Tumor Drug Response Research Articles

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.

Generation and Maintenance of Patient-Derived Endometrial Cancer Organoids.

Endometrial cancer (EC) is the leading cause of gynecologic cancer morbidity and mortality in the U.S. Despite advancements in cancer research, EC death rates are increasing, particularly high-grade endometrial cancers. The development of three-dimensional (3D) patient-derived organoid (PDO) models for EC is crucial, as they provide a more accurate representation of the biological and genetic complexity of a patient's tumor compared to traditional 2D cell lines. Here, we describe a protocol for cultivating PDO models from normal endometrium and EC across different EC subtypes. These EC PDO models can be expanded across multiple passages and facilitate the exploration of tumor behavior and drug responses, thereby advancing our understanding of the disease and potentially leading to more effective and individualized novel therapeutic strategies. Key features • Establishment of patient-derived EC and normal endometrium organoids. • Accurate replication of the various histologic and molecular subtypes of EC within the organoids. • Our approach provides a clinically relevant platform for studying EC development, metastasis, progression, and recurrence. • It offers potential for developing targeted therapeutic interventions tailored to specific EC subtypes. Graphical overview Schematic overview of endometrial cancer and normal organoid preparation from patient-derived samples.

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.

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.

Multi-omics fusion based on attention mechanism for survival and drug response prediction in Digestive System Tumors

In recent decades, digestive system tumors (DST) have become the primary cause of cancer-related deaths worldwide. Improving tumor prognosis and drug response prediction holds significant importance in personalized medicine. In order to construct an effective and interpretable model that integrates multiple omics data, the Multi-omics Fusion Graph Attention Network (MFGAN) is proposed for survival prediction and drug response prediction in DST. In this method, the similarity matrix of each kind of omics data is calculated and a new graph structure is learned by Graph Transformer (GT). Next, to learn the features of every kind of omics data from the TCGA and GDSC databases, the Graph Attention Network (GAN) is used. Lastly, the View Correlation Discovery Network (VCDN) combines different types of omics data features to predict survival risk and drug response. Among them, the survival prediction results showed an improvement of up to 9% relative to other methods in terms of the c-index metric, with a minimum improvement of 2.6%. In terms of drug response prediction performance, there was an improvement of 4%. The ablation experiment demonstrates MFGAN’s feature integration capability, and the functional enrichment analysis for significant genes explains the functional characteristics of the model. Furthermore, the prediction of unknown tumor drug response demonstrates the model’s prediction ability. The proposed high-prediction comprehensive model could have important potential value for the personalized medicine of DST.

Dynamic altruistic cooperation within breast tumors

BackgroundSocial behaviors such as altruism, where one self-sacrifices for collective benefits, critically influence an organism’s survival and responses to the environment. Such behaviors are widely exemplified in nature but have been underexplored in cancer cells which are conventionally seen as selfish competitive players. This multidisciplinary study explores altruism and its mechanism in breast cancer cells and its contribution to chemoresistance.MethodsMicroRNA profiling was performed on circulating tumor cells collected from the blood of treated breast cancer patients. Cancer cell lines ectopically expressing candidate miRNA were used in co-culture experiments and treated with docetaxel. Ecological parameters like relative survival and relative fitness were assessed using flow cytometry. Functional studies and characterization performed in vitro and in vivo include proliferation, iTRAQ-mass spectrometry, RNA sequencing, inhibition by small molecules and antibodies, siRNA knockdown, CRISPR/dCas9 inhibition and fluorescence imaging of promoter reporter-expressing cells. Mathematical modeling based on evolutionary game theory was performed to simulate spatial organization of cancer cells.ResultsOpposing cancer processes underlie altruism: an oncogenic process involving secretion of IGFBP2 and CCL28 by the altruists to induce survival benefits in neighboring cells under taxane exposure, and a self-sacrificial tumor suppressive process impeding proliferation of altruists via cell cycle arrest. Both processes are regulated concurrently in the altruists by miR-125b, via differential NF-κB signaling specifically through IKKβ. Altruistic cells persist in the tumor despite their self-sacrifice, as they can regenerate epigenetically from non-altruists via a KLF2/PCAF-mediated mechanism. The altruists maintain a sparse spatial organization by inhibiting surrounding cells from adopting the altruistic fate via a lateral inhibition mechanism involving a GAB1-PI3K-AKT-miR-125b signaling circuit.ConclusionsOur data reveal molecular mechanisms underlying manifestation, persistence and spatial spread of cancer cell altruism. A minor population behave altruistically at a cost to itself producing a collective benefit for the tumor, suggesting tumors to be dynamic social systems governed by the same rules of cooperation in social organisms. Understanding cancer cell altruism may lead to more holistic models of tumor evolution and drug response, as well as therapeutic paradigms that account for social interactions. Cancer cells constitute tractable experimental models for fields beyond oncology, like evolutionary ecology and game theory.

