tbuckley/cabot-search huggingface dataset:
Abstract 5487: Improved spatial biology analysis of the tumor microenvironment with the next generation of the MultiOmyxTMplatform
Authors: Michael Lazare, Harry Nunns, Fatemeh Jelvhei Moghaddam, Xiaowei Jin, Courtney Todorov, Eric Leones, Marianne Thio, Flora Sahafi, Erinn Parnell, Qingyan Au
Journal: Cancer Research (2024)

Abstract Multiplexed Immunofluorescence is a powerful tool for spatially characterizing and phenotyping cells within the tumor microenvironment. MultiOmyx has been one of the leading platforms for generating multiplexed immunofluorescence data to support translational and clinical research for more than 10 years. However, MultiOmyx and other similar platforms are often hampered by a limited imaging area due to either restricted staining areas or being cost prohibitive due to excessive imaging times. Here, we demonstrate the capabilities of an improved MultiOmyx platform which can be used to generate whole-tissue data from the iterative MultiOmyx multiplexing process using the CyteFinder® II microscope and a customized software package developed by RareCyte, Inc. The new NeoLYTX image analysis pipeline in conjunction with the whole-tissue image output allows for improved interaction with pathologists, better histological context, and unbiased spatial analysis. In this study we utilized a 16-marker panel consisting of CD3, CD4, CD8, FoxP3, CD20, CD68, CD163, HLA-DR, PD-1, PD-L1, CTLA-4, CD34, Vimentin, SOX10, Ki67, and PanCK to generate immune profiles for colorectal carcinoma, prostate carcinoma, non-Small cell lung cancer, and triple negative breast cancer samples. We showed concordance with the current MultiOmyx imager - the IN Cell 2200 - for each of the markers in the panel. Additionally, we demonstrated that the updates made to the proprietary NeoLYTX image analysis pipeline are able to facilitate the significantly larger data sets that will be generated with the new MultiOmyx platform. This study successfully demonstrates the capability of the improved MultiOmyx system to characterize the distribution of T-cell and myeloid phenotypes in the tumor microenvironment using the proven MultiOmyx workflow, now enhanced by whole slide images and analysis. Citation Format: Michael Lazare, Harry Nunns, Fatemeh Jelvhei Moghaddam, Xiaowei Jin, Courtney Todorov, Eric Leones, Marianne Thio, Flora Sahafi, Erinn Parnell, Qingyan Au. Improved spatial biology analysis of the tumor microenvironment with the next generation of the MultiOmyxTMplatform [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 5487.
Abstract 889: Machine learning-enhanced targeted versus whole-exome sequencing as a guide to cancer care
Authors: Hyung Jun Woo, Shweta S. Chavan, Aphrothiti J. Hanrahan, Allison L. Richards, Anne Marie Noronha, Ahmet Zehir, Alexander Drilon, Michael F. Berger, David B. Solit, Mark T. Donoghue
Journal: Cancer Research (2024)

Abstract Targeted DNA sequencing of cancer-associated genes has been widely adopted clinically. In addition, the increasing availability of targeted and whole-exome/genome sequencing (WES/WGS)-derived big data suggests avenues of computationally enhancing power of targeted assays via machine learning. We directly compared targeted (MSK-IMPACT) and WES in a cohort of 1,483 cancer patients using the same DNA libraries. We found high sensitivity and specificity of mutation detection by WES for overlapping genes. Comparatively higher estimates of tumor mutation burden by MSK-IMPACT arose from enrichment of oncogenic mutations in the targeted gene set. WES had minimal value in identifying actionable alterations beyond those from MSK-IMPACT, whereas RNA-sequencing identified additional targetable fusions that led to durable responses. We developed a deep learning-based algorithm (DeepSig) that enhances power of mutational signature detection, enabling detection of a select group of signatures with mutation counts of ~5 or less. Hypermutation-associated signatures, including POLE, temozolomide, and mismatch repair deficiency could be detected robustly using targeted sequencing data. In summary, WES had limited clinical value over targeted sequencing panels and future clinical diagnostic development should focus on transcriptome and WGS that can detect additional signatures and gene fusions. Citation Format: Hyung Jun Woo, Shweta S. Chavan, Aphrothiti J. Hanrahan, Allison L. Richards, Anne Marie Noronha, Ahmet Zehir, Alexander Drilon, Michael F. Berger, David B. Solit, Mark T. Donoghue. Machine learning-enhanced targeted versus whole-exome sequencing as a guide to cancer care [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 889.
Abstract 2841: Development and validation of a systemic human multiple myeloma model utilizing luciferase expressing MM1.S tumor cells
Authors: Bincy John, Christopher Currie, Nicole Westrick, Tyler Rowe, Andrew Wong, Elizabeth Rainbolt, Zachary Ward, Chassidy Hall, David Harris
Journal: Cancer Research (2024)

