their corresponding phenotype. In response, we have created a data-driven, matrix-factorization-based refinement method to build more sensitive and specific gene sets. This method incorporates large-scale datasets from multiple sources including compendia such as the Cancer Dependency Map as well as curated protein-protein interaction networks. We will present the initial results of this refinement method as well as our ongoing work which will yield a new collection of refined gene sets that will be made freely available in MSigDB for use with GSEA and many other applications.
Citation Format: Alexander T. Wenzel, Pablo Tamayo, Jill P. Mesirov. Data driven refinement of gene signatures for enrichment analysis and cell state characterization [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 5032.
Abstract 5039: Visualization and analysis of cancer genomics data using UCSC Xena
Authors: Goldman Mary , Craft Brian , Zhu Jingchun , Haussler David 
Published: June 2022

Abstract
UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. The toll supports Mac, Windows, and Linux.
Questions Xena can help you answer:
1) Is overexpression of this gene associated with lower/higher survival?
2) What genes are differentially expressed between these two groups of samples?
3) What is the relationship between mutation, copy number, expression, etc for this gene?
Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression, and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links.
Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap.
New features include:
- Genome-wide differential gene expression analysis
- New interface for filtering samples and creating subgroups
- New interface to create charts and graphs
- Violin plots on any numerical data
- Loading of Microsoft Excel files
- A new Publication Page showcasing publications and authors that use UCSC Xena
Our beta prototype for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser.
Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [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 5039.
Abstract 5054: Breast cancer survival analysis with the size of the infiltrative cancer area
Authors: Choi JunYoung , Kwak Tae-Yeong , Kim Sun Woo , Chang Hyeyoon 
Published: June 2022

Abstract
Background: Analyzing breast cancer prognosis factors is important for survival analysis. In general, the approximate size of the tumor is considered in pathologic staging, but it is not known whether the exact size of the infiltrative cancer area is related to the patients' survival. To analyze the effectiveness of the size of the infiltrative cancer area for survival analysis of patients, we developed a machine learning algorithm that predicts death risk by analyzing clinical data and the actual size of the infiltrative cancer area from hematoxylin and eosin stained whole slide images (WSIs) of breast cancer.
Design: To obtain an area of infiltrative cancer, an experienced pathologist conducted annotations using WSIs of breast cancer resection tissue. The data analyzed in this study was from The Cancer Genome Atlas’s breast cancer data set (TCGA-BRCA). There were 871 WSIs labeled with survival events and periods, and several clinical features according to patients including the age at diagnosis, ER, PR, HER2 status, and pathologic TNM stages. We extract the size of the infiltrative cancer area from annotations and incorporate them with clinical features. There existed 53 uncensored data, and we splitted the whole dataset into 8:2 for training/validation. We performed 5-fold cross validation with random sampling. We trained a Cox Regression model with features and compared C-index according to features.
Result: The C-index with the infiltrative cancer area for the test set was 0.806. This figure is an improvement of 0.2 from 0.781 without the infiltrative cancer area. The hazard ratio of the infiltrative cancer area was 1.84. The p-value of the infiltrative cancer area was 0.019 for the log-rank test.
Conclusion: This result shows that the infiltrative cancer area is a useful feature even in the presence of the TNM stage.
Citation Format: JunYoung Choi, Tae-Yeong Kwak, Sun Woo Kim, Hyeyoon Chang. Breast cancer survival analysis with the size of the infiltrative cancer area [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 5054.
Abstract 5082: Proteomic analysis of extracellular vesicles from oncology donor samples purified using an alternating current electrokinetic microelectrode chip
Authors: Hinestrosa Juan P. , Lewis Jean M. , Searson David J. , Balcer Heath I. , Gonzalez David J. , Krishnan Rajaram 
Published: June 2022

