bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract 5209: Generation of humanized TREM2 mice for preclinical evaluation of therapeutics targeting tumor-associated macrophages
Authors: Lv Ruili , Yu Jia , Shen Zhiyuan , Jin James 
Published: April 2023

Abstract
In recent years, immune checkpoint inhibitors (ICIs) have improved treatment for several types of cancer. However, the overall response rates remain low, and drug resistance can occur in patients after initial responses. This resistance is thought to be due in part to tumor-associated macrophages (TAMs), which can deploy a number of mechanisms to suppress anti-tumor immunity, resulting in tumor immune escape. Triggering receptor expressed on myeloid cells-2 (TREM2), a transmembrane receptor that is part of the immunoglobulin superfamily, is highly expressed on the surface of TAMs. Moreover, the expression level of TREM2 is reported to be higher in cancerous tissues vs normal tissues, with higher expression associated with poorer patient outcomes across multiple solid tumor types. Conversely, mice that lack Trem2 experience slower tumor cell expansion and are more responsive to anti-PD-1 immunotherapy, suggesting that TREM2 may be a promising therapeutic target for improving responses to ICI immunotherapies. To better assist the efficacy evaluation of anti TREM2 antibodies, Biocytogen has generated a humanized TREM2 mouse model. In these mice, exons 1-5 and the 3’UTR of mouse Trem2 gene, which encodes the full-length protein, were replaced by its human counterpart. In homozygous mice, human TREM2 protein expression is detected. The distribution of basal leukocyte subpopulations of blood, spleen, and lymph nodes in humanized B-hTREM2 mice were similar and comparable to those in wild-type C57BL/6 mice. Using the MC38 tumor model, we showed modest efficacy of an anti-human TREM2 antibody in inhibiting tumor growth in vivo. In summary, TREM2 humanized mice provide a powerful preclinical model for in vivo evaluation of anti-human TREM2 antibodies for cancer treatment.
Citation Format: Ruili Lv, Jia Yu, Zhiyuan Shen, James Jin. Generation of humanized TREM2 mice for preclinical evaluation of therapeutics targeting tumor-associated macrophages. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5209.
Abstract 5226: Exome-wide association study identifies coding variant in IL17RA associated with survival in cancer patients treated with immunotherapy
Authors: Kleeman Sam O. , Chan Michael , Chvasta Matthew , Janowitz Tobias 
Published: April 2023

Abstract
Immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 and CTLA4 are effective treatments in multiple cancer types, but durable responses are seen only in a minority of patients. Resistance to ICI is likely to reflect a combination of tumor-intrinsic, such as tumor mutational burden, and tumor-extrinsic factors, such as systemic immune suppression and host genetics. With the advent of large-scale germline genotyping studies, there is emerging evidence that host-specific genetic factors can substantially modify both disease risk and responsiveness to therapy. There has been limited characterization to date of the interaction between host genetics and response to ICI. Here, we perform harmonized mutation calling in available normal exome sequencing from 668 metastatic cancer patients treated with ICI (derived from n=7 studies, spanning n=3 cancer types and PD-1/PD-L1/CTLA4-directed therapy) and performed exome-wide association studies in European ancestry patients. For each cancer type (melanoma, renal cell carcinoma, urothelial carcinoma), we conducted exome-wide Cox regression (with PC1-4 and sex as covariates) to identify coding single-nucleotide polymorphisms (SNPs) associated with overall survival (OS). On fixed-effect meta-analysis, we identified a single coding variant (rs140221307, C allele, MAF 0.5% in European populations) that met genome-wide significance (p=1.11 × 10−15, HR=4.86, 95% CI 3.30-7.15). This SNP causes a substitution of Trp to Arg at amino acid 320 at the interface between the transmembrane and extracellular domains of interleukin 17 receptor A (IL17RA). While the secretion mechanism is uncharacterized, soluble IL17RA can be readily detected in plasma and the survival-reducing variant is associated with substantially elevated plasma IL17RA (p=1.90 × 10−9, beta=1.01, Sun et al. 2018) as well as reduced monocyte counts in UK Biobank (p=2.20 × 10−308, beta=-0.60). We have established a prime editing strategy to induce this variant in cultured human monocytes to investigate its biochemical and functional consequences, and are performing validation studies in independent patient cohorts. Soluble IL17RA would be expected to function as a decoy receptor to antagonize IL17/IL17RA signaling, and so these data suggest that activation of IL-17 receptor signaling might improve responsiveness to ICI therapy.
Citation Format: Sam O. Kleeman, Michael Chan, Matthew Chvasta, Tobias Janowitz. Exome-wide association study identifies coding variant in IL17RA associated with survival in cancer patients treated with immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5226.
Abstract 5348: Targeting MEK, alone and in combination with BRAF, in metastatic BRAFK601E-mutated melanoma
Authors: Chan Sze Man , Chow Sin Ting , Yeung Sai Fung , Tsui Kwok Wing Stephen 
Published: April 2023

