Improve Cancer Diagnosis Research Articles

HDAC10 inhibition represses melanoma cell growth and BRAF inhibitor resistance via upregulating SPARC expression.

Secreted protein acidic and rich in cysteine (SPARC), a conserved secreted glycoprotein, plays crucial roles in regulating various biological processes. SPARC is highly expressed and has profound implications in several cancer types, including melanoma. Understanding the mechanisms that govern SPARC expression in cancers has the potential to lead to improved cancer diagnosis, prognosis, treatment strategies, and patient outcomes. Here, we demonstrate that histone deacetylase 10 (HDAC10) is a key regulator of SPARC expression in melanoma cells. Depletion or inhibition of HDAC10 upregulates SPARC expression, whereas overexpression of HDAC10 downregulates it. Mechanistically, HDAC10 coordinates with histone acetyltransferase p300 to modulate the state of acetylation of histone H3 at lysine 27 (H3K27ac) at SPARC regulatory elements and the recruitment of bromodomain-containing protein 4 (BRD4) to these regions, thereby fine-tuning SPARC transcription. HDAC10 depletion and resultant SPARC upregulation repress melanoma cell growth primarily by activating AMPK signaling and inducing autophagy. Moreover, SPARC upregulation due to HDAC10 depletion partly accounts for the resensitization of resistant cells to a BRAF inhibitor. Our work reveals the role of HDAC10 in gene regulation through indirect histone modification and suggests a potential therapeutic strategy for melanoma or other cancers by targeting HDAC10 and SPARC.

Unlocking Overexpressed Membrane Proteins to Guide Breast Cancer Precision Medicine.

Breast cancer (BC) is a prevalent form of cancer affecting women worldwide. However, the effectiveness of current BC drugs is limited by issues such as systemic toxicity, drug resistance, and severe side effects. Consequently, there is an urgent need for new therapeutic targets and improved tumor tracking methods. This study aims to address these challenges by proposing a strategy for identifying membrane proteins in tumors that can be targeted for specific BC therapy and diagnosis. The strategy involves the analyses of gene expressions in breast tumor and non-tumor tissues and other healthy tissues by using comprehensive bioinformatics analysis from The Cancer Genome Atlas (TCGA), UALCAN, TNM Plot, and LinkedOmics. By employing this strategy, we identified four transcripts (LRRC15, EFNA3, TSPAN13, and CA12) that encoded membrane proteins with an increased expression in BC tissue compared to healthy tissue. These four transcripts also demonstrated high accuracy, specificity, and accuracy in identifying tumor samples, as confirmed by the ROC curve. Additionally, tissue microarray (TMA) analysis revealed increased expressions of the four proteins in tumor tissues across all molecular subtypes compared to the adjacent breast tissue. Moreover, the analysis of human interactome data demonstrated the important roles of these proteins in various cancer-related pathways. Taken together, these findings suggest that LRRC15, EFNA3, TSPAN13, and CA12 can serve as potential biomarkers for improving cancer diagnosis screening and as suitable targets for therapy with reduced side effects and enhanced efficacy.

Abstract 2338: Benchmarking long- and short-read somatic structural variation callers using a multi-technology panel of six tumor/normal cell lines

