Tumor Drug Response Research Articles

Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine.

The field of oncology has witnessed remarkable progress in personalized cancer therapy. Functional precision medicine has emerged as a promising avenue for achieving superior treatment outcomes by integrating omics profiling and sensitivity testing of patient-derived cancer cells. This review paper provides an in-depth analysis of the evolution of cancer-directed drugs, resistance mechanisms, and the role of functional precision medicine platforms in revolutionizing individualized treatment strategies. Using two-dimensional (2D) and three-dimensional (3D) cell cultures, patient-derived xenograft (PDX) models, and advanced functional assays has significantly improved our understanding of tumor behavior and drug response. This progress will lead to identifying more effective treatments for more patients. Considering the limited eligibility of patients based on a genome-targeted approach for receiving targeted therapy, functional precision medicine provides unprecedented opportunities for customizing medical interventions according to individual patient traits and individual drug responses. This review delineates the current landscape, explores limitations, and presents future perspectives to inspire ongoing advancements in functional precision medicine for personalized cancer therapy.

A pan-cancer analysis unveiling the function of NR4A family genes in tumor immune microenvironment, prognosis, and drug response.

NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis. We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods. We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated. Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A family genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-γ dominant), C3 (inflammatory), and C6 (TGF-β dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer. The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers.

Abstract PO4-15-11: HER2 STATUS AND TUMOR HETEROGENEITY IN INVASIVE BREAST CARCINOMAS: CLINICAL PATHOLOGICAL IMPACT

Abstract Human epidermal growth factor receptor 2 (HER2) is a major player in breast carcinogenesis with a range of 15-20% of cases harboring gene amplification and overexpression turning the tumor eligible to over eight anti-HER2 drugs in the past 15 years. Recently, novel anti-HER2 antibody-drug conjugates (ADCs) treatment was recently approved to metastatic breast cancer bearing some HER2 expression albeit not due to gene amplification. Immunohistochemically classified, by ASCO-CAP Guidelines (2018), as 1+ cases were enrolled in clinical trials and benefited by the addition of ADCs to chemotherapy. Despite being classified as HER2 negative, most tumors exhibit detectable levels of HER2 protein, reinforcing the importance of HER2 expression evaluation. HER-2 intratumoral heterogeneity (ITH) is reported in up to 40% of breast cancers and is defined as the coexistence of subpopulations of tumor cells with different HER2 gene or protein expression. In this scenario, it becomes very important to evaluate the spectrum of HER-2 expression in correlation with pathological factors as ITH is associated with poor prognosis and anti-HER2 resistance. We designed a retrospective linear study to reanalyze all HER2 immunohistochemical core biopsy slides of patients with primary invasive breast carcinomas (IBC) diagnosed and treated at São Paulo Federal University Hospital between 2019 and 2023. Clinical variables (age, laterality, tumor size by ultrasound imaging, BI-RADS) were collected from clinical files. HER2 slides were reviewed by three observers and reported regarding status by ASCO-CAP 2018 guidelines, HER2 heterogeneity and patterns. Statistical analyses were performed with Chi-Square test of independence, Fisher's exact test and Mann–Whitney U test using IBM® SPSS® Statistics (26.0) software. 353 cases were included; the mean age of the patients at the time of diagnosis was 58 years old. 191 cases (54,1%) were left sided and 162 cases (45,9%) were right sided. Regarding BI-RADS report, the major category was 4 (140 cases), followed by 5 (119 cases). Out of the 164 patients who underwent clinical follow-up at our institute, 14 deaths were reported. Concerning pathological features, IBC of no special type was the prevalent histological subtype with 304 cases (86,1%), followed by invasive lobular carcinoma, with 26 cases (7,4%). 296 cases (83,9%) were HER2 negative and 42 (11,9%) were HER2 positive; 15 cases were HER2 2+ (4,2%). Amongst negative cases, 235 were HER2 0+ and 61 were HER2 1+. Considering ITH, 287 cases (81,3%) were HER2 homogenous with 0+ score as the most prevalent, presenting with 232 cases (80%). 66 cases (18,7%) had ITH, with 1+ as the most prevalent primary score, with 34 cases (51,5%), followed by 3+, with 17 cases (25,8%). Not only HER2 positive carcinomas, but also the presence of heterogeneity in HER2 expression was associated with death outcome (p=0,003 and p=0,001, respectively). Also, HER2 positive IBC had larger tumor size compared to HER2 negative carcinomas (mean size of 25,6 mm x 31,3 mm, respectively; p=0,012); HER2 heterogeneity showed no statistical significance when associated with tumor size (p=0,165). We conclude that ITH is prevalent in HER2 expression and should be addressed in pathology reports since it may play an additional role in tumor progression and drug response, especially in the ADCs scenario. Citation Format: Karla Calaça Kabbach Prigenzi, Lisandra Gonzalez Porta Nova, Adilson Monteiro dos Santos Filho, Ana Luiza da Cruz, Lucas de Figueiredo Barbosa, Angela Flavia Waitzberg. HER2 STATUS AND TUMOR HETEROGENEITY IN INVASIVE BREAST CARCINOMAS: CLINICAL PATHOLOGICAL IMPACT [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-15-11.

