bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 3350: The pan-quadruplex drug QN-302 targets up-regulated genes in pancreatic cancer and in other cancer types and correlate with poor patient prognosis
Authors: Neidle Stephen , Ahmed Ahmed A. , Arshad Tariq 
Published: March 2024

Abstract
We have previously disclosed (Ahmed et al, ACS Med Chem Lett, 2020) a novel small molecule G-quadruplex binding compound, QN-302, that is highly potent in pancreatic ductal adenocarcinoma (PDAC) cells and in several in vivo models for this disease. QN-302 was invented at University College London UK and has been subsequently developed by Qualigen Therapeutics Inc. It was cleared by the FDA in August 2023 for clinical evaluation and is currently in a Phase 1 clinical trial at select oncology centers in the USA. Genes that are significantly down-regulated by QN-302 are mostly up-regulated genes in human PDAC. We report here that some of these genes are also up regulated in other cancer types, whose expression frequently correlates with poor patient prognosis. We have used human cancer data from https://www.proteinatlas.org which contains pathology information based on mRNA and protein expression data from 17 different forms of human cancer, together with millions of in-house generated immunohistochemically stained tissue sections, images and Kaplan-Meier plots showing the correlation between mRNA expression of each human protein gene and cancer patient survival. We have revisited the RNA-seq data for QN-302 in PDAC MIA-PACA2 cells exposed to QN-302 for 24 hrs. (GSE151741 at https://www.ncbi.nlm.nih.gov/geo/) in detail and now identify a small group of eleven down-regulated genes, all with &gt;3-fold expression changes. Interestingly all these genes and their gene products have been previously identified as playing a role in human PDAC (they also have varying roles in other cancer types). Some have been validated as anticancer targets. 10/11 of these genes (https://www.proteinatlas.org/) have been previously reported as being over-expressed in significant fractions of large (&gt;150) groups of human cancer patients from The Cancer Genome Atlas (TCGA) project. Members of the PDAC susceptible gene panel are mostly disfavored in prostate, lung and breast cancer. However, the patient data does not discriminate between cancer sub-types nor does it show resistant data. They are especially favored in colorectal, liver and renal cancers, suggesting that QN-302 may have anticancer effects in these cancers as well as in PDAC. We also do not exclude the strong possibility that each cancer type also has its own group of G-quadruplex containing genes that are sensitive to QN-302.
Citation Format: Stephen Neidle, Ahmed A. Ahmed, Tariq Arshad. The pan-quadruplex drug QN-302 targets up-regulated genes in pancreatic cancer and in other cancer types and correlate with poor patient prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3350.
Abstract 3405: CT-assessed body composition and tumor immunologic characteristics in patients with non-small-cell lung cancer
Authors: Cheng Ting-Yuan David , Mahenge Cuthbert Mario , Akasheh Rand T. , Yilmaz Ayse Selen , Kinder Ben , Nguyen Xuan , Pietrzak Maciej , Presley Carolyn J. , Shields Peter G. 
Published: March 2024

