Cancer Clinical Practice Research Articles

Annotated test-retest dataset of lung cancer CT scan images reconstructed at multiple imaging parameters

Quantitative imaging biomarkers (QIB) are increasingly used in clinical research to advance precision medicine approaches in oncology. Computed tomography (CT) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility. Here, we contribute to the cancer imaging community through The Cancer Imaging Archive (TCIA) by providing investigator-initiated, same-day repeat CT scan images of 32 non–small cell lung cancer (NSCLC) patients, along with radiologist-annotated lesion contours as a reference standard. Each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; GE CT equipment), which spans a wide range of CT imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. This holds considerable value for advancing the development of robust Radiomics, Artificial Intelligence (AI) and machine learning (ML) methods.

PALB2 analysis in the diagnostic process of breast cancer: An Italian monocentric experience.

The clinical utility of germline BRCA1 and BRCA2 testing is well established in patients with family history suggestive for hereditary breast and ovarian cancer syndrome. Recently, germline PALB2 pathogenic variants were also associated with an increased risk of breast and other cancers and, in the Italian population, it has been described in few studies without a systematic germline analysis of BRCA1, BRCA2 and PALB2. In this study, we described ASST Sette Laghi cancer genetic counselling services' experience in the analysis of 402 patients with suspected breast and ovarian cancer syndrome, by using BRCA1, BRCA2 and PALB2 germline genetic test. The frequency of PALB2 pathogenic variants was 1.2% compared to 3.5% and 3.2% for BRCA1 and BRCA2, respectively, whereas class 3 variants were detected in 0.3% and 0.5% of the BRCA1 and BRCA2 investigated patients, respectively. PALB2 pathogenic variants were identified in patients with a strong family history for breast cancer. Moreover, PALB2 variants were significantly associated with a younger age of breast cancer onset (mean age, 40.25 years) compared to wild-type patients (mean age 51.2 years, p-value = 0.0331). Similar to BRCA-associated breast cancer, the majority of PALB2 breast cancers were identified at an advanced clinical stage. Pedigree analysis revealed a family history of breast and ovarian cancer syndrome in all PALB2 pathogenic variants carriers (early breast cancer onset, bilateral breast cancer and ovarian cancer). In conclusion, the germline analysis of BRCA1, BRCA2 and PALB2 should be included in breast cancer clinical practice as a not negligible number of PALB2 carriers could be identified and referred to specific surveillance protocols.

O10-4 Current status and future of patient and public involvement in pancreatic cancer clinical practice guidelines revision

O10-4 Current status and future of patient and public involvement in pancreatic cancer clinical practice guidelines revision

Promising Approach for Optimizing In Vivo Fluorescence Imaging in a Tumor Mouse Model: Precision in Cancer Research.

Cancer remains a major global health concern due to its high mortality rates. Advanced diagnostic imaging, such as in vivo near-infrared (NIR) fluorescence imaging, enhances early detection by reducing autofluorescence and enabling deeper tissue penetration, addressing some limitations of conventional methods. Understanding the underlying causes of autofluorescence, even in mouse model fluorescence imaging, is crucial for accurate interpretation. This study investigated the origins of autofluorescence observed in experimental animals under NIR wavelengths, achieving successful fluorescence imaging in a clinically relevant tumor mouse model. Both fasting and non-fasting groups were evaluated to assess the dietary impact on autofluorescence, with various feeds tested. Subcutaneous and lung tumor models were established in C57BL/6 and BALB/c nude mice using LL/2-iRFP cells. Cryo-sectioning and lung tissue imaging were conducted to confirm tumor presence and assess fluorescence signals. It was found that autofluorescence, notably common in the abdomen, is attributed to dietary factors. By selecting feed that lacks autofluorescence, the impact of dietary fluorescence on imaging was evaluated, leading to the establishment of optimized imaging conditions suited to the presence or absence of autofluorescence. Subsequently, utilizing lung cancer cells expressing near-infrared proteins (LL/2-iRFP), intratracheal, and subcutaneous tumor mouse models were developed, and successful in vivo imaging was achieved using the optimized imaging protocols, effectively bypassing autofluorescence. This study emphasizes the importance of understanding and addressing autofluorescence in fluorescence imaging, presenting valuable insights for enhancing the reliability and accuracy of diagnostic imaging techniques in cancer research and clinical practice.

Artificial-Intelligence Cloud-Based Platform to Support Shared Decision-Making in the Locoregional Treatment of Breast Cancer: Protocol for a Multidimensional Evaluation Embedded in the CINDERELLA Clinical Trial.

