Enhanced Cancer Diagnosis Research Articles

Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis

Background/Objectives: This study aims to evaluate the performance of various classification algorithms and resampling methods across multiple diagnostic and prognostic cancer datasets, addressing the challenges of class imbalance. Methods: A total of five datasets were analyzed, including three diagnostic datasets (Wisconsin Breast Cancer Database, Cancer Prediction Dataset, Lung Cancer Detection Dataset) and two prognostic datasets (Seer Breast Cancer Dataset, Differentiated Thyroid Cancer Recurrence Dataset). Nineteen resampling methods from three categories were employed, and ten classifiers from four distinct categories were utilized for comparison. Results: The results demonstrated that hybrid sampling methods, particularly SMOTEENN, achieved the highest mean performance at 98.19%, followed by IHT (97.20%) and RENN (96.48%). In terms of classifiers, Random Forest showed the best performance with a mean value of 94.69%, with Balanced Random Forest and XGBoost following closely. The baseline method (no resampling) yielded a significantly lower performance of 91.33%, highlighting the effectiveness of resampling techniques in improving model outcomes. Conclusions: This research underscores the importance of resampling methods in enhancing classification performance on imbalanced datasets, providing valuable insights for researchers and healthcare professionals. The findings serve as a foundation for future studies aimed at integrating machine learning techniques in cancer diagnosis and prognosis, with recommendations for further research on hybrid models and clinical applications.

Overexpression of SEZ6L2 and Immune Infiltration in Cancer Based on Gene Image Diagnosis.

With the rapid advancement of optical image diagnostic technology, researchers are delving into the potential applications in the field of cancer diagnosis and treatment. The exact link between the SEZ6L2 gene and cancer immune infiltration remains elusive. This study aims to investigate the relationship between SEZ6L2 gene overexpression and cancer immune infiltration using optical image diagnostic technology, thereby presenting novel insights for enhancing cancer diagnosis and treatment strategies. Tissue samples obtained from cancer patients were meticulously analyzed to quantitatively assess the expression of the SEZ6L2 gene through light image diagnostic technology. Additionally, immunohistochemical techniques were employed to assess the nature and quantity of immune infiltrating cells within the cancerous tissues. The enrichment pathways were found to include complement activation, circulating immunoglobulin mediated humoral immune response, protein activation cascade, immunoglobulin complex, and immunoglobulin. In addition, the expression of SEZ6L2 is closely related to the infiltration level of tumor infiltrating immune cells (TIICs), and there is a potential relationship between the expression of SEZ6L2 and different marker genes of TIIC. Increased SEZ6L2 mRNA expression in breast invasive carcinoma was significantly associated with negative prognosis and immune invasion. SEZ6L2 may be a novel prognostic biomarker and a potential immunotherapeutic target in BRCA.

Facile fabrications of poly (acrylic acid)-mesoporous zinc phosphate/polydopamine Janus nanoparticles as a biosafe photothermal therapy agent and a pH/NIR-responsive drug carrier

Balancing biocompatibility and drug-loading efficiency in nanoparticles presents a significant challenge. In this study, we describe the facile fabrication of poly (acrylic acid)-mesoporous zinc phosphate/polydopamine (PAA-mZnP/PDA) Janus nanoparticles (JNPs). The PDA half-shell itself can serve as a photothermal agent for photothermal therapy (PTT), as well as to offers sites for polyethylene glycol (PEG) to enhance biocompatibility. Concurrently, the mesoporous ZnP core allows high loading of doxorubicin (DOX) for chemotherapy and the Cy5.5 dye for fluorescence imaging. The resultant PAA-mZnP/PDA-PEG JNPs exhibit exceptional biocompatibility, efficient drug loading (0.5 mg DOX/1 mg JNPs), and dual pH/NIR-responsive drug release properties. We demonstrate the JNPs’ satisfactory anti-cancer efficacy, highlighting the synergistic effects of chemotherapy and PTT. Furthermore, the potential for synergistic fluorescence imaging-guided chemo-phototherapy in cancer treatment is illustrated. Thus, this work exemplifies the development of biosafe, multifunctional JNPs for advanced applications in cancer theranostics. Statement of significanceFacile fabrication of monodispersed nanomedicine with multi-cancer killing modalities organically integrated is nontrivial and becomes more challenging under the biocompatibility requirement that is necessary for the practical applications of nanomedicines. In this study, we creatively designed PAA-mZnP/PDA JNPs and fabricated them under mild conditions. Our method reliably yields uniform JNPs with excellent monodispersity. To maximize functionalities, we achieve fourfold advantages including efficient drug/fluorescent dye loading, PTT, pH/NIR dual-responsive properties, and optimal biocompatibility. The as-fabricated JNPs exhibit satisfactory anti-cancer performance both in vitro and in vivo, and demonstrate the potential of JNPs in fluorescence imaging-guided synergistic cancer chemo-phototherapy. Overall, our research establishes a pathway in versatile inorganic/polymer JNPs for enhanced cancer diagnosis and therapy.