YY2/BUB3 Axis promotes SAC Hyperactivation and Inhibits Colorectal Cancer Progression via Regulating Chromosomal Instability.

Spindle assembly checkpoint (SAC) is a crucial safeguard mechanism of mitosis fidelity that ensures equal division of duplicated chromosomes to the two progeny cells. Impaired SAC can lead to chromosomal instability (CIN), a well-recognized hallmark of cancer that facilitates tumor progression; paradoxically, high CIN levels are associated with better therapeutic response and prognosis. However, the mechanism by which CIN determines tumor cell survival and therapeutic response remains poorly understood. Here, using a cross-omics approach, YY2 is identified as a mitotic regulator that promotes SAC activity by activating the transcription of budding uninhibited by benzimidazole 3 (BUB3), a component of SAC. While both conditions induce CIN, a defect in YY2/SAC activity enhances mitosis and tumor growth. Meanwhile, hyperactivation of SAC mediated by YY2/BUB3 triggers a delay in mitosis and suppresses growth. Furthermore, it is revealed that YY2/BUB3-mediated excessive CIN causes higher cell death rates and drug sensitivity, whereas residual tumor cells that survived DNA damage-based therapy have moderate CIN and increased drug resistance. These results provide insights into the role of SAC activity and CIN levels in influencing tumor cell survival and drug response, as well as suggest a novel anti-tumor therapeutic strategy that combines SAC activity modulators and DNA-damage agents.

Tumor‐On‐A‐Chip Model Incorporating Human‐Based Hydrogels for Easy Assessment of Metastatic Tumor Inter‐Heterogeneity

AbstractThe coordinated migration of invasive tumor cells is a complex and dynamic mechanism driven by diverse cellular and molecular events. Unfortunately, the inherent heterogeneity within tumors raises multiple challenges in deciphering key biomarkers and novel therapeutic approaches to prevent tumor metastasis. Here, a microengineered tumor‐on‐a‐chip system incorporating human platelet lysate hydrogels is proposed to recreate the early metastatic process of tumor invasion and drug response. By co‐culturing human bone marrow mesenchymal stem cells with two tumor cell lines with distinct metastatic capability, the developed model can emulate the 3D tumor microarchitecture and inter‐heterogeneity regarding its intrinsic metastatic ability. The recreated microenvironment supports tumor and stromal cell movement, evidencing the synergistic tumor‐stromal cell and cell‐extracellular matrix interactions of an invading tumor. Through gene and protein expression analysis and exometabolomic profiling, this tumor‐on‐a‐chip platform provides evidence for the role of a dynamic environment as a key regulator of tumor metastatic ability. Additionally, the effect of doxorubicin treatment on tumor invasiveness and biomarker profile highlights the suitability of the established models for therapy assessment. Overall, this study presents a tumor‐on‐a‐chip model useful to pursue mechanistic studies on early metastatic events in a fully human‐derived microenvironment, while contributing with fundamental insights into biomolecular profiling.

Abstract 3906: Ex vivo breast tumors for understanding the patient specific tumor microenvironment remodeling and drug responses

Abstract Breast cancer is a heterogeneous disease driven by cell-intrinsic and cell-extrinsic signaling cues. Many of these cues derive from the tumor microenvironment (TME), which is composed of adipocytes, vasculature, stem cells, immune cells, and the extracellular matrix (ECM). Through these diverse components, the TME modulates cellular proliferation, survival, and ultimately response to therapy. Despite this high-level understanding of tumor-TME crosstalk, no existing pre-clinical models accurately capture the TME in vitro. We overcame that hurdle by developing a method to maintain healthy human breast tissue (HBT) alive ex vivo for at least 8 weeks. By culturing breast cancer cells in that HBT, we created ex vivo breast tumors (EVBTs) that retained patient-specific features relevant to breast cancer including menopause status, obesity status, and response to therapy. Using these EVBTs, we demonstrate breast cancer subtype-specific alterations in lipid accumulation, ECM remodeling, and cytokine profiles. RNA sequencing showed that breast cancer cell line signatures were retained in the EVBTs system at 1 week and 2 weeks in vitro. We demonstrated donor-specific transcriptional changes to the core matrisome gene set. Finally, we demonstrate that our EVBTs retained patient-specific heterogeneity in response to standard of care endocrine therapy and neoadjuvant chemotherapies. Our novel tumor model allows for 1) real-time examination of breast cancer interactions with HBT and the TME and 2) the isolation of breast cancer-specific factors vs. patient-specific factors in the development and progression of breast cancer. Citation Format: Elizabeth Martin, Megan Benz, Katie Hebert, Ethan Byrne, Frank Lau. Ex vivo breast tumors for understanding the patient specific tumor microenvironment remodeling and drug responses [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 3906.

