Emerging Technologies for the Detection of Cancer Micrometastasis

Lymph Node Metastasis in Gastrointestinal Carcinomas: A View from a Proteomics Perspective

Lymph node metastasis (LNM) is one of the major prognostic factors in human gastrointestinal carcinomas (GICs). The lymph node-positive patients have poorer survival than node-negative patients. LNM is directly associated with the recurrence and poor survival of patients with GICs. The early detection of LNM in patients and designing effective therapies to suppress LNM may significantly impact the survival of these patients. The rapid progress made in proteomic technologies could be successfully applied to identify molecular targets for cancers at high-throughput levels. LC-MS/MS analysis enables the identification of proteins involved in LN metastasis, which can be utilized for diagnostic and therapeutic applications. This review summarizes the studies on LN metastasis in GICs using proteomic approaches to date.

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images

BACKGROUND

Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions.

METHODS

A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts.

RESULTS

The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis.

CONCLUSIONS

In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

A Genuinely Hybrid, Multiscale 3D Cancer Invasion and Metastasis Modelling Framework

We introduce in this paper substantial enhancements to a previously proposed hybrid multiscale cancer invasion modelling framework to better reflect the biological reality and dynamics of cancer. These model updates contribute to a more accurate representation of cancer dynamics, they provide deeper insights and enhance our predictive capabilities. Key updates include the integration of porous medium-like diffusion for the evolution of Epithelial-like Cancer Cells and other essential cellular constituents of the system, more realistic modelling of Epithelial-Mesenchymal Transition and Mesenchymal-Epithelial Transition models with the inclusion of Transforming Growth Factor beta within the tumour microenvironment, and the introduction of Compound Poisson Process in the Stochastic Differential Equations that describe the migration behaviour of the Mesenchymal-like Cancer Cells. Another innovative feature of the model is its extension into a multi-organ metastatic framework. This framework connects various organs through a circulatory network, enabling the study of how cancer cells spread to secondary sites.

Osteosarcoma cells exhibit functional interactions with stromal cells, fostering a lung microenvironment conducive to the establishment of metastatic tumor cells

BACKGROUND

Osteosarcoma (OS) stands out as the most common bone tumor, with approximately 20% of the patients receiving a diagnosis of metastatic OS at their initial assessment. A significant challenge lies in the frequent existence of undetected metastases during the initial diagnosis. Mesenchymal stem cells (MSCs) possess unique abilities that facilitate tumor growth, and their interaction with OS cells is crucial for metastatic spread.

METHODS AND RESULTS

We demonstrated that, in vitro, MSCs exhibited a heightened migration response toward the secretome of non-metastatic OS cells. When challenged to a secretome derived from lungs preloaded with OS cells, MSCs exhibited greater migration toward lungs colonized with metastatic OS cells. Moreover, in vivo, MSCs displayed preferential migratory and homing behavior toward lungs colonized by metastatic OS cells. Metastatic OS cells, in turn, demonstrated an increased migratory response to the MSCs' secretome. This behavior was associated with heightened cathepsin D (CTSD) expression and the release of active metalloproteinase 2 (MMP2) by metastatic OS cells.

CONCLUSIONS

Our assessment focused on two complementary tumor capabilities crucial to metastatic spread, emphasizing the significance of inherent cell features. The findings underscore the pivotal role of signaling integration within the niche, with a complex interplay of migratory responses among established OS cells in the lungs, prometastatic OS cells in the primary tumor, and circulating MSCs. Pulmonary metastases continue to be a significant factor contributing to OS mortality. Understanding these mechanisms and identifying differentially expressed genes is essential for pinpointing markers and targets to manage metastatic spread and improve outcomes for patients with OS.

Overcoming chemoresistance and radio resistance in prostate cancer: The emergent role of non-coding RNAs

Prostate cancer (PCa) continues to be a major health concern worldwide, with its resistance to chemotherapy and radiation therapy presenting major hurdles in successful treatment. While patients with localized prostate cancer generally have a good survival rate, those with metastatic prostate cancer often face a grim prognosis, even with aggressive treatments using various methods. The high mortality rate in severe cases is largely due to the lack of treatment options that can offer lasting results, especially considering the significant genetic diversity found in tumors at the genomic level. This comprehensive review examines the intricate molecular mechanisms governing resistance in PCa, emphasising the pivotal contributions of non-coding RNAs (ncRNAs). We delve into the diverse roles of microRNAs, long ncRNAs, and other non-coding elements as critical regulators of key cellular processes involved in CR & RR. The review emphasizes the diagnostic potential of ncRNAs as predictive biomarkers for treatment response, offering insights into patient stratification and personalized therapeutic approaches. Additionally, we explore the therapeutic implications of targeting ncRNAs to overcome CR & RR, highlighting innovative strategies to restore treatment sensitivity. By synthesizing current knowledge, this review not only provides a comprehension of the chemical basis of resistance in PCa but also identifies gaps in knowledge, paving the way for future research directions. Ultimately, this exploration of ncRNA perspectives offers a roadmap for advancing precision medicine in PCa, potentially transforming therapeutic paradigms and improving outcomes for patients facing the challenges of treatment resistance.

Melanoma identification and classification model based on fine-tuned convolutional neural network

Background

Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing recognition that skin cancer can be lethal to humans. For instance, melanoma is the most unpredictable and terrible form of skin cancer.