Nanotechnology: Revolutionizing Cancer Diagnosis and Treatment

Nanotechnology: Revolutionizing Cancer Diagnosis and Treatment The creation of nanotechnology marks a pivotal transformation within the landscape of cancer diagnosis and treatment. As we stand getting ready to this technological revolution, the potential of nanotechnology to significantly beautify the precision, efficiency, and effectiveness of most cancers care is becoming increasingly evident[1]. This editorial explores the profound impact nanotechnology is having on most cancers analysis and treatment, highlighting key improvements and their implications for the future of oncology[2]. The Promise of Nanotechnology in Cancer Care Nanotechnology, the science of manipulating materials at the atomic and molecular scale, has opened new frontiers in medication, particularly in oncology. Its capacity to interact with organic structures at the mobile and molecular degrees lets in for unparalleled precision in diagnosing and treating most cancers. Nano-enzymes, nanoparticles, and nanocarriers are most of the modern tools being advanced and deployed to combat cancer more correctly. Enhancing Cancer Diagnosis Traditional most cancers diagnostic methods frequently be afflicted by obstacles in sensitivity and specificity, leading to delayed detection and suboptimal remedy outcomes. Nanotechnology addresses these demanding situations via allowing the development of relatively touchy diagnostic gear that could detect cancer at its earliest stages. For example, nanoparticles may be engineered to target particular cancer biomarkers, imparting extra correct and early detection as compared to traditional imaging strategies[3, 4]. Biosensors incorporating nanoparticles have proven exceptional efficacy in detecting trace levels of cancer-associated biomolecules in physical fluids, facilitating non-invasive and fast analysis. This early detection is essential for enhancing prognosis and survival rates, because it lets in for timely intervention and treatment[5]. Revolutionizing Cancer Treatment Nanotechnology's effect on cancer remedy is equally transformative. One of the maximum massive improvements is the improvement of focused drug delivery structures. Traditional chemotherapy, while powerful, regularly consequences in intense aspect effects due to its non-particular nature, affecting each cancerous and healthy cell. Nanoparticles may be designed to supply chemotherapeutic sellers without delay to tumor cells, minimizing harm to healthy tissue and lowering aspect consequences[6]. Moreover, the particular houses of nanoparticles, which include their size, surface place, and functionalization capacity, allow for the controlled release of therapeutic retailers. This guarantees that the drug attention remains in the therapeutic window for an extended period, enhancing its efficacy and decreasing the frequency of administration[7]. Emerging Therapies and Innovations Recent research has established the potential of nanotechnology in developing novel cancer healing procedures. For example, nano-enzymes have shown promise in improving the effectiveness of radiotherapy via growing the sensitivity of tumor cells to radiation. Additionally, nanotechnology is facilitating the improvement of immunotherapies, wherein nanoparticles are used to modulate the immune gadget's response to most cancers cells, improving the body's herbal potential to fight most cancers[8].Furthermore, the combination of nanotechnology with different rising fields, such as artificial intelligence and personalised medication, is paving the way for the next technology of most cancers’ treatments. AI algorithms can analyse substantial datasets to identify patterns and expect responses to nanotechnology-based totally cures, enabling customized treatment plans tailored to character sufferers' genetic and molecular profiles[9]. Challenges and Future Directions While the ability of nanotechnology in most cancers care is sizeable, numerous demanding situations stay. Ensuring the safety and biocompatibility of nanoparticles is paramount, as their lengthy-time period outcomes on the human frame aren't but absolutely understood. Regulatory frameworks want to adapt to hold pace with those improvements, making sure that new nanotechnology-based treatments are thoroughly evaluated for safety and efficacy[9, 10].Future research needs to cognizance on overcoming those challenges and expanding the packages of nanotechnology in cancer care. Interdisciplinary collaborations among oncologists, nanotechnologists, and regulatory our bodies will be important in translating these innovations from the lab to the clinic[11, 12]. CONCLUSION Nanotechnology is certainly revolutionizing cancer analysis and remedy, presenting new hope for patients and remodelling the landscape of oncology. As we hold to discover and harness the capacity of this cutting-edge generation, the dream of greater powerful, less invasive, and customized most cancers care is turning into a reality. The ongoing advancements in nanotechnology promise to not simplest improve patient results but also pave the manner for a destiny in which most cancers are a doable, and possibly even curable, circumstance.

