tbuckley/cabot-search huggingface dataset:
Abstract 3799: Spatial biomarker prediction in papillary renal cell carcinoma using artificial intelligence-based immunofluorescent inference
Authors: Jared Brewer, Joseph Vento, Scott Haake, Anupama Reddy
Journal: Cancer Research (2024)

Abstract Papillary renal cell carcinoma (pRCC) accounts for ~15-20% of the global burden for kidney cancers but still lacks effective mechanisms to predict patient responses to therapeutic intervention. Existing therapies targeting kidney cancers (anti-angiogenesis and immunotherapy) intimately involve the tumor microenvironment in their mechanism of action. However, the role of the physical organization of the tumor in determining therapeutic efficacy remains unknown. Previous efforts have utilized transcriptional profiling to stratify patients into treatment categories but here we propose to leverage artificial neural networks (“AI”) to infer the expression of marker genes for particular cell types of interest based on ubiquitously expressed immunofluorescence channels, unstained brightfield images, and hematoxylin and eosin (H&amp;E) stained images. We have developed AI models that are capable of the prediction of a range of relevant cell types based on the expression profile of DAPI, type IV collagen, and integrin beta-1, including endothelial cells, T cells, macrophages and other cell types. This work applies an AI model class known as generative adversarial networks (GANs) to produce photo-realistic simulated immunofluorescence images based on the input channels. This approach may allow us to accurately predict complex multiplex immunofluorescence (mIF) images from simpler, more routine histological methods, greatly reducing the cost burden required to profile histopathology images for cell types of interest. Our previous work identified a number of interesting cell-cell association clusters correlated with patient prognosis and this extension of that previous work aims to allow for the prediction of these interactions while obviating the need to perform the original, costly multiplex immunofluorescence assay or even a more reduced immunofluorescence staining panel which may still be out of reach for many clinical pathology labs and the interpretation of which may be challenging. By simplifying and increasing the availability of predictive AI models for digital pathology, we hope to further develop and apply predictive biomarkers for patient prognosis and treatment response in papillary renal cell carcinoma. Citation Format: Jared Brewer, Joseph Vento, Scott Haake, Anupama Reddy. Spatial biomarker prediction in papillary renal cell carcinoma using artificial intelligence-based immunofluorescent inference [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 3799.
Abstract 4120: Adherent-to suspension transition by hematopoietic factors in metastatic dissemination of circulating tumor cells
Authors: Hyun Woo Park
Journal: Cancer Research (2024)

Abstract Although metastasis is the foremost cause of cancer-related death, a specialized mechanism that reprograms anchorage dependency of solid tumor cells into circulating tumor cells (CTCs) during the metastatic cascade remains elusive. We discovered a biological phenomenon referred to as Adherent-to-Suspension Transition (AST) that reprograms adherent cells into suspension cells via aberrant induction of hematopoietic transcriptional regulators, which are hijacked by solid tumor cells to disseminate into CTCs. During dissemination, tumor microenvironment triggers epigenetic alteration of AST factors in the invasive front of solid tumors to evoke spontaneous cell-matrix dissociation, acquire anoikis resistance, and bypass immune surveillance of CTCs, however, in the absence of lineage differentiation and EMT. Using mouse models and de novo metastatic patient specimens, we uncovered how Adherent-Suspension Plasticity (ASP) dictates anchorage plasticity during the dissemination and colonization process within the metastatic cascade. Finally, we demonstrate therapeutic strategies that target AST factors to specifically abrogate CTC formation and suppress distant metastases. Together, these findings underscore an important early mechanism of how cancer cells spread and highlight novel targets for the development of anti-metastatic therapy. Furthermore, AST opens unexplored scenarios how reprogramming anchorage dependency orchestrates physiological events such as development, organ morphogenesis and immune cell activation. Citation Format: Hyun Woo Park. Adherent-to suspension transition by hematopoietic factors in metastatic dissemination of circulating tumor cells [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 4120.
Abstract 3822: Oncotype DX testing and differences in use of chemotherapy by age in an integrated system
Authors: Ashley Aller, Liisa Lyon, Laurel Habel, Aida Shirazi, Raymond Liu
Journal: Cancer Research (2024)

