Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Classification performance assessment for imbalanced multiclass data.

The evaluation of diagnostic systems is pivotal for ensuring the deployment of high-quality solutions, especially given the pronounced context-sensitivity of certain systems, particularly in fields such as biomedicine. Of notable importance are predictive models where the target variable can encompass multiple values (multiclass), especially when these classes exhibit substantial frequency disparities (imbalance). In this study, we introduce the Imbalanced Multiclass Classification Performance (IMCP) curve, specifically designed for multiclass datasets (unlike the ROC curve), and characterized by its resilience to class distribution variations (in contrast to accuracy or F ß -score). Moreover, the IMCP curve facilitates individual performance assessment for each class within the diagnostic system, shedding light on the confidence associated with each prediction-an aspect of particular significance in medical diagnosis. Empirical experiments conducted with real-world data in a multiclass context (involving 35 types of tumors) featuring a high level of imbalance demonstrate that both the IMCP curve and the area under the IMCP curve serve as excellent indicators of classification quality.

A spatially localized DNA linear classifier for cancer diagnosis.

Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology.

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images.

Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).

A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.

BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. METHOD: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates 2D Region of Interest (ROI) segmentation and image reconstruction as auxiliary tasks. Subsequently, all lesion features of a single patient are aggregated into a feature vector using a multi-instance aggregation layer. RESULT: Validation experiments were conducted on three PET datasets, specifically the liver cancer dataset, lung cancer dataset, and lymphoma dataset. In the context of lung cancer, our proposed method achieved impressive performance with Area Under Curve (AUC) values of 0.82, 0.84, and 0.83 for the three-classification task. For the binary classification task of lymphoma, our method demonstrated notable results with AUC values of 0.95 and 0.75. Moreover, in the binary classification task of liver tumor, our method exhibited promising performance with AUC values of 0.84 and 0.86. CONCLUSION: The experimental results clearly indicate that our proposed method outperforms alternative approaches significantly. Through the extraction of complementary radiomics features and deep features, our method achieves a substantial improvement in tumor subtype classification performance using small PET datasets.

Determination of Nursing Care Times Based on the Perroca Patient Classification Instrument in the Inpatient Oncology Unit: A Mixed Method Study.

OBJECTIVES: The aim of this study was to determine the daily nursing care times of hospitalized inpatient oncology unit patients according to degree of acuity using the Perroca Patient Classification tool. DATA SOURCES: This study used a mixed method sequential explanatory design. The "Nursing Activity Record Form" and "Perroca Patient Classification Instrument" were used for quantitative data collection, and direct observation was performed for 175 hours via time-motion study. Descriptive statistics, between-group comparison, and correlation analysis were used for data analysis. Using a semistructured questionnaire, qualitative data were collected from individual in-depth interviews with seven nurses who participated in the quantitative part of the study. Qualitative data were analyzed by thematic analysis. The reporting of this study followed GRAMMS checklist. CONCLUSIONS: As a result of the integration of quantitative and qualitative data, daily nursing care duration was determined as 2 to 2.5 hours for Type 1 patients, 2.6 to 3.5 hours for Type 2 patients, 3.6 to 4.75 hours for Type 3 patients, and 4.76 to 5.5 hours for Type 4 patients. The findings showed that in an inpatient oncology unit, nursing care hours increased as patients' Perroca Patient Classification Instrument acuity grade increased; thus, the instrument was discriminative in determining patients' degree of acuity. IMPLICATIONS FOR NURSING PRACTICE: Nurse managers can utilize this study's results to plan daily assignments that are sensitive to patient care needs. The results can also help nurse managers to identify relationships between nurse staffing and patient outcomes at the unit level, as well as to develop ways to analyze such relationships.

Demoralization in cancer survivors: an updated systematic review and meta-analysis for quantitative studies.