Abstract 6785: KIYA-PREDICT™ XPDX: An ex vivo 3D spheroid platform for modeling in vivo responses to multiple classes of oncology therapeutics

Abstract Patient-derived xenografts (PDX) are a powerful and commonly used tool in preclinical drug development as they represent an in vivo platform for screening drug response in human tumors that recapitulate many molecular and histological features of the original patient tumor. However, these models are costly and time-intensive, necessitating the development of ex vivo platforms for assessing drug response to PDX-derived tumors to de-risk in vivo studies by aiding in model selection and reducing the number of animals and costs associated with in vivo experimentation. The KIYA-PREDICT™ platform is a clinically validated, ex vivo 3D cell culture platform with demonstrated ability to predict patient response to chemotherapy in high grade glioma and ovarian cancer. Here, we apply this platform to profiling XPDX from XenoSTART’s comprehensive library of well-characterized models as a tool for assessing drug response and mechanism of action ex vivo while also aiding in model selection for in vivo studies. Over 100 XPDX models have been profiled ex vivo for response to several classes of cancer therapeutics, including chemotherapy, targeted therapy, and antibody-drug conjugates (ADCs), demonstrating robust concordance with in vivo drug response. CDK4/6 inhibitors palbociclib and ribociclib evaluated ex vivo in breast XPDX display strong correlation to in vivo response and recapitulate palbociclib resistance. Similarly, G12C inhibitors sotorasib (AMG-510) and adagrasib (MRTX849) screened against a panel of non-small cell lung cancer XPDX ex vivo match in vivo response and drug resistance. Finally, ex vivo and in vivo responses to FDA approved ADC enfortumab vedotin were evaluated in a panel of bladder cancer PDX, with ex vivo and in vivo responses showing good concordance and expected modulation based on Nectin-4 expression levels. The combination of the KIYA-PREDICT™ platform with XenoSTART’s XPDX catalogue is a flexible and powerful tool that can be utilized for streamlining preclinical drug development for a wide range of therapeutic classes. Citation Format: Aaron L. Carlson, Natalie W. Dance, Kimberly J. Burgess, Danielle Y. Nadeau, Melissa Millard, Alyssa Simonson, Armando Diaz, Michael Wick, Teresa M. DesRochers. KIYA-PREDICT™ XPDX: An ex vivo 3D spheroid platform for modeling in vivo responses to multiple classes of oncology therapeutics [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 6785.

Abstract 907: ScreenDL: A transfer learning framework integrating tumor omics and functional drug screening for personalized clinical drug response prediction

Abstract Precision oncology hinges on accurate prediction of patient-specific treatment response from tumor molecular inputs. While deep learning (DL) models achieve state-of-the-art response prediction in cell lines, existing methods do not readily translate to the clinic where training data is limited. Here, we have developed and validated ScreenDL, a novel DL framework designed explicitly for use in clinical precision oncology applications. The underlying architecture of ScreenDL consists of two fully connected branches dedicated to learning rich embeddings of a drug’s chemical structure and a tumor’s transcriptomic profile respectively. These tumor omic and drug molecular embeddings are fed into a shared response prediction subnetwork which outputs predicted ln(IC50) values. The ScreenDL training schema incorporates three phases, each designed to improve clinical response prediction: (1) an initial pretraining phase leverages large-scale cell line pharmacogenomic datasets to extract patterns/relationships linking tumor omics and drug response; (2) subsequent transfer learning integrates pharmacogenomic data from patient-derived models of cancer, adapting the pretrained model to a more patient-relevant context; (3) patient-level omics and drug screening data (e.g., from matched patient-derived organoid (PDO) models) is integrated through patient-specific fine-tuning, generating a patient-specific response prediction model. Importantly, biomaterial from surgical tumor resection is often available for organoid establishment and patient-specific drug screening in practical clinical scenarios. To assess the utility of our ScreenDL framework for clinical response prediction, we applied ScreenDL to predict treatment response in 50 advanced/metastatic triple negative breast cancer (TNBC) patient-derived xenograft models (PDX). We leveraged tumor omics profiles for each PDX and drug screening data from matched PDX-derived organoids (PDxOs), mirroring the combination of patient-level tumor omic characterization and functional drug screening in matched PDOs, a protocol currently being piloted in Functional Precision Oncology trials at the University of Utah Huntsman Cancer Institute. After cell line pretraining, ScreenDL achieves a modest median Pearson correlation per drug of 0.15 relative to 0.03 for the leading compared model. However, transfer learning and subsequent PDX-specific fine-tuning significantly improve performance, producing median Pearson correlations per drug of 0.39 and 0.51, respectively. These results demonstrate the ability of our ScreenDL framework to dramatically improve response prediction in clinically relevant domains, bringing DL-based precision treatment selection closer to clinical application. Citation Format: Casey Sederman, Tonya Di Sera, Yi Qiao, Xiaomeng Huang, Bryan E. Welm, Alana L. Welm, Gabor Marth. ScreenDL: A transfer learning framework integrating tumor omics and functional drug screening for personalized clinical drug response prediction [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 907.