Abstract
Mitogen-activated protein kinase/ERK signaling pathway has been one of the major mechanisms underlying melanoma haboring BRAF mutation. Of these, K601E mutation belongs to a rare mutation sub-type, comprising only 3% of the BRAF mutations in melanoma. Although patients with BRAF V600E/K mutation respond to MEK inhibitor, whether it also applies to those with BRAF K601E mutation remains elusive. Here, we examined the effects of MEK and BRAF/MEK inhibition on BRAFK601E-expressing melanoma cells. Melanoma cell line (MeWo) was transfected with doxycycline-inducible vector encoding wild-type or K601E-mutant BRAF. Subsequently, cells or xenograft-bearing mice were treated with trametinib, dabrafenib plus trametinib or sorafenib plus trametinib. Compared with BRAFWT, MTT assay demonstrated that BRAFK601E cells were more sensitive towards drug treatment. The effects of combined treatment were more profound. Tumorigenicity assay showed that trametinib treatment inhibited tumor growth of BRAFK601E xenografts. Our preliminary results demonstrated the therapeutic values of MEK and BRAF/MEK inhibitors against BRAFK601E-mutated melanoma. As previous studies report the superior therapeutic effects of BRAF/MEK inhibitors over MEK inhibitor alone, phenotype characterization of the cells, as well as underlying mechanisms behind such response, worth further investigation.
Citation Format: Sze Man Chan, Sin Ting Chow, Sai Fung Yeung, Kwok Wing Stephen Tsui. Targeting MEK, alone and in combination with BRAF, in metastatic BRAFK601E-mutated melanoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5348.
Abstract 542: Synthetic lethal targeting of AURKA in head and neck squamous cell carcinoma
Authors: da Silva Flaviane N , Nguyen Theodore , Lee Jong Woo , Burtness Barbara , Golemis Erica 
Published: April 2023