Abstract Introduction: Oncotype DX (ODX) identifies breast cancer patients who are less likely to benefit from chemotherapy. We investigated patient factors associated with clinical application of ODX testing and chemotherapy use. Methods: We identified a cohort of adult breast cancer patients with stage I-IIA, estrogen receptor positive, human epidermal growth factor receptor 2 negative, lymph node negative disease diagnosed within Kaiser Permanente Northern California between 2015 - 2021 eligible for ODX testing. Chi-square tests were used for bivariate analysis of categorical variables. Multivariable logistic regression was used to identify independent predictors of chemotherapy use in female patients with high risk ODX scores (&amp;gt;25). Results: 8017 patients were eligible for ODX testing; of which 5,136 received testing (64%) and 576 patients (7%) had high risk ODX. ODX testing increased over the years (45% of eligible patients in 2015; 79% of eligible patients in 2021). Testing was frequent in larger lesions (74%), poorly differentiated tumors (73%), and younger patients (83% in patients &amp;lt;40 years at diagnosis; 12% in patients &amp;gt; 80 years). Chemotherapy was frequently administered in younger patient, and for larger, poorly differentiated tumors. For patients who possessed a high risk ODX score, chemotherapy administration differed by age. In comparison with the 90% of high risk patients &amp;lt;45 years who received chemotherapy, 58% of patients 60+ years at diagnosis with high risk ODX received chemotherapy. Comorbidity index and chemotherapy administration was not statistically significant (Table 1). Conclusion: Oncologists frequently use ODX testing to guide chemotherapy management. Despite high risk ODX scores, older patients received less chemotherapy compared to their younger counterparts. Further investigation is needed to determine how age impacts clinician decision making and clinical outcomes following ODX testing. Table 1. Multivariable Logistic Regression Analysis of Factors Associated with Chemotherapy Women &amp;gt; 60 years with high risk oncotype DX scores (N=467) Adjusted Odds Ratio (95% Confidence Interval) P-values (Calculated using the Wald χ2 Statistic) Age at Cancer Diagnosis (Reference: 60 - 69 years old) 70-79 0.62 (0.40-0.96) 0.03 &amp;gt;79 0.11 (0.02-0.55) 0.01 Charlson Comorbidity Index (Reference = 0) 1 0.76 (0.48-1.22) 0.26 2 0.62 (0.30-1.29) 0.20 3 0.65 (0.26-1.60) 0.35 4 0.54 (0.20-1.44) 0.22 5+ 0.37 (0.10-1.35) 0.13 Citation Format: Ashley Aller, Liisa Lyon, Laurel Habel, Aida Shirazi, Raymond Liu. Oncotype DX testing and differences in use of chemotherapy by age in an integrated system [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 3822.
Abstract 3431: Genetically determined circulating lactase/phlorizin hydrolase concentrations and risk of colorectal cancer: A two-sample mendelian randomization study
Authors: Sihao Han, Jiemin Yao, Hajime Yamazaki, Samantha Streicher, Jianyu Rao, Roch Nianogo, Zuo-Feng Zhang, Brian Huang
Journal: Cancer Research (2024)