Abstract 636: Coculture model for investigating cellular interactions between colorectal tumor organoids and cancer-associated fibroblasts

Abstract The tumor microenvironment (TME) is well recognized for its role in tumor progression, chemoresistance, and recurrence. The TME consists of various components, such as extracellular matrix (ECM), chemical and mechanical cues, as well as stromal cells and their interactions. As the most abundant cells in tumor stroma, cancer-associated fibroblasts (CAFs) have drawn a lot of attention. It has been reported that CAFs not only directly interact with cancer cells via surface proteins and cytokines, but also influence their behaviors by remodeling the ECM. However, due to their heterogenous characteristics and diverse functions, the influence of tumor-CAF interactions on drug response is not fully understood. Here, we present a coculture model for investigating CAF effects on patient-derived colorectal tumor organoids (CTOs) growth, differentiation, and drug response. It consists of normal colon fibroblasts, CCD18-Co, or patient-derived CAFs seeded with CTOs in a mixed hydrogel containing 90% basement membrane extract (BME) and 10% collagen type I. Media conditions were optimized (1:1 mix of CTO culture media plus reduced media excluding growth factors and inhibitors) to achieve similar organoid sizes in the monoculture and coculture conditions at the onset of drug testing. To support higher throughput investigations, our workflow incorporates a liquid handler for automated CTO seeding, media and drug exchange, and an automated microscope with a robotic arm for multi-plate imaging. Briefly, single cell CTOs and fibroblasts were seeded at a 1:1 ratio (3000 total cells/well) in 96-well plates. After 4 days in culture, anti-cancer drugs were administrated in reduced media to the samples. Brightfield images were acquired on days 0, 3, and 5 after drug administration. The collected images were analyzed with a neural network (NN) to dynamically monitor the growth and viability of individual organoids. The CellTiter-Glo assay was performed on day 5 to quantify the total CTO cell viability. The normalized drug response (NDR) was calculated from the NN processed images to determine the drug response. An immunostaining panel, which includes protein markers such as CK20, Ki67, α-SMA, Vimentin, and FAP, was used to evaluate the fibroblast phenotypes and CTO differentiation. Additionally, the cultures were subjected to fluorescent lifetime imaging microscopy (FLIM), a label-free imaging technique, to measure the metabolic adaptations of each cell population. Preliminary studies revealed a CAF-dependent increase in CTO growth and glycolytic signatures in the co-culture setting; however, minimal effects on drug response to common chemotherapy agents such as 5-fluorouracil and irinotecan were observed. In the future, we will use this integrated pipeline as a screening method to identify new treatment strategies aimed at targeting the stromal microenvironment in colorectal cancer. Citation Format: Yuyuan Zhou, Yu-Kai Huang, Emma J. Fong, Elizabeth Elton, Erin McManus, Brandon Choi, Seungil Kim, Shannon M. Mumenthaler. Coculture model for investigating cellular interactions between colorectal tumor organoids and cancer-associated fibroblasts [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 636.

Abstract 4241: Development of stroma cells-rich oral cancer organoid for functional analysis of tumor microenvironment

Abstract Background: Tumor microenvironment (TME), composed of cancer cells, endothelial cells, fibroblast, immune cells, and etc, plays crucial roles in tumor progression and treatment resistance. Thus, it is extremely important to find its role involved in the interaction of TME. However, it is more difficult to examine the biological activities of those cells in vitro assay. The purpose of this study is to develop stroma cells-incorporating preclinical model, termed stroma cells-rich oral cancer organoid, for functional analysis of TME. Materials and Methods: We have first developed the stroma cells-rich oral cancer organoid by selecting the types of stroma cells such as endothelial cells, fibroblast, ratio of those cells, culture method, 3-D methods, culture media, and supplement of growth factors. Then, growth rate, morphology, radiosensitivity, and drug response were analyzed to investigate the effect of TME. Finally, the function of tumor-associated macrophage was determined using this organoid model. The growth rate of cancer cells and drug response were investigated using the organoid treated with supernatant of M1 and M2 macrophages polarized from THP-1 monocyte. Results: Oral cancer cells were co-cultured with endothelial cells (ECs) and mesenchymal stem cells (MSCs) in a particular ratio in 3D-culture, and the formed organoid became sphere. The vascular formation in the organoid was confirmed by immunofluorescent analysis. The organoid also showed higher tumorigenicity in nude mice when inoculated, compared to cancer cells alone or mixed cells with ECs and MSCs. Cell viability of luciferase-expressing tongue cancer cells (OSC-19-luc) in the organoid was assessed after treatment with cisplatin using the IVIS system showing that OSC-19-luc organoid was resistance to cisplatin compared to 3D-cultured OSC-19-luc alone or OSC-19-luc co-cultured with ECs/MSCs. The supernatant of M1 or M2 macrophage had no effect on cancer cell growth in the organoid. However, when the organoid was treated with cisplatin in the presence of M2 macrophage supernatant, the drug sensitivity was low compared to those of M1 suggesting that secretory factor from M2 macrophage may lead to drug resistance of oral cancer cells in the presence of TME. Conclusions: We have successfully established stroma cells-incorporating preclinical model that recreates the tumor microenvironment by co-culturing stromal cells. This will serve as a platform that can recapitulate the tumor biology and drug response profiles of cancer cells derived from individual oral cancer patients. This will also enable us to elucidate the role of other factors in TME and to identify novel targets for the treatment of chemo-resistant oral cancer. Citation Format: Ami Shimoda, Shizuka Shiiya, Haruka Yoshii, Kenji Mitsudo, Mitomu Kioi. Development of stroma cells-rich oral cancer organoid for functional analysis of tumor microenvironment [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 4241.

