Precision Cancer Medicine Research Articles

Perceptions of Cancer Through the Ages—From Hippocratic Oncology to Precision Cancer Medicine

The unravelling of the human genome created new perceptions of the origin and evolution of diseases, and for cancer in particular, it established the notion that neoplasia has been a companion of life since its appearance on Earth. It is not surprising that neoplasms, in various forms, develop in numerous species of animals and even in plants. Unmistakable accounts of cancer with clinical features as are understood today begin in the 5th c. B.C. The principles and practice of the Hippocratic and Galenic tradition dominated cancer care virtually into the 20th century. Advanced sequencing technologies at the dawn of the 21st century generated new therapeutic opportunities with immunotherapy, oncolytic virotherapy, and gene transfer, with the latter especially being used in cases of hereditary cancer.

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer

BackgroundNatural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes.MethodsThe data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC).ResultsTumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC.ConclusionsWe developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

Machine learning-assisted identification of potential cancer inhibitors: Multifunctional biomineralized CaCO3-polyethyleneimine nanoparticle carriers and their application in lung cancer therapy

Cancer, as a prevalent phenomenon among malignant tumors, has a late-stage diagnosis rate approaching half, significantly limiting treatment efficacy and imposing substantial physiological and psychological burdens on patients. Therefore, developing innovative therapeutic platforms that simultaneously optimize diagnostic accuracy, enhance therapeutic efficacy, and minimize side effects is a critical scientific challenge in the field of precision cancer medicine. In this study, we employed a simple and efficient one-pot synthesis method combined with polyethyleneimine (PEI)-mediated biomineralization technology to successfully prepare calcium carbonate-polyethyleneimine (CaCO3-PEI) composite nanoparticles. These nanoparticles exhibit high pH sensitivity and can rapidly degrade under the mildly acidic conditions that mimic the tumor microenvironment, providing potential for targeted tumor therapy. Subsequently, we encapsulated compound 1, which has potential for lung cancer treatment, within the CaCO3-PEI nanoparticles, successfully constructing the CaCO3-PEI@1 composite system. Structural analysis of this composite system was conducted through characterization techniques. In vitro cell experiments demonstrated that the CaCO3-PEI@1 composite system exhibited significant anticancer effects by effectively inhibiting cancer cell proliferation through the regulation of apoptosis-related protein Bax expression. This provides new insights and experimental evidence for the development of cancer treatment strategies. Based on the experiment and molecular docking identified activity against lung cancer, the compound 1 was used as the initiator for the machine learning model, and up to 10,000 episodes were generated, after thorough examination, it was found about two-thirds of the generated episodes were entirely different from each other, which demonstrated that the machine learning model was a powerful tool for designing of potential inhibitors against lung cancer.

Innovation in cancer pharmacotherapy through integrative consideration of germline and tumor genomes.

Precision cancer medicine is widely established, and numerous molecularly targeted drugs for various tumor entities are approved or in development. Personalized pharmacotherapy in oncology has so far been based primarily on tumor characteristics, e.g., somatic mutations. However, the response to drug treatment also depends on pharmacological processes summarized under the term ADME (absorption, distribution, metabolism, and excretion). Variations in ADME genes have been the subject of intensive research for more than five decades, considering individual patients' genetic makeup, referred to as pharmacogenomics (PGx). The combined impact of a patient's tumor and germline genome is only partially understood and often not adequately considered in cancer therapy. This may be attributed, in part, to the lack of methods for combined analysis of both data layers. Optimized personalized cancer therapies should, therefore, aim to integrate molecular information about the tumor and the germline, taking into account existing PGx guidelines for drug therapy. Moreover, such strategies should provide the opportunity to consider genetic variants of previously unknown functional significance. Bioinformatic analysis methods and corresponding algorithms for data interpretation need to be developed to consider PGx data in interdisciplinary molecular tumor boards, where cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles. Significance Statement The era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, full potential of targeted therapy remains untapped due to the predominant focus on acquired tumor-specific alterations. Optimized cancer care must integrate tumor and patient genomes, guided by pharmacogenomic principles. An essential prerequisite for realizing truly personalized drug treatment of cancer patients is the development of bioinformatic tools for comprehensive analysis of all data layers generated in modern precision oncology programs.

Microfluidic Chip-Based Automatic System for Sequencing Patient-Derived Organoids at the Single-Cell Level.

