Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

Harnessing the power of artificial intelligence for human living organoid research

As a powerful paradigm, artificial intelligence (AI) is rapidly impacting every aspect of our day-to-day life and scientific research through interdisciplinary transformations. Living human organoids (LOs) have a great potential for in vitro reshaping many aspects of in vivo true human organs, including organ development, disease occurrence, and drug responses. To date, AI has driven the revolutionary advances of human organoids in life science, precision medicine and pharmaceutical science in an unprecedented way. Herein, we provide a forward-looking review, the frontiers of LOs, covering the engineered construction strategies and multidisciplinary technologies for developing LOs, highlighting the cutting-edge achievements and the prospective applications of AI in LOs, particularly in biological study, disease occurrence, disease diagnosis and prediction and drug screening in preclinical assay. Moreover, we shed light on the new research trends harnessing the power of AI for LO research in the context of multidisciplinary technologies. The aim of this paper is to motivate researchers to explore organ function throughout the human life cycle, narrow the gap between in vitro microphysiological models and the real human body, accurately predict human-related responses to external stimuli (cues and drugs), accelerate the preclinical-to-clinical transformation, and ultimately enhance the health and well-being of patients.

Organoids: development and applications in disease models, drug discovery, precision medicine, and regenerative medicine

Organoids are miniature, highly accurate representations of organs that capture the structure and unique functions of specific organs. Although the field of organoids has experienced exponential growth, driven by advances in artificial intelligence, gene editing, and bioinstrumentation, a comprehensive and accurate overview of organoid applications remains necessary. This review offers a detailed exploration of the historical origins and characteristics of various organoid types, their applications-including disease modeling, drug toxicity and efficacy assessments, precision medicine, and regenerative medicine-as well as the current challenges and future directions of organoid research. Organoids have proven instrumental in elucidating genetic cell fate in hereditary diseases, infectious diseases, metabolic disorders, and malignancies, as well as in the study of processes such as embryonic development, molecular mechanisms, and host-microbe interactions. Furthermore, the integration of organoid technology with artificial intelligence and microfluidics has significantly advanced large-scale, rapid, and cost-effective drug toxicity and efficacy assessments, thereby propelling progress in precision medicine. Finally, with the advent of high-performance materials, three-dimensional printing technology, and gene editing, organoids are also gaining prominence in the field of regenerative medicine. Our insights and predictions aim to provide valuable guidance to current researchers and to support the continued advancement of this rapidly developing field.

A cuproptosis-related lncRNAs signature predicts prognosis and reveals pivotal interactions between immune cells in colon cancer

Copper-mediated cell death presents distinct pathways from established apoptosis processes, suggesting alternative therapeutic approaches for colon cancer. Our research aims to develop a predictive framework utilizing long-noncoding RNAs (lncRNAs) related to cuproptosis to predict colon cancer outcomes while examining immune interactions and intercellular signaling. We obtained colon cancer-related human mRNA expression profiles and clinical information from the Cancer Genome Atlas repository. To isolate lncRNAs involved in cuproptosis, we applied Cox proportional hazards modeling alongside the least absolute shrinkage and selection operator technique. We elucidated the underlying mechanisms by examining the tumor mutational burden, the extent of immune cell penetration, and intercellular communication dynamics. Based on the model, drugs were predicted and validated with cytological experiments. A 13 lncRNA-cuproptosis-associated risk model was constructed. Two colon cancer cell lines were used to validate the predicted representative mRNAs with high correlation coefficients with copper-induced cell death. Survival enhancement in the low-risk cohort was evidenced by the trends in Kaplan-Meier survival estimates. Analysis of immune cell infiltration suggested that survival was induced by the increased infiltration of naïve CD4+ T cells and a reduction of M2 macrophages within the low-risk faction. Decreased infiltration of naïve B cells, resting NK cells, and M0 macrophages was significantly associated with better overall survival. Combined single-cell analysis suggested that CCL5-ACKR1, CCL2-ACKR1, and CCL5-CCR1 pathways play key roles in mediating intercellular dialogues among immune constituents within the neoplastic microhabitat. We identified three drugs with a high sensitivity in the high-risk group. In summary, this discovery establishes the possibility of using 13 cuproptosis-associated lncRNAs as a risk model to assess the prognosis, unravel the immune mechanisms and cell communication, and improve treatment options, which may provide a new idea for treating colon cancer.