Abstract 4249: Establishment and analysis of patient-derived high-grade serous ovarian cancer organoids for disease modeling and drug testing

Abstract Purpose: Ovarian cancer is the most lethal gynecologic cancer. Majority of ovarian cancers belong to the subgroup of high-grade serous ovarian cancer (HGSC). Its standard treatment is surgery and chemotherapy with platinum and taxane-based compounds, for which almost all patients become resistant. We have established a panel of patient derived 3-dimensional (3D) HGSC long-term tumor organoid cultures for personalized disease modeling, drug screening and evaluation of the efficiency of potential new treatments to be used in preclinical research. Methods: Tumor tissues or ascites from > 30 HGSC tumors were set up as long-term 3D organoid cultures (PDOs). RNA and whole genome sequencing was performed from the original tumors and from PDOs. Pluripotency and proliferation were assessed via high-throughput immunofluorescent detection of PAX8 and Ki67. Resistance towards standard treatment and response to additional selected drugs and drug combinations were evaluated in cell death and proliferation assays. Imaging and image-based quantifications were performed under 3D setup. PDOs were set up for in vivo drug testing in immunodeficient NOD/Shi-scid/IL-2Rgnull (NOG) mice. Summary: A set of HGSC PDOs of different longitudinal, prospective, and multiregional samples has been established representing the three evolutionary states of HGSC: evolving, maintaining and adaptive state. They are ready to be used as rational and effective models for studying drug interventions. Supportively, results of preclinical studies with PI3K inhibitor Alpelisib matched the published patient responses. Tumor evolutionary analysis based on data of longitudinal, prospective, and multiregional collected samples suggest further targetable pathways that can mediate chemotherapy resistance and pinpoint potential targets for drug intervention. Conclusion: Inhibition of these pathways with simultaneous targeting of other cellular functions, such as lysosomal-mediated cell death, may be highly effective alternative treatment option. 3D imaging and efficient image analysis are necessary tools to understand tumor organoid biology and drug responses to assist development and pre-clinical evaluation of new treatments. This study is a part of the DECIDER project (https://www.deciderproject.eu/, NCT04846933). Citation Format: Tuula Kallunki, Anna R. Lauridsen, Aikaterini Skorda, Alexandra Lahtinen, Marie L. Bay, Benita Rasmussen, Kaisa Huhtinen, Taru Muranen, Jaana Oikkonen, Johanna Hynninen, Sampsa Hautaniemi. Establishment and analysis of patient-derived high-grade serous ovarian cancer organoids for disease modeling and drug testing [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 4249.

Abstract 5633: Unravelling the pleiotropic role of HNF4α in invasive mucinous adenocarcinoma of the lung (IMA)