BACKGROUND: Demoralization can cause impairments across all life aspects of cancer patients. Cancer patients are also vulnerable during their survivorship. The purpose of this review is to examine the risk of demoralization and associated risk factors among cancer survivors who have completed their primary anti-cancer treatment or time since diagnosis ≥5 years without recurrence. METHODS: We searched databases of PubMed, Cochrane, Embase, PsycINFO and ClinicalTrial.gov to identify eligible studies which reported the demoralization level among cancer survivors. A random-effect meta-analysis model was used for calculating mean demoralization level. Heterogeneity was evaluated by I2 statistics. Funnel plots and Egger's regression tests were performed for checking publication bias. We used one-study-removed method for sensitivity analysis. Subgroup analysis was also done to examine the difference of demoralization level between cancer types. Meta-regression was performed to reveal risk factors of demoralization. RESULTS: A meta-analysis of 12 articles involving 2902 cancer survivors was conducted. The mean demoralization score among cancer survivors was 25.98 (95% CI: 23.53-28.43). Higher demoralization level was seen in participants with older age, higher female ratio, higher married/living together status ratio and higher patient health questionnaire-9 score. The literature review revealed correlations between demoralization and suicide risk, anxiety and quality of life. No consistent correlation between demoralization and post-traumatic stress symptoms could be seen. CONCLUSIONS: High demoralization level is noticed among cancer survivors. Risks for females, elder patients or breast cancer survivors are identified. More longitudinal or interventional studies for cancer survivors' demoralization are expected in the future.

Epigenetic regulation during cancer transitions across 11 tumour types.

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.

A Comprehensive Landscape of De Novo Malignancy After Double Lung Transplantation.

Although the association between post-transplant malignancy (PTM) and immunosuppressive therapy after organ transplantation has been studied, an integrated review of PTM after lung transplantation is lacking. We investigated the incidence and types of de novo PTM and its impact on survival following double lung transplantation (DLT). The incidence and type of PTM as well as the annual and cumulative risks of each malignancy after DLT were analyzed. The overall survival (OS) of recipients with or without PTM was compared by the Kaplan-Meier survival method and landmark analysis. There were 5,629 cases (23.52%) with 27 types of PTMs and incidences and OS varied according to the types of PTMs. The recipients with PTM showed a significantly longer OS than those without PTM (p < 0.001). However, while the recipients with PTM showed significantly better OS at 3, and 5 years (p < 0.001, p = 0.007), it was worse at the 10-year landmark time (p = 0.013). And the single PTM group showed a worse OS rate than the multiple PTM group (p < 0.001). This comprehensive report on PTM following DLT can help understand the risks and timing of PTM to improve the implementation of screening and treatment.

Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours.

Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics1. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH2. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.

Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data.

BACKGROUND: Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is "large p and small n" in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer classification. RESULTS: This study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies-Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes selected. CONCLUSIONS: The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.

High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing.

Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide-level retrieval could be more intuitive and practical in clinical applications, most methods are designed for patch-level retrieval. A few recently unsupervised slide-level methods only focus on integrating patch features directly, without perceiving slide-level information, and thus severely limits the performance of WSI retrieval. To tackle the issue, we propose a High-Order Correlation-Guided Self-Supervised Hashing-Encoding Retrieval (HSHR) method. Specifically, we train an attention-based hash encoder with slide-level representation in a self-supervised manner, enabling it to generate more representative slide-level hash codes of cluster centers and assign weights for each. These optimized and weighted codes are leveraged to establish a similarity-based hypergraph, in which a hypergraph-guided retrieval module is adopted to explore high-order correlations in the multi-pairwise manifold to conduct WSI retrieval. Extensive experiments on multiple TCGA datasets with over 24,000 WSIs spanning 30 cancer subtypes demonstrate that HSHR achieves state-of-the-art performance compared with other unsupervised histology WSI retrieval methods.

Recurrent repeat expansions in human cancer genomes.