Abstract Background: Colorectal cancer (CRC) ranks as the third most common cancer worldwide. Despite its established connection with genetic, dietary, and lifestyle factors, gaps remain in the understanding of its etiology. Lactase-phlorizin hydrolase (LPH) plays a critical role in milk digestion and influences crucial nutrient absorption and gut microbiota composition. A direct relationship of CRC risk with LPH level remains unexplored. Our study aims to elucidate the potential causal link between genetically determined LPH levels and CRC risk. Methods: We applied a two-sample Mendelian Randomization (MR) strategy to investigate the causal link between elevated LPH levels and CRC risks. Genetic instruments for LPH (one cis-variant and three trans-variants) were derived from the Fenland Study, and CRC-associated summary statistics for these instruments were extracted from the FinnGen Study, PLCO Atlas Project, and Pan-UK Biobank. Primary MR analyses focused on a cis-variant (rs4988235) for LPH levels, with results integrated via meta-analysis. MR analyses using all variants were also undertaken. This analytical approach was further extended to assess CRC subtypes, specifically colon and rectal cancer. Results: Meta-analysis across the three datasets illustrated an inverse association between genetically predicted LPH level and CRC risk (OR: 0.92 [95% CI, 0.89-0.95]). Subtype analyses revealed associations of genetically predicted elevated LPH levels with reduced risks for both colon cancer (OR: 0.92 [95% CI, 0.89-0.96]) and rectal cancer (OR: 0.92 [95% CI, 0.87, 0.98]). Consistency was observed across varied analytical methods and different datasets. Conclusions: Our study suggests an inverse relationship between genetically determined LPH level and CRC risk. Further exploration is warranted to unveil the underlying mechanisms and validate LPH’s potential role in CRC prevention. Citation Format: Sihao Han, Jiemin Yao, Hajime Yamazaki, Samantha Streicher, Jianyu Rao, Roch Nianogo, Zuo-Feng Zhang, Brian Huang. Genetically determined circulating lactase/phlorizin hydrolase concentrations and risk of colorectal cancer: A two-sample mendelian randomization study [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 3431.
Abstract 7412: METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics
Authors: Jiahui Jiang, Yunhe Liu, Jiangjiang Qin, Jingjing Wu, Jianfeng Chen, Melissa Pizzi, Rossana Segura, Kohei Yamashita, Zhiyuan Xu, Guangsheng Pei, Kyung Serk Cho, Yanshuo Chu, Ansam Sinjab, Fuduan Peng, Guangchun Han, Ruiping Wang, Xinmiao Yan, Enyu Dai, Mingyao Li, Andrew Futreal, Anirban Maitra, Alexander Lazar, Xiangdong Cheng, Humam Kadara, Jaffer Ajani, Amir Jazaeri, Jian Hu, Linghua Wang, Jianjun Gao
Journal: Cancer Research (2024)

Abstract The recent progression in spatial transcriptomics (ST) offers invaluable perspectives on the structure and dynamics of cells within the tumor microenvironment (TME). Though several analytical tools exist for tasks like spatial clustering and spatially variable gene identification, most of them are general methods lacking consideration of histological features in spatial data analysis. This oversight compromises their performance and interpretability when studying the TME. Here, we introduce a computational framework, the Morphology-Enhanced Spatial Transcriptome Analysis Integrator (METI). METI provides a comprehensive framework for spatially mapping both cancer cells and various TME cells, robust stratification of cell type and transcriptional states, and cell co-localization analysis. By integrating spatial transcriptomics with cell morphology and curated gene signatures, METI deepens our insight into the molecular landscape and cellular interactions within tissues, paving the way for intricate studies of the TME and its consequential impacts. The performance of METI has been assessed using ST data derived from a range of tumor tissues, encompassing gastric, lung, and bladder cancers, and premalignant tissues. Throughout these varied tissues and conditions, METI consistently exhibited robust performance. Citation Format: Jiahui Jiang, Yunhe Liu, Jiangjiang Qin, Jingjing Wu, Jianfeng Chen, Melissa Pizzi, Rossana Segura, Kohei Yamashita, Zhiyuan Xu, Guangsheng Pei, Kyung Serk Cho, Yanshuo Chu, Ansam Sinjab, Fuduan Peng, Guangchun Han, Ruiping Wang, Xinmiao Yan, Enyu Dai, Mingyao Li, Andrew Futreal, Anirban Maitra, Alexander Lazar, Xiangdong Cheng, Humam Kadara, Jaffer Ajani, Amir Jazaeri, Jian Hu, Linghua Wang, Jianjun Gao. METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics [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 7412.
Abstract 7411: CAMStool: Cancer-associated somatic mutation selection tool for cancer risk prediction in whole-genome data
Authors: Jae-Yoon Kim, Seung-Jin Park, Seong-Hwan Park, Seon-Kyu Kim
Journal: Cancer Research (2024)