Abstract Better structural variation (SV) detection could improve cancer diagnosis, treatment and prevention. Yet many cancer genomics studies have only reliably detected single nucleotide variants and small indels due to technological limitations of short-read sequencing. While structural variants can be inferred from discordant short read pairs, as much as 70% of SVs remain undetected because of the mappability difficulties. This is especially true for SVs in regions where GC content is greater than 45% or regions which harbor tandem repeats and segmental duplications. Advances in long-read sequencing enabled direct observation of SVs with recall and precision over 95% across a larger portion of the genome. To harness this technology, we developed Severus - a long-read SV caller that provides phased detection of both simple germline SVs as well as complex rearrangements commonly found in cancer genomes. Such rearrangements are built into a breakpoint graph to reconstruct derived tumor chromosome structure. To benchmark Severus and other long-read SV calling methods, we sequenced six tumor/normal cell line pairs using Nanopore, HiFi, short-read and Hi-C sequencing technologies. We combined the calls from multiple tools and technologies to generate benchmarking sets of somatic SV for each tumor cell line, and evaluated short- and long-read tools against this benchmark set. Current tools for SV merging and comparison (such as truvari) were primarily designed for long indels, rather than breakends that are more common in somatic SVs. Another challenge is that different tools may represent the same variants differently in a VCF output. To enable robust somatic SV outputs comparison, we developed a tool called Minda that translates an input VCF entries into a unified representation, and then compares the calls in either pairwise or multi-tool mode. As expected, in our benchmarking of cell six tumor cell line pairs, short-read methods had substantially lower recall and precision, compared to long-read methods. Among the long-read methods, Severus consistently had substantially better recall and precision, compared to Sniffles2, nanomonsv and SAVANA. An analysis of publicly available long-read sequencing of a melanoma cell line (for which curated benchmark SV set is available) produced consistent results. Citation Format: Ayse Keskus, Asher Bryant, Tanveer Ahmad, Ataberk Donmez, Isabel Rodriguez, Nicole Rossi, Yi Xie, Byunggil Yoo, Rose Milano, Hong Lou, Jimin Park, Joshua Gardner, Brandy McNulty, Karen Miga, Mike Dean, Midhat Farooqi, Benedict Paten, Mikhail Kolmogorov. Benchmarking long- and short-read somatic structural variation callers using a multi-technology panel of six tumor/normal cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2338.

Abstract 1571: Upregulation of phosphoglyceric acid mutase (PGAM)-1 is related to lymph node metastasis in gastric cancer

Abstract Gastric cancer (GC) is the most common malignancy of the digestive system, causing over 750,000 deaths worldwide. Gastric cancer mortality is largely due to its high level of invasiveness and metastasis to distal organs. Thus, it is an urgent priority to identify mechanisms underlying GC metastasis to improve cancer diagnosis develop of effective biological targeting agents. In this study, we demonstrate that phosphoglyceric acid mutase-1 (PGAM1) was preferentially over-expressed in lymph node metastasis (LNM) tissues of GC patients and correlated with poor prognosis. PGAM1 knock down in two human GC cell lines resulted in impaired cell proliferation and increased cell cycle arrest. Further, PGAM knockdown inhibited migration of both GC cell lines, which correlated with decreased expression of MMP2, MMP9 and ICAM-1. In a murine xenograft model, GC cells with a PGAM1 knockdown displayed decreased growth capacity in vivo. Western blot analysis showed that PGAM1 knockdown in SGC7901 and BGC823 decreased the phosphorylation of Src, FAK and Paxillin relative to the parental cell lines. These results indicate that PGAM1 may play an important role in promoting GC lymph node metastasis by driving cell proliferation and activating cytoskeletal reorganization and cell migration through the regulating Src/FAK/Paxillin pathway. Citation Format: Helei Wang, Zhicheng Liu, Lei Wang, Zitian Wang, Christina Jamieson, Nicholas A. Cacalano. Upregulation of phosphoglyceric acid mutase (PGAM)-1 is related to lymph node metastasis in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1571.

Abstract 4920: A nanopore sequencing approach characterizes cell-free DNA methylation-fragmentomics profiles indicative of breast cancer in a large cohort