Abstract Multiple Myeloma (MM) is a complex hematologic malignancy characterized by clonal proliferation of transformed plasma cells leading to overproduction of monoclonal immunoglobulins and subsequently end-stage organ damage. Despite therapeutic advances, MM is an incurable disease with poor prognosis in high-risk patients, and thus relevant preclinical models are necessary to develop novel treatment strategies. We developed a Multiple Myeloma in vivo model by systemic implantation of the luciferase-expressing glucocorticoid sensitive tumor cell line MM1.S (MM1.S-Luc) into immunodeficient NCG mice. First, we generated a MM1.S-Luc monoclonal cell line from a polyclonal pool of stable luciferase-expressing cells by limiting dilution. Single cells from the stable pool were expanded under selective pressure and four MM1.S-Luc monoclonal lines were selected based on in vitro growth properties and bioluminescence intensity. Next, we determined the in vivo tumor growth profile by testing the monoclonal lines at two different cell inoculums, and disease progression was monitored by in vivo serial bioimaging. MM1.S-Luc tumor growth kinetics revealed a long latency and based on our in vivo imaging results, the tumor preferentially localized to the long bones, lungs, and mandible. For validation of the MM1.S-Luc in vivo model, we determined the efficacy and survival benefit in response to various clinically relevant standard of care agents including Dexamethasone, Daratumumab, Cyclophosphamide, Vincristine, Panobinostat, and Bortezomib. The quantification of tumor burden by bioluminescent imaging showed reduced tumor burden and prolonged survival with Panobinostat and Cyclophosphamide. In summary, we have developed a systemic MM1.S-Luc model that recapitulates the disease dissemination and invasiveness of multiple myeloma for evaluating novel therapeutic approaches to treatment. Citation Format: Bincy John, Christopher Currie, Nicole Westrick, Tyler Rowe, Andrew Wong, Elizabeth Rainbolt, Zachary Ward, Chassidy Hall, David Harris. Development and validation of a systemic human multiple myeloma model utilizing luciferase expressing MM1.S 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 2841.
Abstract 4844: Epidemiology of bone and soft-tissue sarcomas: A two-decade analysis of the surveillance, epidemiology, and end results program
Authors: Marc El Beaino, Rachel Baum, Katherine M. Connors, Karim Masrouha, Santiago A. Lozano-Calderon, Patrick P. Lin
Journal: Cancer Research (2024)