Abstract
In this study, we evaluated the ability to detect novel proteins on extracellular vesicles (EVs) following isolation using an alternating current electrokinetic (ACE) microarray. EVs circulating in blood contain a wealth of biomarkers that are thought to reflect the tumor’s proteome; however, detecting the full diversity of EV proteins may depend on choice of method to purify EVs for analysis. We compared an ACE microarray isolation method with a conventional chemical precipitation method to isolate EVs from blood plasma cancer and control samples. EVs were isolated from 1 mL each of control or pancreatic cancer patient plasma using the ExoVerita platform (Biological Dynamics) or ExoQuick Ultra kit (System Biosciences). Purified EVs were processed using tandem mass tag (TMT)-based multiplexing mass spectrometry proteomics (ThermoFisher Scientific) and the resulting peptide spectra were analyzed and quantified to determine the proteins present. Additional analyses, such as bioanalyzer measurements of contaminating plasma proteins and nanoparticle tracking analysis were performed in parallel to further characterize the isolated EVs. Cancer EVs isolated by ACE were enriched in proteins related to cancer functions when compared to non-cancer control EVs. They also showed enrichment in ontology clusters such as NABA core matrisome proteins, with depressed expression of wound healing family proteins. In contrast, EVs isolated using chemical purification demonstrated enrichment chiefly in proteins functionally grouped into immune system or blood regulation functions. After analyzing EVs isolated by ACE, a total of fifty-one (51) proteins were observed to be upregulated in the cancer donor samples. EV isolation method choice significantly impacted the proteomic profile observed. EV isolation using ACE-based technology can provide more insights into understanding the proteome by possibly reducing or removing non-specific proteins from the EVs. With better separation of EVs from blood proteins, the EV proteins identified may have greater biological significance.
Citation Format: Juan P. Hinestrosa, Jean M. Lewis, David J. Searson, Heath I. Balcer, David J. Gonzalez, Rajaram Krishnan. Proteomic analysis of extracellular vesicles from oncology donor samples purified using an alternating current electrokinetic microelectrode chip [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 5082.
Abstract 5084: KMT2D and PIK3CA mutation as potential factors to predict adjuvant chemotherapy efficacy in surgical triple negative breast cancer
Authors: Wang Xue , Du Feng , Yue Jian , Si Yiran , Cui Lina , Zhang Bei , Zhao Xiaochen , Xu Binghe , Yuan Peng 
Published: June 2022

Abstract
Background: Chemotherapy is the most common treatment strategy for triple negative breast cancer (TNBC) patients. Nevertheless, due to adverse drug reactionsthe molecular high heterogeneity of TNBC and no appropriate meaningful efficacy markers, it is still difficult to establish preferred therapeutic strategies and predict the outcomes for TNBC. This study was to investigate the potential predictors and therapeutic targets based on genetic features.
Methods: A total of 386 TNBC patients were randomized 1:1 to receive either six cycles of paclitaxel + cisplatin (TP) or four cycles of epirubicin + cyclophosphamide followed by four cycles of docetaxel (EC-T) adjuvant chemotherapy after surgery (NCT01150513), which were described previously. Finally, 149 TNBC patients with clinical and tumor sequencing data availability were retrospectively analysed by NGS for 733 cancer-related genes. All tumor samples were collected during the surgical operation and subjected to NGS for evaluating genomic mutations and the potential predictors. Cox regression model and Kaplan-Meier method were applied to evaluate disease-free survival (DFS).
Results: In the surgical cohort receiving adjuvant chemotherapy, 74 patients received platinum and 75 received platinum-free chemotherapy as adjuvant chemotherapy. The most frequently mutated genes in this surgical TNBC cohort were TP53 (84%), BRCA1 (18%), BRCA2 (15%), POL1 (13%), PTEN (12%), REV3L (11%), FANCC (10%), and PARP4 (10%). We analyzed the associations between 733 cancer-related gene mutations and DFS after adjuvant chemotherapy. For the TP group, PIK3CA mutation (19%, 14/74) was discovered to correlate with poor DFS for patients treated with platinum-containing adjuvant therapy (HR=3.2, P=0.037), and KMT2D mutation (15%, 11/75) display worse DFS for patients treated with EC-T platinum-free group (HR=3.0, p=0.039). However, BRCA1/2 mutation (24%, 35/149) was found to be associated with poor prognosis (HR=2.1 (95% CI: 1-4.6), p=0.047), irrespective of therapeutic regimen.
Conclusions: In this population of surgical TNBC patients, NGS analysis identified potential predictive markers. PIK3CA mutation in TP platinum-containing group and KMT2D mutation in EC-T platinum-free group were significantly associated to poor prognosis for adjuvant chemotherapy in triple negative breast cancer.
Citation Format: Xue Wang, Feng Du, Jian Yue, Yiran Si, Lina Cui, Bei Zhang, Xiaochen Zhao, Binghe Xu, Peng Yuan. KMT2D and PIK3CA mutation as potential factors to predict adjuvant chemotherapy efficacy in surgical triple negative breast cancer [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 5084.
Abstract 509: Tumor inflammation signature (TIS) in residual disease is associated with survival in breast cancer patients with high risk of recurrence
Authors: Galvez-Nino Marco , Castillo Miluska , Bernabe Luis , Suarez Nancy , Sanchez Joselyn , Roque Katia , Neciosup Silvia , Gomez Henry , Castaneda Carlos 
Published: June 2022

