bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 4181: CDK4/6 inhibition with Abemaciclib improves intracranial and extracranial response to checkpoint blockade in pre-clinical models of brain metastasis
Authors: Nayyar Naema , Singh Mohini , de Sauvage Magali , Ijad Nazanin , Dahal Ashish , Chuprin Jane , Brehm Michael , Brastianos Priscilla 
Published: June 2022

Abstract
While immune checkpoint inhibitors (ICI) have revolutionized treatment of advanced cancers, few of the 30% of cancer patients who develop brain metastases benefit from immunotherapy. Inhibition of CDK4/6 pathway - altered in &gt;50% of brain metastatic patients - can reportedly increase tumor inflammation and sensitize extracranial tumors to ICI. To determine whether intracranial tumors can be similarly sensitized, we studied efficacy of combination CDK4/6 inhibitor Abemaciclib and ICI in immunocompetent mouse models of brain metastasis bearing concurrent intracranial and extracranial murine tumors that are ICI-resistant. 8-week-old female C57BL/6 mice received subcutaneous injections of 2x105 YUMM1.7 or B16-F10 melanoma cells 3 days prior to intracranial injections of 1x104 YUMM1.7-FmC (firefly luciferase-tagged) cells or 5x103 B16-F10 cells respectively. 8-week-old female BALB/C mice similarly received 2x105 4T1 mammary carcinoma cells in mammary fat pad 3 days prior to intracranial injections with 2x104 4T1-FmC cells. Mice were randomized into 4 treatment groups (n=5-7/group): Abemaciclib monotherapy (administered daily), anti-PD-1 monotherapy (every 3 days), Abemaciclib and anti-PD-1 (combination therapy), and treatment with vehicle and isotype-matched antibody as control. In all 3 tumor models, extracranial tumor growth was significantly reduced compared to control mice following combination therapy (P&lt;0.05 for all), while Abemaciclib monotherapy only reduced growth of subcutaneous YUMM1.7 tumors (P&lt;0.05). Intracranial tumor growth, calculated using bioluminescence imaging early-in-treatment and post-treatment, was significantly reduced compared to control-treated tumors following combination therapy in both YUMM1.7-FmC and 4T1-FmC models (P&lt;0.05 for both). In mice bearing B16-F10 tumors, where overall survival was used as an indirect measure of intracranial tumor growth, combination therapy improved survival (p&lt;0.05) compared to both control and Abemaciclib monotherapy arms. These results indicate CDK4/6 inhibition with Abemaciclib can improve both extracranial and intracranial responses to ICI and sensitize brain metastases to immunotherapy. Our pre-clinical findings warrant further investigation to determine whether this combination approach can improve patient outcomes.
Citation Format: Naema Nayyar, Mohini Singh, Magali de Sauvage, Nazanin Ijad, Ashish Dahal, Jane Chuprin, Michael Brehm, Priscilla Brastianos. CDK4/6 inhibition with Abemaciclib improves intracranial and extracranial response to checkpoint blockade in pre-clinical models of brain metastasis [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 4181.
