bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract 5215: Clinical features of Li-Fraumeni syndrome in Korea
Authors: Song Ran , Lee Seeyoun , Han Jai Hong , Woo Jae Yeon , Lee Min Jung , Kang Han-Sung , Park Sunhwa , Lee Eun-Gyeong , Kong Sun Young , Jung So-Youn
Published: June 2023

Purpose: Li-Fraumeni syndrome (LFS) is a rare autosomal dominant hereditary disorder caused by a germline mutation in the TP53 gene. Because of the rarity of the disease, there were limited data on the types of the mutation, clinical features and treatment outcomes. The aim of this study is to evaluate the clinical characteristics and prognosis in Korean patients with germline TP53 gene mutation.

Methods: Patients who underwent genetic counseling and confirmed with TP53 gene mutation in National Cancer Center in Korea between 2011 and 2022 were reviewed retrospectively. Data on family history with pedigree, types of mutation, clinical features and prognosis were collected.

Results: Fourteen patients with LFS were included in the study. Missense mutations were shown in 13 cases and nonsense mutation in 1 case. The repeated mutations were p.Arg273His (n=2), p.Ala138Val (n=2) and pPro190Leu (n=2). A sister with breast cancer had the same mutation of p.Ala138Val. The median age at diagnosis of first tumor in 14 LFS patients was 32 (1-67) years. Seven patients (50%) had multiple primary cancers. Breast cancer was most frequently observed (n=9) and other types of tumor included sarcoma (n=5), thyroid cancer (n=3), pancreatic cancer (n=2), ovarian cancer (n=1), endometrial cancer (n=1), colon cancer (n=1), brain tumor (n=1), adrenocortical carcinoma (n=1), vaginal cancer (n=1), skin cancer (n=1) and leukemia (n=1). The median follow-up period was 51.5 (6-188) months. There were two cases of local recurrence and four cases of distant metastasis during the periods. Two patients died from leukemia and pancreatic cancer at three months and 23 months after diagnosis, respectively.

Conclusion: As known in other countries, many Korean patients with LFS also had an early onset and multiple primary tumors. And patients showed various types of mutation, clinical features and prognostic outcomes. Further large-scale studies are required for proper screening and management in Korean patients with LFS. Grant: This study was supported by National cancer center, Korea, Grant no. 2110181

Citation Format: Ran Song, Seeyoun Lee, Jai Hong Han, Jae Yeon Woo, Min Jung Lee, Han-Sung Kang, Sunhwa Park, Eun-Gyeong Lee, Sun Young Kong, So-Youn Jung. Clinical features of Li-Fraumeni syndrome in Korea. [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 5215.
Abstract 5261: Prostate specific sustained anti androgen delivery to delay early stage prostate cancer progression
Authors: Desai Pujan , Rairkar Maithili , Pawlowska Nela , Thomas Scott , Munster Pamela N.
Published: June 2023

Prostate cancer is the most common cancer in men. Commensurate with risk, current approaches to localized prostate cancer include prostatectomy, prostate radiation with or without radiation therapy or active surveillance. Recent data suggest that systemic anti-androgen therapy with enzalutamide reduced the risk of prostate cancer progression in men on active surveillance but was associated with considerable side effects. We developed a novel polymer-based drug delivery method to provide sustained localized drug administration of anti-androgens selectively to the prostate. In vitro and in vivo mouse and canine studies show that this drug eluting implant delivers sustained high prostate to plasma therapeutic anti-androgen levels for an anticipated range of 2 years while preventing accumulation of anti-androgens in the liver, lungs, heart and brain. We performed a proof of concept study in a canine prostate model implanting an 15mm x 1mm bicalutamide eluting implant into each lobe of the prostate. Bicalutamide levels in the prostate measured near the implant at 6 months were 3250 ± 1622 ng/g, n = 3 compared to 3809 ± 638 ng/g, n = 3 to those achieved at steady-state oral administration. Plasma and other off target levels were over 1000-fold lower in the implant cohort compared to oral administration: Plasma: 5.9 ± 0.2 ng/ml, liver: 11.3 ±4.3 ng/g, and brain: 5.4 ±0.8 ng/g versus plasma: 18170 ± 3820 ng/ml, liver: 35407 ±8510 ng/g, and brain: 8870 ±1797 ng/g. The implants were well tolerated without local toxicity and imaged easily with ultrasound. The design characteristics of the implant facilitates minimally invasive surgical implantation under standard image guidance and withstands the concomitant use of prostate radiation. This novel technology warrants future clinical exploration of prostate specific anti-androgen therapy for (neo)adjuvant therapy or in active surveillance.