Advancements in Research and Treatment Applications of Patient-Derived Tumor Organoids in Colorectal Cancer

Colorectal cancer (CRC) remains a significant health burden globally, being the second leading cause of cancer-related mortality. Despite significant therapeutic advancements, resistance to systemic antineoplastic agents remains an important obstacle, highlighting the need for innovative screening tools to tailor patient-specific treatment. This review explores the application of patient-derived tumor organoids (PDTOs), three-dimensional, self-organizing models derived from patient tumor samples, as screening tools for drug resistance in CRC. PDTOs offer unique advantages over traditional models by recapitulating the tumor architecture, cellular heterogeneity, and genomic landscape and are a valuable ex vivo predictive drug screening tool. This review provides an overview of the current literature surrounding the use of PDTOs as an instrument for predicting therapy responses in CRC. We also explore more complex models, such as co-cultures with important stromal cells, such as cancer-associated fibroblasts, and organ-on-a-chip models. Furthermore, we discuss the use of PDTOs for drug repurposing, offering a new approach to identify the existing drugs effective against drug-resistant CRC. Additionally, we explore how PDTOs serve as models to gain insights into drug resistance mechanisms, using newer techniques, such as single-cell RNA sequencing and CRISPR-Cas9 genome editing. Through this review, we aim to highlight the potential of PDTOs in advancing our understanding of predicting therapy responses, drug resistance, and biomarker identification in CRC management.

Advancing cancer therapeutics: Integrating scalable 3D cancer models, extracellular vesicles, and omics for enhanced therapy efficacy

Cancer remains as one of the highest challenges to human health. However, anticancer drugs exhibit one of the highest attrition rates compared to other therapeutic interventions. In part, this can be attributed to a prevalent use of in vitro models with limited recapitulative potential of the in vivo settings. Three dimensional (3D) models, such as tumor spheroids and organoids, offer many research opportunities to address the urgent need in developing models capable to more accurately mimic cancer biology and drug resistance profiles. However, their wide adoption in high-throughput pre-clinical studies is dependent on scalable manufacturing to support large-scale therapeutic drug screenings and multi-omic approaches for their comprehensive cellular and molecular characterization. Extracellular vesicles (EVs), which have been emerging as promising drug delivery systems (DDS), stand to significantly benefit from such screenings conducted in realistic cancer models. Furthermore, the integration of these nanomedicines with 3D cancer models and omics profiling holds the potential to deepen our understanding of EV-mediated anticancer effects. In this chapter, we provide an overview of the existing 3D models used in cancer research, namely spheroids and organoids, the innovations in their scalable production and discuss how omics can facilitate the implementation of these models at different stages of drug testing. We also explore how EVs can advance drug delivery in cancer therapies and how the synergy between 3D cancer models and omics approaches can benefit in this process.

Organoids for Cancer Research: Advances and Challenges

As 3D culture technology advances, new avenues have opened for the development of physiological human cancer models. These preclinical models provide efficient ways to translate basic cancer research into clinical tumor therapies. Recently, cancer organoids have emerged as a model to dissect the more complex tumor microenvironment. Incorporating cancer organoids into preclinical programs have the potential to increase the success rate of oncology drug development and recapitulate the most efficacious treatment regimens for cancer patients. In this review, four main types of cancer organoids are introduced, their applications, advantages, limitations, and prospects are discussed, as well as the recent application of single-cell RNA-sequencing (scRNA-seq) in exploring cancer organoids to advance this field.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Patient-derived tumor organoids: a new avenue for preclinical research and precision medicine in oncology

Over the past decade, the emergence of patient-derived tumor organoids (PDTOs) has broadened the repertoire of preclinical models and progressively revolutionized three-dimensional cell culture in oncology. PDTO can be grown from patient tumor samples with high efficiency and faithfully recapitulates the histological and molecular characteristics of the original tumor. Therefore, PDTOs can serve as invaluable tools in oncology research, and their translation to clinical practice is exciting for the future of precision medicine in oncology. In this review, we provide an overview of methods for establishing PDTOs and their various applications in cancer research, starting with basic research and ending with the identification of new targets and preclinical validation of new anticancer compounds and precision medicine. Finally, we highlight the challenges associated with the clinical implementation of PDTO, such as its representativeness, success rate, assay speed, and lack of a tumor microenvironment. Technological developments and autologous cocultures of PDTOs and stromal cells are currently ongoing to meet these challenges and optimally exploit the full potential of these models. The use of PDTOs as standard tools in clinical oncology could lead to a new era of precision oncology in the coming decade.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