Genetically sequencing patient-derived organoids (PDOs) at the single-cell level has emerged as a promising method to infer cell-level heterogeneity of original organs and improve cancer precision medicine. Unfortunately, because of the limited starting quantity and uncontrolled establishing process of PDOs, the existing single-cell sequencing technologies, either manual-operation-based or microfluid-based, are inefficient in processing PDOs originating from clinical tissue samples. To address such issues, this study presents a microfluidic chip-based automatic system for sequencing organoids at the single-cell level, named as MASSO. By performing all required procedures, including PDO establishment/culturing/digesting and single-cell isolation/lysis/whole-genome amplification, in a single microfluidic chip, the possible loss of precious PDO is avoided, and the high quality of on-chip whole-genome amplification of a single PDO cell is ensured. By automating the entire operation process, possible human error is eliminated, and the data repeatability is improved, therefore bridging the technical gap between laboratorial proof-of-concept studies and clinical practices. After characterizing the organoid single-cell whole-genome amplification chip (named as OSA-Chip) and the MASSO, the first successful attempt, to the best of our knowledge, on whole-genome sequencing lung cancer PDO at the single-cell level was performed by MASSO. The results reveal that the MASSO is capable of not only identifying common cancer-related mutations but also discovering specific mutations that affect drug responses, therefore laying the technical foundation for efficiently understanding the cell-level heterogeneities of PDOs and corresponding original organs.

Modelling the tumor microenvironment in vitro in prostate cancer: current and future perspectives

AbstractProstate cancer is the most common cancer in over 50% of countries and the third most common malignancy worldwide. The tumor microenvironment includes the non‐cancerous cells and components present in the tumor, including the molecules they produce and release. Prostate cancer proliferation, angiogenesis, metastasis, and drug resistance are closely associated with the tumor microenvironment. With the continuous development of in vitro tumor models, they have gradually become important tools for recapitulating in vivo parental tumors and studying the reciprocal interactions between tumors and their microenvironment. In this perspective, we describe the significant in vitro models of prostate cancer, analyze the research results on incorporating the tumor microenvironment into these models, and compare their advantages and disadvantages. Furthermore, we highlight the future developmental direction of in vitro prostate cancer model research according to the hotspots of in vitro model research in other cancer types to facilitate precision medicine in prostate cancer.

KRAS, a New Target for Precision Medicine in Colorectal Cancer?

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality globally, with significant public health concerns. This review examines the landscape of KRAS inhibition in colorectal cancer (CRC), focusing on recent advances in therapeutic strategies targeting this oncogene. Historically deemed undruggable due to its complex structure and essential role in tumorigenesis, KRAS mutations are prevalent in CRC and are associated with poor prognosis. However, breakthroughs in drug development have led to the emergence of KRAS inhibitors as promising treatment options. This review discusses various classes of KRAS inhibitors, including covalent and non-covalent inhibitors, as well as combination therapies aimed at enhancing efficacy and overcoming resistance mechanisms. It highlights recent clinical trials evaluating the efficacy of KRAS inhibitors either as monotherapy or in combination with other agents, such as anti-EGFR antibodies. Despite challenges such as resistance mechanisms and tumor heterogeneity, the development of KRAS inhibitors represents a significant advance in CRC treatment and holds promise for improving patient outcomes in the future.

Tissue-based Profiling Techniques to Achieve Precision Medicine in Cancer: Opportunities and Challenges in Melanoma.

Immunotherapies targeting the programmed cell death 1 (PD-1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) checkpoint receptors have revolutionised the treatment of metastatic melanoma. However, half of treated patients do not respond to or eventually progress on standard therapies and many experience adverse events as a result of drug toxicity. The identification of accurate biomarkers of clinical outcomes are required in order to move away from the one-size-fits-all treatment approach of standard clinical practice, and towards a more personalised approach to enable the administration of the optimal therapy for any given patient and further improve patient outcomes. Recent clinical trials have proven the potential of multi-omics analyses, including genomic, gene expression, and tumour immune profiling, of patients' tumour biopsies, to predict a patient's response to subsequently administered immunotherapies. However, reproducibility of such multi-omics analyses, tissue requirements, and clinical validation have limited the practical application of these approaches in routine clinical workflows. In this review, we discuss several pivotal tissue-based profiling techniques that can be utilised to identify potential genomic, transcriptomic and immune biomarkers predictive of clinical outcomes following treatment with immune checkpoint inhibitors in melanoma. Furthermore, we highlight the key opportunities and challenges associated with the use of each of these techniques. The development and implementation of multimodal predictive models which combine data derived from these various methods is the future for achieving precision medicine for patients with melanoma.

The economic costs of precision medicine for clinical translational research among children with high-risk cancer

Embedding precision medicine in paediatric oncology shows promise to have a positive impact on how children with cancer will be treated in the future. While there are a number of studies of precision medicine in childhood cancer, there is limited evidence available on the cost of implementing the related testing. This is the first Australian study that systematically measures the cost of using precision medicine in the care of high-risk childhood cancers, through the Zero Childhood Cancer Precision Medicine Programme. In 2021 Australian dollars, the estimated costs inclusive of genomic and preclinical testing were: (A) $12,743 per patient for access; (B) $14,262 per identification of molecular cause; and (C) $21,769 per MTB recommendation. The information gained supports the understanding of the cost of reporting clinically significant outcomes relevant to the biology of the tumour, diagnosis, prognosis and potentially improving clinical management for a child.