Abstract
Background: The assessment of tumor inflammation signature (TIS) quantifies the adaptive immune response in the tumor microenvironment and is emerging as a predictor of clinical benefit in breast cancer. In this study we evaluated the ability of TIS to be a prognostic biomarker of survival benefit in patients with moderated and high residual disease after neoadjuvant chemotherapy (NAC) in breast cancer patients and its correlation with residual TILS after NAC.
Methods: We analyzed 46 samples of residual disease of breast cancer patients who received NAC and were under surgery between 2013 - 2018, with high risk of recurrence defined as Residual Cancer Burden (RCB) group II and III. TIS signature was evaluated using the Research Use Only version of 18-gen signature algorithm. Median of TIS score was 5.88, TIS score were categorized in four groups by quartiles and defined as low TIS score (lower quartile, score ≤ 4.82) and high TIS score (score &gt; 4.82). In addition, we quantified the percentage of stromal TILS in residual disease, categorizing level of TILS in high and low level using median (20%) as a cut-off. Survival analysis was performed with Kaplan-Meier method and the associations with Cox regression model. Correlation between TIS and TILS was performed whit Pearson coefficient. Clinical data was collected of medical records.
Results: The median age at diagnosis was 50 years (26y - 78y), 65.2% of patients were Luminal B subtype, 63% were clinical stage IIIB, 26.2% were RCB II and 73.9% RCB III. No correlation between TIS score and percentage of stromal TILS was founded (p = 0.264). After median follow-up of 60 months, estimated median PFS for high TIS score was 71.7 vs 18.2 months for low score (HR = 0.28, 95% CI [0.13-0.64], p = 0.002), and estimated median OS for high TIS score was non-reached vs 40.9 months for low score (HR = 0.28, 95% CI [0.11-0.68], p = 0.005). Did not found differences in PFS (p = 0.408) and OS (p = 0.773) by level of TILS.
Conclusions: High TIS score in moderated/high residual disease after neoadjuvant chemotherapy is associated with survival benefit expressed in prolonged PFS and OS. Level of TILS in residual disease did not show differences in survival and its percentage did not have correlation with TIS score.
Funding: FONDECYT-198-2015/280-INNOVATEPERU-EC-2017.
Citation Format: Marco Galvez-Nino, Miluska Castillo, Luis Bernabe, Nancy Suarez, Joselyn Sanchez, Katia Roque, Silvia Neciosup, Henry Gomez, Carlos Castaneda. Tumor inflammation signature (TIS) in residual disease is associated with survival in breast cancer patients with high risk of recurrence [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 509.
Abstract 5191: BCL-2 inhibitor APG-2575 promotes anti-tumor immunity through converting tumor-associated macrophages into M1 phenotype in non-small cell lung cancer
Authors: Luo Fan , Lu Fei-teng , Qiu Miao-zhen , Pan Wen-tao , Zhang Lin , Zhao Hong-yun , Zhang Li , Yang Da-jun 
Published: June 2022

Abstract
The main challenges for immune checkpoint inhibitors (ICIs) are ascribable to tumor immunosuppressive microenvironment and a lack of sufficient activated CD8+ T cell infiltration. Modifying tumor-associated macrophages (TAMs) strategies have attracted attention for ameliorating tumor immunosuppression and improving anti-tumor immunity. Here, by applying human hematopoietic stem cell (HSC) reconstruction NSG mice and murine C57BL/6 syngeneic tumor xenografts, we demonstrated that APG-2575 combined with PD-1 blockade exerted synergistic antitumor activity, which was associated with the increased recruitment of CD8+ cytotoxic T lymphocytes. Additionally, we found that APG-2575-mediated antitumor T cell immunity is macrophage dependent, and it could function as an anticancer immune regulator that repolarizes TAMs from M2 to pro-inflammatory M1 phenotype. Mechanistically, APG-2575 enhanced M1 polarization through increasing the activated NF-κB binding to the NLRP3 promoter. In parallel, the enhanced expression of NLRP3 mediated the activation of pro-inflammatory caspases and the subsequently elevation of CCL5 and CXCL10 chemokines production by M1 macrophages. As a result, APG-2575-mediated macrophages transition could improve tumor immunosuppression, thus skewing the cytokines profiles into immunostimulatory one in the TME and further enhancing antitumor T cell immunity. These results provided a novel promising strategy for NSCLC treatment and warranted future clinical evaluation of combination therapy of APG-2575 and ICIs.Key words: BCL-2, APG-2575, ICIs, macrophages, non-small cell lung cancer.
Citation Format: Fan Luo, Fei-teng Lu, Miao-zhen Qiu, Wen-tao Pan, Lin Zhang, Hong-yun Zhao, Li Zhang, Da-jun Yang. BCL-2 inhibitor APG-2575 promotes anti-tumor immunity through converting tumor-associated macrophages into M1 phenotype in non-small cell lung cancer [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 5191.