Abstract Predicting cancer risk plays a useful role in presenting guidelines to prevent and manage cancers of individuals. Genomic variants have recently been widely used as a genetic factor, and the utilization of the genetic factor consisting of somatic mutations has shown remarkable predictive performance in many cancer prediction studies. Typically, the accuracy of such prediction studies is significantly affected by the composition of somatic mutations used as the genetic factor; however, in practice, choosing the optimal set of mutations with high prediction accuracy is a challenging task in cancer prediction research. From this perspective, we present a new analysis tool for selecting an optimal set of somatic mutations associated with cancers of interest, named CAMStool. The CAMStool is based on the statistical values, which are the effect size and its p-value of each somatic mutation for the cancers of interest. It heuristically searches for the only somatic mutations that directly increase the prediction accuracy of cancer, through statistical and machine-learning models. Then, our tool presents the finally selected somatic mutations as the final optimal mutation set, and additionally presents an optimal cancer risk prediction model through various machine/deep learning modeling using the optimal set as a genetic factor. For the evaluation, the CAMStool was applied to our real whole-genome dataset which are sequenced from normal-tumor tissues of 459 individuals with thyroid cancer. The CAMStool selected 36 and 55 optimal somatic mutations associated with the benign thyroid and non-invasive follicular thyroid cancer, and presented risk prediction models with sensitivity of 88.04% and 90.32%, respectively. Although the CAMStools has some limitations, it is expected to contribute to various cancer prediction studies that require selecting optimal mutation sets in the future. The CAMStools is written in R and will be released soon on our github (https://github.com/JaeYoonKim72). Citation Format: Jae-Yoon Kim, Seung-Jin Park, Seong-Hwan Park, Seon-Kyu Kim. CAMStool: Cancer-associated somatic mutation selection tool for cancer risk prediction in whole-genome data [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 7411.
Abstract 4454: Manipulation of the amino acid metabolism promotes therapeutic efficacy through targeting lysosome in glioblastoma
Authors: Yongwei Jing, Masahiko Kobayashi, Atsushi Hirao
Journal: Cancer Research (2024)

Abstract Nutrients are converted by the body to smaller molecules, which are utilized for both anabolic and catabolic reactions. Cooperative regulation of these metabolic processes is critical for maintaining tissue homeostasis. Based on these findings, our group has been focusing on how the regulation of nutrient-driven metabolism controls the malignancy of cancer cells. Metabolic regulation has been considered crucial for cellular adaptation to environmental changes and promoting survival and proliferation. Recent studies showed the successful applications of targeting specific nutrients for cancer therapy. In this study, we developed a new lysosome-targeting therapeutic strategy, centered around the manipulation of the amino acid metabolism for GBM. Using patient-derived GBM cells, we found that lysosomal proteolytic activity is a unique metabolic biomarker representing malignant phenotypes of GBM and that it is closely correlated with the efficacy of current conventional therapy for GBM patients. Furthermore, the MiT/TFE family members, TFEB and TFE3, which are the master regulators of lysosomal biogenesis, controlled malignant progression and therapeutic efficacy in vitro and vivo. Combinatory analyses with clinical patient samples and xenograft models demonstrated that the MiT/TFE family plays a crucial role as a regulator of malignant properties in GBM. Analysis of The Cancer Genome Atlas (TCGA) glioma dataset, which includes data from patients with Grade 2, 3, and 4 (GBM) gliomas, showed that higher expression of the MiT/TFE family was significantly associated with shorter survival. Interestingly, we discovered that an essential amino acid controls lysosomal biogenesis and membrane integrity by regulation of nitric oxide signaling pathway. Finally, we screened the potential anti-cancer drugs, including temozolomide, exhibiting the synergy effect in amino acid-restriction conditions by conducting a drug screening using a clinically available anti-cancer drug library. Our study demonstrated that lysosomal function, supported by amino acid metabolism, plays a critical role in the regulation of GBM malignant status and proposed precision nutrition as a promising therapeutic approach. Citation Format: Yongwei Jing, Masahiko Kobayashi, Atsushi Hirao. Manipulation of the amino acid metabolism promotes therapeutic efficacy through targeting lysosome in glioblastoma [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 4454.
Abstract 5234: TGF<i>beta</i>-conditioned K-NK cells are able to counter immune suppression in the tumor micro-environment
Authors: Andre Kunert, Anna Oja, Ieva Cesonyte, Katarzyna Franciszkiewicz, Sukhvinder Sidhu, Hardik Jetani, Giulio Bocconcelli, Robert Igarashi, Valeria R. Fantin
Journal: Cancer Research (2024)