Tissue Bicalutamide oral (2m)(ng/g) Bicalutamide implant (6m)(ng/g) Plasma 18,170 (3820) 5.9 (0.2) Prostate 3,809 (638) 3,250 (1622) Liver 35,407 (8510) 11.3 (4.3) Cerebrum 8,870 (1797) 5.4 (0.8) Cerebellum 8,737 (1550) 6.2 (1.3) Spleen 7,727 (1288) 4.2 (0.9) Lung 27,013 (2896) 11.3 (4.3) Heart 11,737 (3491) 10.0 (1.9) Kidneys 17,413 (6581) 8.5 (2.0)

Citation Format: Pujan Desai, Maithili Rairkar, Nela Pawlowska, Scott Thomas, Pamela N. Munster. Prostate specific sustained anti androgen delivery to delay early stage prostate cancer progression. [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 5261.
Abstract 5283: Glucocorticoid receptor (GR) activity alters invasive lobular breast cancer (ILC) oncogenic properties
Authors: Durdana Ishrat , Frerich Candace , Laine Muriel , Rindhe Smita , Bennett Lynda , Bachoo Robert , Greene Geoffrey , Conzen Suzanne
Published: June 2023

Invasive breast cancer (BC) is commonly divided into two subtypes, invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC). ILC makes up 10-15% of all BC cases. Expression of nuclear hormone receptors further define clinical histology and directs treatment; receptors include estrogen (ER), progesterone (PR), androgen (AR), and glucocorticoid receptors (GR). Tumor GR expression activation is associated with good prognosis in ER+ but poor prognosis in ER- BC. The majority of ILC tumors are ER+, and we have found that GR expression in primary ILC tumors correlates with improved overall survival. We hypothesized that GR activity in ILC may play a role in ILC’s indolent natural history and could alter its metastatic tendency. We used two isogenic ILC cell lines, each with and without GR expression (MM134-GR+/GR- and SUM44PE-GR+/GR-) to study GR-mediated ILC associated phenotypes. Specifically, we describe GR’s effect on proliferation in vitro and tumor formation in the in vivo MIND model. We also measured GR-mediated ILC adherence to major components of the extra cellular matrix (collagen I, collagen II, laminin, fibronectin, vitronectin, and tenascin). To recapitulate early metastasis, we examined ILC cell movement through a 10 uM microchannel device. We found that GR expression and activation in ILC cell lines cause decreased cell proliferation and decreased adherence to laminin. Preliminary experiments suggest that GR activation altered ILC cell’s ability to enter and navigate through restrictive 10 uM microchannels. Given our preliminary findings, we will investigate the effect of selective GR modulators (SGRMs) on ILC phenotypes and gene expression. We hypothesize that the gene expression changes following GR activation will be different in ILC compared to IDC. In addition, we are examining the genes required for survival of ILC cells on collagen using an in vitro CRISPR-Cas9 screen. In vivo experiments examining the association of GR expression with tumor growth in vivo are ongoing.

Citation Format: Ishrat Durdana, Candace Frerich, Muriel Laine, Smita Rindhe, Lynda Bennett, Robert Bachoo, Geoffrey Greene, Suzanne Conzen. Glucocorticoid receptor (GR) activity alters invasive lobular breast cancer (ILC) oncogenic properties. [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 5283.
Abstract 5287: Is testosterone the male sex hormone responsible for increased male mortality in melanoma? In-vitro studies based on human melanoma (BLM) cell model
Authors: Ramaraj Pandurangan
Published: June 2023

Clinical studies showed that menstruating females were better protected in melanoma than post-menopausal women and men of any age. But, these studies did not correlate with the steroid status of females. Our in-vitro study with progesterone (P), a female sex hormone showed significant inhibition of human melanoma cell growth. A literature survey showed that progesterone levels ranged between 1000-1500 ng/dl in menstruating females compared to post-menopausal women (20-100 ng/dl) and men (27-90 ng/dl), suggesting increased progesterone levels could offer protection to the menstruating females. Moreover, Elisarray showed that progesterone action was mediated by specific suppression of proinflammatory cytokine IL-8. So, it was proposed that a low level of progesterone in males leading to non-suppression of IL-8 in melanoma, could be responsible for increased melanoma cell growth and male mortality. This hypothesis prompted us to check the effect of male sex hormones androstenedione (AD) and testosterone (T) on melanoma cell growth and IL-8 secretion. Both AD and T decreased IL-8 secretion along with a decrease in cell growth. However, when progesterone was added along with androgens, there was a significant decrease in cell growth and in IL-8 secretion. This raised the question of why male mortality was high when androgens also decreased cell growth and IL-8 secretion. In order to address this question, it was decided to induce endogenous IL-8 in melanoma cells by pre-incubating with endothelin (50ng/ml) and then add steroids. AD and P individually decreased IL-8 secretion and in combination significantly decreased IL-8 secretion and cell growth even in endothelin pretreated cells when compared to their respective control cells. But, T was not able to suppress endogenous IL-8 induced by endothelin and in fact, cell growth also was slightly increased compared to straight T-treated cells.

Conclusion: So, in males 2 reasons 1) a deficiency of progesterone and 2) inefficiency of T to suppress endogenous IL-8 could possibly result in increased IL-8 levels. This increased IL-8 level in males could lead to increased cell growth and metastasis in melanoma (IL-8 had already been shown to stimulate melanoma cell growth and metastasis by us and others) leading to death. So, a situation arising out of male reproductive endocrine physiology could probably be responsible for increased male mortality in melanoma.

