Precision Cancer Medicine Research Articles

The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients.

Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.

Gastric cancer patient-derived organoids model for the therapeutic drug screening

BackgroundGastric cancer (GC) is a highly heterogeneous disease featuring many various histological and molecular subtypes. Therefore, it is imperative to have well-characterized in vitro models for personalized treatment development. Gastric cancer patient-derived organoids (PDOs), re-capitulating in vivo conditions, exhibit high clinical efficacy in predicting drug sensitivity to facilitate the development of cancer precision medicine. MethodsPDOs were established from surgically resected GC tumor tissues. Histological and molecular characterization of PDOs and primary tissues were performed via IHC and sequencing analysis. We also conducted drug sensitivity tests using PDO cultures with five chemotherapeutic drugs and twenty-two targeted drugs. ResultsWe have successfully constructed a PDOs biobank that included EBV+, intestinal/CIN, diffuse/GS, mixed and Her2+ GC subtypes, and these PDOs captured the pathological and genetic characteristics of corresponding tumors and exhibited different sensitivities to the tested agents. In a clinical case study, we performed an additional drug sensitivity test for a patient who reached an advanced progressive stage after surgery. We discovered that the combination of napabucasin and COTI-2 exhibited a stronger synergistic effect than either drug alone. ConclusionPDOs maintained the histological and genetic characteristics of original cancer tissues. PDOs biobank opens up new perspectives for studying cancer cell biology and personalized medicine as a preclinical study platform.

MVMSGAT: Integrating Multiview, Multi-Scale Graph Convolutional Networks with Biological Prior Knowledge for Predicting Bladder Cancer Response to Neoadjuvant Therapy

The incidence of bladder cancer is on the rise, and its molecular heterogeneity presents significant challenges for personalized cancer therapy. Transcriptome data can characterize the variability among patients. Traditional machine-learning methods often struggle with high-dimensional genomic data, falling into the ’curse of dimensionality’. To address this challenge, we have developed MVMSGAT, an innovative predictive model tailored for forecasting responses to neoadjuvant therapy in bladder cancer patients. MVMSGAT significantly enhances model performance by incorporating multi-perspective biological prior knowledge. It initially utilizes the Boruta algorithm to select key genes from transcriptome data, subsequently constructing a comprehensive graph of gene co-expression and protein–protein interactions. MVMSGAT further employs a graph convolutional neural network to integrate this information within a multiview knowledge graph, amalgamating biological knowledge maps from various scales using an attention mechanism. For validation, MVMSGAT was tested using a five-fold cross-validation approach on two specific GEO datasets, GSE169455 and GSE69795, involving a total of 210 bladder cancer samples. MVMSGAT demonstrated superior performance, with the following metrics (mean ± standard deviation): AUC-ROC of 0.8724±0.0511, accuracy of 0.7789±0.068, F1 score of 0.8529±0.0338, and recall of 0.9231±0.0719. These results underscore the potential of MVMSGAT in advancing personalized treatment and precision medicine in bladder cancer.

Understanding The Influence of Tumour Microenvironment Variability On Therapeutic Effectiveness

Cancer immunotherapy has proven effective in treating malignant diseases, but only a small percentage of patients experience completeand durable responses to current treatments. This highlights the need for more effective immunotherapies, combination treatments, andpredictive biomarkers. The molecular properties of a tumor, intratumor heterogeneity, and the tumor immune microenvironment are key targetsfor precision cancer medicine. Humanized mice that support the engraftment of patient-derived tumors and recapitulate the human tumorimmune microenvironment of patients represent a promising preclinical model for addressing fundamental questions in precision immuno-oncology and cancer immunotherapy. The microenvironmental physiology of tumors is unique from normal tissues, characterized by oxygendepletion, glucose and energy deprivation, high lactate levels, and extracellular acidosis. Hypoxia and other hostile microenvironmental parameterscan confer resistance to irradiation, leading to treatment failure. Pretreatment assessment of critical microenvironmental parameters is neededto select patients who could benefit from special treatment approaches, such as hypoxia-targeting therapy. Acquired resistance to varioustherapeutic interventions is a hallmark of cancer, and the tumor microenvironment (TME) plays a crucial role in cancer progression, particularlytherapeutic resistance. Targeting the TME as an essential strategy to overcome cancer resistance and improve therapeutic outcomes throughprecise intervention is highly desired. Cell stem cells (CSCs) play a pivotal role in tumorigenesis, tumor progression, and metastasis. Theinflammatory microenvironment is an essential component of the tumor microenvironment, and understanding the key factors that exertimportant actions in the tumor process would be important to improve clinical outcomes.