Abstract IMA is a distinct subtype of lung adenocarcinoma (LUAD), marked by a cellular identity shift from a pulmonary to a gastric lineage with the loss of the pulmonary lineage specifier NKX2-1. This distinctive transformation underscores the complexity of lung cancer heterogeneity and highlights the critical role of lineage-specifying transcription factors in dictating tumor behavior. Notably, both human and murine models of IMA overexpress HNF4α, a known lineage specifier for gastric differentiation in the adult mouse mucosa. Also, concomitant deletion of Hnf4a and Nkx2-1 at tumor initiation impairs tumor growth and the extent of HNF4α in regulating IMA tumor growth, cellular identity and drug response remains unknown at large. Here, we utilized genetically engineered mouse models (GEMM) that relies on a sequential recombination strategy to delete Hnf4a in established tumors. We induce tumor formation through intratracheal delivery of Flp-O virus, activating the expression of mutant KRASG12D and Cre in lung epithelial cells. Tamoxifen administration recombines either Nkx2-1 alone (KN) or in combination with Hnf4a (KNH). We have extensively studied the phenotypic role of HNF4α in GEMM and organoids and identified gene sets regulated by HNF4α in IMA at the bulk level. To complement our findings, we profiled both the tumor and stromal compartments via scRNAseq analysis on our GEMM to gain insights into the cellular diversity within the tumor microenvironment (TME) and the role HNF4α plays. Additionally, we performed ChIPSeq in organoids to elucidate the genome occupancy of HNF4α and determine the mechanisms of gene regulation in IMA. We find that deletion of Hnf4a impairs tumor growth in GEMM and this is phenocopied in organoids. We identified numerous differentially expressed genes (DEGs) in KNH compared to KN and validated several DEGs in vivo. GSEA analysis reveals a significant involvement in regulating cellular identity and suggests a potential impact on anti-tumor immune responses. To this, our scRNAseq data revealed an enrichment of N2-TAN (tumor associated neutrophils) associated genes in KNH compared to KN, suggesting a nuanced interplay between HNF4α and the TME. Additionally, HNF4α exhibited dual functionality, acting as an activator and indirectly as a repressor by modulating the activity of other transcription factors like FOXA1. FOXA1 was found to bind de novo target genes following the loss of HNF4α. These findings so far collectively illuminate the multifaceted role of HNF4α in IMA, primarily driving tumorigenesis in a cell autonomous manner and secondarily skewing the TME towards a protumor via N2-TAN following loss of HNF4α in tumors. Citation Format: Headtlove Essel Dadzie, Gabriela Fort, Katherine Gillis, Pengshu Fang, Walter Orellana, Eric L. Snyder. Unravelling the pleiotropic role of HNF4α in invasive mucinous adenocarcinoma of the lung (IMA) [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 5633.

Abstract 2900: BRAFi-induced epigenetic switch drives the reprogramming of resistant cancer-associated fibroblasts

Abstract Cancer-associated fibroblasts (CAFs) are a potential target for optimizing therapeutic strategies against melanoma. However, a major unknown still exists in CAFs, which is how CAFs maintain and even reinforce their functions in drug therapy and influence melanoma cell drug sensitivity. Yes-associated protein 1 (YAP1) is the master regulator that shuttles between the cytoplasm and nucleus in CAFs. Apparently, proteins associated with nuclear YAP1 in CAFs when treated with BRAF inhibitor (BRAFi) can potentially influence tumor drug response. Here we unveil that CAFs require the YAP1 function to proliferate, migrate, remodel the cytoskeletal machinery and matrix, and promote cancer cell invasion. Ablating YAP1 in CAFs increases the response of BRAF-mutant melanoma cells to BRAFi and MEK inhibitor (MEKi) in vivo and in vitro. Using an approach termed RIME (Rapid Immunoprecipitation Mass Spectrometry), we purified nuclear YAP1 and identified protein arginine methyl transferase 1 (PRMT1) as a major YAP1 binding partner in BRAFi-treated CAFs. Binding PRMT1 to YAP1 was confirmed by Co-immunoprecipitation and proximity ligation assay, and YAP1 deficiency in CAFs diminishes the nuclear accumulation of PRMT1, suggesting BRAFi-induced nuclear translocation of PRMT1 in CAFs requires the formation of YAP1 and PRMT1 complex. Silencing PRMT1 expression in CAFs not only deprives them of their ability to proliferate and to induce BRAF mutant melanoma cell resistance to BRAFi and MEKi. PRMT1 is a type I R-methyltransferase that mediates arginine methylation in human cells, which was known to be recruited to chromatin and exert its enzymatic activity to methylate Arg3 of histone H4 (H4R3). Using Histone H4 Modification Multiple Assay, we observed a global increase in Histone H4 methylation, especially H4R3 methylation, in CAFs in response to BRAFi stimulation, suggesting increased chromatin unfolding and accessibility and transcription activation. When YAP1 or PRMT1 expression was silenced, H4R3 methylation was notably suppressed, indicating BRAFi-induced H4R3 methylation was indeed driven by the YAP1 and PRMT1 signaling axis. In conclusion, our data disclose a novel BRAFi-induced YAP1-PRMT1-mediated epigenetic mechanism that reprograms CAFs through H4R3 methylation-directed transcriptional activation to reinforce their drug-resistant functions and modulate melanoma cell drug sensitivity. Moreover, the discovery of PRMT1 as a key player in the intricate molecular mechanisms that govern epigenetic regulation in CAFs in response to BRAFi treatment suggest PRMT1 as a potential CAF target in cancer therapy. Citation Format: Yao Xiao, Linli Zhou, Yuhang Zhang. BRAFi-induced epigenetic switch drives the reprogramming of resistant cancer-associated fibroblasts [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 2900.