Citation Format: Pandurangan Ramaraj. Is testosterone the male sex hormone responsible for increased male mortality in melanoma? In-vitro studies based on human melanoma (BLM) cell model. [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 5287.
Abstract 5352: Targeting DNA methylation in T cells to improve the efficacy of immunotherapy
Authors: Dang Pengtao , Wang Xiao , Zhu Haiqi , Wang Jia , Guo Tingbo , Zhou Xinyu , Swaminathan Paveethran , Zhang Chi , Cao Sha
Published: June 2023

T-cells are critical mediators of immunity and immunologic memory. Their cell fates are regulated in part through epigenetic mechanisms, including DNA methylation. Recent genome-wide methylation analyses have revealed dynamic alterations in the methylome at various stages of development and differentiation of T cells. At single cell level, it is not easy to simultaneously collect RNA-seq and RBBS methylation profiling. An important task is to understand the expression change of which genes and pathways are regulated by DNA methylations, especially for the ones that are associated with functional variations in the T cells from tumor microenvironment.In this study, we developed a computational approach based on our recently developed metabolic flux estimation to estimate cell-wise global DNA methylation activity level by using scRNA-seq data. We also hypothesize that the global DNA methylation activity level in one cell determines most of the DNA methylation level in gene-specific DNA methylations. Hence, the dependency between gene-specific DNA methylation and expression could be imputed by the dependency between predicted global DNA methylation input level and the gene expression. We validated our method to impute cell-wise global DNA methylation level by using four independent sets of paired gene expression and DNA methylation data. Noted, our prediction of global DNA methylation activity is from a pure metabolic perspective. We found that two metabolic reaction rates, named metabolic flux from methionine to SAM and SAM to SAH, purely predicted by using gene expression data can accurately impute DNA methylation activity in all validating data sets. Our method enables further identification of the disease/cell context specific contributor of DNA methylation, i.e., the genes high contribution to DNA methylations in each individual cell or cell groups. We applied our method on scRNA-seq data of different T cell types extracted from TME of lung, liver, and colon cancer. We have seen that exhausted T cells, especially the ones with decreased Granzymes and PRF1 are associated with increased global DNA methylation level and related genes, suggesting the potential clinical implications in targeting DNA methylation to improve the efficacy of immunotherapies.

Citation Format: Pengtao Dang, Xiao Wang, Haiqi Zhu, Jia Wang, Tingbo Guo, Xinyu Zhou, Paveethran Swaminathan, Chi Zhang, Sha Cao. Targeting DNA methylation in T cells to improve the efficacy of 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 5352.
Abstract 5363: MicroArray-based MethylationToActivity: Advancing biological knowledges from DNA methylation profiles of patient tumors
Authors: Jones Karissa Dieseldorff , Quarni Waise , Putnam Daniel , Singh Shivendra , Wu Qiong , Yang Jun , Chen Xiang
Published: June 2023

DNA methylation (DNAm) is a relatively stable regulatory epigenetic mechanism. Although accurate DNAm classifiers have been built for early detection of cancer and subtype classifications, its transcriptional regulatory roles remain unclear. To address this challenge, we developed a deep-learning framework MethylationToActivity (M2A) that accurately predicts individual promoter activity from WGBS methylomes. As array-based methylome is the most common epigenetic data from patients’ tumor samples, we redesigned M2A’s raw features and model topology for array methylomes, which shows an accurate prediction of the promoter activity (R2 = 0.74), approaching its WGBS counterpart (R2 = 0.79). Transfer learning improves the prediction accuracy to 0.77. In a primary rhabdomyosarcoma cohort, M2A prioritized candidate genes with strong subtype-specific alternative promoter usage (APU) and identified distinct isoforms of a subgroup of APU genes including NAV2 expressed in the two major subtypes (aRMS and eRMS). Molecular experiments revealed that PAX3-FOXO1 directly binds to the aRMS-specific promoter and is required for its active transcription. Genetic deletion of PAX3-FOXO1 leads to NAV2 APU. Overexpression of aRMS-specific NAV2 isoform significantly promoted cell proliferation, supporting its oncogenic function. These data indicate that PAX3-FOX1 is critical for APU in aRMS. We conclude that the new M2A provides vast insight into downstream functional interpretation of differential DNAm patterns.