Abstract 4215: Combination of STING and TLR 7/8 agonists as vaccine adjuvants for cancer immunotherapy
Authors: Bhatnagar Shubhmita , Revuri Vishnu , Shah Manan , Larson Peter , Ferguson David , Panyam Jayanth 
Published: June 2022

Abstract
Various classes of molecules including toll-like receptor (TLR) agonists, exosomes, and metallo compounds have been evaluated as adjuvants for cancer vaccines. However, their wide-spread clinical use has been limited by transient immune responses and serious side-effects. Here, we propose the use of a multi-adjuvant approach that combines two different classes of adjuvants, STING and TLR 7/8 agonists, based on their distinct immune cell targets, signalling pathways, and significant roles in the activation and maintenance of immune responses. We evaluated the potential of combining 522, a novel TLR 7/8 agonist, and DMXAA, a STING agonist, for stronger DC activation and greater CD8 T cell responses. Mouse bearing B16F10-OVA (murine melanoma cell line which expresses ovalbumin) tumors were immunized with OVA mixed with 522 or DMXAA or a combination of 522 and DMXAA daily for 5 days. Immunization with OVA+DMXAA+522 resulted in significant tumor growth inhibition (p &lt; 0.05) and improved survival (p&lt;0.05) compared to other controls. Using flow cytometry, the costimulatory molecule expression and immune cell infiltration in mouse lymph node, spleen and tumor were evaluated. Immunization with OVA+DMXAA+522 resulted in the activation of antigen presenting cells (APCs) in lymph nodes, spleen and tumor (additive or equivalent to single treated groups). The combination also elicited stronger antigen specific CD8 T cell and natural killer (NK) cell responses than control or individual treatment groups. OVA + DMXAA+ 522 immunization increased the number of OVA-specific CD44high CD8 T cells by over 4-fold compared to other treatment groups and control mice in spleen and lymph nodes. A reduction in the frequency of M2 macrophages was observed with OVA+DMXAA+522 treatment. Cytokine analysis demonstrated higher levels of pro-inflammatory cytokines like IFNγ and lower levels of pro-tumorigenic cytokines in the serum of OVA+DMXAA+522 immunized mice compared to that in untreated or OVA-only treated mice. Taken together, these results suggest that combination of TLR7/8 and STING agonists is a promising multi-adjuvant approach for cancer vaccination
Citation Format: Shubhmita Bhatnagar, Vishnu Revuri, Manan Shah, Peter Larson, David Ferguson, Jayanth Panyam. Combination of STING and TLR 7/8 agonists as vaccine adjuvants for cancer immunotherapy [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 4215.
Abstract 4248: HFB200603, a novel anti-BTLA monoclonal antibody that provides therapeutic potential for immune escape and synergizes with anti-PD-1 treatment
Authors: Li Juying , Zhang Qian , Shen Bingqing , Shah Manan , Chen Mingjie , Li Sharon , Dong Ling , Adrian Francisco , Schweizer Liang 
Published: June 2022

Abstract
BTLA is a co-inhibitory immune checkpoint molecule sharing sequence and structural homology with PD-1 and CTLA4. Ligation of HVEM with BTLA induces recruitment of SHP1 and SHP2, and triggers inhibition of T cell proliferation and cytokine production. In melanoma, HVEM is described to have a broader expression than PD-L1 and constituted a negative prognostic marker; in PD-L1 negative NSCLC, the expression of HVEM has been shown to contribute to immune escape. Therefore BTLA-HVEM axis could play an important role in the immune escape, thus BTLA blockade in combination with PD-1/PD-L1 blockade could represent an effective therapeutic option. Using our proprietary microfluidic-based single cell platform CelliGO™, we identified a series of anti-human BTLA antibodies that were characterized for their binding affinity, cross-reactivity, selectivity and functional activity. Amongst them, HFB200603 was identified as a single-digit nanomolar binder to human and cynomolgus BTLA, capable of reversing HVEM-mediated immune suppression in a BTLA-HVEM reporter system and in a primary CD4+ T cell proliferation assay. HFB200603 showed synergistic effect with anti-PD-1 to enhance IFN-γ production in an MLR assay, and demonstrated favorable developability and pharmacokinetic profiles. Profiling of tumor infiltrating lymphocytes demonstrated that BTLA+PD-1+ T cells are present in melanoma, NSCLC, and HCC. Blockade of the BTLA-HVEM interaction with HFB200603 alone or in combination with anti-PD-1 led to increases in IFN-γ, CXCL9, IP-10 and other proinflammatory cytokines in primary dissociated tumor cultures. Based on its favorable pharmacological activity and excellent developability, HFB200603 is currently being developed as a potential novel immunotherapy coupled with a patient biomarker strategy derived from HiFiBiO’s Drug Intelligent Science (DIS™) single-cell immune profiling platform.
Citation Format: Juying Li, Qian Zhang, Bingqing Shen, Manan Shah, Mingjie Chen, Sharon Li, Ling Dong, Francisco Adrian, Liang Schweizer. HFB200603, a novel anti-BTLA monoclonal antibody that provides therapeutic potential for immune escape and synergizes with anti-PD-1 treatment [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 4248.