Abstract The detection of breast cancer from cell-free DNA (cfDNA) offers a new approach with the potential to benefit individual screening and early detection. However, identifying breast cancer from cfDNA remains a major challenge when one uses conventional short-read sequencing. Methylation and DNA fragmentation characteristics provide highly informative biomarkers for detecting and monitoring cancer. To characterize these genomic features from cfDNA, we developed an approach using single molecule Nanopore sequencing. Single cfDNA molecules are sequenced intact with the methylation and fragment size directly extrapolated from Nanopore data. Moreover, our approach requires small amounts of cfDNA to discern specific genomic features from individual molecules. Thus, the use of Nanopore sequencing provides both methylation and fragment size as directly measured from native cfDNA without the use of any chemical processing or other molecular manipulation. We studied an extended breast cancer cohort with in total 1080 of samples, consisting of 440 untreated breast cancer patients and 640 healthy controls. Nanopore sequencing analysis of these cfDNA samples identified a range of 1 to 4 million CpG sites from 1 ml of plasma. Leveraging the cohort, we developed an ensemble classifier for breast cancer detection based on cfDNA methylation and fragmentation. The classifier leveraged a leave-one-out cross-validation (LOOCV) to generate predicted probability. We created nucleosome-specific methylation scores as the input features to the classifier, which scores quantified the similarity of each sample's methylation profile to a training set on a selected set of CpG sites associated with cancers. Furthermore, we also identified DNA fragmentation features, revealing distinct mono-nucleosome and di-nucleosome patterns that distinguish cancers from healthy controls. Aggregating the DNA nucleosome-specific scores and fragmentation features, we built an ensemble LOOCV random forest model. We applied the model to the breast cancer cohort and achieved a detection specificity of 74%, a sensitivity of 71%, and an overall accuracy of approximately 73%. The area under the receiver operating characteristic curve (AUROC) reached 80%. We validated the classifier with two external independent cohorts. Validation cohort 1 demonstrated as a specificity of 85%, a sensitivity of 80%, and overall accuracy of 83% with AUROC of 90%. Validation cohort 2 without controls demonstrated that the classifier achieved a sensitivity of 94%. Both validation sets confirm that our model can achieve excellent performance for early breast cancer detection. In summary, our findings demonstrate that nanopore sequencing provides cfDNA methylation and fragment profiling that enables breast cancer detection. This approach holds the potential to improve cancer diagnosis and treatment outcomes. Citation Format: Xiangqi Bai, Billy T. Lau, Alison Almada, Sue Grimes, Tianqi Chen, Hojoon Lee, Hanlee P. Ji. A nanopore sequencing approach characterizes cell-free DNA methylation-fragmentomics profiles indicative of breast cancer in a large cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4920.

Nanotheranostics: A Cutting-Edge Technology for Cancer Management.

Cancer is the second-leading cause of death in the 21st century, where early detection and appropriate therapeutic interventions are two components essential for effective cancer management. Despite the availability of several conventional diagnostics and therapeutic agents, cancer mortality rates are rising due to an increase in the frequency of recurrence and metastasis in cancer patients. Therefore, tremendous efforts have been expended to address this significant clinical issue and improve therapeutic efficacy. In this regard, nanotheranostic is a multipotential single platform for both cancer diagnosis and treatment through enhanced aqueous solubility and bioavailability of the encapsulated agent, stimulus responsiveness, tumor-specific targeting ability, precise tumor imaging, and real-time drug delivery. Nonetheless, the translational success of nanotheranostic platforms is still in its infancy and requires more extensive research in the context of tumor heterogeneity, safety profile, and regulatory issues, which pose one of the largest technological limitations. The present review summarizes different nanotheranostic platforms and nanotheranostic candidates in clinical trials (AGuIX® , NBTXR3, Ferumoxtran, MM-398, EndoTAG-1, etc.), along with disadvantages and challenges to improving cancer diagnosis and treatment. Overall, the concept, platform, and technical knowledge of nanotheranostics are really helpful to academic and pharmaceutical researchers.

Biomimetic bright optotheranostics for metastasis monitoring and multimodal image-guided breast cancer therapeutics

Nanoparticle formulations blending optical imaging contrast agents and therapeutics have been a cornerstone of preclinical theranostic applications. However, nanoparticle-based theranostics clinical translation faces challenges on reproducibility, brightness, photostability, biocompatibility, and selective tumor targeting and penetration. In this study, we integrate multimodal imaging and therapeutics within cancer cell-derived nanovesicles, leading to biomimetic bright optotheranostics for monitoring cancer metastasis. Upon NIR light irradiation, the engineered optotheranostics enables deep visualization and precise localization of metastatic lung, liver, and solid breast tumors along with solid tumor ablation. Metastatic cell-derived nanovesicles (∼80 ± 5 nm) are engineered to encapsulate imaging (emissive organic dye and gold nanoparticles) and therapeutic agents (anticancer drug doxorubicin and photothermally active organic indocyanine green dye). Systemic administration of biomimetic bright optotheranostic nanoparticles shows escape from mononuclear phagocytic clearance with (i) rapid tumor accumulation (3 h) and retention (up to 168 h), (ii) real-time monitoring of metastatic lung, liver, and solid breast tumors and (iii) 3-fold image-guided solid tumor reduction. These findings are supported by an improvement of X-ray, fluorescence, and photoacoustic signals while demonstrating a tumor reduction (201 mm3) in comparison with single therapies that includes chemotherapy (134 mm3), photodynamic therapy (72 mm3), and photothermal therapy (88mm3). The proposed innovative platform opens new avenues to improve cancer diagnosis and treatment outcomes by allowing the monitorization of cancer metastasis, allowing the precise cancer imaging, and delivering synergistic therapeutic agents at the solid tumor site.