Lung Organoids: Systematic Review of Recent Advancements and its Future Perspectives

Organoids are essentially an in vitro (lab-grown) three-dimensional tissue culture system model that meticulously replicates the structure and physiology of human organs. A few of the present applications of organoids are in the basic biological research area, molecular medicine and pharmaceutical drug testing. Organoids are crucial in connecting the gap between animal models and human clinical trials during the drug discovery process, which significantly lowers the time duration and cost associated with each stage of testing. Likewise, they can be used to understand cell-to-cell interactions, a crucial aspect of tissue biology and regeneration, and to model disease pathogenesis at various stages of the disease. Lung organoids can be utilized to explore numerous pathophysiological activities of a lung since they share similarities with its function. Researchers have been trying to recreate the complex nature of the lung by developing various "Lung organoids" models.This article is a systematic review of various developments of lung organoids and their potential progenitors. It also covers the in-depth applications of lung organoids for the advancement of translational research. The review discusses the methodologies to establish different types of lung organoids for studying the regenerative capability of the respiratory system and comprehending various respiratory diseases.Respiratory diseases are among the most common worldwide, and the growing burden must be addressed instantaneously. Lung organoids along with diverse bio-engineering tools and technologies will serve as a novel model for studying the pathophysiology of various respiratory diseases and for drug screening purposes.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep learning (MFMDL). This model utilizes graph neural networks to map gene-gene relationships in gene networks to low dimensional vector spaces, and then fuses biological pathway features and immune cell infiltration features to make robust predictions of ICT. We used five datasets to validate the predictive performance of the MFMDL. These five datasets span multiple types of cancer, including melanoma, lung cancer, and gastric cancer. We found that the prediction performance of multi-modal feature fusion model based on deep learning is superior to other traditional ICT biomarkers, such as ICT targets or tumor microenvironment-associated markers. In addition, we also conducted ablation experiments to demonstrate the necessity of fusing different modal features, which can improve the prediction accuracy of the model.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Exploring tumor organoids for cancer treatment

As a life-threatening chronic disease, cancer is characterized by tumor heterogeneity. This heterogeneity is associated with factors that lead to treatment failure and poor prognosis, including drug resistance, relapse, and metastasis. Therefore, precision medicine urgently needs personalized tumor models that accurately reflect the tumor heterogeneity. Currently, tumor organoid technologies are used to generate in vitro 3D tissues, which have been shown to precisely recapitulate structure, tumor microenvironment, expression profiles, functions, molecular signatures, and genomic alterations in primary tumors. Tumor organoid models are important for identifying potential therapeutic targets, characterizing the effects of anticancer drugs, and exploring novel diagnostic and therapeutic options. In this review, we describe how tumor organoids can be cultured and summarize how researchers can use them as an excellent tool for exploring cancer therapies. In addition, we discuss tumor organoids that have been applied in cancer therapy research and highlight the potential of tumor organoids to guide preclinical research.

Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning

Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools, capable of classifying cancer cells based on their functions, may substantially enhance therapy and extend patient life span. The connection between cell biomechanics and cancer cell functions is used here to classify cells through mechanical measurements, via particle uptake. Machine learning (ML) was used to classify cells based on single-cell patterns of uptake of particles with diverse sizes. Three pairs of human cancer cell subpopulations, varied in their level of drug resistance or malignancy, were studied. Cells were allowed to interact with fluorescently labeled polystyrene particles ranging in size from 0.04 to 3.36 μm and analyzed for their uptake patterns using flow cytometry. ML algorithms accurately classified cancer cell subtypes with accuracy rates exceeding 95%. The uptake data were especially advantageous for morphologically similar cell subpopulations. Moreover, the uptake data were found to serve as a form of "normalization" that could reduce variation in repeated experiments.

