bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5048: Imaging based EGFR mutation subtype classification using EfficientNet
Authors: Franklin Daniel L. , Pattilachan Tara , Magliocco Anthony 
Published: June 2022

Abstract
This study aimed to determine whether EfficientNet-B0 was able to classify EGFR mutation subtypes with H&E stained whole slide images of lung and lymph node tissue.
Background: Non-small cell lung cancer (NSCLC) accounts for the majority of all lung adenocarcinomas, with estimates that up to a third of such cases have a mutation in their epidermal growth factor receptor (EGFR). EGFR mutations can occur in various subtypes, such as Exon19 deletion, and L858R substitution, which are important for early therapy decisions. Here, we propose a deep learning approach for detecting and classifying EGFR mutation subtypes, which will greatly reduce the cost of determining mutation status, allowing for testing in a low resource setting.
Methods: An EfficientNet-B0 model was trained with whole slide images of lung tissue or metastatic lymph nodes with known EGFR mutation subtype (wild type, exon19 deletion or L858R substitution). Regions of interest were tiled into 512x512 pixel images. The RGB .jpeg tiles are augmented by rotating 90°, 180°, 270°, and mirroring. The model was initialized with random parameters and trained with a batch size of 32, a learning rate of 0.0001 for 1 epoch before the validation loss increased for the next 5 epochs.
Results: The model achieved a slide AUC of 0.8333, and a tile AUC of 0.8010. Slide AUC is the result of averaging all tiles within a slide and measuring performance based on correctly predicted slides (n=18). Tile AUC is the result of measuring performance based on correctly predicted tiles (n=102,000).
Conclusion: Using EfficientNet-B0 architecture as the basis for our EGFR mutation classification system, we were able to create a top performing model and achieve a slide AUC of 0.833 and tile AUC of 0.801. Healthcare providers and researchers may utilize this AI model in clinical settings to allow for detection of EGFR mutation from routinely captured images and bypass expensive and time consuming sequencing methods.
Table 1. Number of image tiles used and the number of slides they were extracted from. Train Validation Test Exon19 tiles 187,384 47,904 33,096 L858R tiles 166,288 19,512 26,136 Wild type tiles 225,944 27,696 42,768 Exon19 slides 47 6 6 L858R slides 46 6 6 WIld type slides 43 6 6
Citation Format: Daniel L. Franklin, Tara Pattilachan, Anthony Magliocco. Imaging based EGFR mutation subtype classification using EfficientNet [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 5048.
Abstract 5080: Age- and pregnancy-related impact on soluble immune checkpoint protein profiles in mice as analyzed by a novel multiplex immunoassay
Authors: Lie Wen-Rong , Kornmeier Christine , Zeng Ling , Hoberg James 
Published: June 2022

Abstract
Immune checkpoint proteins are key regulators of the immune system and drug targets of cancer immunotherapy. There is extensive literature on the use of various types of mouse models for biomarker discovery and drug testing. Only a few studies have examined the association between soluble immune checkpoint proteins and the immune status. Following the development of multiplex immunoassay panels for profiling human co-inhibitory and co-stimulatory immune checkpoint proteins, here we report the development of a novel magnetic bead-based Luminex multiplex assay for the quantitative detection of 28 mouse immune checkpoint proteins and immune regulators in 25 μL of sample (PD-1, PD-L1, PD-L2, CTLA-4/CD152, TIM-3, LAG-3, B7-H2/ICOSL, B7-H3/CD276, BTLA, GITR, HVEM, CD27, CD40, CD80/B7-1, CD226/DNAM-1, 4-1BBL/TNFSF9, CD137/4-1BB, 5’-NT/CD73, BCA-1/CXCL13, CD25/IL-2Rα, Granzyme B, E-cadherin, Galectin-1, Galectin-3, IFNγ, IL-10, TLR-2, and TNFα). Immune function declines with age, while the incidence of cancer rises. In this study, we examined age-related serum samples by quantitative profiling soluble checkpoint proteins from young (age of 