RNA binding proteins in cancer chemotherapeutic drug resistance.

Drug resistance has been a major obstacle in the quest for a cancer cure. Many chemotherapeutic treatments fail to overcome chemoresistance, resulting in tumor remission. The exact process that leads to drug resistance in many cancers has not been fully explored or understood. However, the discovery of RNA binding proteins (RBPs) has provided insight into various pathways and post-transcriptional gene modifications involved in drug tolerance. RBPs are evolutionarily conserved proteins, and their abnormal gene expression has been associated with cancer progression. Additionally, RBPs are aberrantly expressed in numerous neoplasms. RBPs have also been implicated in maintaining cancer stemness, epithelial-to-mesenchymal transition, and other processes. In this review, we aim to provide an overview of RBP-mediated mechanisms of drug resistance and their implications in cancer malignancy. We discuss in detail the role of major RBPs and their correlation with noncoding RNAs (ncRNAs) that are associated with the inhibition of chemosensitivity. Understanding and exploring the pathways of RBP-mediated chemoresistance will contribute to the development of improved cancer diagnosis and treatment strategies.

FedCSCD-GAN: A secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and GAN

Digital technologies present unrivaled opportunities to improve healthcare services worldwide. Medical devices and hospitals are now using innovative techniques to diagnose cancer patients. Despite the vast amount of data generated, stored, and communicated to the cloud and edge devices, patient data privacy remains a crucial concern. Federated learning (FL) is a revolutionary distributed learning method with significant potential in medical data processing. However, data privacy, data quality, and model performance issues can make it challenging to develop a resilient model. As a solution to the above-mentioned challenges, a secure and collaborative FedCSCD-GAN framework is proposed for clinical cancer diagnosis under the federated learning and GAN optimization principle. By leveraging the collective intelligence of distributed data sources, this framework seeks to improve cancer diagnosis accuracy while maintaining appropriate security measures for sensitive patient data. In the proposed system, the quasi-identifiers (QIden) are identifiedas independent QIden attributes in health data, while the others areclassified as confidential information (CI). Consequently, to enhance security and privacy f- differential privacy anonymization is performed on the QIden attributes, and the resulting data is mixed with the CI attribute. Using anonymized data, the Cramer GAN was trained using Cramer distance for efficiency, and privacy was assessed. Notably, the suggested architecture obtains diagnosis accuracy of 97.80 % for lung cancer, 96.95 % for prostate cancer, and 97 % for breast cancer. Based on the experimental analysis, the proposed architecture detects cancer accurately and addresses security and collaboration issues. This paradigm has the potential to transform healthcare and improve patient outcomes globally.

Development of a xanthene-based NIR fluorescent probe for accurate and sensitive detection of γ-glutamyl transpeptidase in cancer diagnosis and treatment.

γ-Glutamyl transpeptidase (GGT) regulates glutathione (GSH), essential for cell functions and linked to cancer. High GGT levels in tumors make it a valuable cancer biomarker. Current GGT detection methods often lack sensitivity and specificity. To address this, we developed XM-Glu, a new near-infrared (NIR) fluorescent probe. XM-Glu features a xanthene-based structure with a hydroxy xanthene fluorophore and a malononitrile group for NIR emission and reduced background noise. It has a self-immolating linker masked with glutamate acid, which activates fluorescence when GGT is present. XM-Glu can detect GGT in the range of 1.0 to 20 mU with a low detection limit of 0.067 mU mL-1. It showed high specificity and minimal interference in cellular assays. In mice, XM-Glu effectively detected GGT in tumor, liver, and kidney tissues. Its NIR properties provide real-time insights into GGT activity, improving cancer diagnosis and monitoring. This new technology enhances cancer research and helps better understand GGT's role in cancer progression.

Unlocking the potential of RGD-conjugated gold nanoparticles: a new frontier in targeted cancer therapy, imaging, and metastasis inhibition.