Interpreting and integrating genomic tests results in clinical cancer care: Overview and practical guidance.

The last decade has seen rapid progress in the use of genomic tests, including gene panels, whole-exome sequencing, and whole-genome sequencing, in research and clinical cancer care. These advances have created expansive opportunities to characterize the molecular attributes of cancer, revealing a subset of cancer-associated aberrations called driver mutations. The identification of these driver mutations can unearth vulnerabilities of cancer cells to targeted therapeutics, which has led to the development and approval of novel diagnostics and personalized interventions in various malignancies. The applications of this modern approach, often referred to as precision oncology or precision cancer medicine, are already becoming a staple in cancer care and will expand exponentially over the coming years. Although genomic tests can lead to better outcomes by informing cancer risk, prognosis, and therapeutic selection, they remain underutilized in routine cancer care. A contributing factor is a lack of understanding of their clinical utility and the difficulty of results interpretation by the broad oncology community. Practical guidelines on how to interpret and integrate genomic information in the clinical setting, addressed to clinicians without expertise in cancer genomics, are currently limited. Building upon the genomic foundations of cancer and the concept of precision oncology, the authors have developed practical guidance to aid the interpretation of genomic test results that help inform clinical decision making for patients with cancer. They also discuss the challenges that prevent the wider implementation of precision oncology.

Prostate Cancer Organoids for Tumor Modeling and Drug Screening.

Prostate cancer (PCa) is the second most common malignancy and the fifth leading cause of cancer death in men worldwide. Despite its prevalence, the highly heterogenic PCa has shown difficulty to establish representative cell lines that reflect the diverse phenotypes and different stages of the disease in vitro and hence hard to model in preclinical research. The patient-derived organoid (PDO) technique has emerged as a groundbreaking three-dimensional (3D) tumor modeling platform in cancer research. This versatile assay relies on the unique ability of cancer stem cells (CSCs) to self-organize and differentiate into organ-like mini structures. The PDO culture system allows for the long-term maintenance of cancer cells derived from patient tumor tissues. Moreover, it recapitulates the parental tumor features and serves as a superior preclinical model for in vitro tumor representation and personalized drug screening. Henceforth, PDOs hold great promise in precision medicine for cancer. Herein, we describe the detailed protocol to establish and propagate organoids derived from isolated cell suspensions of PCa patient tissues or cell lines using the 3D semisolid Matrigel™-based hanging-drop method. In addition, we highlight the relevance of PDOs as a tool for evaluating drug efficacy and predicting tumor response in PCa patients.

Cancer genomic panels for lymphoid malignancies

Cancer precision medicine (genome-based individualized treatment for cancer patients) has already been introduced for solid tumors, and involves identifying driver genes in the development and progression of tumors and suggesting optimal treatments targeting those genes. So far, many patients have received this style of treatment. Meanwhile, preparations for cancer genomic medicine based on cancer gene panel testing are also underway for hematopoietic tumors. In this article, I would like to share fundamental information about the main genetic mutations in malignant lymphomas and their clinical significance, and discuss how this information should be utilized in cancer genomic medicine in the future.

Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development

Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development

Ex vivo platform en route to functional precision medicine: clinical relevance in gynecological cancers.