Abstract 44: Evolution of large copy number variants in breast cancer through genetic network rewiring
Authors: Kuzmin Elena , Monlong Jean , Bourgey Mathieu , Barber Jarry , Lesluyes Tom , Baker Toby , Morin Genevieve , Zou Dongmei , Schwartz Michael , Yang Yang , Pacis Alain , Martinez Constanza , Kuasne Hellen , Fortier Anne-Marie , Li Rui , Kleinman Claudia , Huang Sidong , van Loo Peter , Morris Quaid , Ragoussis Jiannis , Bourque Guillaume , Park Morag 
Published: June 2022

Abstract
Large chromosomal alterations are common in cancer and often show preferential gain or loss across many cancer types indicating their selective advantage. Triple negative breast cancer (TNBC) exhibits complex mutational spectrum without common oncogenic drivers yet displays consistent loss of large chromosomal regions. Here, we characterize selection pressures that maintain a recurrently deleted region of chromosome 4p in TNBC. We used single cell and bulk WGS phylogenetic analysis of TNCB PT/PDX panel to show that chr4p deletion is an early event in tumor evolution. We used scRNAseq gene expression and inferred copy number analysis to show that chr4p loss is associated with a proliferative state. This finding was confirmed by a combination of RNA in situ hybridization and immunofluorescence. We then tested the dosage sensitivity of genes residing within this region by individual and dual overexpression in TNBC PDX-derived cell lines and control normal cell line by assessing their effect on cell proliferation. The overexpression of genes within chr4p elicited a strong cell proliferation defect in cancer but not normal cell line models. We also characterized an unknown gene within chr4p region as a novel member of the STRIPAK complex. Genome-wide pooled ORFeome library screens identified a global pattern of background-specific dosage sensitive regions. Our study shows that large chromosomal deletions are maintained due to evolutionary early genetic network rewiring rendering multiple genes within such regions to be dosage sensitive. Ultimately, this work enhances our understanding of genetic events that modulate TNBC.
Citation Format: Elena Kuzmin, Jean Monlong, Mathieu Bourgey, Jarry Barber, Tom Lesluyes, Toby Baker, Genevieve Morin, Dongmei Zou, Michael Schwartz, Yang Yang, Alain Pacis, Constanza Martinez, Hellen Kuasne, Anne-Marie Fortier, Rui Li, Claudia Kleinman, Sidong Huang, Peter van Loo, Quaid Morris, Jiannis Ragoussis, Guillaume Bourque, Morag Park. Evolution of large copy number variants in breast cancer through genetic network rewiring [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 44.
Abstract 459: Identification of the PDAC immunogenic subtype using deep learning with multi-scaled digital H&E images
Authors: Si Han , Xu Steven , Muthuswamy Anantharaman , Liang Ryan , Sasser Kate , Hamadeh Hisham K. , Couto Suzana , Higgs Brandon 
Published: June 2022

Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) has proven a difficult cancer to treat. To improve treatment strategies, molecular subtypes have been identified, one being immunogenic with significant immune infiltrate and better prognosis. These subtypes have been determined by RNA profiling, which often exhibits technical challenges in a clinical setting. Here we present a deep learning-based method to identify PDAC immunogenic patients using H&E images. This method is robust to RNA quality issues and can elucidate unique morphological features not obtainable with RNA profiling to refine the selection of patients with this phenotype.
Methods: Patients (n=265) were primarily stage II or IV, treated previously with gemcitabine+nab-paclitaxel or FOLFIRINOX. Ground truth for immunogenic subtype (n=105) was defined with RNASeq. Whole slide images were subdivided into non-overlapping, fixed-size tiles at 4 magnifications: 2.5x, 5x, 10x, 20x, followed by feature extraction using a ResNet50 convolutional neural network. Principal component (PC) analysis reduced dimensionality of extracted features in each tile. The dataset was split into 80% training, 20% testing. Posterior probabilities from a linear model were inputs to a support vector machine to predict outcome (2-step model). Progression- free survival (PFS) was evaluated using the Kaplan Meier method.