Citation Format: Karissa Dieseldorff Jones, Waise Quarni, Daniel Putnam, Shivendra Singh, Qiong Wu, Jun Yang, Xiang Chen. MicroArray-based MethylationToActivity: Advancing biological knowledges from DNA methylation profiles of patient tumors. [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 5363.
Abstract 537: Validating USP27X as a drug target in lung cancer
Authors: Alam Shamshad , Zunic Amanda , Venkat Swati , Feigin Michael E , Atanassov Boyko
Published: June 2023

Over the past two decades, tremendous efforts have been dedicated to developing and improving receptor tyrosine kinase (RTK) targeted therapies to combat the development and progression of different cancers. Despite the significant progress made, these therapies are not durable: not all patients respond to treatment, and primary or acquired resistances are common. Interestingly, however, while RTK-targeted therapies have no effect on resistant lung cancer cell lines, si/shRNA-mediated RTK depletion or combined treatments that affect RTK protein levels profoundly impact the growth and survival of these cells, indicating that RTKs are still critical for their proliferation. Thus, strategies to target RTK protein levels hold promise as an option for treating RTK-driven cancers. In our recent studies, we discovered that ubiquitin-specific protease 27X (USP27X) functions are crucial for properly recycling and maintaining EGFR steady-state levels in lung cancer cells. Ablation of USP27X leads to a severe decrease of EGFR steady levels, inhibition of xenograft tumor growth, and sensitization of lung cancer cell lines to targeted therapy. To fully understand the in vivo functions of USP27X in EGFR regulation and lung carcinogenesis, we created a new mouse line carrying a conditional Usp27X allele and introduced this allele into the well-established and clinically relevant EGFRL858R mouse lung cancer model. Ongoing studies will determine whether USP27X can be a critical therapeutic in EGFR-driven lung cancer.

Citation Format: Shamshad Alam, Amanda Zunic, Swati Venkat, Michael E Feigin, Boyko Atanassov. Validating USP27X as a drug target in lung 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 537.
Abstract 5372: Supervised manifold learning and classification; application to expression data visualization and cancer prediction
Authors: Webber James , Elias Kevin
Published: June 2023

Background: Manifold Learning (ML) has become essential in recent years for expression data visualization and dimension reduction. ML operates under the assumption that the data points lie on a manifold embedded in Euclidean space. The goal of ML is to learn an inverse embedding function to decipher the manifold structure. Cancer classification is a widely investigated application of expression profile data (e.g., miRNA) currently in the literature. The non-linear ML techniques commonly applied to expression data, such as t-distributed Stochastic Neighborhood Embedding (TSNE), Isomap, and Locally Linear Embedding (LLE), are intended for visualization, and cannot easily be applied to classification or unseen data, more generally. Such methods are also unsupervised, and do not factor in class information. Conventional linear ML methods, such as Principal Component Analysis (PCA), while applicable to unseen data and easy to use, are limited and can learn only linear manifolds.

Results: We introduce a novel, non-linear ML technique, which incorporates class memberships, and is readily applicable to unseen data and classification. Specifically, we construct a series of neighborhood graphs which describe the manifold structure locally within each cluster and among the cluster centroids. Our objective function uses the neighborhood graph information to preserve the class separations and manifold structure in reduced dimension space, and we train a neural network to learn an explicit, inverse embedding function. This allows for fast visualization and classification of unseen data. The technique is compared against similar methods from the literature which use neural networks for ML, and is shown to offer improved cancer classification performance on expression data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA).

Conclusion: We present a new, supervised ML technique designed specifically for classification, which can be efficiently applied to unseen data. The results show promise on multiple, large-scale expression data sets, and thus warrant further research into supervised ML methods for cancer classification and expression data visualization.

Citation Format: James Webber, Kevin Elias. Supervised manifold learning and classification; application to expression data visualization and cancer prediction. [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 5372.
Abstract 5379: Deep learning enables label-free tracking of heterogeneous subpopulations
Authors: Jost Tyler , Gardner Andrea , Brock Amy
Published: June 2023

Observing and quantifying the proliferation of subpopulations in cancer is key to understanding how heterogeneous groups of cells interact and respond to therapy, their environment, and each other. Prior research has demonstrated that cell-state properties such as metastatic potential and genotype perturbation are encoded in cellular morphology and can be identified with various machine-learning approaches. This encoding spans multiple imaging modalities such as brightfield, phase contrast, and stained whole-slide images, and phenotype prediction can be accomplished using both classical and deep machine learning methods. Here we show that not only do these prediction capabilities extend to transcriptomic subpopulations, but that they can be used to track these populations in high throughput longitudinal live-cell imaging experiments. Using single-cell RNA sequencing, we observed that, among untreated MDA-MB-231 triple negative breast cancer cells, there exist two transcriptomically distinct populations of cells. Using fluorescence-activated cell sorting based on the differentially expressed surface marker ESAM, we isolated these subpopulations and fluorescently labeled them with mCherry or GFP depending on their transcriptomic cluster. Cells were then grown in monoculture or coculture and imaged every 4 hours at 20x resolution. To identify the associated transcriptomic cluster, we trained an instance segmentation algorithm, Mask R-CNN, to both segment and classify cells. We find that, despite being derived from the same cell line, these phenotypes can be predicted using phase contrast images alone. These results demonstrate that cellular phenotypes manifested by distinctive RNA expression signatures can now be surveilled in a high-throughput manner across multiple samples and conditions without further RNA sequencing or biomarker labeling. We anticipate that this methodology of high throughput tracking will be applicable to other heterogeneous subpopulations, such as isolated therapy-resistant or sensitive cells. Tracking transcriptomically distinct populations using only high-throughput imaging will increase the granularity of population analysis and enable more rapid assessment of cancer cell evolutionary dynamics.