Microfluidic characterization of single-cell biophysical properties and the applications in cancer diagnosis.

Single-cell biophysical properties play a crucial role in regulating cellular physiological states and functions, demonstrating significant potential in the fields of life sciences and clinical diagnostics. Therefore, over the last few decades, researchers have developed various detection tools to explore the relationship between the biophysical changes of biological cells and human diseases. With the rapid advancement of modern microfabrication technology, microfluidic devices have quickly emerged as a promising platform for single-cell analysis offering advantages including high-throughput, exceptional precision, and ease of manipulation. Consequently, this paper provides an overview of the recent advances in microfluidic analysis and detection systems for single-cell biophysical properties and their applications in the field of cancer. The working principles and latest research progress of single-cell biophysical property detection are first analyzed, highlighting the significance of electrical and mechanical properties. The development of data acquisition and processing methods for real-time, high-throughput, and practical applications are then discussed. Furthermore, the differences in biophysical properties between tumor and normal cells are outlined, illustrating the potential for utilizing single-cell biophysical properties for tumor cell identification, classification, and drug response assessment. Lastly, we summarize the limitations of existing microfluidic analysis and detection systems in single-cell biophysical properties, while also pointing out the prospects and future directions of their applications in cancer diagnosis and treatment.

Construction of a novel disulfidptosis-related signature for improving outcomes in hepatocellular carcinoma: Observational study.

Disulfidptosis is a novel form of metabolic-related regulated cell death (RCD) that is caused by disulfide stress caused by the accumulation of excess cystine in the cell. Targeting disulfide metabolism imbalance is an emerging strategy for the treatment of cancer. However, it is undetermined how disulfidptosis-related genes (DRGs) influence hepatocellular carcinoma (HCC). Unsupervised clustering analysis was performed on the TCGA-LIHC cohort to identify various phenotypes of disulfidptosis. GSVA was used to measure the activation of characteristic gene sets, while CIBERSORT was employed to estimate the infiltration of immune cells. Disulfidptosis-related signature was generated to quantify the phenotype of disulfidptosis in HCC patients. Next, we examined the disparities among the high and low disulfidptosis score categories by considering clinical characteristics, infiltration of immune cells, functions related to the immune system, sensitivity to chemotherapeutic drugs, and effectiveness of immunotherapy. Two different disulfidptosis phenotypes with different prognoses, clinical traits, biological pathways, and immune cell infiltration were identified. Based on differently expressed genes (DEGs) among 2 disulfidptosis phenotypes, a disulfidptosis-related signature was built. The prognostic value of this signature was then evaluated in the TCGA and GEO datasets. Low disulfidptosis score indicated favorable clinical outcomes, higher levels of immune cell infiltration, lower tumor purity, and enhanced immune responses. Furthermore, we noticed a clear disparity in tumor mutation load and drug responsiveness when comparing the high and low disulfidptosis score categories. Finally, a quantitative nomogram was built with disulfidptosis score and several clinical characteristics. The disulfidptosis-related signature provides new insights into the tumor immune microenvironment and complexity in HCC. The disulfidptosis score can serve as a promising tool for personalized prognostic prediction of HCC patients and for customizing more effective immunotherapeutic strategies.

Colorectal cancer tissue-originated spheroids reveal tumor intrinsic signaling pathways and mimic patient clinical chemotherapeutic response as a rapid and valid model

Locally advanced colorectal cancer requires preoperative chemotherapy to reduce local recurrence and metastasis rates, but it remains difficult to predict the tumor will be sensitive to which treatments. The patient-derived organoids (PDOs) are considered an effective platform for predicting tumor drug responses in precision oncology. However, it has the limitation of being time-consuming in practical applications, especially in neoadjuvant treatment. Here we used cancer tissue-originated spheroids (CTOS) method to establish organoids from a heterogeneous population of colorectal cancer specimens, and evaluated the capacity of CTOS to predict clinical drug responses. By analyzing the relationship of the activities of drug-treated CTOS, drug targets and target-related pathways, tumor intrinsic effective-target-related pathways can be identified. These pathways were highly matched to the abnormal pathways indicated by whole-exome sequencing. Based on this, we used half effective concentration gradients to classify CTOS as sensitive or resistant to chemotherapy regimens within a week, for predicting neoadjuvant treatment outcomes for colorectal cancer patients. The drug sensitivity test results are highly matched to the clinical responses to treatment in individual patients. Thus, our data suggested that CTOS models can be effectively screened ex vivo to identify pathways sensitive to chemotherapies. These data also supported organoid research for personalized clinical medication guidance immediately after diagnosis in patients with advanced colorectal cancer.