8-month-old, n=8) and old-aged mice (48-month-old, n=8) of C57BL/6 strain. A significant age effect was observed on the levels of soluble checkpoint proteins in mouse serum samples tested. Comparing with the young mice, decreased levels of circulating HVEM, Granzyme B, LAG-3, PD-L1, CD73, B7-H2, CD25, and CD27; and increased levels of circulating TIM-3, PD-1, PD-L2, CD40, Galectin-3, IL-10, BCA-1, and TLR-2 were observed in aged C57BL/6 mice (Mann Whitney test, P value &lt;0.05). The immune system also changes during pregnancy. We used the similar multiplex array approach to profile the soluble checkpoint proteins in non-pregnant (n=16) and pregnant CD-1 mice (n=9). Significant decreased levels of soluble LAG-3, B7-H2, B7-H3, CD25, CD27, and Galectin-1; and significant increase of soluble CD137, Galectin-3, and CD73 expression levels were detected in the serum samples of pregnant mice, as compared to the samples from the non-pregnant mice (Mann Whitney test, P value &lt;0.05). Altogether, using bead-based quantitative multiplex analysis, our results demonstrate that the changes on circulating immune checkpoint protein profiles occur in the immunologic adaptations with age and during pregnancy in mice.
Citation Format: Wen-Rong Lie, Christine Kornmeier, Ling Zeng, James Hoberg. Age- and pregnancy-related impact on soluble immune checkpoint protein profiles in mice as analyzed by a novel multiplex immunoassay [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 5080.
Abstract 5081: Proteomic analysis of ascites- and cancer cell-derived EVs for identifying ovarian cancer diagnostic biomarkers
Authors: Wang Wenyu , Han Dohyun , Song Yong Sang 
Published: June 2022

Abstract
Ovarian cancer is the most lethal gynecologic cancer in the world. Most patients are diagnosed at advanced stages with the formation of ascites. Extracellular vesicles (EVs) have been elucidated to play a pivotal role in cancer development and progression, also emerging as promising resources for clinical biomarkers in liquid biopsy. In this study, we aim to identify cancer-specific protein biomarkers in ascites-derived EVs for ovarian cancer diagnosis. Malignant ascites from high grade ovarian cancer patients (HGSOC) and benign peritoneal fluids from female patients with benign gynecologic diseases were collected with informed consent. EVs were isolated from these biofluids using the Exoquick kit. Meanwhile, HGSOC cell lines and normal cell lines were cultured in conditioned media supplemented with EVs-depleted FBS. EVs were isolated from the culture media. Proteins were extracted from EVs and fragmented into peptides. Tandem mass tag (TMT) hyper multiplex quantitation assay was carried out to detect the EV proteomic profiling. EVs isolated from biofluids and cell culture media were identified as double-membrane nanoparticles sizing ranging from 30nm to 200nm. Proteomic analysis of biofluids-derived EVs showed that malignant ascites-derived EVs displayed different proteomic profiling compared to benign peritoneal fluids-derived EVs. The differently expressed proteins between malignant ascites-derived EVs and benign peritoneal fluids-derived EVs were defined as candidate EV protein biomarkers. In addition, proteins that were expressed in at least one cancer cell line-derived EVs but not expressed in normal cell line-derived EVs were also identified as ovarian cancer-specific proteins. A diagnostic model constructed based on the integrative analysis of candidate EV protein biomarkers and ovarian cancer-specific proteins yielded a good discrimination ability between benign and cancer patients.In conclusion, this study demonstrates the distinct proteomic profiling in ascites- and cancer cell-derived EVs in ovarian cancer, which sheds new light on implementing EV proteins as a potential diagnostic strategy in clinical settings.
Citation Format: Wenyu Wang, Dohyun Han, Yong Sang Song. Proteomic analysis of ascites- and cancer cell-derived EVs for identifying ovarian cancer diagnostic biomarkers [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 5081.