In the rapidly evolving field of cancer therapeutics, the potential of gold nanoparticles (AuNPs) conjugated with RGD peptides has emerged as a promising avenue for targeted therapy and imaging. Despite numerous studies demonstrating the effectiveness of RGD-conjugated AuNPs in specifically targeting tumor cells and enhancing radiation therapy (RT), a comprehensive review of these advancements is currently lacking. This review aims to fill this critical gap in the literature. Our analysis reveals that RGD-conjugated AuNPs have shown significant promise in improving the diagnosis and treatment of various types of cancer, including breast cancer. However, the full potential of this technology is yet to be realized. The development of multifunctional nanoplatforms incorporating AuNPs has opened new horizons for targeted therapy, dual-mode imaging, and inhibition of tumor growth and metastasis. This review is of paramount importance as it provides a comprehensive overview of the current state of research in this area, and highlights the areas where further research is needed. It is hoped that this review will inspire further investigations into this promising nanotechnology, ultimately leading to improved cancer diagnosis and therapy. Therefore, the findings presented in this review underscore the potential of AuNPs conjugated with RGD peptides as a revolutionary approach in cancer therapeutics. It is our fervent hope that this review will serve as a catalyst for further research in this exciting field.

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diagnosis of cancer is a formidable challenge. Mass spectrometry-based proteomics offers a robust technical foundation for cancer diagnosis. However, mass spectrometry data are characterized by high dimensionality, large data volume, and noise interference, which can lead to diagnostic errors in clinical applications. To address this challenge, an improved algorithm combining principal component analysis (PCA) with a convolutional neural network (CNN) algorithm (denoted as PCA-1DSE-ResCNN) was proposed to assist in analyzing high-dimensional mass spectral data. The algorithm initially reduced the dimensionality of the data through the PCA technique. Subsequently, the convolutional neural network algorithm (1DSE-ResCNN) integrating residual blocks and squeeze-and-excitation blocks was used as a classifier. This approach can not only alleviate the issues of overfitting and gradient vanishing caused by deep network layers but also reduce redundant information, enabling the algorithm to effectively learn high-dimensional data features and deal with nonlinear relationships. To validate the effectiveness of the algorithm, the high-dimensional ovarian cancer mass spectrometry dataset was selected as an example to examine its application performance in early diagnosis of ovarian cancer. The experimental results demonstrated that the PCA-1DSE-ResCNN algorithm outperforms other methods in terms of accuracy, specificity, and sensitivity on three high-dimensional ovarian cancer datasets. This study will contribute to the rapid diagnosis and early detection of cancer.

Attitudes toward Cancer Diagnosis Disclosure and Resilience among Suspicious Lung Cancer Patients, Lung Cancer Patients, and Their Families

Objective. This study aimed to compare the attitudes and preferences on cancer diagnosis disclosure (CDD) among suspicious lung cancer patients (SLCPs), lung cancer patients (LCPs), and their family members and to explore their associations with resilience. Methods. A cross-sectional study was conducted at Yunnan Cancer Hospital in China, from March to August 2022. A total of 1016 participants including 254 SLCP-family pairs and 254 LCP-family pairs completed self-administered questionnaires to assess their attitudes toward CDD and resilience. Continuous variables were expressed by means and standard deviations, while categorical variables were presented by numbers and percentages. The comparisons between groups were tested by using a t-test or chi-squared test. Associations between resilience and CDD attitudes in the four groups were estimated by multivariate logistic regression models. Results. Compared with LCPs, more SLCPs believed that patients should be informed of their cancer diagnoses (63.8% vs 43.7%, p<0.001), and the distribution of the first one to know the diagnosis was disequilibrium (p<0.05). The significant difference was identified in participants’ attitudes toward patients being told the facts by resilience levels among the different groups. Subsequent multivariate logistic regression analyses showed that resilience was associated with participants’ preference for patients being informed of their cancer diagnoses in the SLCPs group (adjusted OR: 1.87, 95% CI: 1.08–3.25), LCPs group (adjusted OR: 2.21, 95% CI: 1.32–3.74), and family of LCPs group (adjusted OR: 1.79, 95% CI: 1.04–3.12). We further performed a sensitivity analysis using quantiles of resilience. Conclusion. SLCPs and LCPs exhibited different attitudes towards CDD. Resilience plays a positive role in CDD. Our study suggests that healthcare practitioners should consider patients’ diagnosis state when disclosing a cancer diagnosis and tailor their disclosure methods based on the patients’ and families’ preferences and resilience. Our findings provide important implications to guide future research and intervention programs to improve cancer diagnosis disclosure for SLCPs, LCPs, and their families.