Abstract 6210: The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction

Abstract Background: Determining a patient’s response to a specific therapy is a vital step in developing personalized cancer treatment. Personalized treatment relies on two key technological advancements: algorithms tailored to each patient’s molecular profiles, and comprehensive datasets detailing the effects of patient diversity on drug response. Despite recent advancements in machine learning-based models, each new algorithm requires curation of numerous datasets, which poses a significant burden to researchers who need to evaluate and compare various algorithms. Currently, datasets include extensive genomics, transcriptomics, proteomics and metabolomics measurements along with drug response data (i.e., cell viability) for each cell line, collected through the Cancer Cell Line Encyclopedia (CCLE) and other initiatives. Additional omics data is available for tumor organoids and patient samples generated by consortia such as Human Cancer Model Initiative (HCMI) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). However, to date there has been little public effort to annotate and harmonize these data across repositories. Methods: To address this need, we developed the Python package coderdata that collates the most up-to-date cancer omics and drug response data. We curated four public cancer datasets, establishing reproducible pipelines that standardize data retrieval, formatting, and integration so the datasets can be built on a cluster if needed. The datasets undergo bi-monthly synchronization with FigShare, ensuring the data is up to date and available. Scientists can use coderdata to retrieve and format data on the command line interface as well as within Python scripts. Results: Coderdata is a straightforward Python package that will enable the development and benchmarking of diverse machine learning applications in cancer research. At its core, it represents reproducible data harmonization that enables heterogeneous datasets to be analyzed in bulk. This Python package downloads and standardizes data from multiple consortia-related resources including HCMI, CPTAC, CCLE, and BeatAML, together representing 4931 multi-omics samples from across 250+ cancer types. These datasets include cancer data metrics such as copy number, mutation, transcriptomics, proteomics, miRNA, methylation, and drug response data. Conclusions: The applications of machine learning in cancer biology are hampered by the distributed nature of existing datasets. As such, the collection and standardization of data by coderdata substantially reduces the time investment required for data curation in cancer research. Direct access to benchmark multi-omics and drug response datasets enables scientists to focus on algorithm development for tumor drug response prediction, potentially accelerating the discovery of therapeutic strategies. Citation Format: Jeremy Jacobson, Sydney Schwartz, M. Ryan Weil, Neeraj Kumar, Sara Gosline. The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction [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 6210.

Abstract 7393: Tumor model to tumor treatment: Applying deep learning approaches to map multimodal data from cancer model systems to patients

Abstract Despite large-scale efforts to measure the effect of drug screens in cancer cell lines, mapping the effects of drugs to patient samples has been a challenge. Biological differences between cell lines and patients, such as lack of immune system or microbiome, in-vitro survival adaptations, and biases in measurement technologies create differences across sample modalities that can confound analysis including prediction with machine learning. In this work, we propose a multiway batch correction strategy to enable algorithmic prediction of tumor drug response across model systems and patient data.Recent advances in batch correction algorithms have been motivated by the need to correct for batch effects in single-cell omics and include diverse approaches such as variational autoencoders (VAEs) and generative adversarial networks (GANs). Given the successes of these generative deep learning methods in single cell sequencing analysis, we worked to employ similar approaches to correct large omics measurements across various cancer datasets. Here, we describe mapping of datasets from diverse data sources and model systems to the same space, so that a predictive model of drug response built in a system such as cell lines can be used in biologically relevant models such as organoids, patient derived xenografts, and tumor data. Specifically, we introduce a modified loss function in a VAE using cosine similarity distance to minimize the effect of different cancer model systems in predicting cancer types. We evaluate the method on standard data types for drug response prediction - gene expression, copy number variation, and protein abundance. For this method, the cosine similarity is added as an additional term to the VAE reconstruction and Kullback-Leibler divergence loss terms. This injects a quantification of the dissimilarity between the tumor and tumor model distributions into the backpropagation and gradient descent for updating the model parameters resulting in an encoded representation of the data where the effect of data source has been attenuated while preserving the phenotypic signal. We evaluate our approach for biological signal preservation while reducing model system-specific noise with logistic regression and Euclidean distance. Our results show that the proposed VAE can effectively correct for platform effects and improve the accuracy of downstream integrative analyses. This study has the potential to improve the accuracy and translatability of proteogenomic drug response studies. The proposed modified VAE could be used to correct for platform effects in a variety of datasets, including those from different studies, different platforms, and different cancer types. This could lead to new insights into cancer biology, calibration of cancer patient digital twins, and the development of new diagnostic and therapeutic strategies. Citation Format: Brian Karlberg, Raphael Kirchgaessner, Jeremy R. Jacobson, Kyle Ellrott, Sara J. Gosline. Tumor model to tumor treatment: Applying deep learning approaches to map multimodal data from cancer model systems to patients [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 7393.