Citation Format: Tyler Jost, Andrea Gardner, Amy Brock. Deep learning enables label-free tracking of heterogeneous subpopulations. [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 5379.
Abstract 5395: Improving survival prediction using flexible late fusion machine learning framework for multi-omics data integration
Authors: Nikolaou Nikos , Salazar Domingo , RaviPrakash Harish , Goncalves Miguel , Argoty Gustavo Alonso Arango , Burlutsky Nikolay , Markuzon Natasha , Jacob Etai
Published: June 2023

Improving cancer patients Overall Survival (OS) prognosis is critical for personalization of treatment using model-identified drivers of cancer progression. Current cancer prognosis models largely rely on clinical and demographic patient characteristics. Adding ‘omics’-based modalities can help improve patient OS prediction and lead to better disease categorization and understanding. We introduce a data driven methodology for combining multi-omics and clinical data, including clinical/demographics, mutations, gene expression, long non-coding and micro-RNA expression, DNA methylation, and proteomics for improving prediction of OS in cancer patients. High dimensionality of ‘omics’ modalities present challenges to combining them into one model. We propose a late stage modalities fusion where we construct a separate data driven model for OS prediction for each modality, later combining individual predictions in a final linear OS prediction model. With a limited number of patients to develop the model, such an approach helps to better protect against overfitting, and allows to account for different degrees of informativeness of modalities by weighting them according to individual success. We introduce a robust machine learning pipeline with rigorous training, testing and evaluation capabilities, and demonstrate its effectiveness on a suit of TCGA data. When comparing early vs. late fusion of omics and clinical modalities for survival prediction using NSCLC TCGA data, we observe the C-index improvement from 0.57±.04 to 0.61±.01. Best individual modality performance was at 0.59±.02 using clinical modality. Dominant modalities in unimodal survival analysis varied between cancers, with clinical, RNA, and miRNA for LUAD, and clinical, RNA, and RPPA for LUSC. Using pan-cancer TCGA data for survival prediction, the best C-index = 0.77 was achieved using multi-omics model, followed by 0.76±.01 for clinical, 0.75±.01 for RNA seq, and 0.73±.01 for RPPA unimodal models.

Citation Format: Nikos Nikolaou, Domingo Salazar, Harish RaviPrakash, Miguel Goncalves, Gustavo Alonso Arango Argoty, Nikolay Burlutsky, Natasha Markuzon, Etai Jacob. Improving survival prediction using flexible late fusion machine learning framework for multi-omics data integration. [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 5395.
Abstract 5396: Early detection of ovarian cancer using mRNA sample data to investigate the correlation of specific gene mutations with ovarian cancer
Authors: Saghaie Zahra , Richardson Christine , Mostafavi M. Taghi
Published: June 2023

Introduction: Ovarian cancer (OC) is the second most common gynecologic cancer in the U.S, and is the deadliest cancer of the female reproductive system. Because of the complex nature of OC, diagnoses are often by display of symptoms at late stages of disease. Identifying OC at early stages can increase the 5-year survival rate from 40% in stage IV to 94% in stage I. Therefore, effective screening and detection of OC at early stages is imperative. The main objective of this study is to develop a diagnostic panel of markers that correlate with early-stage OC, and ultimately, improve the survival rate of patients.

Background: Approaches in the past several years to detect malignant OC have focused on processing relevant images or screening genomic data. These studies use liquid-based biopsies, sonographic images, cyst images, CT images, MR imaging and genomics data. Some studies have focused on distinguishing malignant tumors versus benign tumor cells. In addition to statistical methods to examine available genomic datasets, Machine Learning models including K-Nearest Neighbor, K-means, Convolutional Neural Networks, Deep Learning, Support Vector Machine, Random Forest, and Fuzzy algorithms have been used.

Methods: In this study we proposed a method to determine a possible correlation between some actively expressed genes and OC, using publicly available mRNA data sets. The samples were retrieved from the GSE106817 dataset via the NCBI GEO site, with 333 cancerous and 2759 Non-cancerous samples. The objective is to define a set of genes which is able to illustrate prediction of correlation through the use of PCA, NMF and SVD. We further verified our findings by testing on one additional different mRNA datasets. For this, we compared several machine learning algorithms to potentially correlate active genes and OC stages, from the available data.

Results and Discussions: Through our study we have developed Machine Learning models valuable to identify patterns of disease at different stages, thus, potentially be useful in developing predictive models. Our initial experimental results have been demonstrated with a set of mRNA signatures, which may be used to create a panel of biomarkers valuable for detecting early-stage OC. Our results confirm the correlation between OC and PAX8, PEG3, and BIRC5, and MYB and RAC2. Continuation of this study highlights methods towards the early detection of OC and to improve disease identification outcome.