Organoid bioinks: construction and application

Organoids have emerged as crucial platforms in tissue engineering and regenerative medicine but confront challenges in faithfully mimicking native tissue structures and functions. Bioprinting technologies offer a significant advancement, especially when combined with organoid bioinks-engineered formulations designed to encapsulate both the architectural and functional elements of specific tissues. This review provides a rigorous, focused examination of the evolution and impact of organoid bioprinting. It emphasizes the role of organoid bioinks that integrate key cellular components and microenvironmental cues to more accurately replicate native tissue complexity. Furthermore, this review anticipates a transformative landscape invigorated by the integration of artificial intelligence with bioprinting techniques. Such fusion promises to refine organoid bioink formulations and optimize bioprinting parameters, thus catalyzing unprecedented advancements in regenerative medicine. In summary, this review accentuates the pivotal role and transformative potential of organoid bioinks and bioprinting in advancing regenerative therapies, deepening our understanding of organ development, and clarifying disease mechanisms.

Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection

Colorectal cancer (CRC) poses significant challenges in chemotherapy response prediction due to its molecular heterogeneity. This study introduces an innovative methodology that leverages gene expression data generated from matched colorectal tumor and organoid samples to enhance prediction accuracy. By applying Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identify critical gene modules and hub genes that correlate with patient responses, particularly to 5-fluorouracil (5-FU). This integrative approach advances precision medicine by refining chemotherapy regimen selection based on individual tumor profiles. Our predictive model demonstrates superior accuracy over traditional methods on independent datasets, illustrating significant potential in addressing the complexities of high-dimensional genomic data for cancer biomarker research.

Integration of 3D bioprinting and multi-algorithm machine learning identified glioma susceptibilities and microenvironment characteristics

Glioma, with its heterogeneous microenvironments and genetic subtypes, presents substantial challenges for treatment prediction and development. We integrated 3D bioprinting and multi-algorithm machine learning as a novel approach to enhance the assessment and understanding of glioma treatment responses and microenvironment characteristics. The bioprinted patient-derived glioma tissues successfully recapitulated molecular properties and drug responses of native tumors. We then developed GlioML, a machine learning workflow incorporating nine distinct algorithms and a weighted ensemble model that generated robust gene expression-based predictors, each reflecting the diverse action mechanisms of various compounds and drugs. The ensemble model superseded the performance of all individual algorithms across diverse in vitro systems, including sphere cultures, complex 3D bioprinted multicellular models, and 3D patient-derived tissues. By integrating bioprinting, the evaluative scope of the treatment expanded to T cell-related therapy and anti-angiogenesis targeted therapy. We identified promising compounds and drugs for glioma treatment and revealed distinct immunosuppressive or angiogenic myeloid-infiltrated tumor microenvironments. These insights pave the way for enhanced therapeutic development for glioma and potentially for other cancers, highlighting the broad application potential of this integrative and translational approach.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Elaboration and validation of a novelty nomogram for the prognostication of anxiety susceptibility in individuals suffering from low back pain

Low back pain (LBP) constitutes a distressing emotional ordeal and serves as a potent catalyst for adverse emotional states, notably anxiety. We dedicated to discerning methodologies for identifying patients who are predisposed to heightened levels of anxiety and pain. A self-assessment questionnaire was administered to patients afflicted with LBP. The pain scores were subjected to analysis in conjunction with anxiety scores, and a clustering procedure was executed using the scientific k-means methodology. Subsequently, six machine learning algorithms, including Logistics Regression (LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB), were employed. Next, five pertinent variables were identified, namely Age, Course, Body Mass Index (BMI), Education, and Marital status. Furthermore, a LR model was utilized to construct a nomogram, which was subsequently subjected to assessment for discrimination, calibration, and evaluation of its clinical utility. As a result, 599 questionnaires were valid (effective rate: 99 %). The correlation analysis revealed a significant association between anxiety and pain scores (r = 0.31, P < 0.001). LBP patients could be divided into two clusters, Cluster1 had higher pain scores (P < 0.05) and SAS scores (P < 0.001). The proposed nomogram demonstrated an area under the receiver operating characteristics curve (ROC) of 0.841 (95 %CI: 0.804-0.878) and 0.800 (95 %CI: 0.733-0.867) in the training and test groups, respectively. Briefly, the established nomogram has demonstrated remarkable proficiency in discerning individuals afflicted with LBP who are at a heightened risk of experiencing anxiety.

