Molecular Oncology最新文献

Colorectal cancer (CRC) cells express sialylated Lewis antigens (sLe), crucial for metastasis via E-selectin binding. However, these glycoepitopes lack cancer specificity, and E-selectin-targeted glycoproteins remain largely unknown. Here, we established a framework for identifying metastasis-linked glycoproteoforms. More than 70% of CRC tumors exhibited overexpression of sLeA/X, yet without discernible associations with metastasis or survival. However, The Cancer Genome Atlas (TCGA) analysis unveiled differing expression patterns of sLeA/X-related glycogenes correlating with disease severity, indicating context-dependent regulation by distinct glycosyltransferases. Deeper exploration of metastatic tumor sialoglycoproteome identified nearly 600 glycoproteins, greatly expanding our understanding of the metastasis-related glycoproteome. These glycoproteins were linked to cell adhesion, oncogenic pathways, and neuroendocrine functions. Using an in-house algorithm, the secretin receptor (SCTR) emerged as a top-ranked targetable glycoprotein. Tumor screening confirmed SCTR's association with poor prognosis and metastasis, with N-glycosylation adding cancer specificity to this glycoprotein. Prognostic links were reinforced by TCGA-based investigations. In summary, SCTR, a relatively unknown CRC glycoprotein, holds potential as a biomarker of poor prognosis and as an E-selectin ligand, suggesting an unforeseen role in disease dissemination. Future investigations should focus on this glycoprotein's biological implications for clinical applications.

Advances in therapeutic approaches for melanoma urge the need for biomarkers that can identify patients at risk for recurrence and to guide treatment. The potential use of liquid biopsies in identifying biomarkers is increasingly being recognized. Here, we present a head-to-head comparison of several techniques to analyze circulating tumor cells (CTCs) and cell-free DNA (cfDNA) in 20 patients with metastatic melanoma. In this study, we investigated whether diagnostic leukapheresis (DLA) combined with multimarker flow cytometry (FCM) increased the detection of CTCs in blood compared to the CellSearch platform. Additionally, we characterized cfDNA at the level of somatic mutations, extent of aneuploidy and genome-wide DNA methylation. Both CTCs and cfDNA measures were compared to tumor markers and extracranial tumor burden on radiological imaging. Compared to the CellSearch method applied on peripheral blood, DLA combined with FCM increased the proportion of patients with detectable CTCs from 35% to 70% (P = 0.06). However, the median percentage of cells that could be recovered by the DLA procedure was 29%. Alternatively, cfDNA mutation and methylation analysis detected tumor load in the majority of patients (90% and 93% of samples successfully analyzed, respectively). The aneuploidy score was positive in 35% of all patients. From all tumor measurements in blood, lactate dehydrogenase (LDH) levels were significantly correlated to variant allele frequency (P = 0.004). Furthermore, the presence of CTCs in DLA was associated with tumor burden (P < 0.001), whereas the presence of CTCs in peripheral blood was associated with number of lesions on radiological imaging (P < 0.001). In conclusion, DLA tended to increase the proportion of patients with detectable CTCs but was also associated with low recovery. Both cfDNA and CTCs were correlated with clinical parameters such as LDH levels and extracranial tumor burden.

Patients with gastro-intestinal stromal tumors (GISTs) undergoing tyrosine kinase inhibitor therapy are monitored with regular computed tomography (CT) scans, exposing patients to cumulative radiation. This exploratory study aimed to evaluate circulating tumor DNA (ctDNA) testing to monitor treatment response and compare changes in ctDNA levels with RECIST 1.1 and total tumor volume measurements. Between 2014 and 2021, six patients with KIT proto-oncogene, receptor tyrosine kinase (KIT) exon-11-mutated GIST from whom long-term plasma samples were collected prospectively were included in the study. ctDNA levels of relevant plasma samples were determined using the KIT exon 11 digital droplet PCR drop-off assay. Tumor volume measurements were performed using a semi-automated approach. In total, 94 of 130 clinically relevant ctDNA samples were analyzed. Upon successful treatment response, ctDNA became undetectable in all patients. At progressive disease, ctDNA was detectable in five out of six patients. Higher levels of ctDNA correlated with larger tumor volumes. Undetectable ctDNA at the time of progressive disease on imaging was consistent with lower tumor volumes compared to those with detectable ctDNA. In summary, ctDNA levels seem to correlate with total tumor volume at the time of progressive disease. Our exploratory study shows promise for including ctDNA testing in treatment follow-up.

The emergence of targeted therapies has transformed ovarian cancer treatment. However, biomarker profiling for precision medicine is limited by access to quality, tumour-enriched tissue samples. The use of cell-free DNA (cfDNA) in ascites presents a potential solution to this challenge. In this study, next-generation sequencing was performed on ascites-derived cfDNA samples (26 samples from 15 human participants with ovarian cancer), with matched DNA from ascites-derived tumour cells (n = 5) and archived formalin-fixed paraffin-embedded (FFPE) tissue (n = 5). Similar tumour purity and variant detection were achieved with cfDNA compared to FFPE and ascites cell DNA. Analysis of large-scale genomic alterations, loss of heterozygosity and tumour mutation burden identified six cases of high genomic instability (including four with pathogenic BRCA1 and BRCA2 mutations). Copy number profiles and subclone prevalence changed between sequential ascites samples, particularly in a case where deletions and chromothripsis in Chr17p13.1 and Chr8q resulted in changes in clinically relevant TP53 and MYC variants over time. Ascites cfDNA identified clinically actionable information, concordant to tissue biopsies, enabling opportunistic molecular profiling. This advocates for analysis of ascites cfDNA in lieu of accessing tumour tissue via biopsy.