Abstract In the United States, ~15000 cases of bone and soft-tissue sarcomas are diagnosed annually, representing roughly 1% of all incident cancers. While several studies have analyzed independent predictors of outcomes for these sarcomas, small samples and confounding bias have limited their ability to establish reliable conclusions. The present analysis provides a comprehensive epidemiological description of patients with any of 16 distinct bone and soft-tissue sarcomas by querying the Surveillance, Epidemiology, and End Results database between 2000 and 2020. Incidence, prevalence, incidence-based mortality, survival rates, and case-fatality ratios were computed. Using patient age, sex, race, and ethnicity, as well as tumor size, grade, and stage as covariates, multivariable survival Cox proportional hazards regression analyses were performed to model hazard ratios (HRs) and identify independent predictors of worse prognosis. Only patients with myosarcoma and fibrous histiocytoma witnessed a decrease in the prevalence of their disease over time (both, p&amp;lt;0.05). The incidence of chondrosarcoma and synovial sarcoma increased in White and Asian patients but decreased in Black individuals (both, p&amp;lt;0.001), while their case-fatality rates remained constant and increased, respectively, regardless of race and ethnicity (all, p&amp;lt;0.05). Case-fatality rates only declined significantly for White and Non-Hispanic patients with solitary fibrous tumor (both, p&amp;lt;0.05). Aside from alveolar soft-part sarcoma and clear cell sarcoma, a 10-year increase in age of patients with bone and soft-tissue sarcomas resulted in 18% to 61% higher likelihood of death (all, p&amp;lt;0.05). For most malignancies, having a tumor size greater than 5 cm yielded a 37% to 189% higher likelihood of death. While incidence trends suggest improvements in diagnostic techniques and early tumor detection, survival and case-fatality trends indicate potential for advancement in treatment for most bone and soft-tissue sarcomas. Citation Format: Marc El Beaino, Rachel Baum, Katherine M. Connors, Karim Masrouha, Santiago A. Lozano-Calderon, Patrick P. Lin. Epidemiology of bone and soft-tissue sarcomas: A two-decade analysis of the surveillance, epidemiology, and end results program [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 4844.
Abstract 891: Turning data into information: Using PD3D® models to guide colorectal cancer therapy by Optim.AITM
Authors: Ulrike Pfohl, Masturah Mohd Abdul Rashid, Jhin Jieh Lim, Juergen Loskutov, Lena Wedeken, Edward Kai-Hua Chow, Hugo Saavedra, Christoph Reinhard, Christian R. Regenbrecht
Journal: Cancer Research (2024)

Abstract Colorectal cancer (CRC) is one of the most prevalent and lethal malignancies globally with up to 50 % of patients eventually progressing to metastatic disease. The mitogen-activated-protein kinase (MAPK) pathway emerges as a key player, being one of the most frequently mutated signaling pathways in the oncogenesis of CRC. However, given the numerous genomic aberrations and tumor heterogeneity in CRC, patients may benefit from combinatorial therapies, particularly those who have inoperable or metastatic tumors. In the present study, we investigated the feasibility of combining two platforms, patient-derived 3D (PD3D®) models with Optim.AI™, to identify more effective cancer therapies. PD3D® models can robustly retain the genotypic and histopathologic features of the primary patient tumor, model tumor heterogeneity and were shown to predict a patient’s drug response. Optim.AI™ is a hybrid computational-experimental platform that uses small data sets to rationally converge to optimal drug combinations within a defined drug search space. By mapping experimental data points to a second-order quadratic function, Optim.AI™ can predict every possible 531k data points and thus the cell-killing efficacy for all other possible combinations without testing each individual drug-dose combination. Two CRC PD3D® models with different mutation profiles were tested with 155 different combinations of 12 drugs at variant concentrations. The post-treatment cell viability was measured and used for Optim.AI™ analysis to evaluate and compare the best therapies. Optim.AI™ analysis revealed differential drug sensitivity between tested CRC PD3Ds®. The top-ranked drug combinations included SN-38, active compound of commonly used chemotherapeutic irinotecan, paired with MEK-inhibitors trametinib or cobimetinib which we could confirm with both platforms. With this study, we successfully demonstrated the feasibility, the robustness, and the efficiency of combining PD3Ds® and Optim.AI™ in identifying effective drug combination therapies, here for CRC, within one month. This combined technology provides precise insights into tumor treatability and its functional causes of treatment outcomes, leading to new treatment combinations and accelerating the development of new cancer drugs in a time- and cost-effective manner. Citation Format: Ulrike Pfohl, Masturah Mohd Abdul Rashid, Jhin Jieh Lim, Juergen Loskutov, Lena Wedeken, Edward Kai-Hua Chow, Hugo Saavedra, Christoph Reinhard, Christian R. Regenbrecht. Turning data into information: Using PD3D® models to guide colorectal cancer therapy by Optim.AITM [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 891.
Abstract 897: Synthetic lethality screening with Recursive Feature Machines
Authors: Adityanarayanan Radhakrishnan, Cathy Cai, Barbara A. Weir, Christopher Moy, Caroline Uhler
Journal: Cancer Research (2024)