Abstract
The purpose of this study was to identify new combination strategies for improving treatment of head and neck squamous cell carcinomas (HNSCCs). HNSCCs often express high levels of Aurora kinase A (AURKA), most studied as a kinase important for mitotic progression in normal cells. Notably the overexpression of AURKA in many cancers allows it to act throughout the cell cycle, acquiring new cancer-promoting activities. While several AURKA inhibitors are in clinical trials, limited efficacy is observed with single treatment, causing us to evaluate potential AURKA combination therapeutic combinations. Particularly relevant to HNSCC, which often overexpress and depend on EGFR, overexpression of AURKA can promote resistance to EGFR inhibitors by mechanisms variously identified as including activation of the EGFR effectors AKT, ERK, and BIM. Using viability and clonogenic assays, we have found that the AURKA inhibitor VIC-1911 synergizes with the EGFR inhibitors afatinib and erlotinib in multiple HNSCC cell lines and retains activity in HNSCC cell models selected for resistance to these EGFR inhibitors. However, AURKA inhibition increases levels of AURKA protein and its protein-stabilizing partner TPX2, counteracting drug effect. We also combined inhibition of AURKA with inhibition of WEE1, a regulator of the G2/M checkpoint, finding this dual inhibition enhanced levels of apoptosis in EGFR inhibitor resistant HNSCC cell lines. Intriguingly, one recent study reported AURKA can also modulate the glycolytic signaling pathway, increasing the cancer-promoting Warburg effect. We have found that inhibition of AURKA can significantly reduce glycolytic flux in HNSCC cell lines and alter expression and phosphorylation of key glycolytic enzymes. Dual inhibition of AURKA in combination with inhibitors of glycolytic enzymes, or enzymes regulating oxidative phosphorylation, showed marked synergy. These and other results investigating AURKA signaling mechanisms in HNSCC demonstrate that the non-canonical functions of AURKA include viable therapeutic targets that can enhance the efficacy of AURKA inhibition in HNSCC.
Citation Format: Flaviane N da Silva, Theodore Nguyen, Jong Woo Lee, Barbara Burtness, Erica Golemis. Synthetic lethal targeting of AURKA in head and neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 542.
Abstract 5428: Skeletal muscle gauge prediction by a machine learning model in patients with colorectal cancer
Authors: Kang Jeonghyun 
Published: April 2023

Abstract
Background: Skeletal muscle gauge (SMG) was recently introduced as an imaging indicator of sarcopenia for the prediction of clinical outcomes, including chemotherapy toxicity and prognosis, in patients with cancer. Computed tomography (CT) is essential for measuring SMG; thus, the use of SMG is limited to patients who undergo CT.
Objective: We aimed to develop a machine learning algorithm using clinical and inflammatory markers to predict SMG in patients with colorectal cancer (CRC)
Methods: The least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of the LASSO model, defined as LP-SMG was compared using the area under the receiver operating characteristic (AUROC) and decision curve analysis (DCA) in the test set.
Results: A total of 1,094 patients with CRC were enrolled and randomly categorized into training (n=656) and test (n=438) sets. Low SMG was identified in 142 (21.6%) and 90 (20.5%) patients in the training and test sets, respectively. According to multivariable analysis of the test sets, LP-SMG was identified as an independent predictor of low SMG (OR: 1329.431, CI: 271.684-7667.996, p&lt;.001). Its predictive performance was similar in the training and test sets (AUROC: 0.846 vs. 0.869, p=.427). In the test set, LP-SMG showed better outcomes in predicting SMG than single clinical variables, such as sex, height, weight, and hemoglobin, as measured by AUROC and DCA.
Conclusions: LP-SMG, incorporating clinical variables and serum inflammatory indicators, showed superior performance compared to single variables in predicting low SMG. This machine learning model can be used as a screening tool to detect sarcopenic status without using CT during the treatment period. Applying a machine learning model might be beneficial in reducing the effort, cost, and radiation exposure from conventional CT-based diagnosis.
Citation Format: Jeonghyun Kang. Skeletal muscle gauge prediction by a machine learning model in patients with colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5428.
Abstract 562: Anti-tumor efficacy of GMF-1A3, an MMAE-based antibody drug conjugate targeting cell surface cleaved Amphiregulin in breast cancer
Authors: Lofgren Kristopher A. , Sreekumar Sreeja , Kenny Paraic A. 
Published: April 2023