Materials and Methodology

This paper aims to support Internet of Medical Things (IoMT) applications by developing a robust image classification model for the early detection of melanoma, a deadly skin cancer. It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based on Deep Learning (DL). We analyze dermatoscopic images from publicly available datasets, including DermIS, DermQuest, DermIS&Quest, and ISIC2019. Our model applies convolutional and pooling layers to extract meaningful features, followed by fully connected layers for classification.

Results

The proposed CNN model achieves high accuracy demonstrates the model's effectiveness in distinguishing between malignant and benign skin lesions. We developed deep features and used transfer learning to improve the categorization accuracy of medical images. Soft-max classification layer and support vector machine have been used to assess the classification performance of deep features. The proposed model's efficacy is rigorously evaluated using benchmark datasets: DermIS, DermQuest, and ISIC2019, having 621, 1233, and 25000 images, respectively. Its performance is compared to current best practices showing an average of 5% improved detection accuracy in DermIS, 6% improvement in DermQuest, and 0.81% in ISIC2019 datasets.

Conclusion

Our study showcases the potential of CNN in melanoma detection, contributing to early diagnosis and improved patient outcomes. The developed model proves its capability to aid dermatologists in accurate decision-making, paving the way for enhanced skin cancer diagnosis.

Comparing Methods to Determine Complete Response to Chemoradiation in Patients with Locally Advanced Cervical Cancer

OBJECTIVES

There is no consensus on the most reliable procedure to determine remission of cervical cancer after chemoradiotherapy (CRT). Therefore, this study aims to assess the diagnostic performance of two different imaging techniques, MRI and 18F[FDG]-PET/CT, in determining the presence of locoregional residual disease after CRT in patients with locally advanced cervical cancer.

METHODS

Patients diagnosed with locally advanced cervical cancer (FIGO 2009) treated with CRT were retrospectively identified from a regional cohort. The accuracy of MRI and 18F[FDG]-PET/CT in detecting locoregional residual disease was assessed with histology as the reference standard.

RESULTS

The negative predictive value (NPV) and positive predictive value (PPV) for locoregional residual disease detection of MRI and 18F[FDG]-PET/CT combined were 84.2% (95% CI 73.2-92.1), and 70.4% (95% CI 51.8-85.2), respectively. The NPV and PPV of MRI alone were 80.2% (95% CI 71.2-87.5) and 47.7% (95% CI 35.8-59.7), respectively, and values of 81.1% (95% CI 72.2-88.3) and 55.8 (95% CI 42.2-68.7), respectively, were obtained for 18F[FDG]-PET/CT alone.

CONCLUSION

In this study, the reliability of MRI and 18F[FDG]-PET/CT in detecting locoregional residual disease was limited. Combining MRI and 18F[FDG]-PET/CT did not improve predictive values. Routine use of both MRI and 18F[FDG]-PET/CT in the follow-up after CRT should be avoided. MRI during follow-up is the advised imaging technique. Pathology confirmation of the presence of locoregional residual disease before performing salvage surgery is warranted.

Glycolysis-based drug delivery nanosystems for therapeutic use in tumors and applications

Tumor cells are able to use glycolysis to produce energy under hypoxic conditions, and even under aerobic conditions, they rely mainly on glycolysis for energy production, the Warburg effect. Conventional tumor therapeutic drugs are unidirectional, lacking in targeting and have limited therapeutic effect. The development of a large number of nanocarriers and targeted glycolysis for the treatment of tumors has been extensively investigated in order to improve the therapeutic efficacy. This paper reviews the research progress of nanocarriers based on targeting key glycolytic enzymes and related transporters, and combines nanocarrier systems with other therapeutic approaches to provide a new strategy for targeted glycolytic treatment of tumors, providing a theoretical reference for achieving efficient targeted treatment of tumors.

Circulating Tumor Cells as Biomarkers for Renal Cell Carcinoma: Ready for Prime Time?

Renal cell carcinoma (RCC) is among the 15 most common cancers worldwide, with rising incidence. In most cases, this is a silent disease until it reaches advance stages, demanding new effective biomarkers in all domains, from detection to post-therapy monitoring. Circulating tumor cells (CTC) have the potential to provide minimally invasive information to guide assessment of the disease's aggressiveness and therapeutic strategy, representing a special pool of neoplastic cells which bear metastatic potential. In some tumor models, CTCs' enumeration has been associated with prognosis, but there is a largely unexplored potential for clinical applicability encompassing screening, diagnosis, early detection of metastases, prognosis, response to therapy and monitoring. Nonetheless, lack of standardization and high cost hinder the translation into clinical practice. Thus, new methods for collection and analysis (genomic, proteomic, transcriptomic, epigenomic and metabolomic) are needed to ascertain the role of CTC as a RCC biomarker. Herein, we provide a critical overview of the most recently published data on the role and clinical potential of CTCs in RCC, addressing their biology and the molecular characterization of this remarkable set of tumor cells. Furthermore, we highlight the existing and emerging techniques for CTC enrichment and detection, exploring clinical applications in RCC. Notwithstanding the notable progress in recent years, the use of CTCs in a routine clinical scenario of RCC patients requires further research and technological development, enabling multimodal analysis to take advantage of the wealth of information they provide.