Abstract Synthetic lethality refers to the concept that simultaneous perturbation of gene pairs leads to cell death but individual perturbation does not. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selective targeting of cancer cells based on underlying genetic vulnerabilities. The rise of large scale gene perturbation screens such as the Cancer Dependency Map (DepMap) offers the opportunity to identify SL pairs automatically using machine learning. Yet, a key difficulty in using machine learning models for this task is the lack of labeled data in the form of known SL pairs. Thus, prior approaches framed SL pair identification as a feature learning problem where the goal is to identify the genomic features most influential for predicting low cellular viability under a gene knockout. These prior approaches have primarily utilized random forests since these machine learning models are one of the only nonlinear models for which feature importances are provided explicitly. On the other hand, if we could identify features learned by state-of-the-art models on screening tasks such as kernel machines, we would be better powered in finding SL pairs. In this work, we present a computationally efficient and effective pipeline for SL pair screening built on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs). We show that our pipeline more accurately recovers experimentally verified SL pairs than prior work including the best model from the DepMap portal and previous random forest based approaches (see the table below). Moreover, our pipeline identifies several new candidate SL pairs for further analysis, opening new avenues for targeting genetic vulnerabilities in cancer. Rank of verified SL pairs across unsupervised methods (lower is better with a minimum value of 1). Experimentally Verified SL Pairs Pearson Correlation PARIS DepMap RFMs (Ours) SMARCA2/SMARCA4 1 1 1 1 ARID1A/ARID1B 1 1 1 1 STAG1/STAG2 1 1 1 1 CREBBP/EP300 1 1 1 1 VPS4A/VPS4B 7 &amp;gt; 10 5 1 DDX17/DDX5 4 1 1 1 ENO1/ENO2 &amp;gt; 10 1 1 1 SMARCC1/SMARCC2 &amp;gt; 10 1 1 1 UBB/UBC &amp;gt; 10 1 &amp;gt; 10 1 MAGOH/MAGOHB 1 1 1 1 FAM50A/FAM50B 1 1 1 1 Citation Format: Adityanarayanan Radhakrishnan, Cathy Cai, Barbara A. Weir, Christopher Moy, Caroline Uhler. Synthetic lethality screening with Recursive Feature Machines [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 897.
Abstract 570: The role of CD5L in chronic liver disease
Authors: Handan Hong, Taojian Tu, Lina He, Meisam Razaviyayn, Sze-Chuan Suan, Liyun Yuan, Bangyan L. Stiles
Journal: Cancer Research (2024)

Abstract Macrophage migration inhibitory factor (MIF) is a CD5 molecule like glycoprotein synthesized and secreted by macrophages, also termed CD5L. In liver cancer, serum levels of CD5L has been reported to be higher in steatohepatitis and hepatocellular carcinoma (HCC) patients and proposed to be a serum marker for these conditions. In the liver, steatosis and steatohepatitis is a major comorbid factor for HCC development and associated with poor response to therapy, particularly immune-checkpoint therapy. Here, we assessed the levels of CD5L in plasma samples from 50 advanced HCC patients obtained at baseline prior to initiation of systemic immunotherapy and compared them with that of healthy controls. The data demonstrated a 50% higher plasma CD5L levels in HCC patients vs. healthy donors. Furthermore, CD5L concentrations are higher in patients with higher Child Pugh score. In patients treated with immune checkpoint single or combination therapy, serum CD5L was found to correlate with clinical benefit. To address the role of CD5L and CD5L secreting macrophage in HCC development, we performed in-silico analysis of single cell RNA-seq data and identified Cd5l as a unique gene expressed on liver resident macrophages, the Kupffer cells (KCs). This finding was validated with additional datasets and using two steatosis mouse models representing ALD and NAFLD. Furthermore, our immunofluorescent staining and flow cytometry further confirmed the main source of Cd5l is KCs. In HFD fed mice, we showed that the expression and serum levels of CD5L became more significant with the severity of liver injury, particularly steatotic liver injury. We hypothesize that CD5L may be involved in establish the tumor microenvironment established by steatohepatitis, we performed a free fatty acid (FFA) uptake assay and found that CD5L increased the uptake of FFA in hepatic stellate cells. This ability of CD5L may have contributed to therapy resistance observed in the CD5L high patients who showed reduced clinical benefit. Citation Format: Handan Hong, Taojian Tu, Lina He, Meisam Razaviyayn, Sze-Chuan Suan, Liyun Yuan, Bangyan L. Stiles. The role of CD5L in chronic liver disease [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 570.
Abstract 548: Exploiting pronounced selection pressure from therapeutic bottlenecks to identify orthogonal immunotherapy targets
Authors: J. Nic Fisk, Jeffrey P. Townsend
Journal: Cancer Research (2024)