The Era of Immunotherapy in Hepatocellular Carcinoma: The New Mission and Challenges of Magnetic Resonance Imaging.

In recent years, significant advancements in immunotherapy for hepatocellular carcinoma (HCC) have shown the potential to further improve the prognosis of patients with advanced HCC. However, in clinical practice, there is still a lack of effective biomarkers for identifying the patient who would benefit from immunotherapy and predicting the tumor response to immunotherapy. The immune microenvironment of HCC plays a crucial role in tumor development and drug responses. However, due to the complexity of immune microenvironment, currently, no single pathological or molecular biomarker can effectively predict tumor responses to immunotherapy. Magnetic resonance imaging (MRI) images provide rich biological information; existing studies suggest the feasibility of using MRI to assess the immune microenvironment of HCC and predict tumor responses to immunotherapy. Nevertheless, there are limitations, such as the suboptimal performance of conventional MRI sequences, incomplete feature extraction in previous deep learning methods, and limited interpretability. Further study needs to combine qualitative features, quantitative parameters, multi-omics characteristics related to the HCC immune microenvironment, and various deep learning techniques in multi-center research cohorts. Subsequently, efforts should also be undertaken to construct and validate a visual predictive tool of tumor response, and assess its predictive value for patient survival benefits. Additionally, future research endeavors must aim to provide an accurate, efficient, non-invasive, and highly interpretable method for predicting the effectiveness of immune therapy.

Continuous glucose metabolism monitoring platform for long-term analysis of tumor cell proliferation and drug response

Glucose metabolism monitoring has emerged as a crucial method for analyzing the metabolic state of tumor cells. Conventional glucose measurement techniques only provide endpoint information and lack the ability for real-time monitoring. Therefore, it is imperative to develop a continuous and long-term monitoring approach to gain insights into cellular behavior and metabolic regulation. We developed a selective permeation outer membrane specifically designed for high glucose concentrations in cell culture, allowing continuous operation in the range of 0–32 mM, significantly exceeding the linear range of typical continuous glucose sensing systems (CGMS). The platform enabled real-time and continuous monitoring of glucose consumption during tumor cell proliferation, demonstrating high linearity, stability, and anti-interference in the cell culture medium. More importantly, by optimizing the electrode design, we achieved accurate and long-term online glucose monitoring for over seven days, double the current capacity. The prepared sensors have enabled continuous monitoring of glucose metabolism parameters in different cell lines, cell numbers and cells treated with different drugs, providing a promising strategy to explore cellular metabolism. The glucose monitoring platform holds tremendous potential in various fields, including drug screening, toxicology, cancer therapy and personalized medicine.

Three-dimensional heterotypic colorectal cancer spheroid models for evaluation of drug response.

Colorectal cancer (CRC) is a leading cause of death worldwide. Improved preclinical tumor models are needed to make treatment screening clinically relevant and address disease mortality. Advancements in 3D cell culture have enabled a greater recapitulation of the architecture and heterogeneity of the tumor microenvironment (TME). This has enhanced their pathophysiological relevance and enabled more accurate predictions of tumor progression and drug response in patients. An increasing number of 3D CRC spheroid models include cell populations such as cancer-associated fibroblasts (CAFs), endothelial cells (ECs), immune cells, and gut bacteria to better mimic the in vivo regulation of signaling pathways. Furthermore, cell heterogeneity within the 3D spheroid models enables the identification of new therapeutic targets to develop alternative treatments and test TME-target therapies. In this mini review, we present the advances in mimicking tumor heterogeneity in 3D CRC spheroid models by incorporating CAFs, ECs, immune cells, and gut bacteria. We introduce how, in these models, the diverse cells influence chemoresistance and tumor progression of the CRC spheroids. We also highlight important parameters evaluated during drug screening in the CRC heterocellular spheroids.

Identification of a distinct tumor endothelial cell-related gene expression signature associated with patient prognosis and immunotherapy response in multiple cancers.