Shared decision-making (SDM) plays a crucial role in breast cancer care by empowering patients and reducing decision regret. Patient decision aids (PtDAs) are valuable tools for facilitating SDM, now available in digital and artificial intelligence (AI)-powered formats to offer increasingly personalized contents. The ongoing CINDERELLA clinical trial (ClinicalTrials.gov: NCT05196269) evaluates an innovative AI cloud-based approach using a web platform and a mobile application (CINDERELLA APProach) versus the conventional approach to support SDM in breast cancer patients undergoing locoregional treatment. This protocol outlines a trial-based multidimensional evaluation, encompassing economic, financial, implementability, and environmental considerations associated with the CINDERELLA APProach. A within-trial cost-consequence and cost-utility analysis from a societal perspective will be performed using patient-level data on outcomes and resource use. The latter will be valued in monetary terms using country-specific unit costs or patient valuations. A budget impact analysis will be performed over 1 and 5 years from the budget holder perspectives. The CINDERELLA APProach implementability will be assessed through an evaluation of its usability, acceptability, organizational impact, and overall feasibility. The environmental impact will be quantitatively assessed across several dimensions, such as quantity, appropriateness, and emissions, supplemented by qualitative insights. Overall, data for the evaluation will be gathered from patient questionnaires, interviews with patients and managers, focus groups with healthcare professionals, and app electronic data. A thorough understanding of the broad consequences of the CINDERELLA APProach may foster its successful translation into real-world settings, hopefully benefiting breast cancer patients and clinical practice.

Soluble CD40 Ligand as a Promising Biomarker in Cancer Diagnosis.

Cancer remains a significant challenge in medicine due to its complexity and heterogeneity. Biomarkers have emerged as vital tools for cancer research and clinical practice, facilitating early detection, prognosis assessment, and treatment monitoring. Among these, CD40 ligand (CD40L) has gained attention for its role in immune response modulation. Soluble CD40 ligand (sCD40L) has shown promise as a potential biomarker in cancer diagnosis and progression, reflecting interactions between immune cells and the tumor microenvironment. This review explores the intricate relationship between sCD40L and cancer, highlighting its diagnostic and prognostic potential. It discusses biomarker discovery, emphasizing the need for reliable markers in oncology, and elucidates the roles of CD40L in inflammatory responses and interactions with tumor cells. Additionally, it examines sCD40L as a biomarker, detailing its significance across various cancer types and clinical applications. Moreover, the review focuses on therapeutic interventions targeting CD40L in malignancies, providing insights into cellular and gene therapy approaches and recombinant protein-based strategies. The clinical effectiveness of CD40L-targeted therapy is evaluated, underscoring the need for further research to unlock the full potential of this signaling pathway in cancer management.

Comparative Analysis of Gene Expression Analysis Methods for RNA in Situ Hybridization Images

Gene expression analysis is pivotal in cancer research and clinical practice. While traditional methods lack spatial context, RNA in situ hybridization (RNA-ISH) is a powerful technique that retains spatial tissue information. Here, RNAscope score, RT-droplet digital PCR (RT-ddPCR), and automated QuantISH and QuPath were employed for quantifying RNA-ISH expression values from formalin-fixed paraffin-embedded samples. The methods were compared using high-grade serous ovarian carcinoma samples, focusing on CCNE1, WFDC2, and PPIB genes. The findings demonstrate good concordance between automated methods and RNAscope, with RT-ddPCR showing less concordance. Additionally, QuantISH exhibits robust performance, even for low-expressed genes like CCNE1, showcasing its modular design and enhancing accessibility as a viable alternative for gene expression analysis.

Engineered Exosomes for Drug Delivery in Cancer Therapy: A Promising Approach and Application.

A significant amount of research effort is currently focused on investigating the role of exosomes in various cancers. These tiny vesicles, apart from acting as biomarkers, also play a crucial role in tumor formation and development. Several studies have demonstrated that exosomes can be a drug delivery vehicle for cancer therapy. In this paper, we highlight the key advantages of exosomes as a drug delivery candidate, with a particular focus on their low immunogenicity, natural targeting ability and suitable mechanical properties. Furthermore, we propose that the selection of appropriate exosomes and drug loading methods based on therapeutic goals and product heterogeneity is essential for preparing engineered exosomes. We comprehensively analyzed the superiorities of current drug-loading methods to improve the creation of designed exosomes. Moreover, we systematically review the applications of engineered exosomes in various therapies such as immunotherapy, gene therapy, protein therapy, chemotherapy, indicating that engineered exosomes have the potential to be reliable and, safe drug carriers that can address the unmet needs in cancer clinical practice.

Artificial Intelligence for the Management of Breast Cancer: An Overview.

Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast cancer research and clinical practice tools in recent years. Various projects are underway in early detection, diagnosis, prognosis, drug discovery, advanced image analysis, precision medicine, predictive modeling, and personalized treatment planning using artificial intelligence and machine learning. These projects use different algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and deep learning methods, to analyze and improve different types of data, such as clinical, genomic, and imaging data for breast cancer management. The success of these projects has the potential to transform breast cancer care, and continued research and development in this area is likely to lead to more accurate and personalized breast cancer diagnosis, treatment, and outcomes.