A scientific interrogation-driven approach to the clinical management of cancer patients is based on molecular profiling of the tumor. Empowered by the knowledge of oncogenic drivers and biomarkers, oncologists chart an optimal treatment path toward increasing the mathematical probability of a positive outcome. In this entire chain of events, an experimental proof of logical interrogation has never been incorporated before. Here, we provide the first evidence that the result of ex vivo testing of a drug matched to the genomic profiling of an N-of-1 tumor can deliver meaningful insight connecting scientific interrogation and a clinical event. Using resected tissues from endometrial (EC) and ovarian (OC) cancer patients, we designed a personalized ex vivo platform to test combinations of drugs in the default histological architecture of the individual tumors. Following the CART-T cells' principle, we co-cultured with autologous T-cells to test targeted drugs and immune checkpoint inhibitors. The study was designed with a limited clinical information window from patient registration/consent to obtaining the tumor tissues, and adjuvant treatment/post-surgery event (PSE) data were accessed retrospectively. Using a checkerboard analysis, we found that PSE-free survival time was longer in patients whose therapy "matched" the effective drug combination in ex vivo culture/co-cultures compared to those with no effect. Specifically, out of 32 EC patients in the "test & treatment-matched" category whose tumor cells failed to respond to ex vivo drug testing, none achieved > 4 and > 3 years of PSE-free survival. In contrast, out of 38 EC patients in the "test & treatment-matched" category, 4 and 6 patients, whose tumor cells responded to drugs in ex vivo culture, achieved > 4 and > 3 years of PSE-free survival, respectively. Cases with genomically-guided ex vivo testing showed that a "match" between an effective ex vivo drug combination and therapy resulted in late PSE, whereas a "match" between prescribed treatment and an ineffective drug combination in ex vivo testing led to early PSE. Our study demonstrates that integrating genomic data with personalized drug testing on an ex vivo culture/co-culture platform is an effective tool for modeling functional precision medicine in gynecological cancers. This approach bridges the gap between next-generation drug testing in translational research and patient care, providing insight for improved treatment outcomes.

Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

Big data in breast cancer: Towards precision treatment.

Breast cancer is the most prevalent and deadliest cancer among women globally, representing a major threat to public health. In response, the World Health Organization has established the Global Breast Cancer Initiative framework to reduce breast cancer mortality through global collaboration. The integration of big data analytics (BDA) and precision medicine has transformed our understanding of breast cancer's biological traits and treatment responses. By harnessing large-scale datasets - encompassing genetic, clinical, and environmental data - BDA has enhanced strategies for breast cancer prevention, diagnosis, and treatment, driving the advancement of precision oncology and personalised care. Despite the increasing importance of big data in breast cancer research, comprehensive studies remain sparse, underscoring the need for more systematic investigation. This review evaluates the contributions of big data to breast cancer precision medicine while addressing the associated opportunities and challenges. Through the application of big data, we aim to deepen insights into breast cancer pathogenesis, optimise therapeutic approaches, improve patient outcomes, and ultimately contribute to better survival rates and quality of life. This review seeks to provide a foundation for future research in breast cancer prevention, treatment, and management.

A potential therapeutic approach for gastric cancer: inhibition of LACTB transcript 1.

This study sought to investigate the role of LACTB transcript 1 in regulating adaptive immune resistance and stemness in gastric cancer and its potential as a therapeutic target for precision medicine. Bioinformatics analysis and RT-qPCR were used to analyze the expression level of LACTB and its transcripts in gastric cancer cells. The effects of LACTB transcript 1 on adaptive immune resistance and stemness were evaluated using in vitro cell experiments and western blotting experiments. Our study findings revealed that LACTB transcript 1 modulated adaptive immune resistance and inhibited the stemness of gastric cancer cells. Knocking down the expression level of LACTB transcript 1 activated autophagy and inhibited EMT. As expected, overexpression of LACTB transcript 1 yielded the opposite findings. The expression level of LACTB transcript 1 in the peripheral blood of gastric cancer patients was consistent with the bioinformatics analysis, suggesting its potential as a biomarker of gastric cancer. LACTB transcript 1 is a promising therapeutic target for precision medicine in gastric cancer by modulating immune evasion mechanisms and stemness. These findings provide insights into leveraging long non-coding RNAs (lncRNAs) in immunotherapy, radiotherapy, and chemotherapy to enhance cancer therapy efficacy, particularly in the context of targeting tumor heterogeneity and stemness.