Effective Cancer Diagnosis through High-Dimensional Microarray Data Analysis by Integrating DCT and UFS

Cancer remains a global health challenge, with various types such as lung, breast, and colon cancer posing significant threats. Timely and accurate diagnosis is crucial for effective treatment and improved survival rates. Genetic research offers promising avenues in the fight against cancer, as identifying gene mutations and expression levels enables the development of targeted therapies and a deeper understanding of disease subtypes and progression. This study explores an innovative hybrid approach to enhance cancer diagnosis and classification. By combining Discrete Cosine Transformation (DCT) and Univariate Feature Selection (UFS) methods, the feature selection process is optimized for the dataset. The extracted features are then rigorously tested using established classifiers to assess their effectiveness in cancer classification. The proposed method's performance was evaluated using eight distinct datasets, and metrics such as MF1, K-score, and sensitivity were calculated and compared with various methods in the literature. Empirical evidence demonstrates that the proposed method outperforms others on 5 out of 8 datasets in terms of both accuracy and computational efficiency. The introduced method represents a reliable tool for cancer diagnosis and classification.

“Building” Human Health: When Doctors and Physicists Work Together

In this article, we explore the importance of cooperation in science. Just as various construction trades must work together to build a skyscraper, scientists from separate fields can cooperate to tackle complex scientific challenges. This is called interdisciplinary collaboration, and it is a great way to do science. By bringing together knowledge and tools from multiple fields, scientists can uncover creative solutions and make meaningful connections that they might not have reached on their own. We give an example of how collaboration between particle physics and medicine—two fields that seem very different from one another—come together to improve healthcare. Using the tools of particle physics, scientists are enhancing cancer diagnosis and treatment. Interdisciplinary collaboration is the best way to address many of the complex issues we face today, like controlling climate change or fighting cancer, and it can help scientists and doctors make a lasting impact on human lives and the health of our planet.

Abstract A023: Stratification of bladder cancer patients with the use of flow cytometry is increasing the diagnostic potential of molecular classifiers

Abstract Recent research suggests the potential categorization of bladder cancer (BLCa) patients into molecular subtypes to aid in the selection of appropriate treatment strategies, be it conventional, targeted, or a combination thereof. However, the practical implementation of molecular classifiers has faced obstacles due to the intricate nature of gene regulation, tumor heterogeneity, and challenges related to tumor sampling caused by substantial stromal infiltration and tumor multifocality. Hence, there is a pressing need for more straightforward marker-based methodologies to assist in genomic and transcriptomic subtyping efforts. Our study addressed this gap by establishing and validating a panel of antibodies for application in flow cytometry (FCM) to enhance the stratification of BLCa patients. We curated a set of 17 FCM markers capable of delineating four cell clusters (epithelial, immune, endothelial, and stromal) and defining tumor classification (basal and luminal), immune infiltration, and hot-target expression on BLCa tissue samples. This FCM panel was deployed on ex vivo tissue samples treated with chemotherapy and targeted therapy agents. Initially tested on a cohort of 10 BLCa tissue samples, our validation of the FCM panel revealed correlations between marker expression and clinical-pathological features, such as the presence of metastasis and heightened immune and stromal infiltration. Notably, samples exhibiting lower claudin-4 levels displayed increased immune infiltration, while those with metastases at diagnosis showcased elevated basal marker expression. The translational potential of the FCM panel was demonstrated in ex vivo experiments where tissue slices from six patients were subjected to chemotherapy and targeted therapy agents, leading to observable shifts in marker expression post-treatment, particularly pronounced with chemotherapy. Noteworthy effects included reductions in epithelial cells expressing FGFR3 and EGFR following targeted therapy with erdafitinib or lapatinib, alongside alterations in the basal/luminal cell ratio indicated by increased CD44, CD90, and CD239 marker expressions. Our findings underscore the value of FCM in categorizing BLCa patients based on specific marker expression, supplementing conventional histological analyses to enhance cancer diagnosis, guide personalized therapeutic approaches, and monitor treatment efficacy. Citation Format: Martina Radic, Federico La Manna, Martina Minoli, Panagiotis Chouvardas, George N. Thalmann, Roland Seiler-Blarer, Marianna Kruithof-de Julio, Bernhard Kiss. Stratification of bladder cancer patients with the use of flow cytometry is increasing the diagnostic potential of molecular classifiers [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr A023.