Tumor endothelial cells (TECs) play a significant role in regulating the tumor microenvironment, drug response, and immune cell activities in various cancers. However, the association between TEC gene expression signature and patient prognosis or therapeutic response remains poorly understood. We analyzed transcriptomics data of normal and tumor endothelial cells obtained from the GEO database to identify differentially expressed genes (DEGs) associated with TECs. We then compared these DEGs with those commonly found across five different tumor types from the TCGA database to determine their prognostic relevance. Using these genes, we constructed a prognostic risk model integrated with clinical features to develop a nomogram model, which we validated through biological experiments. We identified 12 TEC-related prognostic genes across multiple tumor types, of which five genes were sufficient to construct a prognostic risk model with an AUC of 0.682. The risk scores effectively predicted patient prognosis and immunotherapeutic response. Our newly developed nomogram model provided more accurate prognostic estimates of cancer patients than the TNM staging method (AUC = 0.735) and was validated using external patient cohorts. Finally, RT-PCR and immunohistochemical analyses indicated that the expression of these 5 TEC-related prognostic genes was up-regulated in both patient-derived tumors and cancer cell lines, while depletion of the hub genes reduced cancer cell growth, migration and invasion, and enhanced their sensitivity to gemcitabine or cytarabine. Our study discovered the first TEC-related gene expression signature that can be used to construct a prognostic risk model for guiding treatment options in multiple cancers.

Metabonomic analysis of tumor microenvironments: a mini-review.

Metabolomic analysis is a vital part of studying cancer progression. Metabonomic crosstalk, such as nutrient availability, physicochemical transformation, and intercellular interactions can affect tumor metabolism. Many original studies have demonstrated that metabolomics is important in some aspects of tumor metabolism. In this mini-review, we summarize the definition of metabolomics and how it can help change a tumor microenvironment, especially in pathways of three metabonomic tumors. Just as non-invasive biofluids have been identified as early biomarkers of tumor development, metabolomics can also predict differences in tumor drug response, drug resistance, and efficacy. Therefore, metabolomics is important for tumor metabolism and how it can affect oncology drugs in cancer therapy.

Impact of hydrogel biophysical properties on tumor spheroid growth and drug response

The extracellular matrix (ECM) plays a critical role in regulating cell-matrix interactions during tumor progression. These interactions are due in large part to the biophysical properties responding to cancer cell interactions. Within in vitro models, the ECM is mimicked by hydrogels, which possess adjustable biophysical properties that are integral to tumor development. This work presents a systematic and comparative study on the impact of the biophysical properties of two widely used natural hydrogels, Matrigel and collagen gel, on tumor growth and drug response. The biophysical properties of Matrigel and collagen including complex modulus, loss tangent, diffusive permeability, and pore size, were characterised. Then the spheroid growth rates in these two hydrogels were monitored for spheroids with two different sizes (140 μm and 500 μm in diameters). An increased migratory growth was observed in the lower concentration of both the gels. The effect of spheroid incorporation within the hydrogel had a minimal impact on the hydrogel's complex modulus. Finally, 3D tumor models using different concentrations of hydrogels were applied for drug treatment using paclitaxel. Spheroids cultured in hydrogels with different concentrations showed different drug response, demonstrating the significant effect of the choice of hydrogels and their concentrations on the drug response results despite using the same spheroids. This study provides useful insights into the effect of hydrogel biophysical properties on spheroid growth and drug response and highlights the importance of hydrogel selection and in vitro model design.

Abstract 202: Perfused patient-derived tumor organoid models with autologous immune cells for preclinical drug development