Unveiling the characteristics of D4 and R4 aptamers for their future use in prostate cancer clinical practice

The DNA and RNA aptamers D4 and R4, respectively, emerged from the modification of PC-3 cell-binding aptamer A4. Our objective was to characterize the aptamers in silico and in vitro and begin to identify their target molecules. We represented their structures using computational algorithms; evaluated their binding to several prostate cell lines and their effects on the viability and migration of these cells; and determined their dissociation constant by flow cytometry. We analyzed circulating prostate tumor cells from patients using D4, R4, anti-CD133 and anti-CD44. Finally, the target proteins of both aptamers were precipitated and identified by mass spectrometry to simulate their in silico docking. The aptamers presented similar structures and bound to prostate tumor cells without modifying the cellular parameters studied, but with different affinities. The ligand cells for both aptamers were CD44+, indicating that they could identify cells in the mesenchymal stage of the metastatic process. The possible target proteins NXPE1, ADAM30, and MUC6 need to be further studied to better understand their interaction with the aptamers. These results support the development of new assays to determine the clinical applications of D4 and R4 aptamers in prostate cancer.

The Japanese breast cancer society clinical practice guidelines for radiation treatment of breast cancer, 2022 edition.

The Breast Cancer Clinical Practice Guidelines, organized by the Japanese Breast Cancer Society (JBCS), were published in 2022. We present the English version of the Radiation Therapy (RT) section of the guidelines. The JBCS formed a task force to update the 2018 version of the JBCS Clinical Practice Guidelines. The Background Questions (BQs) contain the standard treatments for breast cancer in clinical practice, whereas the Clinical Questions (CQs) address daily clinical questions that remain controversial. Future Research Questions (FRQs) explore the subjects that are considered important issues, despite there being insufficient data for inclusion as CQs. The task force selected the 12 BQs, 8 CQs, and 6 FRQs for the RT section. For each CQ, systematic literature reviews and meta-analyses were conducted according to the Minds Manual for Guideline Development 2020, version 3.0. The recommendations, strength of recommendation, and strength of evidence for each CQ were determined based on systematic reviews and meta-analyses, and finalized by voting at the recommendation decision meeting.

Application of enzyme-linked immunosorbent assay in cancer detection

The enzyme-linked immunosorbent assay (ELISA) has emerged as an indispensable method in the realm of cancer detection., serving as a significant factor in the timely identification, prediction, and surveillance of many forms of cancer. ELISA as a highly sensitive immunological assay, utilizes the binding affinity between antibodies and antigens to detect and quantify cancer-associated biomolecules, such as tumor markers, oncogenic proteins, and specific antibodies. The recently adopted methodology facilitates the identification of cancer-associated biomarkers in various types of samples, such as plasma, urine, and tissue extracts. In the context of cancer diagnosis, ELISA aids in the early detection of malignancies by detecting cancer-specific biomarkers at low concentrations, allowing for timely intervention and improved patient outcomes. This research provides a concise overview of the multifaceted applications of ELISA in the field of oncology, especially in electrochemical ELISA and gold nanoparticle–based ELISA. It discusses the diverse biomarkers employed, and elucidates its significance in cancer research and clinical practice.

Identification of germline population variants misclassified as cancer-associated somatic variants.

Databases used for clinical interpretation in oncology rely on genetic data derived primarily from patients of European ancestry, leading to biases in cancer genetics research and clinical practice. One practical issue that arises in this context is the potential misclassification of multi-ancestral population variants as tumor-associated because they are not represented in reference genomes against which tumor sequencing data is aligned. To systematically find misclassified variants, we compared somatic variants in census genes from the Catalogue of Somatic Mutations in Cancer (COSMIC) V99 with multi-ancestral population variants from the Genome Aggregation Databases' Linkage Disequilibrium (GnomAD). By comparing genomic coordinates, reference, and alternate alleles, we could identify misclassified variants in genes associated with cancer. We found 192 of 208 genes in COSMIC's cancer-associated census genes (92.31%) to be associated with variant misclassifications. Among the 1,906,732 variants in COSMIC, 6,957 variants (0.36%) aligned with normal population variants in GnomAD, concerning for misclassification. The African / African American ancestral population included the greatest number of misclassified variants and also had the greatest number of unique misclassified variants. The direct, systematic comparison of variants from COSMIC for co-occurrence in GnomAD supports a more accurate interpretation of tumor sequencing data and reduces bias related to genomic ancestry.

Advancements in Circulating Tumor Cell Research: Bridging Biology and Clinical Applications.