Unveiling tumor invasiveness: enhancing cancer diagnosis with phase-contrast microtomography for 3D virtual histology

Malignant diseases are characterized by a critical trait known as invasiveness, where tumor cells tend to spread from the primary tissue layer into surrounding healthy tissues and distant organs. Presently, histopathology offers essential insights for diagnosing, classifying, predicting outcomes, and guiding patient-specific treatments. However, histology offers two-dimensional data from chosen cutting planes. Although 3D histological volumes can be generated through serial sectioning or whole slide imaging, this method is laborious, may introduce processing artefacts, and lacks isotropic spatial resolution. These limitations pose a considerable challenge to accurate diagnoses, particularly when dealing with micro-infiltrating carcinomas. These lesions, characterized by minute infiltrations, demand a three-dimensional representation for comprehensive visualization, essential for precise identification and assessment. Emerging X-ray-based virtual histology technology offers three-dimensional visualization of soft-tissue specimens, enabling virtual slicing in any direction or at any point. This approach can assist in guiding tissue sectioning for optimal representation of tumor cross sections during histological analysis. Micro-infiltrating carcinomas from the breast, cervix, and thyroid were imaged using X-ray phase-contrast microtomography (PhC-μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}CT) at the Elettra synchrotron facility in Trieste, Italy. Comparative assessment of histological and CT slices by pathologists revealed that PhC-μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}CT aids in classifying lesions by highlighting distinct tissue components and, notably, identifying tissue invasion. Reviewing a volume image allows pathologists to trace the entire lesion, identifying invasion sites that might be overlooked in individual or serial histological sections. Consequently, this proposed method could complement pathologists’ tools, potentially enhancing diagnoses by minimizing under-staging and reducing false negative results.

The 3D Revolution in Cancer Discovery.

The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.

Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.

Cancer diagnosis using label-free SERS-based exosome analysis.

Exosomes, carrying distinctive biomolecules reflective of their parent cell's status and origin, show promise as liquid biopsy biomarkers for cancer diagnosis. However, their clinical translation remains challenging due to their relatively low concentration in body fluids. Surface-Enhanced Raman spectroscopy (SERS) has recently gained significant attention as a label-free and sensitive technique for exosome analysis. This review explores label-free SERS for exosome detection, covering exosome isolation and characterization methods, advancements in SERS substrates, and fingerprint analysis techniques using machine learning. Furthermore, we emphasize the challenges and offer insights into the future prospects of SERS-based exosome analysis to enhance cancer diagnosis.

CRISPR-Cas9 technology: As an efficient genome modification tool in the cancer diagnosis and treatment.