Abstract The paradigm shift towards inclusion of primary patient samples in preclinical drug development bottlenecks at the tissue procurement step due to an on-going lack of ’research-ready’ tumor samples. Organoid technology was developed as a solution to this hurdle. Their ability to expand in culture makes them a renewable resource similar to cell lines while recapitulating key properties of the primary tissue, including tumor heterogeneity, structural organization and drug responses. As preclinical drug development continues to adopt organoid-based screening technology and immune-oncology agents continue to be advanced, there is high demand for more complex organoid-based models capable of more accurately predicting individual patient drug responses. Due to their development process, the vast majority of organoids do not contain immune cells, resulting in the need to utilize allogeneic immune cells for any immune cell involved interrogation of the models. Maintaining the immune cells for incorporation with the organoid model can overcome this hindrance. Additionally, perfused organoid models offer a complex, ex vivo model with the ability to replicate the mechanical forces imparted by blood flow and interstitial pressure. Compared to 2D and static 3D cultures, perfused 3D culture models show enhanced proliferative and migratory capacities and can be sustained as long-term cultures. In our previous studies, DNA methylation signatures in patient-derived breast microtumors grown in Kiyatec’s 3DKUBE™ perfusion bioreactor matched the primary tissues of derivation. Given the need for more complex biology when assessing immune-oncology agents, the physiological relevance in maintaining gene expression ex vivo, and the capability to sustain long-term viability, the impact of autologous immune cells and perfusion on the success of organotypic culture was investigated across a panel of primary patient tumors. Immune cells were profiled for status and activity and imaging and viability assays were used to evaluate static and perfused 3D organoid models for differences in viability and morphology. PCR was used to examine how mechanically transduced forces alter gene expression and DNA methylation signatures. Drug responses were measured using our KIYA-Predict™ platform, comparing organoid drug responses to primary tissue drug responses. Results of this study provide insight on alternative methods of organotypic culture and evidence for the utilization of more complex tissue modeling in preclinical drug development. Citation Format: Melissa Millard, Natalie Dance, Teresa M. DesRochers. Perfused patient-derived tumor organoid models with autologous immune cells for preclinical drug development [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 202.

Abstract 196: A platform for rapid patient-derived cutaneous neurofibroma organoid establishment and screening

Abstract Localized cutaneous neurofibromas (cNFs) are benign tumors that arise in the dermis of patients affected by Neurofibromatosis Type 1 syndrome, a common disorder driven by alterations in the NF1 gene. cNF tumors are heterogenous and comprised of different cell types such as Schwann cells, fibroblasts, and mast cells. While cNFs do not undergo malignant transformation, they carry significant co-morbidities, including itching, pain, and socio-emotional repercussions. There is currently no therapy for cNF beside surgical removal or desiccation, which are typically unable to eliminate all lesions due to their number, with patients developing as many as several hundred cNFs, as well as additional concerns of scarring and potential re-growth. A lack of models to investigate cNFs growth and perform drug screenings has hindered drug discovery and development studies thus far. We set out to develop high throughput screening-compatible patient-derived cNF organoids to perform translational studies. Tumor organoids are promising ex vivo models to recapitulate a patient’s tumor histology, molecular features, and drug responses. To support drug discovery efforts focused on identifying effective systemic therapies for cNF, we have developed an approach to routinely establish and screen cNF tumor organoids. Here we present of our systematic characterization of media conditions together with molecular and functional analysis to validate cNF organoids as a model system. We enrolled n=12 Neurofibromatosis Type 1 syndrome patients in this study and procured cNF tumors that we dissociated to single cells to establish organoids in nine different media compositions. We performed a detailed comparison to the tumor of origin using a combination of immunohistochemistry, flow cytometry, DNA methylation and RNAseq for a subset of samples (n=6 tumors from 5 patients). cNF organoids were successfully established and grown in all cases, regardless of the NF1 alteration present. We determined the optimal medium that had the highest combined correlation (parental vs organoid) across all assays as well as promoted the highest rate of growth ex vivo. We also demonstrated feasibility of screening cNF organoids using our established mini-ring pipeline (Phan et al, 2019; Nguyen and Soragni, 2020; Al Shihabi et al, 2022). In summary, we have optimized conditions for ex vivo growth of cNF organoids that closely recapitulate the molecular and cellular heterogeneity of these tumors as measured by immunohistopathology, DNA methylation, RNAseq and flow cytometry. Our tractable patient-derived cNF organoid platform enables the rapid screening of hundreds of FDA-approved drugs in a patient- and tumor-specific manner. Citation Format: Huyen T. Nguyen, Emily Kohl, Jessica Bade, Stefan E. Eng, Anela Tosevska, Ahmad Al Shihabi, Jenny J. Hong, Sarah Dry, Paul C. Boutros, Andre Panossian, Sara Gosline, Alice Soragni. A platform for rapid patient-derived cutaneous neurofibroma organoid establishment and screening [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 196.

Establishing a Microfluidic Tumor Slice Culture Platform to Study Drug Response.