Citation Format: Zahra Saghaie, Christine Richardson, M. Taghi Mostafavi. Early detection of ovarian cancer using mRNA sample data to investigate the correlation of specific gene mutations with ovarian 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 5396.
Abstract 5397: Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis
Authors: Yoon Hyunseok , Kim Kyu-Pyo , Park Inkeun , Jeong Jae Ho , Chang Heung-Moon , Ryoo Baek-Yeol , Yoo Changhoon
Published: June 2023

Background: Neoadjuvant modified (m) FOLFIRINOX is a standard therapy for medically fit patients with borderline resectable pancreatic cancer (BRPC) and locally advanced unresectable pancreatic cancer (LAPC). Completion of conversion surgery following mFOLFIRINOX is an important prognostic factor. In this study, we evaluated multiple machine learning models to predict completion of conversion surgery following neoadjuvant mFOLFIRINOX in BRPC and LAPC.

Methods: Between January 2017 and December 2020, a total of 647 patients with BRPC and LAPC treated with neoadjuvant mFOLFIRINOX at Asan Medical Center, Seoul, South Korea were enrolled. All demographic and clinicopathological data were retrospectively collected; age, ECOG performance status, BMI, resectability as per the NCCN guidelines, tumor location, tumor size, maximum standardized uptake value assessed by 18F-FDG PET/CT, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and serum CA 19-9. Several machine learning models, such as logistic regression and random forest classifier, were trained to predict curative-intent resection and were then internally validated. The relative importance of each variable was analyzed using both Gini importance and permutation importance.

Results: Among 647 patients, 173 (26.7%) underwent curative-intent conversion surgery (R0 or R1) following mFOLFIRINOX. The patients who underwent surgery showed significantly better overall survival (median 42.1 vs 17.2 months, P<0.0001) than those who did not. In multivariable analysis using logistic regression, age, resectability as per the NCCN guidelines, tumor size, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and CA 19-9 change rate (percentage change in CA 19-9 after mFOLFIRINOX) were significantly correlated to tumor removal with curative-intent conversion surgery. Random forest classifier showed the best predictive capability with AUC at 0.832. The most important feature was the CA 19-9 change rate.

Conclusions: In this large cohort-based analysis, completion of conversion surgery could be predicted successfully in BRPC and LAPC patients treated with neoadjuvant mFOLFIRINOX using machine learning models. Further external validation is necessary for generalization.

Citation Format: Hyunseok Yoon, Kyu-Pyo Kim, Inkeun Park, Jae Ho Jeong, Heung-Moon Chang, Baek-Yeol Ryoo, Changhoon Yoo. Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis. [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 5397.
Abstract 5464: Predictive biomarkers for PD-1 and PD-L1 checkpoint inhibitor response in NSCLC: An analysis of clinical trial and real-world data
Authors: So WeiQing Venus , Dejardin David , Rossmann Eva , Charo Jehad
Published: June 2023

Importance: Largest analysis combining clinical trial and real-world data (RWD) to assess the clinical utility of biomarkers for anti-PD-1/PD-L1 checkpoint inhibitors (CPI) in NSCLC patients.

Objective: To assess the association between tumor PD-L1, TMB and blood analytes with clinical outcomes in patients with advanced NSCLC following CPI therapy.

Design: Retrospective cohort study with patients stratified into high and low biomarker groups. Correlation with treatment outcome in the different biomarker groups was investigated and compared between patients treated with CPI versus chemotherapy.

Setting: Data derived from electronic health records mostly from community oncology settings were combined with clinical trial data.

Participants: Starting with 71850 patients with advanced NSCLC who received CPI or chemotherapy, we selected 24152 patients from an electronic health record-derived de-identified NSCLC clinicogenomic database (CGDB) from a real-world cohort and nine Roche atezolizumab trials. Patients were diagnosed between January 2011 and February 2020 (data collection cut-off date).

Main Outcomes and Measures: Drug response was assessed by RECIST criteria for clinical trials and real-world response for RWD. Durable response was defined as having CR/PR without progression during the study period of 270 days. Associations of biomarker levels with treatment outcomes were analyzed using Fisher’s exact test.

Results: High expression of PD-L1 on tumors (≥50%) were associated with lower risk of resistance to CPI (odds ratio [OR] 0.21; 95% CI = 0.14, 0.32; P< .001). The association was stronger in patients who had prior chemotherapy than first line CPI, with non-squamous than squamous histology, and smokers than non-smokers. Higher TMB (≥9.57 mut/Mb) was also associated with durable response (OR = 0.41; CI = 0.30, 0.55; P< .001). The combination of high TMB and PD-L1 expression was the strongest predictor of durable response (OR = 0.04; CI = 0.00, 0.20; P< .001). There was no evidence of an association between PD-L1/TMB and response to chemotherapy, suggesting a CPI-specific predictive effect. In contrast, blood analytes were prognostic, having correlation with clinical outcome irrespective of treatment type.

Conclusion: This study supports the predictive utility of PD-L1 expression and TMB for NSCLC patients receiving CPI therapy and further elucidates their utility in patient sub-populations.