Investigation of cullin-3 gene expression changes in people with colorectal cancer and polyps in China.

Colorectal cancer (CRC) is one of the most common fatal malignancies caused by environmental and genetic factors. Considering the increasing frequency of CRC worldwide, especially in China, the importance of research on CRC is more widely defined. A recent study focused on molecular pathways involved in colon cancer carcinogenesis to improve cancer diagnosis and treatment to identify new biomarkers. Colon cancer is the result of dysplasia in primary growths of the intestine, known as polyps. These early growths are unknown and different in terms of morphology, molecular mechanisms, and the ability to cause colon cancer. This study aims to investigate the expression level of the CUL3 gene in polyps and colorectal cancer. This cross-sectional study collected 300 colorectal tissue biopsy samples, including 40 tumor tissue samples, 73 precancerous lesions with their adjacent tissue, and 31 normal tissue samples. The expression of the CUL3 gene was investigated by the Real-time PCR method. There was no significant difference in CUL3 mRNA expression between polyp tissues and their adjacent samples (p = 0.41). Our results showed no statistically significant difference in CUL3 gene expression between tumor tissues and their adjacent thermal samples (p = 0.78) and between tumor and polyp groups (p = 0.53). CUL3 may play an essential role in regulating cancer and CRC progression by stimulating the proteasomal degradation of various tumor suppressors or oncogenes. Studies on the effective substrates of CUL3 in colorectal cancer are essential.

Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations

BackgroundMachine learning (ML) represents a powerful tool to capture relationships between molecular alterations and cancer types and to extract biological information. Here, we developed a plain ML model aimed at distinguishing cancer types based on genetic lesions, providing an additional tool to improve cancer diagnosis, particularly for tumors of unknown origin.MethodsTCGA data from 9,927 samples spanning 32 different cancer types were downloaded from cBioportal. A vector space model type data transformation technique was designed to build consistently homogeneous new datasets containing, as predictive features, calls for somatic point mutations and copy number variations at chromosome arm-level, thus allowing the use of the XGBoost classifier models. Considering the imbalance in the dataset, due to large difference in the number of cases for each tumor, two preprocessing strategies were considered: i) setting a percentage cut-off threshold to remove less represented cancer types, ii) dividing cancer types into different groups based on biological criteria and training a specific XGBoost model for each of them. The performance of all trained models was mainly assessed by the out-of-sample balanced accuracy (BACC) and the AUC scores.ResultsThe XGBoost classifier achieved the best performance (BACC 77%; AUC 97%) on a dataset containing the 10 most represented tumor types. Moreover, dividing the 18 most represented cancers into three different groups (endocrine-related carcinomas, other carcinomas and other cancers),such analysis models achieved 78%, 71% and 86% BACC, respectively, with AUC scores greater than 96%. In addition, the model capable of linking each group to a specific cancer type reached 81% BACC and 94% AUC. Overall, the diagnostic potential of our model was comparable/higher with respect to others already described in literature and based on similar molecular data and ML approaches.ConclusionsA boosted ML approach able to accurately discriminate different cancer types was developed. The methodology builds datasets simpler and more interpretable than the original data, while keeping enough information to accurately train standard ML models without resorting to sophisticated Deep Learning architectures. In combination with histopathological examinations, this approach could improve cancer diagnosis by using specific DNA alterations, processed by a replicable and easy-to-use automated technology. The study encourages new investigations which could further increase the classifier’s performance, for example by considering more features and dividing tumors into their main molecular subtypes.

Trisulfide Bond-Mediated Molecular Phototheranostic Platform for "Activatable" NIR-II Imaging-Guided Enhanced Gas/Chemo-Hypothermal Photothermal Therapy.