Abstract
Objectives: Obesity is associated with higher immunotherapy efficacy, but body mass index is a convoluted marker of adipose and muscle tissue. We conducted a discovery scale study to assess the association of skeletal muscle and adipose tissue areas annotated on computed tomography (CT) scan images with selected tumor immunologic characteristics among patients with non-small-cell lung cancer (NSCLC).
Methods: We analyzed publicly available data from 102 patients (mean age = 67.2, 47.1% female, and 70.6% stage I-II) who had diagnostic CT images in The Cancer Image Archive. All patients had the 12th thoracic vertebra (T12) image, and a subset (62 patients) had the 3rd lumber vertebra (L3) image. Paravertebral muscle areas on T12 and skeletal muscle and adipose tissue areas on L3 were annotated using Slice-O-Matic. RNA-seq data were provided by The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium; percentages of CD8, regulatory T cells, and activated NK cells were identified using the CIBERSORT. ESTIMATE algorithm was used to derive the stromal score and the immune score. Differences in the immune markers were examined using ANOVA adjusting for stage, histology, and study.
Results and Conclusion: Patients with higher paravertebral muscle area (3rd tertile) had the lowest stromal scores, compared to the 2nd and 1st tertile (P&lt;0.05). Patients in the 2nd tertile of paravertebral muscle areas had the highest immune score and CD274 (PD-L1) gene expression. Among the body composition types classified using L3 images, low muscle/high adiposity was associated with lower stromal scores, and low muscle was associated with lower immune scores, compared to high adiposity and high muscle/low adiposity types. Other immunological markers did not differ across the body composition types.
Significance: These findings suggest associations between deconvoluted skeletal and adipose tissue components and tumor markers relevant to immune checkpoint inhibitor efficacies in NSCLC. Further validation using immune phenotyping in a large patient sample is warranted. Funding: The study is in part supported by NIH/NCI R37CA248371 and OSU Comprehensive Cancer Center - James.
Citation Format: Ting-Yuan David Cheng, Cuthbert Mario Mahenge, Rand T. Akasheh, Ayse Selen Yilmaz, Ben Kinder, Xuan Nguyen, Maciej Pietrzak, Carolyn J. Presley, Peter G. Shields. CT-assessed body composition and tumor immunologic characteristics in patients with non-small-cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3405.
Abstract 3436: Circadian rhythm dysregulation with risk of gastrointestinal cancers: A large-scale prospective analysis
Authors: Kang Yi , Mok Shinghei , Zong Xiaoyu , Cao Yin 
Published: March 2024

Abstract
Introduction: The incidence of multiple gastrointestinal (GI) cancers has been rising among younger adults with unknown etiology. Circadian rhythm dysregulation due to increasing exposures to artificial lighting, irregular work hours, and frequent travel, has been recently linked with multiple diseases. Disruption of the circadian clock drives Apc LOH to hyperactivate Wnt signaling and enhances MYC-dependent glycolytic metabolism to accelerate colorectal cancer progression. However, the association between circadian rhythm dysregulation, quantified by relative amplitude (RA)–a parameter derived from rest-activity patterns, and risk of colorectal and other GI cancers has not been evaluated in epidemiologic studies.
Methods: We prospectively examined the associations between RA and risk of incident GI cancers among 87,653 UK Biobank participants who wore an Axivity AX3 triaxial accelerometer over 7 days (2013-15) with follow-up to 2021. RA was derived and averaged across all valid days, using the difference between the mean activity levels during the most active 10-hour period and the least active 5-hour period. Cox models were used to estimate relative risks (RRs) and 95% confidence intervals (CIs).
Results: During 512,074 person-years, 1,079 incident GI cancer cases occurred. After multivariable adjustment, lower RA was associated with an increased risk of overall GI cancer (RR per SD [standard deviation] = 1.12, 95% CI 1.04-1.21, P for trend = 0.01). Compared to individuals in the highest tertile (T1), those with RA in the lowest tertile (T3) had a 25% increased risk of overall GI cancer (RR = 1.25, 95% CI 1.05-1.50). The positive associations with GI cancers were mainly driven by gastric cancer (RR per SD = 1.33, 95% CI 1.02-1.74, P for trend= 0.04) and colon cancer (RR per SD = 1.13, 95% CI 1.00-1.28, P for trend = 0.03).
Conclusion: In this large-scale, prospective analysis of accelerometer data, circadian rhythm dysregulation was associated with subsequent risk of GI cancers, primarily gastric and colon cancer. To our knowledge, this is among the first population-based studies reporting the positive link between circadian rhythm dysregulation with cancer risk.
Citation Format: Yi Kang, Shinghei Mok, Xiaoyu Zong, Yin Cao. Circadian rhythm dysregulation with risk of gastrointestinal cancers: A large-scale prospective analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3436.
Abstract 3518: Next generation H&E cell modeling: Using same-slide multiplex immunofluorescence (mIF) data to train H&E
Authors: Arbiv Becky , Bart Amit , Dankovich Tal , Elran Ron , Bookstein Shai , Laniado Avi , Dicker Tomer , Dagan Sun , Puig Oscar , Bloom Kenneth , Markovits Ettai 
Published: March 2024