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases1,2. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs, a phenomenon termed microsatellite instability; however, larger repeat expansions have not been systematically analysed in cancer3-8. Here we identified TR expansions in 2,622 cancer genomes spanning 29 cancer types. In seven cancer types, we found 160 recurrent repeat expansions (rREs), most of which (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with enrichment near candidate cis-regulatory elements, suggesting a potential role in gene regulation. One rRE, a GAAA-repeat expansion, located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, in preliminary experiments, treating cells that harbour this rRE with a GAAA-targeting molecule led to a dose-dependent decrease in cell proliferation. Overall, our results suggest that rREs may be an important but unexplored source of genetic variation in human cancer, and we provide a comprehensive catalogue for further study.

IMP-1 inhibits renal cell carcinoma 786-O cell growth by targeting EphrinB2 signaling pathway

Abstract EphrinB2 plays a critical role in tumor growth. In this study, we studied the antitumor activity of imperatorin derivative IMP-1 in renal cell carcinoma (RCC) by regulating EphrinB2 pathway.. Results showed that IMP-1 inhibited the proliferation of 786-O cells in a dose- and time-dependent manner. More importantly, knockdown and transfection of EphrinB2 altered the inhibitory effect of IMP-1 on the activity of 786-O cells. IMP-1 arrested 786-O cell cycle at G0/G1 phase by decreasing the expression of cyclin D1 and cyclin E. Moreover, IMP-1 regulated Bcl-2 family proteins' expression, thus inducing apoptosis of 786-O cells. IMP-1 down-regulated the expression of EphrinB2, Syntenin1 and PICK1. Then, IMP-1 decreased the phosphorylation of Erk1/2 and AKT. In all, IMP-1 could regulate the EphrinB2 pathway in order to inhibit 786-O cell growth by arresting the cell cycle at G0/G1 phase and inducing cell apoptosis. Thus, IMP-1 may present as a potential strategy for RCC treatment.

Câncer, disparidades sociais e acesso aos serviços de saúde / Cancer, social disparities and access to health services

O crescimento desordenado de células no organismo, que pode invadir tecidos adjacentes ou órgãos em outras regiões do corpo, é denominado câncer (WHO, 2022a). A nomenclatura da doença corresponde ao local de foco inicial, e assim, os cânceres de mama, próstata, pulmão, colorretal, colo uterino e estômago são os mais frequentes. Globalmente, uma em cada seis mortes são relacionadas à doença, que configura a segunda principal causa de morte. E os tipos de câncer que mais evoluem para óbito são pulmão, mama, colorretal, fígado, próstata e estômago (WHO, 2022b)

The disordered growth of cells in the body, which can invade adjacent tissues or organs in other regions of the body, is called cancer (WHO, 2022a). The disease nomenclature corresponds to the initial focus site, and thus, breast, prostate, lung, colorectal, uterine cervix and stomach cancers are the most frequent. Globally, one in every six deaths are related to the disease, which is the second leading cause of death. And the types of cancer that most evolve to death are lung, breast, colorectal, liver, prostate and stomach (WHO, 2022b)

Multiplexed nanomaterial-assisted laser desorption/ionization for pan-cancer diagnosis and classification.

As cancer is increasingly considered a metabolic disorder, it is postulated that serum metabolite profiling can be a viable approach for detecting the presence of cancer. By multiplexing mass spectrometry fingerprints from two independent nanostructured matrixes through machine learning for highly sensitive detection and high throughput analysis, we report a laser desorption/ionization (LDI) mass spectrometry-based liquid biopsy for pan-cancer screening and classification. The Multiplexed Nanomaterial-Assisted LDI for Cancer Identification (MNALCI) is applied in 1,183 individuals that include 233 healthy controls and 950 patients with liver, lung, pancreatic, colorectal, gastric, thyroid cancers from two independent cohorts. MNALCI demonstrates 93% sensitivity at 91% specificity for distinguishing cancers from healthy controls in the internal validation cohort, and 84% sensitivity at 84% specificity in the external validation cohort, with up to eight metabolite biomarkers identified. In addition, across those six different cancers, the overall accuracy for identifying the tumor tissue of origin is 92% in the internal validation cohort and 85% in the external validation cohort. The excellent accuracy and minimum sample consumption make the high throughput assay a promising solution for non-invasive cancer diagnosis.