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

Precision cancer medicine (PCM) to support the treatment of solid tumors requires minimally invasive diagnostics. Here, we describe the development of fine-needle aspiration biopsy-based (FNA) molecular cytology which will be increasingly important in diagnostics and adaptive treatment. We provide support for FNA-based molecular cytology having a significant potential to replace core needle biopsy (CNB) as a patient-friendly potent technique for tumor sampling for various tumor types. This is not only because CNB is a more traumatic procedure and may be associated with more complications compared to FNA-based sampling, but also due to the recently developed molecular methods used with FNA. Recent studies show that image-guided FNA in combination with ultrasensitive molecular methods also offers opportunities for characterization of the tumor microenvironment which can aid therapeutic decisions. Here we provide arguments for an increased implementation of molecular FNA-based sampling as a patient-friendly diagnostic method, which may, due to its repeatability, facilitate regular sampling that is needed during different treatment lines, to provide tumor information, supporting treatment decisions, shortening lead times in healthcare, and benefit healthcare economics.

The incorporation of novel therapeutic agents such as antibody-drug conjugates, radio-conjugates, T-cell engagers, and chimeric antigen receptor cell therapies represents a paradigm shift in oncology. Cell-surface target quantification, quantitative assessment of receptor internalization, and changes in the tumor microenvironment (TME) are essential variables in the development of biomarkers for patient selection and therapeutic response. Assessing these parameters requires capabilities that transcend those of traditional biomarker approaches based on immunohistochemistry, in situ hybridization and/or sequencing assays. Computational pathology is emerging as a transformative solution in this new therapeutic landscape, enabling detailed assessment of not only target presence, expression levels, and intra-tumor distribution but also of additional phenotypic features of tumor cells and their surrounding TME. Here, we delineate the pivotal role of computational pathology in enhancing the efficacy and specificity of these advanced therapeutics, underscoring the integration of novel artificial intelligence models that promise to revolutionize biomarker discovery and drug development.

Proteomics has been little used for the identification of novel prognostic and/or therapeutic markers in isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GB). In this study, we analyzed 50 tumor and 30 serum samples from short- and long-term survivors of IDH-wildtype GB (STS and LTS, respectively) by data-independent acquisition mass spectrometry (DIA-MS)-based proteomics, with the aim of identifying such markers. DIA-MS identified 5422 and 826 normalized proteins in tumor and serum samples, respectively, with only three tumor proteins and 26 serum proteins displaying significant differential expression between the STS and LTS groups. These dysregulated proteins were principally associated with the detoxification of reactive oxygen species (ROS). In particular, GB patients in the STS group had high serum levels of malate dehydrogenase 1 (MDH1) and ribonuclease inhibitor 1 (RNH1) and low tumor levels of fatty acid-binding protein 7 (FABP7), which may have enabled them to maintain low ROS levels, counteracting the effects of the first-line treatment with radiotherapy plus concomitant and adjuvant temozolomide. A blood score built on the levels of MDH1 and RNH1 expression was found to be an independent prognostic factor for survival based on the serum proteome data for a cohort of 96 IDH-wildtype GB patients. This study highlights the utility of circulating MDH1 and RNH1 biomarkers for determining the prognosis of patients with IDH-wildtype GB. Furthermore, the pathways driven by these biomarkers, and the tumor FABP7 pathway, may constitute promising therapeutic targets for blocking ROS detoxification to overcome resistance to chemoradiotherapy in potential GB STS.

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a 5-year survival rate of 7.2% in China. However, effective approaches for diagnosis of PDAC are limited. Tumor-originating genomic and epigenomic aberration in circulating free DNA (cfDNA) have potential as liquid biopsy biomarkers for cancer diagnosis. Our study aims to assess the feasibility of cfDNA-based liquid biopsy assay for PDAC diagnosis. In this study, we performed parallel genomic and epigenomic profiling of plasma cfDNA from Chinese PDAC patients and healthy individuals. Diagnostic models were built to distinguish PDAC patients from healthy individuals. Cancer-specific changes in cfDNA methylation landscape were identified, and a diagnostic model based on six methylation markers achieved high sensitivity (88.7% for overall cases and 78.0% for stage I patients) and specificity (96.8%), outperforming the mutation-based model significantly. Moreover, the combination of the methylation-based model with carbohydrate antigen 19-9 (CA19-9) levels further improved the performance (sensitivity: 95.7% for overall cases and 95.5% for stage I patients; specificity: 93.3%). In conclusion, our findings suggest that both methylation-based and integrated liquid biopsy assays hold promise as non-invasive tools for detection of PDAC.