Abstract
Haematoxylin & Eosin (H&E)-based deep learning algorithms require a large number of human-based cell annotations for model training, and are limited by what a human eye can see. Multiplex immunofluorescence (mIF) offers a more comprehensive and accurate method for classifying cells because annotations are based on cell status, i.e. whether a cell expresses certain markers or not. In this study, we leverage mIF-based cell type predictions1 to enhance H&E model performance.We used a publicly available CODEX/PhenoCycler dataset2 consisting of 140 tissue cores obtained from 35 patients with colorectal cancer, which were stained with 56 protein markers and subsequently with H&E. mIF data was used to create over 60,000 prediction-based annotations on the corresponding H&E-stained images that were used to train deep learning H&E classifiers.
The final model performance reached AUC of 0.91 when evaluated on the H&E test annotations and 0.90 on the mIF predictions. Notably, the mIF annotations-based H&E model surpassed the manual annotations-based H&E model in effectively identifying challenging cell types, demonstrating its superior performance. These findings highlight the potential of leveraging mIF cell type predictions to train accurate and automated H&E models.
1Markovits et al, BioRxiv, https://doi.org/10.1101/2022.11.09.5157762Schürch et al, Cell. 2020,182(5): 1341-1359.PMID: 32763154
Citation Format: Becky Arbiv, Amit Bart, Tal Dankovich, Ron Elran, Shai Bookstein, Avi Laniado, Tomer Dicker, Sun Dagan, Oscar Puig, Kenneth Bloom, Ettai Markovits. Next generation H&E cell modeling: Using same-slide multiplex immunofluorescence (mIF) data to train H&E [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3518.
Abstract 3537: Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft
Authors: Chien Yuan-Hung , Pippa Raffaella , Andrews Warren , Rodriguez Javier , Buck Kristen , Gorospe Derrick , Valencia Elizabeth , Carapia Bianca , Eastwood Emily , Sperry Jantzen , Nakashima Jonathan , Do Long 
Published: March 2024

Abstract
Accurate prediction of drug response using machine learning (ML) remains a significant challenge in drug development as well as in personalized cancer therapy. Furthermore, there is a scarcity of rigorous external validation studies for evaluating drug response prediction models. Even when available, such validations are often confined to in-vitro studies. Orthotopic patient-derived xenograft (O-PDX) mice serve as essential preclinical models that closely replicate the human tumor microenvironment and assess the therapeutic response to cancer treatments. We present CertisAITM, a novel ensemble of machine learning models that have been trained on a vast array of experimental high-throughput screenings of both monotherapies and combination therapies, incorporating over 4500 investigational and FDA-approved drugs across 10 major cancer indications. The results of our ML drug response prediction models show an average R2 of 0.75 and RMSE of 0.62 (across internal 5-fold cross-validation) for all 10 cancer prediction models. Our evaluation of CertisAI's drug response predictions within O-PDX pharmacology studies has shown a notable overall correlation between actual observed tumor growth inhibition (TGI) and predicted TGI, with an r value of 0.45 across seven indications. The most accurate prediction model achieved an r value of 0.7 for colorectal cancer, encompassing 37 treatments in 6 O-PDX studies. Altogether, these results highlight CertisAI’s potential in enhancing pre-clinical model selection as well as personalized treatment strategies in oncology.
Citation Format: Yuan-Hung Chien, Raffaella Pippa, Warren Andrews, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Bianca Carapia, Emily Eastwood, Jantzen Sperry, Jonathan Nakashima, Long Do. Assessing machine learning models of drug response predictions using orthotopic patient-derived xenograft [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3537.
Abstract 3547: The ISB Cancer Gateway in the Cloud (ISB-CGC): Access, explore and analyze large-scale cancer data through the Google Cloud
Authors: Seidl Fabian , Hagen Lauren , Wilson Jacob , Aguilar Boris , Bleich Deena , Wolfe Lauren , Gundluru Poojitha , Venkatesan Prema , Tian Mi , Paquette Suzanne , Lee Elaine , Huffman Danna , Pot David , Longabaugh William 
Published: March 2024