Precision medicine for breast cancer: advances and challenges

Precision medicine for breast cancer: advances and challenges

JS6-1 Cancer precision medicine and multi-gene panel testing (MGPT) overview

JS6-1 Cancer precision medicine and multi-gene panel testing (MGPT) overview

Diagnosis, Treatment Patterns, and Outcomes in Real-World Patients with RET Fusion-Positive Non-small Cell Lung Cancer in China.

Epidemiological studies on non-small cell lung cancer (NSCLC) have noted RET fusions as an oncogenic driver. However, real-world data on RET biomarker testing and treatment patterns in China remain limited. This study aimed to examine demographics, clinical and molecular features, and RET testing and treatment patterns and outcomes in patients with RET fusion-positive NSCLC. Utilizing real-world data from the Chinese Multi-center Lung Cancer Precision Medicine Registry, this retrospective cohort study focused on Chinese patients diagnosed with RET fusion-positive NSCLC between January 1, 2016, and November 30, 2021. The cohort was divided into early-stage and advanced-stage subgroups. Demographics, clinical and molecular profiles, treatment received, and outcomes including real-world event free survival (rwEFS), real-world progression free survival (rwPFS), and overall survival (OS) were assessed. The study included 121 patients with RET fusion-positive NSCLC, comprising 80 early-stage and 58 advanced-stage patients. High biomarker testing rates were observed at diagnosis (75% for early-stage, 78% for advanced-stage). RET testing was often conducted via tissue samples (95.9%) and next-generation sequencing (89.3%). KIF5B (57.0%) and CCDC6 (20.7%) were the most common gene fusion partners. The most frequent oncogenic mutations were TP53 (15.7%) and EGFR (6.6%). Platinum-based chemotherapy was the most common first-line treatment among advanced-stage patients. Median rwPFS was 9.22months for advanced-stage patients on first-line chemotherapy, and median OS was 30.7months for all advanced-stage patients. The 2-year rwEFS rate for early-stage patients was 86.0%, with a median OS of 91.9months. The study observed high biomarker testing rates at initial diagnosis for early- and advanced-stage RET fusion-positive NSCLC patients in China. The heterogeneous treatment pattern of advanced patients suggests the need for more precise, evidence-based treatment to guide clinical decisions. Given the existing therapeutic regimens fall short of adequately addressing treatment needs, targeted therapies are essential to improve outcomes.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Patient-derived organoids in precision cancer medicine.

Organoids are three-dimensional (3D) cultures, normally derived from stem cells, that replicate the complex structure and function of human tissues. They offer a physiologically relevant model to address important questions in cancer research. The generation of patient-derived organoids (PDOs) from various human cancers allows for deeper insights into tumor heterogeneity and spatial organization. Additionally, interrogating non-tumor stromal cells increases the relevance in studying the tumor microenvironment, thereby enhancing the relevance of PDOs in personalized medicine. PDOs mark a significant advancement in cancer research and patient care, signifying a shift toward more innovative and patient-centric approaches. This review covers aspects of PDO cultures to address the modeling of the tumor microenvironment, including extracellular matrices, air-liquid interface and microfluidic cultures, and organ-on-chip. Specifically, the role of PDOs as preclinical models in gene editing, molecular profiling, drug testing, and biomarker discovery and their potential for guiding personalized treatment in clinical practice are discussed.

Optimized biomarker evaluation and molecular testing in the era of breast cancer precision medicine

ABSTRACT Ground breaking advances in medicine, driven in part by major technologic developments in molecular biology have led us to a new model for cancer care that has been termed personalized, or precision medicine. Precision medicine is a model for making medical decisions that employs an innovative clinical approach and advanced tumor testing methods that are tailored to understanding an individual patient’s tumor biology and the molecular drivers of their disease. This medical model includes a combination of diagnostic testing and specific treatment options that can be offered to patients at presentation and in theory throughout the course of their disease as new mutations arise with the development of disease recurrence. Although the precision medicine model offers incredible potential to transform cancer care, these advances are only meaningful when they reach the correct patients. The evolving paradigm of precision medicine is changing the practice of pathology, and the pathology community needs to be mindful of these changes because every tissue specimen represents a patient’s life, and those patients are depending on the pathology community to handle their tissue correctly. The diagnostic tests performed in the pathology laboratory for precision medicine are increasingly complex, and pathologists along with the entire laboratory and clinical communities need to take steps to ensure that the right diagnosis is given to the right patient to inform the right treatment options, at the right time, along every step of the continuum of care for cancer patients. While hormone receptors and human epidermal growth factor receptor 2 (HER2) overexpression and/or amplification have been the mainstay for risk-stratification, and treatment decision making in breast cancer since the early 2000’s, the seminal work on gene expression by Perou and colleagues in the early 2000’s opened the door for molecular testing in the prognostic and predictive assessment of breast cancer. Molecular testing is now part of the standard of care in the precision medicine model for breast cancer care. In this article, the reader will gain a better understanding of how the lack of standardization of pre-analytic factors has the potential to negatively impact the quality of the tissue specimen for downstream biomarker and molecular testing, which ultimately can negatively affect patient care. The reader will also gain insight into the current climate surrounding molecular testing in breast cancer.