Abstract Evolutionary processes play a crucial role in the development of cancer, where tumor cells continuously adapt, leading to the dominance of cell populations over their non-tumor counterparts, evading treatment and ultimately resulting in death. Effective cancer therapies induce significant and targeted selection pressures. During and in response to the induced bottleneck by therapy, the heterogeneous cancer cell population either diminishes or evolves, causing a subsequent selective sweep and the emergence of cells with resistance traits. However, this bottleneck phase presents a unique opportunity to predictably reduce the diversity of tumor cells. In this context, we propose a framework to quantify the selection on variants before, during, and after the therapeutic bottleneck, enhancing our understanding of the mechanisms underlying the acquisition of therapeutic resistance. Furthermore, we combine measures of selection intensity with the prediction of neoantigens in a patient cohort to identify potential alternative targets for immunotherapy. The outcome is a strategy that exploits the predictability introduced by the primary therapeutic bottleneck to eliminate the remaining tumor cell population. Finally, we illustrate the efficacy of this strategy in the context of several cancer types and empirically identify potential cancer type specific and pan-cancer immunotherapy targets orthogonal to primary, first-line therapies. Citation Format: J. Nic Fisk, Jeffrey P. Townsend. Exploiting pronounced selection pressure from therapeutic bottlenecks to identify orthogonal immunotherapy targets [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 548.
Abstract 5499: Spatially-resolved transcriptomics analyses of liver cancer
Authors: Liang Wu
Journal: Cancer Research (2024)

Abstract Dissecting and understanding the cancer ecosystem, especially that around the tumor margins, which have strong implications for tumor cell infiltration and invasion, are essential for exploring the mechanisms of tumor metastasis and developing effective new treatments. Using a novel tumor border scanning and digitization model enabled by nanoscale resolution-SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we identified a 500 μm-wide zone centered around the tumor border in patients with liver cancer, referred to as “the invasive zone”. We detected strong immunosuppression, metabolic reprogramming, and severely damaged hepatocytes in this zone. We also identified a subpopulation of damaged hepatocytes with increased expression of serum amyloid A1 and A2 (referred to collectively as SAAs) located close to the border on the paratumor side. Overexpression of CXCL6 in adjacent malignant cells could induce activation of the JAK-STAT3 pathway in nearby hepatocytes, which subsequently caused SAAs’ overexpression in these hepatocytes. Furthermore, overexpression and secretion of SAAs by hepatocytes in the invasive zone could lead to the recruitment of macrophages and M2 polarization, further promoting local immunosuppression, potentially resulting in tumor progression. Clinical association analysis in additional five independent cohorts of patients with primary and secondary liver cancer (n = 423) showed that patients with overexpression of SAAs in the invasive zone had a worse prognosis. Further in vivo experiments using mouse liver tumor models in situ confirmed that the knockdown of genes encoding SAAs in hepatocytes decreased macrophage accumulation around the tumor border and delayed tumor growth. The identification and characterization of a novel invasive zone in human cancer patients not only add an important layer of understanding regarding the mechanisms of tumor invasion and metastasis, but may also pave the way for developing novel therapeutic strategies for advanced liver cancer and other solid tumors. Citation Format: Liang Wu. Spatially-resolved transcriptomics analyses of liver cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5499.