Abstract
Rapid growth of cancer data in recent decades has made data discovery and wrangling difficult for the average cancer research lab. Our mission at the ISB Cancer Gateway in the Cloud (ISB-CGC), part of the NCI’s Cancer Research Data Commons ecosystem, is to democratize access to large cancer datasets. Funded by the NCI, we have performed ETL processes on data from GDC and PDC projects such as TCGA, TARGET, and CPTAC. We generated hundreds of BigQuery tables containing data such as mutations, gene expression, and protein abundance, which enable data analysis in the cloud via SQL. BigQuery analyses are inexpensive and rapid even when scaled to petabyte sized inputs, for example we ran 6.6 billion correlations in 2.5 hours with a total cost of about one dollar. These data can also be accessed affordably from Google Cloud VMs where researchers can develop analysis pipelines in Python, R, and workflow languages such as CWL. We present two recent collaborations: In one BigQuery was used to develop machine learning algorithms that calculated genetic risk scores from TCGA glioblastoma and ovarian cancer copy number variation. In another example researchers combined SQL queries of our BQ tables with data from the ISPY2 Trial initiative and generated an R shiny app that can dynamically create data visualizations for genes of interest in different TCGA cohorts.
Citation Format: Fabian Seidl, Lauren Hagen, Jacob Wilson, Boris Aguilar, Deena Bleich, Lauren Wolfe, Poojitha Gundluru, Prema Venkatesan, Mi Tian, Suzanne Paquette, Elaine Lee, Danna Huffman, David Pot, William Longabaugh. The ISB Cancer Gateway in the Cloud (ISB-CGC): Access, explore and analyze large-scale cancer data through the Google Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3547.
Abstract 3660: Plasma cell free DNA hydroxymethylation profiling reveals anti-PD1 treatment response and resistance biology in non-small cell lung cancer
Authors: Guler Gulfem D. , Ning Yuhong , Coruh Ceyda , Phillips Tierney , Nabiyouni Maryam , Hazen Kyle , Scott Aaron , Volkmuth Wayne , Levy Samuel 
Published: March 2024

Abstract
Background: Treatment with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) can yield durable anti-tumor responses, yet not all patients respond to ICIs. Current approaches to select patients who may benefit from anti-PD-1 treatment are insufficient. 5-hydroxymethylation (5hmC) analysis of plasma-derived cell free DNA (cfDNA) presents a novel non-invasive approach for identification of therapy response biomarkers which can tackle challenges associated with tumor biopsies such as tumor heterogeneity and serial sample collection.
Methods: 151 blood samples were collected from 31 non-small cell lung cancer (NSCLC) patients before therapy start and at multiple timepoints whilst on therapy. Blood samples were processed to obtain plasma-derived cfDNA, followed by enrichment of 5hmC-containing cfDNA fragments through biotinylation via a two-step chemistry and binding to streptavidin coated beads. 5hmC-enriched cfDNA and whole genome libraries were prepared in parallel and sequenced to obtain whole hydroxymethylome and whole genome plasma profiles, respectively.
Results: Comparison of on-treatment timepoint to matched pre-treatment samples from same patients revealed that anti-PD-1 treatment induced distinct changes in plasma cfDNA 5hmC profiles of responders, as judged by RECIST, relative to non-responders. In responders, 5hmC accumulated over genes involved in immune activation such as IFNγ and IFNα response, inflammatory response, and TNFα signaling, whereas in non-responders 5hmC increased over epithelial to mesenchymal transition genes. The Molecular Response to anti-PD-1 treatment, as measured by 5hmC changes in plasma cfDNA profiles were observed early, starting with the first cycle of treatment. Comparison of pre-treatment plasma samples revealed that anti-PD-1 treatment response- and resistance-associated genes can be captured by 5hmC profiling of plasma-derived cfDNA.
Conclusions: These results demonstrate that 5hmC profiling can identify response and resistance associated biological pathways in plasma samples, offering a novel method for non-invasive prediction and monitoring of immunotherapy response in NSCLC.
Citation Format: Gulfem D. Guler, Yuhong Ning, Ceyda Coruh, Tierney Phillips, Maryam Nabiyouni, Kyle Hazen, Aaron Scott, Wayne Volkmuth, Samuel Levy. Plasma cell free DNA hydroxymethylation profiling reveals anti-PD1 treatment response and resistance biology in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3660.
Abstract 3666: Multi-cancer early detection using metrics of DNA methylation based epigenetic instability
Authors: Thursby Sara-Jayne , Jin Zhicheng , Baylin Steve B. , Brock Malcolm , Pisanic Thomas , Easwaran Hariharan 
Published: March 2024