Citation Format: WeiQing Venus So, David Dejardin, Eva Rossmann, Jehad Charo. Predictive biomarkers for PD-1 and PD-L1 checkpoint inhibitor response in NSCLC: An analysis of clinical trial and real-world data. [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 5464.
Abstract 5471: Integrating tumor-intrinsic and immunological factors to identify immunogenic breast cancers from a low-risk cohort; results from the randomized SweBCG91RT trial
Authors: Tullberg Axel Stenmark , Sjöström Martin , Niméus Emma , Killander Fredrik , Chang Laura , Feng Felix Y. , Speers Corey W. , Pierce Lori J. , Kovács Anokó , Lundstedt Dan , Holmberg Erik , Karlsson Per
Published: June 2023

Purpose: The local immune infiltrate’s influence on tumor progression may be linked to tumor-intrinsic factors. This study aimed to investigate whether integrating immunological and tumor-intrinsic factors could identify patients who may be candidates for de-escalation of radiotherapy (RT).Experimental

Design: The practice-changing SweBCG91RT trial included 1178 patients with stage I-IIA breast cancer, randomized to breast-conserving surgery with or without adjuvant RT with sparse use of systemic therapy (95% without systemic therapy) and followed for a median time of 15.2 years. We developed two gene expression models to capture immunological activity and immunomodulatory tumor-intrinsic qualities, respectively. We then analyzed if combining these two variables could identify a subgroup where RT de-escalation is feasible.

Results: The immunomodulatory model correlated with proliferation and was named Proliferative Index. The immunological model was enriched for T cell signatures and was named Immunescore. Increased Proliferative Index predicted an increased prognostic benefit from Immunescore (pinteraction=0.01). Combining the two models stratified patients who may be recommended an escalated RT (RT boost) by RT benefit and prognosis. Patients with a low predicted risk benefited from standard RT (HR: 0.28, CI 95% 0.09-0.85, p=0.025) and had a 5.4% 10-year incidence of ipsilateral breast tumor recurrence (IBTR) after radiation. In contrast, the group with a high predicted risk, characterized by rapidly proliferating tumors without an immune infiltrate, had a clinically meaningful high 10-year incidence of IBTR despite RT treatment (19.5% (CI 95% 12.2-30.3)).

Conclusions: Integrating tumor-intrinsic and immunological factors may identify immunogenic tumors in early-stage breast cancer populations and identify patients for whom de-escalated RT may be appropriate. Patients with rapidly proliferating tumors and an immune infiltrate may be candidates for RT de-escalation.

Citation Format: Axel Stenmark Tullberg, Martin Sjöström, Emma Niméus, Fredrik Killander, Laura Chang, Felix Y. Feng, Corey W. Speers, Lori J. Pierce, Anokó Kovács, Dan Lundstedt, Erik Holmberg, Per Karlsson. Integrating tumor-intrinsic and immunological factors to identify immunogenic breast cancers from a low-risk cohort; results from the randomized SweBCG91RT trial. [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 5471.
Abstract 5489: Synergistic activity of SCD1 blockade in combination with tyrosine kinase inhibitors lenvatinib and cabozantinib in hepatocellular carcinoma (HCC)
Authors: Gleba Justyna J. , Alasonyalilar-Demirer Aylin , Pawlush Matthew L. , Bilgili Ahmet , Hickman Peyton G. , Mody Kabir , Roberts Lewis R. , Alberts Steven R. , Truty Mark J. , Patel Tushar C. , Tun Han W. , Copland John A.
Published: June 2023

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Tyrosine kinase inhibitors (TKIs) are approved as a first-line treatment for unresectable HCC. Lenvatinib and cabozantinib are two of the most used TKIs, but the therapeutic duration is limited due to the development of drug resistance. Therefore, understanding the mechanisms of resistance and combining TKIs with other drugs antagonizing resistance should lead to antitumor synergy, eliminating drug resistance. It turns out that the simultaneous use of TKIs together with an inhibitor of stearoyl-CoA desaturase 1 (SCD1) prevents the development of drug resistance, leading to a durable response. SCD1 is the enzyme responsible for de novo fatty acid (FAs) synthesis, converting saturated fatty acids (SFAs) into monounsaturated fatty acids (MUFAs). MUFAs are an alternative energy source to glucose, integral to cellular membranes, and prevent endoplasmic reticulum (ER) stress and other signaling pathways. In our laboratory, we developed a novel, highly specific SCD1 inhibitor - SSI-4. We have tested the biological activity of SSI-4 against different HCC cell lines and patient-derived xenografts (PDX) mouse models. Of the twelve tested HCC cell lines, four were highly sensitive to SSI-4 (IC50 1-50 nM). Other cell lines showed moderate or no sensitivity to SSI-4. We tested the concomitant use of SSI-4 with lenvatinib and cabozantinib, tyrosine kinase inhibitors (TKIs) FDA-approved for HCC, in HCC cell lines in vitro and using HCC PDX in vivo mouse models. Our studies showed that the combination of the SCD1 inhibitor with both lenvatinib and cabozantinib showed a highly synergistic effect and no development of drug resistance i.e. durable response versus single TKI therapy. Ongoing mechanistic studies are examining whether known molecular targets of tested TKI’s such as VEGFR1, 2, and 3, PDGFRα, FGFR, KIT, and RET dominate in HCC drug resistance to lenvatinib and cabozantinib, and how the use of SSI-4 overcomes the phenomenon of resistance.