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry.

Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases. RAIT dosimetry can be employed to determine the optimal treatment dose of I-131 to effectively treat cancer cells while safeguarding against undesirable radiation effects such as bone marrow toxicity or radiation pneumonitis. Conventional dosimetry protocols for RAIT, however, are complex and time-consuming, involving multiple days of imaging and blood sampling. This study explores the use of Artificial Intelligence (AI) in simplifying and optimizing RAIT. A retrospective analysis was conducted on 83 adult patients with DTC who underwent RAIT dosimetry at our institution between 1996 and 2023. The conventional MIRD-based dosimetry protocol involved imaging and blood sampling at 4, 24, 48, 72, and 96 h post-administration of a tracer activity of I-131. An AI system based on a deep-learning neural network was developed to predict the maximum permissible activity (MPA) for RAIT using only the data obtained from the initial 4, 24, and 48 h time points. The AI system predicted the MPA values with high accuracy, showing no significant difference compared to the results obtained from conventional MIRD-based analysis utilizing a paired t-test (p = 0.351, 95% CI). The developed AI system offers the potential to streamline the dosimetry process, reducing the number of imaging and blood sampling sessions while also optimizing resource allocation. Additionally, the AI approach can uncover underlying relationships in data that were previously unknown. Our findings suggest that AI-based dosimetry may be a promising method for patient-specific treatment planning in differentiated thyroid carcinoma, representing a step towards applying precision medicine for thyroid cancer. Further validation and implementation studies are warranted to assess the clinical applicability of the AI system.

Urine-derived bladder cancer organoids (urinoids) as a tool for cancer longitudinal response monitoring and therapy adaptation.

Bladder cancer is one of the most common cancer types worldwide. Generally, research relies on invasive sampling strategies. Here, we generate bladder cancer organoids directly from urine (urinoids). In this project, we establish 12 urinoid lines from 22 patients with non-muscle and muscle-invasive bladder tumours, with an efficiency of 55%. The histopathological features of the urinoids accurately resemble those of the original bladder tumours. Genetically, there is a high concordance of single nucleotide polymorphisms (92.56%) and insertions & deletions (91.54%) between urinoids and original tumours from patient 4. Furthermore, these urinoids show sensitivity to bladder cancer drugs, similar to their tissue-derived organoid counterparts. Genetic analysis of longitudinally generated tumoroids and urinoids from one patient receiving systemic immunotherapy, identify alterations that may guide the choice for second-line therapy. Successful treatment adaptation was subsequently demonstrated in the urinoid setting. Therefore, urinoids can advance precision medicine in bladder cancer as a non-invasive platform for tumour pathogenesis, longitudinal drug-response monitoring, and therapy adaptation.

Precision cancer medicine and the doctor-patient relationship: a systematic review and narrative synthesis

BackgroundThe implementation of precision medicine is likely to have a huge impact on clinical cancer care, while the doctor-patient relationship is a crucial aspect of cancer care that needs to be preserved. This systematic review aimed to map out perceptions and concerns regarding how the implementation of precision medicine will impact the doctor-patient relationship in cancer care so that threats against the doctor-patient relationship can be addressed.MethodsElectronic databases (Pubmed, Scopus, Web of Science, Social Science Premium Collection) were searched for articles published from January 2010 to December 2021, including qualitative, quantitative, and theoretical methods. Two reviewers completed title and abstract screening, full-text screening, and data extraction. Findings were summarized and explained using narrative synthesis.ResultsFour themes were generated from the included articles (n = 35). Providing information addresses issues of information transmission and needs, and of complex concepts such as genetics and uncertainty. Making decisions in a trustful relationship addresses opacity issues, the role of trust, and and physicians’ attitude towards the role of precision medicine tools in decision-making. Managing negative reactions of non-eligible patients addresses patients’ unmet expectations of precision medicine. Conflicting roles in the blurry line between clinic and research addresses issues stemming from physicians’ double role as doctors and researchers.ConclusionsMany findings have previously been addressed in doctor-patient communication and clinical genetics. However, precision medicine adds complexity to these fields and further emphasizes the importance of clear communication on specific themes like the distinction between genomic and gene expression and patients’ expectations about access, eligibility, effectiveness, and side effects of targeted therapies.