Tumor microenvironment (TME)-triggered phototheranostic platform offers a feasible strategy to improve cancer diagnosis accuracy and minimize treatment side effects. Developing a stable and biocompatible molecular phototheranostic platform for TME-activated second near-infrared (NIR-II) fluorescence imaging-guided multimodal cascade therapy is a promising strategy for creating desirable anticancer agents. Herein, a new NIR-II fluorescence imaging-guided activatable molecular phototheranostic platform (IR-FEP-RGD-S-S-S-Fc) is presented for actively targeted tumor imaging and hydrogen sulfide (H2 S) gas-enhanced chemodynamic-hypothermal photothermal combined therapy (CDT/HPTT). It is revealed for the first time that the coupling distance between IR-FE and ferrocene is proportional to the photoinduced electron transfer (PET), and the aqueous environment is favorable for PET generation. The part of Cyclic-RGDfK (cRGDfk) peptides can target the tumor and benefit the endocytosis of nanoparticles. The high-concentration glutathione (GSH) in the TME will separate the fluorescence molecule and ferrocene by the GSH-sensitive trisulfide bond, realizing light-up NIR-II fluorescence imaging and a cascade of trimodal synergistic CDT/HPTT/gas therapy (GT). In addition, the accumulation of hydroxyl radicals (•OH) and down-regulation of glutathione peroxidase 4 (GPX4) can produce excessive harmful lipid hydroperoxides, ultimately leading to ferroptosis.

Test, evidence, transition projects in Scotland: developing the evidence needed for transition of effective interventions in cancer care from innovation into mainstream practice

BackgroundA robust evidence base is required to assist healthcare commissioners and providers in selecting effective and sustainable approaches to improve cancer diagnosis and treatment. Such evidence can be difficult to build, given the fast-paced and highly pressured nature of healthcare delivery, the absence of incentives, and the presence of barriers in conducting pragmatic yet robust research evaluations. Cancer Research UK (CRUK) has played an active part in building the evidence base through its funding of programmes to identify, evaluate and scale-up innovative approaches across the UK. The aim of this paper is to describe and explain the research design and intended approach and activities for two cancer services improvement projects in Scotland funded by CRUK.MethodsA hybrid effectiveness-implementation study design will assess both the efficiency of the new pathways and their implementation strategies, with the aim of generating knowledge for scale-up. A range of implementation, service and clinical outcomes will be assessed as determined by the projects’ Theories of Change (ToCs). A naturalistic case study approach will enable in-depth exploration of context and process, and the collection and synthesis of data from multiple sources including routine datasets, patient and staff surveys, in-depth interviews and observational and other data. The evaluations are informed throughout by a patient/public representatives’ group, and by small group discussions with volunteer cancer patients.DiscussionOur approach has been designed to provide a holistic understanding of how (well) the improvement projects work (in relation to their anticipated outcomes), and how they interact with their wider contexts. The evaluations will help identify barriers, facilitators, and unanticipated consequences that can impact scalability, sustainability and spread. By opting for a pragmatic, participatory evaluation research design, we hope to inform strategies for scaling up successful innovations while addressing challenges in a targeted manner.

ExplORRNet: An interactive web tool to explore stage-wise miRNA expression profiles and their interactions with mRNA and lncRNA in human breast and gynecological cancers

BackgroundMicroRNAs (miRNAs) are key regulators of gene expression that have been implicated in gynecological and breast cancers. Understanding the cancer stage-wise expression patterns of miRNAs and their interactions with other RNA molecules in cancer is crucial to improve cancer diagnosis and treatment planning. Comprehensive web tools that integrate data on the transcriptome, circulating miRNAs, and their validated targets to derive beneficial conclusions in cancer research are lacking. MethodsUsing the Shiny R package, we developed a web tool called ExplORRNet that integrates transcriptomic profiles from The Cancer Genome Atlas and miRNA expression data derived from various sources, including tissues, cell lines, exosomes, serum, and plasma, available in the Gene Expression Omnibus database. Differential expression analyses between normal and tumor tissue samples as well as different stages of cancer, accompanied by gene enrichment and survival analyses, can be performed using specialized R packages. Additionally, a miRNA-messenger RNA (mRNA)-long non-coding RNA (lncRNA) networks are constructed to identify regulatory modules. ResultsOur tool identifies cancer stage-wise differentially regulated miRNAs, mRNAs, and lncRNAs in gynecological and breast cancers. Survival analysis identifies miRNAs associated with patient survival, and functional enrichment analysis provides insights into dysregulated miRNA-related biological processes and pathways. The miRNA–mRNA–lncRNA networks highlight interconnected regulatory molecular modules driving cancer progression. Case studies demonstrate the utility of the ExplORRNet for studying gynecological and breast cancers. ConclusionExplORRNet is an intuitive and user-friendly web tool that provides a deeper understanding of dysregulated miRNAs and their functional implications in gynecological and breast cancers. We hope our ExplORRNet tool has potential utility among the clinical and basic researchers and will be beneficial to the entire cancer genomics community to encourage and facilitate mining the rapidly growing public databases to progress the field of precision oncology. The ExplORRNet is available at https://mirna.cs.ut.ee.