Abstract Recent advances have enabled the use of autologous K-NK cells to treat patients suffering from hematological malignancies. When aiming to treat patients suffering from solid tumors however, the suppressive effects of the tumor microenvironment (TME) and the involvement of other immune cells need to be considered to make a cell therapeutic potentially more effective. TGFβ-conditioning (TGFβ-c), the co-culture of K-NK cells with low-dose TGFβ while undergoing cytokine-mediated expansion, imbues these cells with unique qualities suitable to mitigate immuno-suppressive effects of the TME, making them more efficacious toward solid tumors. The exposure to TGFβ during expansion results in significant downregulation of SMAD3, rendering these cells insensitive to TGFβ-mediated down-regulation of NK cell receptors. TGFβ-c K-NK cells display enhanced in vitro cytotoxicity towards 2D, 3D cultured cell lines as well as against organoid models derived of various indications. These cells infiltrate into patient-derived tumor organoids at higher frequency and have increased IFNγ secretion upon target-specific activation. The effect of the higher IFNγ output on a complementary anti-tumor immune subset was investigated in complex immune cell cultures and was indeed shown to enhance DC maturation. In vivo studies revealed prolonged persistence of TGFβ-c K-NK cells in mice while retaining both altered gene-expression and functional hallmarks for up to 28 days, indicating that the conditioning results in a stable phenotype. In summary, TGFβ-c K-NKs are a platform with enhanced potency to transition cell therapy from treating hematological to solid tumor malignancies. Citation Format: Andre Kunert, Anna Oja, Ieva Cesonyte, Katarzyna Franciszkiewicz, Sukhvinder Sidhu, Hardik Jetani, Giulio Bocconcelli, Robert Igarashi, Valeria R. Fantin. TGFbeta-conditioned K-NK cells are able to counter immune suppression in the tumor micro-environment [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 5234.
Abstract 3281: Silencing of necroptosis related genes in endocrine-resistant breast cancer cells causes PFKFB3 induced arrest of necroptosis and contributes to resistance
Authors: Brandon C. Jones, Tuna Onal, Surojeet Sengupta, Catherine M. Sevigny, Lu Jin, Paula R. Pohlmann, Ayesha Shajahan-Haq, Robert Clarke
Journal: Cancer Research (2024)

Abstract Approximately 70% of newly diagnosed breast cancer patients are estrogen receptor positive. Patients treated with approved treatment strategies may develop resistance to these therapies. Because of this problem in treatment, new treatment regimens need to emerge. Altered reprogramming of metabolism and an intense dependence on glycolysis are hallmarks of cancer, which is necessary to support the high energy demand of rapidly proliferating cancer cells. Rapidly dividing cancer cells are known to have increased energy requirements and altered reprogramming of glucose metabolism, leading to an increased reliance on glycolysis. The conversion of fructose-6-phosphate to fructose-1,6-bisphosphate, a critical rate-limiting step in the glycolysis pathway, is greatly influenced by an allosteric activator produced by the enzyme PFKFB3 (6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3), thus making PFKFB3 an important regulator of glycolytic flux. Furthermore, PFKFB3 has been shown to be overexpressed in many human cancers, including breast cancer, and upregulated by estrogen. In our previous studies, we have shown that endocrine-resistant BC cells growth is strongly reduced by PFKFB3 targeting using PFKFB3 inhibitors, glucose uptake is reduced, triggered to necroptosis by RIPK1 and MLKL phosphorylation, and PFKFB3 inhibition reduces tumor size in mouse xenografts. In this study, we showed that silencing of RIPK1 and MLKL, genes involved in necroptosis, by siRNAs followed by inhibition of PFKFB3 by PFK158 did not induce necroptosis in endocrine-resistant BC cells. Furthermore, we further confirmed that phosphorylation of MLKL and RIPK occurs with PFKFB3 inhibition. In summary, this study reveals that necroptosis induced by PFKFB3 inhibition contributes to resistance by arresting necroptosis in endocrine-resistant BC cells after silencing of necroptosis-related genes. Citation Format: Brandon C. Jones, Tuna Onal, Surojeet Sengupta, Catherine M. Sevigny, Lu Jin, Paula R. Pohlmann, Ayesha Shajahan-Haq, Robert Clarke. Silencing of necroptosis related genes in endocrine-resistant breast cancer cells causes PFKFB3 induced arrest of necroptosis and contributes to resistance [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 3281.