Simulation of avascular tumor growth and drug response in a microfluidic device with a cellular automaton model

Simulation of avascular tumor growth and drug response in a microfluidic device with a cellular automaton model

A pan-cancer analysis uncovering the function of CRHBP in tumor immunity, prognosis and drug response: especially its function in LIHC

Corticotropin-releasing hormone-binding protein (CRHBP) is involved in many physiological processes. However, it is still unclear what role CRHBP has in tumor immunity and prognosis prediction. Using databases such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Tumor Protein Database, Timer Database, and Gene Expression Profiling Interactive Analysis (GEPIA), we evaluated the potential role of CRHBP in diverse cancers. Further research looked into the relationships between CRHBP and tumor survival prognosis, immune infiltration, immune checkpoint (ICP) indicators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), DNA methylation, tumor microenvironment (TME), and drug responsiveness. The anticancer effect of CRHBP in liver hepatocellular carcinoma (LIHC) was shown by Western blotting, EdU staining, JC-1 staining, transwell test, and wound healing assays. CRHBP expression is significantly low in the majority of tumor types and is associated with survival prognosis, ICP markers, TMB, and microsatellite instability (MSI). The expression of CRHBP was found to be substantially related to the quantity of six immune cell types, as well as the interstitial and immunological scores, showing that CRHBP has a substantial impact in the TME. We also noticed a link between the IC50 of a number of anticancer medicines and the degree of CRHBP expression. CRHBP-related signaling pathways were discovered using functional enrichment. Cox regression analysis showed that CRHBP expression was an independent prognostic factor for LIHC. CRHBP has a tumor suppressor function in LIHC, according to cell and molecular biology trials. CRHBP has a significant impact on tumor immunity, treatment, and prognosis, and has the potential as a cancer treatment target and prognostic indicator.

Lactylation-related gene signature for prognostic prediction and immune infiltration analysis in breast cancer

BackgroundLactylation is implicated in various aspects of tumor biology, but its relation to breast cancer remains poorly understood. This study aimed to explore the roles of the lactylation-related genes in breast cancer and its association with the tumor microenvironment. MethodsThe expression and mutation patterns of lactylation-related genes were analyzed using the breast cancer data from The Cancer Genome Atlas (TCGA) database and GSE20685 datasets. Unsupervised clustering was used to identify two lactylation clusters. A lactylation-related gene signature was developed and validated using the training and validation cohorts. Immune cell infiltration and drug response were assessed. ResultsWe analyzed the mRNA expression, copy number variations, somatic mutations, and correlation networks of 22 lactylation-related genes in breast cancer tissues. We identified two distinct lactylation clusters with different survival outcomes and immune microenvironments. We further classified the patients into two gene subtypes based on lactylation clusters and identified a 7-gene signature for breast cancer survival prognosis. The prognostic score based on this signature demonstrated prognostic value and predicted the therapeutic response. ConclusionLactylation-related genes play a critical role in breast cancer by influencing tumor growth, immune microenvironment, and drug response. This lactylation-related gene signature may serve as a prognostic marker and a potential therapeutic target for breast cancer.

Empowering personalized medicine: unleashing the potential of patient-derived explants in clinical practice.

In recent decades, significant progress has been made in understanding the molecular characteristics of cancer and its microenvironment, leading to the development of life-saving treatments. However, patients often experience side effects from standard therapies, highlighting the need for personalized medicine. Personalized medicine aims to customize drug therapy and preventive care based on individual patients' specific requirements. The heterogeneity within tumors and among patients necessitates personalized medicine approaches. Patient-derived organoids (PDOs), xenografts (PDXs), and explants (PDEs) have emerged as valuable models for studying tumor behaviour and drug response. This paper aims to summarize the latest advancements in patient-derived explants, focusing on their potential utility in the clinic. Different methods for culturing PDEs, including the free-floating approach, the grid method, and sponge scaffolds, are discussed. These approaches provide opportunities for long-term viability, oxygen and nutrient supply, and maintenance of tissue integrity. Additionally, various solid tumor models using PDEs are highlighted, together with assays to study PDE viability, characteristics, and response to drug treatment.

Development of a Three-Dimensional Multicellular Model of Human Neuroblastoma Using Matrigel as an Extracellular Matrix Analogue.

Neuroblastoma, the most prevalent extracranial solid tumor in children, poses therapeutic challenges due to its variable clinical course and propensity for metastasis. Despite advances in treatment strategies like chemotherapy, drug resistance remains a significant concern, highlighting the need for improved models to study tumor behavior and drug responses. This chapter proposes the development of a three-dimensional multicellular model of human neuroblastoma using Matrigel as an ECM analogue. Such models aim to replicate the complexity of the tumor microenvironment, providing valuable insights into tumor progression and drug resistance mechanisms. By recapitulating key features of neuroblastoma within a physiologically relevant context, these models offer a platform for preclinical drug screening and therapeutic development.