The Transcriptional Landscape of Immune-Response 3'-UTR Alternative Polyadenylation in Melanoma

The prognosis of patients with malignant melanoma has been improved in recent decades due to advancements in immunotherapy. However, a considerable proportion of patients are refractory to treatment, particularly at advanced stages. This underscores the necessity of developing a new strategy to improve it. Alternative polyadenylation (APA), as a marker of crucial posttranscriptional regulation, has emerged as a major new type of epigenetic marker involved in tumorigenesis. However, the potential roles of APA in shaping the tumor microenvironment (TME) are largely unexplored. Herein, we collected two cohorts comprising melanoma patients who received immune checkpoint inhibitor (ICI) immunotherapy to quantify transcriptome-wide discrepancies in APA. We observed a global change in 3'-UTRs between responders and non-responders, which might involve DNA damage response, angiogenesis, PI3K-AKT signaling pathways, etc. Ten putative master APA regulatory factors for those APA events were detected via a network analysis. Notably, we established an immune response-related APA scoring system (IRAPAss), which exhibited a great performance of predicting immunotherapy response in multiple cohorts. Furthermore, we examined the correlation of APA with TME at the single-cell level using four single-cell immune profiles of tumor-infiltrating lymphocytes (TILs), which revealed an overall discrepancy in 3'-UTR length across diverse T cell populations, probably contributing to immunoregulation in melanoma. In conclusion, our study provides a transcriptional landscape of APA implicated in immunoregulation, which might lay the foundation for developing a new strategy for improving immunotherapy response for melanoma patients by targeting APA.

Cancer organoids: A platform in basic and translational research

An accumulation of previous work has established organoids as good preclinical models of human tumors, facilitating translation from basic research to clinical practice. They are changing the paradigm of preclinical cancer research because they can recapitulate the heterogeneity and pathophysiology of human cancers and more closely approximate the complex tissue environment and structure found in clinical tumors than in vitro cell lines and animal models. However, the potential applications of cancer organoids remain to be comprehensively summarized. In the review, we firstly describe what is currently known about cancer organoid culture and then discuss in depth the basic mechanisms, including tumorigenesis and tumor metastasis, and describe recent advances in patient-derived tumor organoids (PDOs) for drug screening and immunological studies. Finally, the present challenges faced by organoid technology in clinical practice and its prospects are discussed. This review highlights that organoids may offer a novel therapeutic strategy for cancer research.

Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli. This synergy empowers the development of predictive models, precise disease simulations, and personalized medicine approaches, revolutionizing our understanding of human development, disease mechanisms, and therapeutic interventions. Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling, propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.

OBJECTIVE

This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer.

RESULTS

A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort.

CONCLUSIONS

Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

AI-enabled organoids: Construction, analysis, and application

Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.

Advances towards the use of gastrointestinal tumor patient-derived organoids as a therapeutic decision-making tool

In December 2022 the US Food and Drug Administration (FDA) removed the requirement that drugs in development must undergo animal testing before clinical evaluation, a declaration that now demands the establishment and verification of ex vivo preclinical models that closely represent tumor complexity and that can predict therapeutic response. Fortunately, the emergence of patient-derived organoid (PDOs) culture has enabled the ex vivo mimicking of the pathophysiology of human tumors with the reassembly of tissue-specific features. These features include histopathological variability, molecular expression profiles, genetic and cellular heterogeneity of parental tissue, and furthermore growing evidence suggests the ability to predict patient therapeutic response. Concentrating on the highly lethal and heterogeneous gastrointestinal (GI) tumors, herein we present the state-of-the-art and the current methodology of PDOs. We highlight the potential additions, improvements and testing required to allow the ex vivo of study the tumor microenvironment, as well as offering commentary on the predictive value of clinical response to treatments such as chemotherapy and immunotherapy.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer

OBJECTIVE

To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches.

METHODS

Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality.

RESULTS

Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts.

CONCLUSIONS

We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.

Application of colloidal photonic crystals in study of organoids

As alternative disease models, other than 2D cell lines and patient-derived xenografts, organoids have preferable in vivo physiological relevance. However, both endogenous and exogenous limitations impede the development and clinical translation of these organoids. Fortunately, colloidal photonic crystals (PCs), which benefit from favorable biocompatibility, brilliant optical manipulation, and facile chemical decoration, have been applied to the engineering of organoids and have achieved the desirable recapitulation of the ECM niche, well-defined geometrical onsets for initial culture, in situ multiphysiological parameter monitoring, single-cell biomechanical sensing, and high-throughput drug screening with versatile functional readouts. Herein, we review the latest progress in engineering organoids fabricated from colloidal PCs and provide inputs for future research.