Results: ImageNet-trained ResNet50 model extracted 2048 features from each tile and the first 75 PCs, explaining 87% variance, were input into the 2-step model; average AUC=0.77 (95% CI=0.70,0.83) across the 4 magnification datasets. A multi-scale ensemble approach combining these 4 magnifications improved the AUC to 0.80 (95% CI=0.66,0.93). Accuracy, sensitivity, and specificity were 0.79, 0.76, and 0.81, respectively. The predicted immunogenic subtype showed significantly improved PFS compared to the other 3 subtypes (HR 95%CI = 0.54 (0.36,0.81), p=0.003), while the RNASeq-derived immunogenic subtype had less differentiation (HR 95%CI = 0.64 (0.42,0.98), p=0.04).
Conclusions: This study presents a deep learning 2-step model approach using tumor H&E images to identify PDAC immunogenic subtype, with improved prognostic potential to that identified by RNA profiling, suggesting possible application in clinical settings for patient stratification. Future work will expand the model to larger independent cohorts.
Citation Format: Han Si, Steven Xu, Anantharaman Muthuswamy, Ryan Liang, Kate Sasser, Hisham K. Hamadeh, Suzana Couto, Brandon Higgs. Identification of the PDAC immunogenic subtype using deep learning with multi-scaled digital H&E images [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 459.
Abstract 467: Integrative deep learning analysis identifies cross-talk between morphology, mutation, and clinical variables for colon adenocarcinoma patient outcomes
Authors: Zhou Jie , Foroughi pour Ali , Beydoun Rafic , Deirawan Hany , Daaboul Fayez , Ahmed Fahad Shabbir , Chuang Jeffrey H. 
Published: June 2022

Abstract
Colorectal cancer is one of the most common cancers in both men and women. Patient stratification in colorectal cancers is challenging and affects therapy decisions and patient care. In this study, we develop an integrative Inception V3-based deep learning approach to stratify TCGA-COAD patients from H&E images, showing that combining deep learning extracted morphological features with clinical and mutational status improves patient stratification. Morphological features within tumor regions can distinguish patients by short or long overall survival (OS) (OS&lt;3 years v.s. OS&gt;5 years, AUC 0.81±0.12), while they are less informative for intermediate OS (3&lt;OS&lt;5 years). Models that integrate morphological features with additional data modalities yield superior stratification to those that use only a single datatype or that use only clinical information and genomic markers. Remarkably, combining deep learning morphological features with clinical information and genomic markers also boosts stratification of patients with 3&lt;OS&lt;5 years, both at the 3-year time point (p &lt;0.001) and 5-year time point (p &lt;0.001). We validated our model on an external dataset (OS&lt;3 years v.s. OS&gt;5 years, AUC 0.78±0.20), and again found that combining morphological features and clinical data is superior to only using a single datatype. Our deep learning models were also able to perform reliable tumor segmentation (AUC&gt;0.92). However, survival predictions based on deep learning tumor segmentation were inferior to those based on pathologist tumor annotations (OS&lt;3 years v.s. OS&gt;5 years, AUC 0.79±0.14). Furthermore, we show that the cross-talk between different data modalities is informative of patient risk. More precisely, models that directly combine image and clinical features performed superior to a naïve Bayes classifier combining predictions of models trained on each data type (integrative model AUC=0.81±0.06 and naïve Bayes AUC=0.76±0.11, p&lt;0.01). Our results show integrative analysis utilizes the cross-talk between data modalities to boost patient risk stratification, and that deep learning is an efficient methodology for taking advantage of cross-talk between various data modalities.
Citation Format: Jie Zhou, Ali Foroughi pour, Rafic Beydoun, Hany Deirawan, Fayez Daaboul, Fahad Shabbir Ahmed, Jeffrey H. Chuang. Integrative deep learning analysis identifies cross-talk between morphology, mutation, and clinical variables for colon adenocarcinoma patient outcomes [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 467.
Abstract 480: Consensus subtypes of hepatocellular carcinoma associated with clinical outcomes, response to therapies, and multiple biomarkers
Authors: Lee Sung Hwan , Lee Ju-Seog 
Published: June 2022

Abstract
Background & aims: While many studies revealed genomic subtypes of hepatocellular carcinoma (HCC), they are not translated to the clinic yet due to lack of consensus. We aim to examine consensus of genomic subtypes and uncover their clinical significance.
Methods: We integrated 15 previously established genomic signatures for HCC to uncover consensus genomic subtypes. We also developed and validated a robust predictor of consensus subtype with 100 genes (PICS100). Informatics and statistics approaches were applied to find clinical relevant association of genomic features. Patient derived xenograft (PDX) models were used for testing hypothesis from analysis of genomic data.