Abstract
The Epidermal Growth Factor Receptor ligand, Amphiregulin, is a key proliferative effector of estrogen receptor signaling in breast cancer and also plays a role in other malignancies. Amphiregulin is a single-pass transmembrane protein proteolytically processed by TACE/ADAM17 to release the soluble EGFR ligand, leaving a residual transmembrane stalk that is subsequently internalized. Here, we describe the development of an antibody drug conjugate, GMF-1A3-MMAE, targeting an AREG neo-epitope revealed following ADAM17-mediated cleavage. The antibody does not interact with uncleaved Amphiregulin, providing a novel means of targeting cells with high rates of Amphiregulin shedding. Using fluorescent dye conjugation, we demonstrated that the antibody is internalized by cancer cells in a manner dependent on the presence of cell surface cleaved Amphiregulin. Antibodies conjugated with monomethyl auristatin E (MMAE) were cytotoxic in vitro and induced rapid regression of established breast tumor xenografts in immunocompromised mice. We further demonstrate that these antibodies recognize the Amphiregulin neo-epitope in formalin fixed paraffin embedded tumor tissue, suggesting their utility as a companion diagnostic for patient selection. A pan-cancer tissue microarray analysis indicates that the target is commonly detected (&gt; 50% of cases) in breast, prostate, liver and lung cancer.
Citation Format: Kristopher A. Lofgren, Sreeja Sreekumar, Paraic A. Kenny. Anti-tumor efficacy of GMF-1A3, an MMAE-based antibody drug conjugate targeting cell surface cleaved Amphiregulin in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 562.
Abstract 5643: High-spatial-resolution multi-omics analysis of cancer tissues
Authors: Neil Emily , Park Dongju , Lloyd Erica , Dibuono Michael , Hosono Seiyu , Muller Reto , Lafayette Hanna , Smith Hsinyi , Pinard Robert 
Published: April 2023

Abstract
Recent technological breakthroughs in single cell sequencing have revealed that malignant cells and nonmalignant cells are both highly dynamic and in the case of tumor cells, that the intratumoral heterogeneity is quite remarkable. However, several questions remain, especially in a spatial context, and a rapidly growing need has been observed for imaging-based approaches with single-cell resolution to visualize and characterize the tumor microenvironment. In the current study, we used Miltenyi Biotec newly developed spatial gene expression technology and automated imaging platform, to acquire spatial microscopy data for multiple biological analytes and multiple cancer tissues. The approach is based on fluorescence microscopy and the MICS technology principle of cyclic staining (MACSima™ Imaging Cyclic Staining) along with validated fluorochrome-conjugated antibodies and specifically amplified oligonucleotide probes combined with a proprietary signal removal system. FFPE tissue blocks were analyzed and each gene, which display a unique emission spectrum during a single cycle, was decoded. Due to the non-destructive nature of the workflow, H&E staining, immunofluorescence and RNA data can be collected together into a single image and on a single section. Using our antibody and targeted RNA immune-oncology panel coupled with our smart segmentation and flexible gating software suite, we have generated a topographical map of the expression profile level for several cancer tissues i.e.: tonsil, pancreas, kidney and ovary. This technology allows for the analysis of hundreds of protein markers and RNA transcripts on a single tissue, the characterization of the spatial architecture and the expression topography of tumor tissue sections. A better understanding of tissue microenvironment and implementation of spatial biology approaches will help advance the identification of more suitable biomarkers and for cancer therapy.
Citation Format: Emily Neil, Dongju Park, Erica Lloyd, Michael Dibuono, Seiyu Hosono, Reto Muller, Hanna Lafayette, Hsinyi Smith, Robert Pinard. High-spatial-resolution multi-omics analysis of cancer tissues. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5643.
Abstract 5647: Single cell, multiomic spatial phenotyping of immunotherapy responses in head and neck cancer
Authors: Ji HaYeun , Jhaveri Niyati , Ma Ning , Cheikh Bassem B. , Pratapa Aditya , Monkman James , O’Byrne Ken , Hughes Brett , Kulasinghe Arutha , Braubach Oliver 
Published: April 2023