Transparent precision: Explainable AI empowered breast cancer recommendations for personalized treatment

<p>Breast cancer stands as a prevalent global concern, prompting extensive research into its origins and personalized treatment through Artificial Intelligence (AI)-driven precision medicine. However, AI's black box nature hinders result acceptance. This study delves into Explainable AI (XAI) integration for breast cancer precision medicine recommendations. Transparent AI models, fuelled by patient data, enable personalized treatment recommendations. Techniques like feature analysis and decision trees enhance transparency, fostering trust between medical practitioners and patients. This harmonizes AI's potential with the imperative for clear medical decisions, propelling breast cancer care within the precision medicine era. This research work is dedicated to leveraging clinical and genomic data from samples of metastatic breast cancer. The primary aim is to develop a machine learning (ML) model capable of predicting optimal treatment approaches, including but not limited to hormonal therapy, chemotherapy, and anti-HER2 therapy. The objective is to enhance treatment selection by harnessing advanced computational techniques and comprehensive data analysis. A decision tree model developed here for the prediction of suitable personalized treatment for breast cancer patients achieves 99.87% overall prediction accuracy. Thus, the use of XAI in healthcare will build trust in doctors as well as patients.</p>

IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression.

Precision medicine in breast cancer (Review).

Precision medicine in breast cancer is a revolutionary approach that customizes diagnosis and treatment based on individual and tumor characteristics, departing from the traditional one-size-fits-all approach. Breast cancer is diverse, with various subtypes driven by distinct genetic mutations. Understanding this diversity is crucial for tailored treatment strategies that target specific vulnerabilities in each tumor. Genetic testing, particularly for mutations in breast cancer gene (BRCA) DNA repair-associated genes, helps assess hereditary risks and influences treatment decisions. Molecular subtyping guides personalized treatments, such as hormonal therapies for receptor-positive tumors and human epidermal growth factor receptor 2 (HER2)-targeted treatments. Targeted therapies, including those for HER2-positive and hormone receptor-positive breast cancers, offer more effective and precise interventions. Immunotherapy, especially checkpoint inhibitors, shows promise, particularly in certain subtypes such as triple-negative breast cancer, with ongoing research aiming to broaden its effectiveness. Integration of big data and artificial intelligence enhances personalized treatment strategies, while liquid biopsies provide real-time insights into tumor dynamics, aiding in treatment monitoring and modification. Challenges persist, including accessibility and tumor complexity, but emerging technologies and precision prevention offer hope for improved outcomes. Ultimately, precision medicine aims to optimize treatment efficacy, minimize adverse effects and enhance the quality of life for patients with breast cancer.

Precision Cancer Medicine 2.0-Oncology in the postgenomic era.

Genomic medicine has transformed the lives of patients with cancer byenabling individualised and evidence-based clinical decision-making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single-cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high-resolution personalised clinical decision-making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.

Precision medicine in breast cancer: Targeting molecular subtypes with gold nanoparticle-loaded liposomes

PurposeBreast cancer is a complex disease with several molecular subtypes that respond differently to therapy. This paper describes liposomes loaded with gold nanoparticles as a targeted drug delivery method in the rapidly developing precision breast cancer treatment area. The aim was to investigate the cytotoxicity level and cellular uptake using several breast cancer cell lines and a normal breast cell line. Materials and methodsWe synthesized gold nanoparticles incorporated in liposomes. Nanostructures were incubated with breast cancer cell lines of different subtypes. The analysis included MTT assay, flow cytometry and immunofluorescence. ResultsCell viability varied among different cancer cells. Moreover, the time- and concentration-dependent manner of viability change was observed. The internalization of liposomes with gold nanoparticles and nanoparticles alone determined different results depending on molecular breast cancer subtypes. The luminal B and triple-negative breast cancer cells demonstrated the highest resistance and sensitivity, respectively. The intensity of cells’ interaction with the proposed nanostructures was observed in both cell lines. In this study, we compare the molecular subtypes of breast cancer and discuss how this novel method might improve the therapy success. ConclusionsOur research sheds light on the possibility of new individualized treatments for breast cancer patients, opening the path for better results and a more detailed cancer therapy strategy.