Flourishing tumor organoids: History, emerging technology, and application

Malignant tumors are one of the leading causes of death which impose an increasingly heavy burden on all countries. Therefore, the establishment of research models that closely resemble original tumor characteristics is crucial to further understanding the mechanisms of malignant tumor development, developing safer and more effective drugs, and formulating personalized treatment plans. Recently, organoids have been widely used in tumor research owing to their advantages including preserving the structure, heterogeneity, and cellular functions of the original tumor, together with the ease of manipulation. This review describes the history and characteristics of tumor organoids and the synergistic combination of three-dimensional (3D) culture approaches for tumor organoids with emerging technologies, including tissue-engineered cell scaffolds, microfluidic devices, 3D bioprinting, rotating wall vessels, and clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 (CRISPR-Cas9). Additionally, the progress in research and the applications in basic and clinical research of tumor organoid models are summarized. This includes studies of the mechanism of tumor development, drug development and screening, precision medicine, immunotherapy, and simulation of the tumor microenvironment. Finally, the existing shortcomings of tumor organoids and possible future directions are discussed.

The role of organoids in cancer research

Organoids are established through in vitro 3D culture, and they can mimic the structure and physiological functions of organs or tissues in vivo. Organoids have attracted much attention in recent years. They can provide a reliable technology platform for cancer research and treatment and are a valuable preclinical model for academic research and personalized medicine. A number of studies have confirmed that organoids have great application prospects in new drug development, drug screening, tumour mechanism research, and precision medicine. In this review, we mainly focus on recent advances in the application of organoids in cancer research. We also discussed the opportunities and challenges facing organoids, hoping to indicate directions for the development of organoids in the future.

Tumor organoid model of colorectal cancer (Review)

The establishment of self-organizing 'mini-gut' organoid models has brought about a significant breakthrough in biomedical research. Patient-derived tumor organoids have emerged as valuable tools for preclinical studies, offering the retention of genetic and phenotypic characteristics of the original tumor. These organoids have applications in various research areas, including in vitro modelling, drug discovery and personalized medicine. The present review provided an overview of intestinal organoids, focusing on their unique characteristics and current understanding. The progress made in colorectal cancer (CRC) organoid models was then delved into, discussing their role in drug development and personalized medicine. For instance, it has been indicated that patient-derived tumor organoids are able to predict response to irinotecan-based neoadjuvant chemoradiotherapy. Furthermore, the limitations and challenges associated with current CRC organoid models were addressed, along with proposed strategies for enhancing their utility in future basic and translational research.

Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation

Drug combination therapy is a highly effective approach for enhancing the therapeutic efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable possible drug combinations make it impractical to screen all synergistic drug pairs. Moreover, biological insights into synergistic drug pairs are still lacking. To address this challenge, we systematically analyzed drug combination datasets curated from multiple databases to identify drug pairs more likely to show synergy. We classified drug pairs based on their MoA and discovered that 110 MoA pairs were significantly enriched in synergy in at least one type of cancer. To improve the accuracy of predicting synergistic effects of drug pairs, we developed a suite of machine learning models that achieve better predictive performance. Unlike most previous methods that were rarely validated by wet-lab experiments, our models were validated using two-dimensional cell lines and three-dimensional tumor slice culture (3D-TSC) models, implying their practical utility. Our prediction and validation results indicated that the combination of the RTK inhibitors Lapatinib and Pazopanib exhibited a strong therapeutic effect in breast cancer by blocking the downstream PI3K/AKT/mTOR signaling pathway. Furthermore, we incorporated molecular features to identify potential biomarkers for synergistic drug pairs, and almost all potential biomarkers found connections between drug targets and corresponding molecular features using protein-protein interaction network. Overall, this study provides valuable insights to complement and guide rational efforts to develop drug combination treatments.

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Pharmacogenomic-guided dosing of fluoropyrimidines beyond DPYD: time for a polygenic algorithm?