Circulating tumor cells (CTCs) are cells released from the primary and metastatic tumor and intravasate into the blood or lymphatic vessels, where they are transported to distant sites and act as seeds that initiate cancer metastases or the development of further lesions. Recent advances in CTC research have shown their relevance as prognostic markers for early and metastatic disease detection, predictive biomarkers for relapse, and response to medical intervention or therapy. The rapidly evolving landscape of CTC biology has opened new avenues for understanding cancer progression, metastasis, and treatment response. Additionally, translating these findings into clinical applications holds promise for improving cancer diagnostics, prognosis, and personalized therapeutic strategies. This review discusses the significance of CTCs in cancer research and their associated challenges. We explore recent developments in the detection and characterization of CTCs and their implications in cancer research and clinical practice.

Characterization and mitigation of artifacts derived from NGS library preparation due to structure-specific sequences in the human genome

BackgroundHybridization capture-based targeted next generation sequencing (NGS) is gaining importance in routine cancer clinical practice. DNA library preparation is a fundamental step to produce high-quality sequencing data. Numerous unexpected, low variant allele frequency calls were observed in libraries using sonication fragmentation and enzymatic fragmentation. In this study, we investigated the characteristics of the artifact reads induced by sonication and enzymatic fragmentation. We also developed a bioinformatic algorithm to filter these sequencing errors.ResultsWe used pairwise comparisons of somatic single nucleotide variants (SNVs) and insertions and deletions (indels) of the same tumor DNA samples prepared using both ultrasonic and enzymatic fragmentation protocols. Our analysis revealed that the number of artifact variants was significantly greater in the samples generated using enzymatic fragmentation than using sonication. Most of the artifacts derived from the sonication-treated libraries were chimeric artifact reads containing both cis- and trans-inverted repeat sequences of the genomic DNA. In contrast, chimeric artifact reads of endonuclease-treated libraries contained palindromic sequences with mismatched bases. Based on these distinctive features, we proposed a mechanistic hypothesis model, PDSM (pairing of partial single strands derived from a similar molecule), by which these sequencing errors derive from ultrasonication and enzymatic fragmentation library preparation. We developed a bioinformatic algorithm to generate a custom mutation “blacklist” in the BED region to reduce errors in downstream analyses.ConclusionsWe first proposed a mechanistic hypothesis model (PDSM) of sequencing errors caused by specific structures of inverted repeat sequences and palindromic sequences in the natural genome. This new hypothesis predicts the existence of chimeric reads that could not be explained by previous models, and provides a new direction for further improving NGS analysis accuracy. A bioinformatic algorithm, ArtifactsFinder, was developed and used to reduce the sequencing errors in libraries produced using sonication and enzymatic fragmentation.

Adoption of electronic patient-reported outcomes in cancer clinical practice: the point of view of Italian patients

Adoption of electronic patient-reported outcomes in cancer clinical practice: the point of view of Italian patients

The use of patient reported outcome measures in oncology clinical practice across Australia and New Zealand.

While there is increasing evidence on the benefits of PROMs in cancer care, the extent of routine collection and use of PROMs in clinical cancer practice across Australia and New Zealand (ANZ) is unknown. This study examined the prevalence and characteristics of PROMs use in routine clinical cancer care in ANZ. An online survey was designed and disseminated via professional societies and organisations using a snowball sampling approach to clinical and health administration professionals managing cancer care in ANZ. A poster advertising the study was also circulated on professional social media networks via LinkedIn and Twitter inviting health professionals from ANZ to participate if they were using or intending to use PROMs in clinical cancer practice. Responders opted into the survey via the survey link. From 132 survey views, 91(response rate, 69%) respondents from 56 clinical practices across ANZ agreed to participate in the survey, and of these 55 (n = 55/91, 60%) respondents reported collecting PROMs within their clinical practice. The majority of the respondents were from the State of New South Wales in Australia (n = 21/55, 38%), hospital (n = 35/55, 64%), and a public setting (n = 46/55, 83%). PROMs were collected in all cancer types (n = 21/36, 58%), in all stages of the disease (n = 31/36, 86%), in an adult population (n = 33/36, 92%), applied in English (n = 33/36, 92%), and used to facilitate communication with other reasons (27/36, 75%). A geospatial map analysis provided insights into the variation in PROMs uptake between the two countries and in certain jurisdictions within Australia. This study also highlights the limited resources for PROMs implementation, and a lack of systematic priority driven approach. PROM use across Australia and New Zealand seems variable and occurring predominantly in larger metropolitan centres with limited standardisation of approach and implementation. A greater focus on equitable adoption of PROMs in diverse cancer care settings is urgently needed.

Multi-omics based artificial intelligence for cancer research.

With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.

The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide.

Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.

Deep learning-based multimodal spatial transcriptomics analysis for cancer.

The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networksexcel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.