Abstract 5091: The early change of serum interleukin 14α levels predicts the response to anti-PD-1 therapy in cancer
Authors: Wang Bu Hai , Chen Cai Yue , Zhang Xian , Huang Yu Xiang , Zeng Yi Chun , Li Lei , Wang Mao Qi , Guo Jing Liang , Li Qiu Xian , Shen Long , Gu Juan J. , Liang Yi Chen 
Published: June 2022

Abstract
Background: Targeting programmed cell death-1 (PD-1) or programmed cell death ligand-1 (PD-L1) has been shown improved clinical efficacy in a wide range of tumor types. We evaluated serum interleukin 14α (IL14α) as a biomarker to predict the response of anti- PD-1 therapy.
Patients and methods: Thirty advanced cancer patients treated with PD-1 inhibitor were enrolled in this study. Serum levels of IL-14α were tested at baseline and after 2 cycles of treatment.
Result: Among these 30 patients, the mean expression level of IL14α before treatment was 2.1±1.21, whereas the mean level of IL14α after 2 cycles was 1.99±0.82. There were no association between the expression levels of IL14α and clinical outcome. Early change of IL14α after 2-cycles of anti-PD-1 therapy was calculated as delta IL14α % change = (IL14α level after 2 cycles - IL14α level before treatment)/IL14α level before treatment*100%. Receiver operating characteristic (ROC) was analyzed to get a cutoff point of delta IL14α % change as 2.46% (Sensitivity 85.71%, Specificity 62.5%; AUC=0.7277, P=0.034). Using this cutoff to subgroup the patients, we found higher delta IL14α % change was significantly associated with superior overall response to anit-PD-1 therapy (p=0.0072) and had a better progression free survival (p=0.0039).
Conclusion: Early changes of serum IL14α level may be a useful predicting factor in advanced cancer patients with anti-PD-1 therapy.
Keywords: IL14α, serum biomarker, anti-PD-1 therapy response, cancer
Citation Format: Bu Hai Wang, Cai Yue Chen, Xian Zhang, Yu Xiang Huang, Yi Chun Zeng, Lei Li, Mao Qi Wang, Jing Liang Guo, Qiu Xian Li, Long Shen, Juan J. Gu, Yi Chen Liang. The early change of serum interleukin 14α levels predicts the response to anti-PD-1 therapy in 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 5091.
Abstract 5100: Analysis of genetic alterations related to hyper-progressive disease after immunotherapy in patients with non-small cell lung cancer
Authors: Xiang Xudong , Zhang Hushan , Wang Deguang , Li Heng , Lei Qing , Ma Qianli 
Published: June 2022

Abstract
Background Hyper-progressive disease (HPD) is an unexpected response pattern observed in immune checkpoint therapy, such patients experience a very rapid disease progression (usually progressed seriously in three months). Predictors of HPD are one of the keys to the management of patients receiving immune checkpoint inhibitors. Some factors, such as MDM2/4 amplification, EGFR mutations and old age may be risk factors for HPD, but the results of different studies are inconsistent. Herein, we evaluated the characteristics of MDM2/4 alterations in Chinese patients with NSCLC.
Methods The formalin-fixed paraffin-embedded specimens of cancer patients who have underwent next-generation sequencing (NGS) in 3D Medicines, a laboratory accredited by CAP and Clinical Laboratory Improvement Amendments (CLIA) from May 2017 to May 2020, were included in this study. NGS was performed to detect gene mutation, and tumor mutational burden (TMB) measured by a 733 gene panel. PD-L1 expression detected by using Dako PD-L1 IHC 22C3 pharmDx.