Cancer is the second most common cause of death globally and is a major public health concern. Managing this disease is difficult due to its multiple stages and numerous genetic and epigenetic changes. Traditional cancer diagnosis and treatment methods have limitations, making it crucial to develop new modalities to combat the increasing burden of cancer. The clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) system has transformed genetic engineering due to its simplicity, specificity, low cytotoxicity, and cost-effectiveness. It has been proposed as an effective technology to enhance cancer diagnosis and treatment strategies. This article presents the most recent discoveries regarding the structure, mechanism, and delivery methods of the highly powerful genome editing tool, CRISPR-Cas9. In terms of diagnosis, the article examines the role of CRISPR-Cas9 in detecting microRNAs and DNA methylation, and discusses two popular gene detection techniques that utilize the CRISPR-Cas system: DNA endonuclease-targeted CRISPR trans reporterand specific high sensitivity enzymatic reporter unlocking. Regarding treatment, the article explores several genes that have been identified and modified by CRISPR-Cas9 for effective tumorigenesis of common cancers such as breast, lung, and colorectal cancer. The present review also addresses the challenges and ethical issues associated with using CRISPR-Cas9 as a diagnostic and therapeutic tool. Despite some limitations, CRISPR-Cas9-based cancer diagnosis has the potential to become the next generation of cancer diagnostic tools, and the continuous progress of CRISPR-Cas9 can greatly aid in cancer treatment.

Recent Progress in Enhanced Cancer Diagnosis, Prognosis, and Monitoring Using a Combined Analysis of the Number of Circulating Tumor Cells (CTCs) and Other Clinical Parameters.

Analysis of circulating tumor cells (CTCs) holds promise to diagnose cancer or monitor its development. Among the methods, counting CTC numbers in blood samples could be the simplest way to implement it. Nevertheless, its clinical utility has not yet been fully accepted. The reasons could be due to the rarity and heterogeneity of CTCs in blood samples that could lead to misleading results from assays only based on single CTC counts. To address this issue, a feasible direction is to combine the CTC counts with other clinical data for analysis. Recent studies have demonstrated the use of this new strategy for early detection and prognosis evaluation of cancers, or even for the distinguishment of cancers with different stages. Overall, this approach could pave a new path to improve the technical problems in the clinical applications of CTC counting techniques. In this review, the information relevant to CTCs, including their characteristics, clinical use of CTC counting, and technologies for CTC enrichment, were first introduced. This was followed by discussing the challenges and new perspectives of CTC counting techniques for clinical applications. Finally, the advantages and the recent progress in combining CTC counts with other clinical parameters for clinical applications have been discussed.

Beyond human capacity: How artificial intelligence (AI) is enhancing cancer diagnosis and treatment

Beyond human capacity: How artificial intelligence (AI) is enhancing cancer diagnosis and treatment

Enhancing Cancer Diagnosis with Real-Time Feedback: Tumor Metabolism through Hyperpolarized 1-13C Pyruvate MRSI.

This review article discusses the potential of hyperpolarized (HP) 13C magnetic resonance spectroscopic imaging (MRSI) as a noninvasive technique for identifying altered metabolism in various cancer types. Hyperpolarization significantly improves the signal-to-noise ratio for the identification of 13C-labeled metabolites, enabling dynamic and real-time imaging of the conversion of [1-13C] pyruvate to [1-13C] lactate and/or [1-13C] alanine. The technique has shown promise in identifying upregulated glycolysis in most cancers, as compared to normal cells, and detecting successful treatment responses at an earlier stage than multiparametric MRI in breast and prostate cancer patients. The review provides a concise overview of the applications of HP [1-13C] pyruvate MRSI in various cancer systems, highlighting its potential for use in preclinical and clinical investigations, precision medicine, and long-term studies of therapeutic response. The article also discusses emerging frontiers in the field, such as combining multiple metabolic imaging techniques with HP MRSI for a more comprehensive view of cancer metabolism, and leveraging artificial intelligence to develop real-time, actionable biomarkers for early detection, assessing aggressiveness, and interrogating the early efficacy of therapies.

Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA

Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer detection and tissue-of-origin localization is achieved using our algorithm, which incorporates cancer type-specific profiles of mutation distribution and chromatin organization in tumor tissues as model references. Our integrative model detects early-stage cancers, including those of pancreatic origin, with high sensitivity that is comparable to that of late-stage detection. Model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. Our methodologies may lay the groundwork for accurate cfDNA-based cancer diagnosis, especially at early stages.

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine.

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care.