Citation Format: Justyna J. Gleba, Aylin Alasonyalilar-Demirer, Matthew L. Pawlush, Ahmet Bilgili, Peyton G. Hickman, Kabir Mody, Lewis R. Roberts, Steven R. Alberts, Mark J. Truty, Tushar C. Patel, Han W. Tun, John A. Copland. Synergistic activity of SCD1 blockade in combination with tyrosine kinase inhibitors lenvatinib and cabozantinib in hepatocellular carcinoma (HCC). [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 5489.
Abstract 555: Gene expression profiling in recurrence of oral cancer involved in tumor microenvironment change and cancer stem cells
Authors: Kioi Mitomu
Published: June 2023

Background: Local recurrence or lymph node metastasis is one of the critical events for the mortality of patients with oral cancer. Thus, it is essential to elucidate the mechanism of recurrence of oral cancer after treatment. It is recently thought that the cause of recurrence in advanced oral cancer is involvement of microenvironmental changes after treatment and signaling from cancer stem cells. In this study, we aimed to elucidate the molecular mechanism of cancer stem cell signaling that causes microenvironmental changes and the influx of bone marrow-derived cells that lead to revascularization following radiation.

Materials and Methods: In order to analyze the function of cancer stem cells, we used YCUOC, a primary tissue obtained from biopsy sample of oral cancer. YCUOC and OSC-19 cell line as a control were inoculated at pre-irradiated site where local blood vessels were eradicated (recurrence model) and non-irradiated sites, and gene expression analysis in cancer tissues was performed.

Results: Induction of CD11b-positive bone marrow-derived cells was observed in YCUOC cells, and the higher influx of Tie2-positive cells was observed at the pre-IR site, consistent with activation of HIF-1α. We identified several genes that were up-regulated only in YCUOC at pre-IR site, but neither OSC-19 nor non-IR site.

Conclusion: The development of new therapies targeting tumor vasculogenesis is expected by elucidation of cancer stem cell-specific signals.

Citation Format: Mitomu Kioi. Gene expression profiling in recurrence of oral cancer involved in tumor microenvironment change and cancer stem cells [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 555.
Abstract 5556: Lemur tyrosine kinase 3 serves as a predictor of patient outcome and a target for the treatment of ovarian cancer
Authors: Kirsch-Mangu Thea K. , Tullberg Axel S. , Portela Anna , Helou Khalil , Saed Ghassan M.
Published: June 2023

The objective of these in vitro and in vivo studies was to validate Lemur Tyrosine Kinase 3 (LMTK3) as a specific target and predictor of clinical outcome in ovarian cancer. LMTK3 belongs to a family of regulated tyrosine kinases with three structurally related isoforms, LMTK1, LMTK2, and LMTK3. Both nuclear and cytoplasmic LMTK3 expressions correlated with tumor grade and patient survival in cancers such as breast and colorectal cancer. We tested the clinical significance of the LMTK3 gene by immunohistochemistry (IHC) using LMTK3 monoclonal antibody on formalin-fixed paraffin-embedded sections (FFPE) collected from 204 early-stage (stage I-II) ovarian cancer patients. Results from this IHC LMTK3 study revealed a higher cytoplasmic to nuclear localization of LMTK3 correlated with worse overall survival (P<0.01). Further investigation of LMTK3’s prognostic value by screening LMTK3 signaling in 270 stage III-IV ovarian cancer patients is ongoing. We utilized the MTT ((3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay for testing the killing efficacy of targeting LMTK3 by a monoclonal antibody, siRNA, and specific LMTK3 binding peptides (LMTK3BP) in ovarian cancer cell lines SKOV3, MDAH-2774, A2780, and TOV-21G. Treatment with LMTK3 specific monoclonal antibody, siRNA, and LMTK3BP significantly induced killing of both chemosensitive and chemoresistant ovarian cancer cells without affecting normal cells in vitro. Moreover, we observed this killing as synergistic with both Cisplatin and Taxotere treatment in vitro. Lastly, we used an A2780 cell line derived orthotopic xenograft mouse model of ovarian cancer to test the efficacy of specific LMTK3BP in vivo. Strikingly, LMTK3BP 20 mg/kg IV dose given three times a week for three weeks showed a 35% tumor reduction. Furthermore, in vivo safety studies showed no signs of toxicity of the LMTK3BP, even at a very high dose of 40 mg/kg. Thus, with dose optimization in ongoing experiments, we could expect a higher efficacy. In conclusion, this study highlighted the importance of LMTK3 as a predictor of patient clinical outcome and potential target for treatment in ovarian cancer.

Citation Format: Thea K. Kirsch-Mangu, Axel S. Tullberg, Anna Portela, Khalil Helou, Ghassan M. Saed. Lemur tyrosine kinase 3 serves as a predictor of patient outcome and a target for the treatment of ovarian 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 5556.