Abstract
Cancers present significant changes in DNA methylation, which has proven to be highly useful in cell-free DNA (cfDNA) based cancer detection. Cancer epigenomes are marked by a high degree of intra- and inter-tumor epigenetic variation, indicative of the high level of epigenetic instability. However, the nature of the regions with high epigenetic instability and its utility as biomarker has not been explored. Here we developed a novel methodology and metric to measure the degree of epigenetic perturbation, termed the Epigenetic Instability Index (EII), for multiple cancer screening through cfDNA. Our novel methodology provides a sample specific score of epigenetic perturbation in relation to the expected normal state as opposed to only looking at CpG sites that change between samples. Through machine learning, we have elucidated 269 CpG-island regions which sufficiently capture the epigenetic instability in cancers. We have built classifier models using the EII metrics of these 269 regions and demonstrate that they can efficiently identify breast and lung cancer cases from cfDNA methylation data. Particularly, the models can differentiate even Stage IA of NSCLC with ~75% sensitivity at 95% specificity and early-stage breast cancer at ~68% sensitivity and 95% specificity. The EII metrics perform significantly better than metrics that measure absolute DNA methylation levels. Our studies highlight the potential of measuring epigenetic instability from cfDNA in any given sample in addition to traditional methods of methylation measurement for non-invasive screenings via liquid biopsies.
Citation Format: Sara-Jayne Thursby, Zhicheng Jin, Steve B. Baylin, Malcolm Brock, Thomas Pisanic, Hariharan Easwaran. Multi-cancer early detection using metrics of DNA methylation based epigenetic instability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3666.
Abstract 3698: Characterization of circulating rare events in non-small cell lung cancer
Authors: Seo Jiyoun , Shishido Stephanie N. , Mason Jeremy , Velasco Carmen Ruiz , Mullin Jaden , Prabakar Rishvanth , Hicks James , Kuhn Peter 
Published: March 2024

Abstract
Non-small cell lung cancer (NSCLC) is the most frequent type of lung cancer, accounting for about 80 - 85% of all diagnoses. Unfortunately, 75% of NSCLC diagnoses are made when cancer has already spread and the prognosis is poor. Therefore, biomarker investigation that might allow for early detection of NSCLC is critical to improving patient outcomes. The purpose of this study is to demonstrate the feasibility of liquid biopsy to detect and identify circulating rare events, such as circulating tumor cells (CTCs), associated with NSCLC. Here we apply our non-enrichment-based High Definition Single Cell Assay (HDSCA) workflow which utilizes an immunofluorescence staining technique and an unsupervised rare event detection algorithm to identify the circulating rare cells and acellular events in samples collected from 206 NSCLC patients and 60 healthy individuals. We detected a heterogeneous population of circulating rare events in NSCLC, including epithelial, mesenchymal, and endothelial cells, as well as acellular events such as oncosomes. Cancer-associated circulating rare events with statistically significant prevalence in NSCLC compared to healthy individuals were observed. Molecular characterization using multiplexed targeted proteomics based on imaging mass cytometry and genomic analysis by single-cell copy number profiling confirmed the phenotypic heterogeneity and genomic profiles of the detected circulating rare events. In this study, our results demonstrate the use of the extended capabilities of liquid biopsy to identify a comprehensive overview of circulating rare events that can provide clinically valuable biomarkers in NSCLC.
Citation Format: Jiyoun Seo, Stephanie N. Shishido, Jeremy Mason, Carmen Ruiz Velasco, Jaden Mullin, Rishvanth Prabakar, James Hicks, Peter Kuhn. Characterization of circulating rare events in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3698.