Comprehensive single-cell sequencing reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of nasopharyngeal carcinoma

The tumor microenvironment (TME) of nasopharyngeal carcinoma (NPC) harbors a heterogeneous and dynamic stromal population. A comprehensive understanding of this tumor-specific ecosystem is necessary to enhance cancer diagnosis, therapeutics, and prognosis. However, recent advances based on bulk RNA sequencing remain insufficient to construct an in-depth landscape of infiltrating stromal cells in NPC. Here we apply single-cell RNA sequencing to 66,627 cells from 14 patients, integrated with clonotype identification on T and B cells. We identify and characterize five major stromal clusters and 36 distinct subpopulations based on genetic profiling. By comparing with the infiltrating cells in the non-malignant microenvironment, we report highly representative features in the TME, including phenotypic abundance, genetic alternations, immune dynamics, clonal expansion, developmental trajectory, and molecular interactions that profoundly influence patient prognosis and therapeutic outcome. The key findings are further independently validated in two single-cell RNA sequencing cohorts and two bulk RNA-sequencing cohorts. In the present study, we reveal the correlation between NPC-specific characteristics and progression-free survival. Together, these data facilitate the understanding of the stromal landscape and immune dynamics in NPC patients and provides deeper insights into the development of prognostic biomarkers and therapeutic targets in the TME.

Cancer-Associated Fibroblasts as Players in Cancer Development and Progression and Their Role in Targeted Radionuclide Imaging and Therapy.

Simple SummaryThe tumour microenvironment contains a unique population of cells, of complex origin and diverse functionality, known as Cancer associated fibroblasts (CAFs). In recent years there has been a growing interest in targeting CAFs to aid cancer diagnosis and therapy. Amongst the approaches utilized so far, targeting Fibroblast activation protein (FAP) has shown a lot of promise. In this review, we will focus on our current knowledge of the biology of CAFs as well as theranostic applications that will enhance cancer diagnosis and therapy in cancers carrying a distinct CAF signature.Cancer Associated Fibroblasts (CAFs) form a major component of the tumour microenvironment, they have a complex origin and execute diverse functions in tumour development and progression. As such, CAFs constitute an attractive target for novel therapeutic interventions that will aid both diagnosis and treatment of various cancers. There are, however, a few limitations in reaching successful translation of CAF targeted interventions from bench to bedside. Several approaches targeting CAFs have been investigated so far and a few CAF-targeting tracers have successfully been developed and applied. This includes tracers targeting Fibroblast Activation Protein (FAP) on CAFs. A number of FAP-targeting tracers have shown great promise in the clinic. In this review, we summarize our current knowledge of the functional heterogeneity and biology of CAFs in cancer. Moreover, we highlight the latest developments towards theranostic applications that will help tumour characterization, radioligand therapy and staging in cancers with a distinct CAF population.

Targeted theranostics of lung cancer: PD-L1-guided delivery of gold nanoprisms with chlorin e6 for enhanced imaging and photothermal/photodynamic therapy

Peptide modified nanoparticles have emerged as powerful tools for enhanced cancer diagnosis and novel treatment strategies. Here, human programmed death-ligand 1 (PD-L1) peptides were used for the first time for the modification of gold nanoprisms (GNPs) to enhance targeting efficiency. A multifunctional nanoprobe was developed that the GNPs@PEG/Ce6-PD-L1 peptide (GNPs@PEG/Ce6-P) was used for imaging-guided photothermal/photodynamic therapy by using the targeting effect of PD-L1. Both confocal imaging and flow cytometry experiments demonstrated a remarkable affinity of the as-prepared nanoprobes GNPs@PEG/Ce6-P to lung cancer cells (HCC827), which have a high PD-L1 expression. Subsequent in vitro and in vivo experiments further demonstrated that the nanoprobes GNPs@PEG/Ce6-P not only allowed for real-time visualization via fluorescence (FL) imaging and photoacoustic (PA) imaging, but also served as phototherapy agents for synergistic photothermal therapy (PTT) and photodynamic therapy (PDT). Furthermore, treatments on human lung cancer cells-derived tumors demonstrated that the nanoprobes GNPs@PEG/Ce6-P could significantly suppress tumor growth through PTT and PDT from GNPs and Ce6, respectively. In conclusion, the as-prepared new nanoprobes show promising potential for nanomedicine with remarkable targeting ability for dual-mode imaging and enhanced PDT and PTT effects on lung cancer.