Results The proportion of MDM2/4 amplification in Chinese NSCLC patients is 0.86%, the proportion of MDM2 and MDM4 amplification in Chinese NSCLC patients were 0.74% and 0.12%. respectively. Further analysis found lower TMB level in patients with MDM2/4 amplification than patients without MDM2/4 amplification (p&lt;0.001). The median TMB level was also found lower in patients with MDM2/4 amplification than without, and higher ratio of patients with TMB-high (defined as TMB≥10mutant/Mb) was displayed in group with MDM2/4 amplification. Conversely, patients with TMB-high showed lower copy number gain in MDM2/4. However, no difference was found in level of PD-L1 expression between patients with and without MDM2/4 amplification.
Conclusion HPD happened during ICI treatment in solid tumor with MDM2/4 amplification may associated with lower TMB level, and PD-L1 expression level has limit effect on MDM2/4 alteration related HPD.
Citation Format: Xudong Xiang, Hushan Zhang, Deguang Wang, Heng Li, Qing Lei, Qianli Ma. Analysis of genetic alterations related to hyper-progressive disease after immunotherapy in patients with 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 5100.
Abstract 5126: PIANOS: A platform independent and normalization free single-sample classifier for colorectal cancer
Authors: Cai Du , Qi Haoning , Wang Wei , Zhong Min-Er , Fan Dejun , Li Cheng-Hang , Wu Xiaojian , Gao Feng 
Published: June 2022

Abstract
Objective: Identifying robust prognostic biomarkers of colorectal cancer (CRC) will significantly improve patients’ outcomes. However, due to the difference in detection methods, sequencing platforms and cohort populations, most of the previous molecular biomarkers were unable to cope with batch effects and rely on normalization algorithms, which might result in “test set bias”. This study aims to develop and validate a platform-independent single-sample prognostic model for colorectal cancer without the need for normalization.
Methods: A total of 2510 colorectal cancer patients from 13 microarray or sequencing cohorts with complete follow-up data were included in this study. The resampling algorithm and gene pair method were combined to improve the stability of the model. After 1000 times of resampling in the training cohort, an ensemble model was constructed by selecting the k-TSP (k-Top Scoring Pairs) classifiers with high balanced accuracy. The Decision-making process in each level was generated by voting, without the need for any cutoff value. C index and D index were used to evaluate the performance of the model.
Results: We constructed an ensemble model called “PIANOS”, which could significantly predict the prognosis of CRC in all 13 cohorts. The meta-estimated C index and D index of the PIANOS model were 0.74 (95%CI: 0.71-0.78, P &lt; 0.001) and 2.77 (95%CI: 2.37-3.18, P &lt; 0.001), respectively. After adjusting for clinicopathological factors, PIANOS was still independently associated with the prognosis of CRC. PIANOS also showed superior prognostic value when compared with previously published gene signatures or consensus molecular subtypes (CMS). GSEA analysis found that the high-risk group was enriched in angiogenesis, TGF-β and epithelial-mesenchymal transition (EMT) pathway, which was consistent with the characteristics of the CMS4 subtype in colorectal cancer.
Conclusion: The PIANOS could robustly predict the prognosis of colorectal cancer across different cohorts and platforms, without the need for any normalization, which is promising for refining treatment decisions in clinical practice.
Citation Format: Du Cai, Haoning Qi, Wei Wang, Min-Er Zhong, Dejun Fan, Cheng-Hang Li, Xiaojian Wu, Feng Gao. PIANOS: A platform independent and normalization free single-sample classifier for colorectal 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 5126.
Abstract 5141: Using single cell gene expression to develop treatment response predictions in multiple myeloma
Authors: Scolnick Jonathan , Xu Stacy , de Mel Sanjay , Soekojo Cinnie Yentia , Chng Wee Joo 
Published: June 2022

Abstract
The promise of precision medicine in oncology is to identify the ideal treatment for each patient, eliminating failed treatment cycles and reducing treatment burdens on patients and payors. While gene sequencing can predict effective treatment options in some cases, studies have estimated that only 10-15% of cancer patients are treated with genotype matched drugs. Additional treatment selection tools for predicting patient response thus remains a critical clinical need.