Clinical outcomes of oral epithelial dysplasia managed by observation versus excision.

Oral potentially malignant disorders, including oral epithelial dysplasia (OED), are a group of conditions with an increased risk of progression to oral cancer. Clinical management of OED is challenging and usually involves monitoring with repeated incisional biopsies or complete surgical excision. To determine if complete surgical excision of OED impacts malignant transformation or improves survival outcomes in lesions that progress to malignancy. A retrospective review of all patients diagnosed with OED between 2009 and 2016 was completed, and patients were followed until January 2022 for disease course and outcomes. Hundred and fifty-five cases of OED met the inclusion criteria. Among the 61 lesions managed by observation, 15 progressed to cancer. Among the 94 lesions managed by surgical excision, 27 progressed to cancer. The overall malignant transformation rate was 27%, with an annual rate of 6.4%. Surgical excision with or without histologically negative margins did not decrease malignant transformation but was associated with lower oncologic staging at the time of diagnosis and improved survival. Surgical excision of OED with or without negative margins did not reduce the rate of transformation to oral cancer but resulted in lower oncologic staging at diagnosis, leading to improved patient outcomes. Our results support the implementation of more extensive tissue sampling to improve cancer diagnosis and patient outcomes.

Abstract PO-044: Correlation between circulating tumor DNA and tumor volume in head and neck cancer

Abstract Purpose: NavDx is a DNA test that has been used widely in head and neck cancer to quantify fragments of circulating tumor tissue modified viral DNA (TTMV HPV DNA), an HPV-specific cancer biomarker that is shed into the blood. Circulating tumor DNA has shown promise in monitoring tumor progression and therapeutic efficacy and therefore has potential to assist in improved cancer diagnosis and treatment. Recent studies have demonstrated a correlation between circulating tumor DNA and tumor volume in metastatic pancreatic ductal adenocarcinoma and metastatic breast cancer. The purpose of the study is to determine whether there is a correlation between positive vs. negative circulating tumor DNA at baseline as well as the quantity of circulating fragments and tumor volume in head and neck cancer patients. Methods: In this retrospective cohort study, data was extracted from our NavDx database (Naveris Systems) for 39 patients with p16 positive head and neck cancer with subsites in the base of tongue and tonsil. Logistic regression analyses were performed to compare tumor volume and tumor site in all patients and then by the subsites of base of tongue and tonsil. To address multicollinearity, different models were created for primary tumor volume, nodal volume, and total tumor volumes. Contrast enhanced diagnostic or radiation treatment planning CT scans were imported into Varian Aria (Varian Medical Systems, Palo Alto, CA). The primary and nodal disease was delineated to determine the total and sub-volumes. The relationship was examined between the tumor volumes and positive vs. negative/indeterminate circulating tumor DNA. Linear regression analysis was also performed to assess the relationship between tumor volumes and circulating fragments of TTMV-HPV 16. Results: A total of 39 patients were included in the study, including 36 males and 3 females, ages ranging from 42 to 79, and patients staged from Stage I to Stage IVA. 13 patients underwent surgical intervention. Subsites included the tonsil for 21 patients and base of tongue for 19 patients that were included in the analysis. There was a positive correlation between nodal volume and circulating TTMV-HPV16 fragments (p= 0.024) in all patients. There was a difference in tumor volume with an odds ratio of 1.41 and nodal volume with an odds ratio of 1.04 compared with positive vs. negative circulating DNA in all patients (p = 0.374; p = 0.906). Conclusions: The data demonstrated a positive correlation between nodal volume and circulating TTMV-HPV 16 fragments (p = 0.024). There was a difference in tumor and nodal tumor volumes with positive vs. negative circulating tumor DNA in all patients, however this did not reach statistical significance. While there was an association with nodal volume, further investigation is needed to determine why patients with p16 positive head and neck cancer may have negative serum circulating tumor DNA. Additional studies with a larger sample size can also better elucidate the relationship between circulating tumor DNA and tumor burden in H&N patients. Citation Format: Oghenefejiro Okifo, Vivian Wu, Samantha Tam, Sondos Al Khatib, Farzan Siddiqui. Correlation between circulating tumor DNA and tumor volume in head and neck cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-044.