Fluoropyrimidines are chemotherapeutic agents widely used for the treatment of various solid tumors. Commonly prescribed FPs include 5-fluorouracil (5-FU) and its oral prodrugs capecitabine (CAP) and tegafur. Bioconversion of 5-FU prodrugs to 5-FU and subsequent metabolic activation of 5-FU are required for the formation of fluorodeoxyuridine triphosphate (FdUTP) and fluorouridine triphosphate, the active nucleotides through which 5-FU exerts its antimetabolite actions. A significant proportion of FP-treated patients develop severe or life-threatening, even fatal, toxicity. It is well known that FP-induced toxicity is governed by genetic factors, with dihydropyrimidine dehydrogenase (DPYD), the rate limiting enzyme in 5-FU catabolism, being currently the cornerstone of FP pharmacogenomics. DPYD-based dosing guidelines exist to guide FP chemotherapy suggesting significant dose reductions in DPYD defective patients. Accumulated evidence shows that additional variations in other genes implicated in FP pharmacokinetics and pharmacodynamics increase risk for FP toxicity, therefore taking into account more gene variations in FP dosing guidelines holds promise to improve FP pharmacotherapy. In this review we describe the current knowledge on pharmacogenomics of FP-related genes, beyond DPYD, focusing on FP toxicity risk and genetic effects on FP dose reductions. We propose that in the future, FP dosing guidelines may be expanded to include a broader ethnicity-based genetic panel as well as gene*gene and gender*gene interactions towards safer FP prescription.

Prediction model for drug response of acute myeloid leukemia patients

Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort (n = 278), and then validated in the BeatAML (n = 183) and two external cohorts, including a Swedish AML cohort (n = 45) and a relapsed/refractory acute leukemia cohort (n = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score >0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, -0.49 (95% CI: [-0.53, -0.44]) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/ .

Emerging roles of m6A RNA modification in cancer therapeutic resistance

Marvelous advancements have been made in cancer therapies to improve clinical outcomes over the years. However, therapeutic resistance has always been a major difficulty in cancer therapy, with extremely complicated mechanisms remain elusive. N6-methyladenosine (m6A) RNA modification, a hotspot in epigenetics, has gained growing attention as a potential determinant of therapeutic resistance. As the most prevalent RNA modification, m6A is involved in every links of RNA metabolism, including RNA splicing, nuclear export, translation and stability. Three kinds of regulators, "writer" (methyltransferase), "eraser" (demethylase) and "reader" (m6A binding proteins), together orchestrate the dynamic and reversible process of m6A modification. Herein, we primarily reviewed the regulatory mechanisms of m6A in therapeutic resistance, including chemotherapy, targeted therapy, radiotherapy and immunotherapy. Then we discussed the clinical potential of m6A modification to overcome resistance and optimize cancer therapy. Additionally, we proposed existing problems in current research and prospects for future research.

Tumor organoid biobank-new platform for medical research

Organoids are a new type of 3D model for tumor research, which makes up for the shortcomings of cell lines and xenograft models, and promotes the development of personalized precision medicine. Long-term culture, expansion and storage of organoids provide the necessary conditions for the establishment of biobanks. Biobanks standardize the collection and preservation of normal or pathological specimens, as well as related clinical information. The tumor organoid biobank has a good quality control system, which is conducive to the clinical transformation and large-scale application of tumor organoids, such as disease modeling, new drug development and high-throughput drug screening. This article summarized the common tumor types of patient-derived organoid (PDO) biobanks and the necessary information for biobank construction, such as the number of organoids, morphology, success rate of culture and resuscitation, pathological types. In our results, we found that patient-derived tumor organoid (PDTO) biobanks were being established more and more, with the Netherlands, the United States, and China establishing the most. Biobanks of colorectal, pancreas, breast, glioma, and bladder cancers were established more, which reflected the relative maturity of culture techniques for these tumors. In addition, we provided insights on the precautions and future development direction of PDTO biobank building.

Development of bioinformatics and multi-omics analyses in organoids

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

In modern terminology, "organoids" refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions.