While gene sequencing has clear utility in predicting therapeutic response, gene expression is less commonly used in clinical tests to guide treatment selection. Gene expression is theoretically attractive to predicting therapeutic response because, unlike mutational analysis, it provides a direct readout of the dysregulated biochemical activities in the cells which may be targeted by particular therapies. One of the major problems with conventional approaches to using gene expression to predict therapeutic response is tumor heterogeneity; that is, a biopsied tumor contains multiple cells types and even different tumor clones that can confound a conventional bulk gene expression analysis. More recently, single-cell RNA expression analysis technologies have grown in popularity. These technologies enable the fine characterization of tumors and their cellular makeup and hold the promise of increasing the power of gene expression measurements for identifying therapy response prediction tools.
Here we show the application of single cell gene expression analysis with Proteona’s MapResponse™ machine-learning algorithms to developing a predictive classifier for patient response to Daratumumab treatment in multiple myeloma. Starting with publicly available data (Cohen et al. Nat. Medicine 2021), we developed a classifier that accurately predicted the response of 94% of subjects in the published study. This classifier was able to accurately predict response in an independent cohort of Daratumumab-treated patients. In contrast, the classifier performed poorly in a cohort of patients who did not receive Daratumumab. Together these data suggest the classifier has specificity to predicting response to Daratumumab and is not a general prognostic signature. These early findings point to the potential value of combining single-cell RNA sequencing with machine-learning methods such as MapResponse™ to bring precision medicine to more patients.
Citation Format: Jonathan Scolnick, Stacy Xu, Sanjay de Mel, Cinnie Yentia Soekojo, Wee Joo Chng. Using single cell gene expression to develop treatment response predictions in multiple myeloma [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 5141.
Abstract 5144: Using proteomic approach to identify potential tumor-associated antigens (TAAs) as biomarkers in Hispanic hepatocellular carcinoma
Authors: Ma Yangcheng , Qiu Cuipeng , Zhang Xiaojun , Wang Xiao , Zhang Jianying 
Published: June 2022

Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide with high prevalence and poor prognosis. The incidence rate and mortality rate in Hispanic HCC is relative higher than other ethnic groups. Autoantibodies against tumor associated antigens (TAAs) have long been proved as useful biomarkers in cancer diagnosis. In the present study, sera from 25 Hispanic patients with HCC and 25 sera from normal individuals were used in a proteomic approach to identify HCC-related TAAs. Twenty-four immunoreactive protein spots (16 from HepG2 cells, and 8 from SUN449 cells) were excised from the two-dimensional electrophoresis (2DE) gels, digested with trypsin, and subsequently analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Of 24 immunoreactive protein spots, 21 were identified with compatible molecular weights and isoelectric points. After further analysis, a total of 9 proteins were found to be potential TAAs, including endoplasmic reticulum chaperone BiP (HSPA5), 60 kDa heat shock protein (HSP60), triosephosphate isomerase (TPI1), protein disulfide-isomerase (PDI), Actin-cytoplasmic 2 (ACTG1), calreticulin (CRP55), alpha-enolase (ENO1), heat shock 70 kDa protein (HSP70), tropomyosin alpha-3 chain (TPM3). Among these proteins, HSPA5 was corresponded to 5 protein spots on the 2DE gels, while 4 proteins (HSPA5, HSP60, TPI1 and PDI) were found in both HepG2 and SNU449 cell lines. Enrichment analysis show that these proteins are mostly responsible for protein folding in endoplasmic reticulum as well as ubiquitin ligase binding function. In the subsequent study, autoantibodies against these proteins will be detected by immunoassay, and further evaluated as biomarkers in detection of Hispanic HCC.
Citation Format: Yangcheng Ma, Cuipeng Qiu, Xiaojun Zhang, Xiao Wang, Jianying Zhang. Using proteomic approach to identify potential tumor-associated antigens (TAAs) as biomarkers in Hispanic hepatocellular carcinoma [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 5144.