Precision cancer medicine in ovarian cancer

Precision cancer medicine in ovarian cancer

Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine

BackgroundWith the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing.MethodsIn this study, we have developed “Consensus Cancer Core” (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis.ResultsThe knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial.ConclusionsThe novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org, and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study.

Patients with lung cancer are among the most frequent visitors to emergency departments due to cancer-related problems, and the prognosis for those who seek emergency care is dismal. Given that patients with lung cancer frequently visit health care facilities for treatment or follow-up, the ability to predict emergency department visits based on clinical information gleaned from their routine visits would enhance hospital resource utilization and patient outcomes. This study proposed a machine learning-based prediction model to identify risk factors for emergency department visits by patients with lung cancer. This was a retrospective observational study of patients with lung cancer diagnosed at Seoul National University Bundang Hospital, a tertiary general hospital in South Korea, between January 2010 and December 2017. The primary outcome was an emergency department visit within 30 days of an outpatient visit. This study developed a machine learning-based prediction model using a common data model. In addition, the importance of features that influenced the decision-making of the model output was analyzed to identify significant clinical factors. The model with the best performance demonstrated an area under the receiver operating characteristic curve of 0.73 in its ability to predict the attendance of patients with lung cancer in emergency departments. The frequency of recent visits to the emergency department and several laboratory test results that are typically collected during cancer treatment follow-up visits were revealed as influencing factors for the model output. This study developed a machine learning-based risk prediction model using a common data model and identified influencing factors for emergency department visits by patients with lung cancer. The predictive model contributes to the efficiency of resource utilization and health care service quality by facilitating the identification and early intervention of high-risk patients. This study demonstrated the possibility of collaborative research among different institutions using the common data model for precision medicine in lung cancer.

MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.

Cancer is a complex disease that results in a significant number of global fatalities. Treatment strategies can vary among patients, even if they have the same type of cancer. The application of precision medicine in cancer shows promise for treating different types of cancer, reducing healthcare expenses, and improving recovery rates. To achieve personalized cancer treatment, machine learning models have been developed to predict drug responses based on tumor and drug characteristics. However, current studies either focus on constructing homogeneous networks from single data source or heterogeneous networks from multiomics data. While multiomics data have shown potential in predicting drug responses in cancer cell lines, there is still a lack of research that effectively utilizes insights from different modalities. Furthermore, effectively utilizing the multimodal knowledge of cancer cell lines poses a challenge due to the heterogeneity inherent in these modalities. To address these challenges, we introduce MMCL-CDR (Multimodal Contrastive Learning for Cancer Drug Responses), a multimodal approach for cancer drug response prediction that integrates copy number variation, gene expression, morphology images of cell lines, and chemical structure of drugs. The objective of MMCL-CDR is to align cancer cell lines across different data modalities by learning cell line representations from omic and image data, and combined with structural drug representations to enhance the prediction of cancer drug responses (CDR). We have carried out comprehensive experiments and show that our model significantly outperforms other state-of-the-art methods in CDR prediction. The experimental results also prove that the model can learn more accurate cell line representation by integrating multiomics and morphological data from cell lines, thereby improving the accuracy of CDR prediction. In addition, the ablation study and qualitative analysis also confirm the effectiveness of each part of our proposed model. Last but not least, MMCL-CDR opens up a new dimension for cancer drug response prediction through multimodal contrastive learning, pioneering a novel approach that integrates multiomics and multimodal drug and cell line modeling. MMCL-CDR is available at https://github.com/catly/MMCL-CDR.

Precision medicine in head and neck cancer: from immunotherapy to microbiota, circulating DNA and robotic surgery

Precision medicine in head and neck cancer: from immunotherapy to microbiota, circulating DNA and robotic surgery