Abstract
Background: Immune checkpoint inhibitors (ICI) have proven to be game-changing treatments for mucosal head and neck squamous cell cancer (HNSCC). Emerging successes with anti-PD-1/PD-L1 therapy have led to durable responses and prolonged survival in both human papillomavirus-positive (HPV+) and negative (HPV-) patients. There is now a need for predictive biomarkers to guide patient selection for highly targeted ICI therapies as currently available diagnostic biomarkers have a limited value. The tumor microenvironment (TME) composition, contexture, and cellular architecture are now recognized as key to understanding immune responsive and resistant phenotypes. Here, we are using a spatial biology approach to explore the TME in metastatic/recurrent HNSCC tumors treated with Pembrolizumab/Nivolumab.
Methods: In this study, we used single cell, multiomic spatial phenotyping utilizing the PhenoCycler-Fusion spatial biology platform to characterize the TME of HNSCC tumors from a cohort of n=40 patients. The discovery cohort consisted of patients who had complete vs. partial vs. stable vs. progressive responses to ICI therapy. We first analyzed tissues using an ultrahigh-plex antibody panel of &gt;60 antibodies that label immune cell lineages, checkpoints, activation markers as well as tissue structure and the stroma. We then conducted whole-slide, single cell resolution RNA detection with complementary markers on serial sections from the same tissue blocks; the combination of these data allowed us to obtain multiomic spatial signatures that offered uniquely comprehensive insight into the TME of our tissue cohorts.
Results: Our study identified highly resolved tissue immune contexture analysis and metabolic tissue signatures associated with resistance and sensitivity to immunotherapy. Most notably, multiomic profiling of HNSCC tumours provided deeper insights into ICI therapy resistance than the single omics based approaches alone.
Conclusions: Our study demonstrates the power of unbiased, multiomic spatial phenotyping with whole-slide imaging to identify biomarkers associated with response to ICI therapy in HNSCC.
Citation Format: HaYeun Ji, Niyati Jhaveri, Ning Ma, Bassem B. Cheikh, Aditya Pratapa, James Monkman, Ken O’Byrne, Brett Hughes, Arutha Kulasinghe, Oliver Braubach. Single cell, multiomic spatial phenotyping of immunotherapy responses in head and neck cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5647.
Abstract 5718: Additivity predicts the clinical efficacy of most approved combination therapies for advanced cancer
Authors: Hwangbo Haeun , Patterson Sarah , Dai Andy , Plana Deborah , Palmer Adam C. 
Published: April 2023

Abstract
Most advanced cancers are treated with drug combinations. Rational designs aim to identify synergistic drug interactions to produce superior treatments. However, metrics of drug interaction (i.e., synergy, additivity, antagonism) apply to pre-clinical experiments, and there has been no established method to quantify synergy versus additivity in clinical settings. Here, we propose and apply a model of drug additivity for progression-free survival (PFS) to assess if the clinical efficacies of approved drug combinations are more than, or equal to, the sum of their parts. This model accounts for the benefit from patient-to-patient variability in the best single drug response, plus the added benefit of the weaker drug per patient. Among all FDA approvals of combination therapies for advanced cancer between 1995 and 2020, we identified 37 combinations across 13 cancer types (24,723 patients) where the efficacies of monotherapies and combination therapies could be analyzed at matched doses. 95% of combination therapies exhibited progression-free survival times that were additive, or less than additive. Across all trials and follow-up times, the coefficient of determination of the additivity model was R2=0.90, with no fitting or training, only the operation of addition. While there were deviations from additivity, with several combinations being less than additive, 100% of the approved combinations studied would have been predicted to succeed by the additivity model. This study has two key findings. First, a synergistic effect (more than additive) is neither a necessary nor even common property of clinically effective drug combinations. Second, the predictable efficacy of many of the best drug combinations established over the past 25 years suggests that additivity can be used as a design principle for novel drug combinations and clinical trials, which could be helpful as the number of possible therapies grows and as cancers are increasingly divided into subtypes with distinct drug sensitivities.
Citation Format: Haeun Hwangbo, Sarah Patterson, Andy Dai, Deborah Plana, Adam C. Palmer. Additivity predicts the clinical efficacy of most approved combination therapies for advanced cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5718.