Results: We identified 5 clinically and molecularly distinct consensus subtypes. STM (STeM) is characterized by high stem cell features, vascular invasion, and poor prognosis. CIN (Chromosomal INstability) has moderate stem cell features but high genomic instability and low immune activity. IMH (IMmune High) is characterized by high immune activity. BCM (Beta-Catenin with high Male predominance) is characterized by prominent β-catenin activation, low miRNA expression, hypomethylation, and high sensitivity to sorafenib. DLP (Differentiated and Low Proliferation) is differentiated with high HNF4A activity. We also identified potential serum biomarkers that can stratify patients into 5 subtypes. Because these subtypes are highly associated with currently available treatments, our findings may provide the foundation for rationalized biomarker-based clinical trials.
Conclusions: Five HCC subtypes are highly associated with response to standard and experimental treatments and highly conserved in pre-clinical models, providing a framework for selecting the most appropriate models for preclinical studies and rationalized clinical trials of new drugs.
Citation Format: Sung Hwan Lee, Ju-Seog Lee. Consensus subtypes of hepatocellular carcinoma associated with clinical outcomes, response to therapies, and multiple 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 480.
Abstract 5005: PSMB9/LMP2 as a controversial diagnostic biomarker for uterine leiomyosarcoma
Authors: Maia Raul , Kokaraki Georgia , Souza Jorge E. , Carlson Joseph W. , Petta Tirzah B. 
Published: June 2022

Abstract
Uterine leiomyosarcoma (uLMS) are rare and malignant tumors that arise in the cells of the myometrium. Diagnosis is based on histopathological features. Developing effective therapies against uLMS is a challenge due its resistance to conventional radiation and chemotherapy. In vivo and in vitro models for uLMS are urgently needed and it has been proposed that knockout mice for the gene PSMB9 (MIM:177045), also known by LMP2, could be a good model to study uLMS. Previous studies using mice homozygous deficiency for PSMB9 presented abnormalities in the biological functions of the immunoproteasome and spontaneously developed uLMS thus suggesting this gene as a tumor suppressor gene. Furthermore, expression of PSMB9 was suggested to be a specific biomarker for the diagnosis of uLMS. The goal of this study was to evaluate the role of PSMB9 in uLMS using in silico analysis of transcriptomics data from a patient cohort, combined with publicly available datasets. Molecular data were integrated and used for a meta-analysis of RNA-seq in order to find Differentially Expressed Genes (DEGs). A total of 68, 66 and 67 samples from normal myometrium (MM), uterine leiomyoma (LM) and uLMS, respectively, were analyzed. A quality control followed by adapter removal and alignment step was assessed using FastQC, Trim-galore and HISAT2 software respectively and counting reads was performed using htseq-count. Expression values were calculated as Transcripts Per Million (TPM) in order to remove libraries dependencies. Differential expression analysis was done using DESeq2 and edgeR and genes were classified as DEG when that had adjusted p-value &lt; 0.01 and |log2FC| ≥ 2 in both packages. Gene Set Enrichment Analysis was performed using fgsea package with 100,000 permutations and MSigDB collections (KEGG and Reactome) as input parameters. Our results showed an over-expression of PMSB9 in uLMS samples and immune-related pathways were significantly enriched. Moreover, the overall survival analysis for the outcome of uLMS patients based on PSMB9 expression in TCGA showed no significant changes, further questioning the importance of PSMB9 in uLMS malignancy. Finally, based on phylogenetic analysis we showed that PSMB9 conservation between Mus musculus and Homo sapiens is not closely related. This divergence suggests that the biological role of PSMB9 may differ between M. musculus and H. sapiens. Human uLMS exhibits an over-expression in PSMB9 and these findings suggest PSMB9-deficient mice are not an appropriate diagnostic-biomarker for uLMS.
Citation Format: Raul Maia, Georgia Kokaraki, Jorge E. Souza, Joseph W. Carlson, Tirzah B. Petta. PSMB9/LMP2 as a controversial diagnostic biomarker for uterine leiomyosarcoma [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 5005.