Accurate models for tumor biology and prediction of drug responses of individual tumors require novel technology to grow tumor tissue ex vivo to maintain tumor growth characteristics in situ. Models containing only tumor cells, without the stromal components of the tumor, are suboptimal for many purposes and are generally problematic because the cells are passed through extensive culture and selection. Therefore, direct culture of (human) tumors is of considerable interest for basic tumor biology and diagnostic purposes. Microfluidic technologies have been proposed to accurately mimic physiological conditions for tissue growth. Most published systems build tissues from individual cell types in so-called Organ-on-Chip (OoC) cultures. We here describe a novel OoC device for growing tumor specimens. Thin tumor slices are grown in a microfluidic 'chip' that allows precisely controlled in vitro culture conditions. The performance of the OoC device was extensively validated for predicting therapeutic responses in human breast cancer patient-derived xenograft (PDX) tumor material. The system is amenable to primary tumor material from surgery or biopsies. In addition to using the model to predict and evaluate therapeutic responses, the model can also be used for mechanistic studies of human cancers, such as clonal evolution or immune responses, or to validate new or repurposed (cancer) drugs. The Bi/ond Cancer-on-Chip (CoC) device is designed to culture tumor slices and investigate aspects of tumor growth and drug responses. Here, we describe the step-by-step process of setting up tumor slice cultures using a Bi/ond CoC device and performing in vitro drug response evaluation. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Establishment of breast cancer tumor slice culture using a microfluidic cancer-on-chip platform for chemotherapy testing ex vivo Basic Protocol 2: Histology and immunohistochemistry-based analysis of tumor tissue architecture, cell proliferation, and cell death.

Identifying CDC7 as a synergistic target of chemotherapy in resistant small-cell lung cancer via CRISPR/Cas9 screening

There is currently a lack of efficacious treatments for patients with chemo-resistant small-cell lung cancer (SCLC), leading to poor prognoses. We examined a chemo-resistant SCLC cell line using genome-wide CRISPR/Cas9 screening and identified serine/threonine kinase cell division cycle 7 (CDC7) as a potential synergistic target. Silencing CDC7 in chemo-resistant SCLC cells decreased the IC50 and improved the efficacy of chemotherapy. Based on the highest single agent model, the CDC7 inhibitor XL413 had a synergistic effect with both cisplatin and etoposide in chemo-resistant SCLC cells, but had no such effect in chemo-sensitive SCLC cells; the combination of XL413 and chemotherapy significantly inhibited cell growth. Western blot and flow cytometry showed that the combined treatments increased apoptosis, whereas XL413 alone had little effect on apoptosis. An analysis of cell cycle and cyclin protein levels indicated that the combination of XL413 and chemotherapy-induced G1/S phase arrest and DNA damage in chemo-resistant SCLC cells. Xenografted tumor and histoculture drug response assays using patient-derived xenografts showed that XL413 improved the efficacy of chemotherapy in vivo and with SCLC tissues. These results suggest that XL413 exerts a synergistic effect with chemotherapy on chemo-resistant SCLC.

Papillary thyroid cancer organoids harboring BRAFV600E mutation reveal potentially beneficial effects of BRAF inhibitor-based combination therapies

BackgroundsPapillary thyroid cancer (PTC), which is often driven by acquired somatic mutations in BRAF genes, is the most common pathologic type of thyroid cancer. PTC has an excellent prognosis after treatment with conventional therapies such as surgical resection, thyroid hormone therapy and adjuvant radioactive iodine therapy. Unfortunately, about 20% of patients develop regional recurrence or distant metastasis, making targeted therapeutics an important treatment option. Current in vitro PTC models are limited in representing the cellular and mutational characteristics of parental tumors. A clinically relevant tool that predicts the efficacy of therapy for individuals is urgently needed.MethodsSurgically removed PTC tissue samples were dissociated, plated into Matrigel, and cultured to generate organoids. PTC organoids were subsequently subjected to histological analysis, DNA sequencing, and drug sensitivity assays, respectively.ResultsWe established 9 patient-derived PTC organoid models, 5 of which harbor BRAFV600E mutation. These organoids have been cultured stably for more than 3 months and closely recapitulated the histological architectures as well as mutational landscapes of the respective primary tumors. Drug sensitivity assays of PTC organoid cultures demonstrated the intra- and inter-patient specific drug responses. BRAFV600E inhibitors, vemurafenib and dabrafenib monotherapy was mildly effective in treating BRAFV600E-mutant PTC organoids. Nevertheless, BRAF inhibitors in combination with MEK inhibitors, RTK inhibitors, or chemotherapeutic agents demonstrated improved efficacy compared to BRAF inhibition alone.ConclusionsThese data indicate that patient-derived PTC organoids may be a powerful research tool to investigate tumor biology and drug responsiveness, thus being useful to validate or discover targeted drug combinations.