Graphic abstract

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Systematic comparison of differential expression networks in MTB mono-, HIV mono- and MTB/HIV co-infections for drug repurposing

The synergy between human immunodeficiency virus (HIV) and Mycobacterium tuberculosis (MTB) could accelerate the deterioration of immunological functions. Previous studies have explored the pathogenic mechanisms of HIV mono-infection (HMI), MTB mono-infection (MMI) and MTB/HIV co-infection (MHCI), but their similarities and specificities remain to be profoundly investigated. We thus designed a computational framework named IDEN to identify gene pairs related to these states, which were then compared from different perspectives. MMI-related genes showed the highest enrichment level on a greater number of chromosomes. Genes shared by more states tended to be more evolutionarily conserved, posttranslationally modified and topologically important. At the expression level, HMI-specific gene pairs yielded higher correlations, while the overlapping pairs involved in MHCI had significantly lower correlations. The correlation changes of common gene pairs showed that MHCI shared more similarities with MMI. Moreover, MMI- and MHCI-related genes were enriched in more identical pathways and biological processes, further illustrating that MTB may play a dominant role in co-infection. Hub genes specific to each state could promote pathogen infections, while those shared by two states could enhance immune responses. Finally, we improved the network proximity measure for drug repurposing by considering the importance of gene pairs, and approximately ten drug candidates were identified for each disease state.

A path to translation: How 3D patient tumor avatars enable next generation precision oncology

3D patient tumor avatars (3D-PTAs) hold promise for next-generation precision medicine. Here, we describe the benefits and challenges of 3D-PTA technologies and necessary future steps to realize their potential for clinical decision making. 3D-PTAs require standardization criteria and prospective trials to establish clinical benefits. Innovative trial designs that combine omics and 3D-PTA readouts may lead to more accurate clinical predictors, and an integrated platform that combines diagnostic and therapeutic development will accelerate new treatments for patients with refractory disease.

Advances in Tumor Organoids for the Evaluation of Drugs: A Bibliographic Review

Tumor organoids are defined as self-organized three-dimensional assemblies of heterogeneous cell types derived from patient samples that mimic the key histopathological, genetic, and phenotypic characteristics of the original tumor. This technology is proposed as an ideal candidate for the evaluation of possible therapies against cancer, presenting advantages over other models which are currently used. However, there are no reports in the literature that relate the techniques and material development of tumor organoids or that emphasize in the physicochemical and biological properties of materials that intent to biomimicry the tumor extracellular matrix. There is also little information regarding the tools to identify the correspondence of native tumors and tumoral organoids (tumoroids). Moreover, this paper relates the advantages of organoids compared to other models for drug evaluation. A growing interest in tumoral organoids has arisen from 2009 to the present, aimed at standardizing the process of obtaining organoids, which more accurately resemble patient-derived tumor tissue. Likewise, it was found that the characteristics to consider for the development of organoids, and therapeutic responses of them, are cell morphology, physiology, the interaction between cells, the composition of the cellular matrix, and the genetic, phenotypic, and epigenetic characteristics. Currently, organoids have been used for the evaluation of drugs for brain, lung, and colon tumors, among others. In the future, tumor organoids will become closer to being considered a better model for studying cancer in clinical practice, as they can accurately mimic the characteristics of tumors, in turn ensuring that the therapeutic response aligns with the clinical response of patients.

Les organoïdes dérivés de tumeurs (ou tumoroïdes), des outils de choix pour la médecine de précision en oncologie

Il est désormais possible d’établir des tumoroïdes à partir de presque tout type de tumeur, notamment en vue de la mise en place de tests fonctionnels prédictifs et/ou de l’identification de signatures moléculaires prédictives. Bien que l’optimisation des conditions de culture ou la complexification du micro-environnement des tumoroïdes soit encore nécessaire, de nombreuses applications sont déjà envisageables dans le domaine de la prédiction de la réponse aux traitements et de l’orientation de la décision thérapeutique. Par l’introduction de leur utilisation en clinique, l’oncologie de précision pourrait bien entrer dans une nouvelle ère dans le courant de la décennie à venir.

The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance

The comprehensive regulation effect of eRNA on tumor immune cell infiltration and the outcome remains obscure. We comprehensively identify the eRNA-mediated immune infiltration patterns of gastric cancer (GC) samples. We creatively proposed a random forest machine-learning (ML) algorithm to map eRNA to mRNA expression patterns. The eRNA score was constructed using principal component analysis algorithms and validated in an independent cohort. Three subtypes with distinct eRNA expression patterns were determined in GC. There were significant differences between the three subtypes in the overall survival rate, immune cell infiltration characteristics, and immunotherapy response indicators. The patients in the high eRNA score group have a higher overall survival rate and might benefit from immunotherapy. This work revealed that eRNA regulation might be a new prognostic index and might offer a potential biomarker in the response of immunotherapy. Evaluating the eRNA regulation manner of GC will contribute to guiding more effective immunotherapy strategies.