One-step tumor detection from dynamic morphology tracking on aptamer-grafted surfaces

Aptamers: ushering in new hopes in targeted glioblastoma therapy

Glioblastoma, a formidable brain cancer, has remained a therapeutic challenge due to its aggressive nature and resistance to conventional treatments. Recent data indicate that aptamers, short synthetic DNA or RNA molecules can be used in anti-cancer therapy due to their better tumour penetration, specific binding affinity, longer retention in tumour sites and their ability to cross the blood-brain barrier. With the ability to modify these oligonucleotides through the selection process, and using rational design to modify them, post-SELEX aptamers offer several advantages in glioblastoma treatment, including precise targeting of cancer cells while sparing healthy tissue. This review discusses the pivotal role of aptamers in glioblastoma therapy and diagnosis, emphasising their potential to enhance treatment efficacy and also highlights recent advancements in aptamer-based therapies which can transform the landscape of glioblastoma treatment, offering renewed hope to patients and clinicians alike.

Quantitative cell imaging approaches to metastatic state profiling

Genetic heterogeneity of metastatic dissemination has proven challenging to identify exploitable markers of metastasis; this bottom-up approach has caused a stalemate between advances in metastasis and the late stage of the disease. Advancements in quantitative cellular imaging have allowed the detection of morphological phenotype changes specific to metastasis, the morphological changes connected to the underlying complex signaling pathways, and a robust readout of metastatic cell state. This review focuses on the recent machine and deep learning developments to gain detailed information about the metastatic cell state using light microscopy. We describe the latest studies using quantitative cell imaging approaches to identify cell appearance-based metastatic patterns. We discuss how quantitative cancer biologists can use these frameworks to work backward toward exploitable hidden drivers in the metastatic cascade and pioneering new Frontier drug discoveries specific for metastasis.

Aptamers: Novel Therapeutics and Potential Role in Neuro-Oncology

A relatively new paradigm in cancer therapeutics is the use of cancer cell-specific aptamers, both as therapeutic agents and for targeted delivery of anticancer drugs. After the first therapeutic aptamer was described nearly 25 years ago, and the subsequent first aptamer drug approved, many efforts have been made to translate preclinical research into clinical oncology settings. Studies of aptamer-based technology have unveiled the vast potential of aptamers in therapeutic and diagnostic applications. Among pediatric solid cancers, brain tumors are the leading cause of death. Although a few aptamer-related translational studies have been performed in adult glioblastoma, the use of aptamers in pediatric neuro-oncology remains unexplored. This review will discuss the biology of aptamers, including mechanisms of targeting cell surface proteins, various modifications of aptamer structure to enhance therapeutic efficacy, the current state and challenges of aptamer use in neuro-oncology, and the potential therapeutic role of aptamers in pediatric brain tumors.

The Role of RNA and DNA Aptamers in Glioblastoma Diagnosis and Therapy: A Systematic Review of the Literature

Glioblastoma (GBM) is the most lethal primary brain tumor of the central nervous system in adults. Despite advances in surgical and medical neuro-oncology, the median survival is about 15 months. For this reason, initial diagnosis, prognosis, and targeted therapy of GBM represent very attractive areas of study. Aptamers are short three-dimensional structures of single-stranded nucleic acids (RNA or DNA), identified by an in vitro process, named systematic evolution of ligands by exponential enrichment (SELEX), starting from a partially random oligonucleotide library. They bind to a molecular target with high affinity and specificity and can be easily modified to optimize binding affinity and selectivity. Thanks to their properties (low immunogenicity and toxicity, long stability, and low production variability), a large number of aptamers have been selected against GBM biomarkers and provide specific imaging agents and therapeutics to improve the diagnosis and treatment of GBM. However, the use of aptamers in GBM diagnosis and treatment still represents an underdeveloped topic, mainly due to limited literature in the research world. On these bases, we performed a systematic review aimed at summarizing current knowledge on the new promising DNA and RNA aptamer-based molecules for GBM diagnosis and treatment. Thirty-eight studies from 2000 were included and investigated. Seventeen involved the use of aptamers for GBM diagnosis and 21 for GBM therapy. Our findings showed that a number of DNA and RNA aptamers are promising diagnostic and therapeutic tools for GBM management.

Classification of cancer cells using computational analysis of dynamic morphology

BACKGROUND AND OBJECTIVE

Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cancer cells, and especially metastatic tumor cells, show very distinctive morphological behavior compared to their healthy counterparts on aptamer functionalized substrates. The ability to quickly analyze the data and quantify the cell morphology for an instant real-time feedback can certainly contribute to early cancer diagnosis. A supervised machine learning approach is presented for identification and classification of cancer cell gestures for early diagnosis.

METHODS

We quantified the morphologically distinct behavior of metastatic cells and their healthy counterparts captured on aptamer-functionalized glass substrates from time-lapse optical micrographs. As a proof of concept, the morphologies of human glioblastoma (hGBM) and astrocyte cells were used. The cells were captured and imaged with an optical microscope. Multiple feature vectors were extracted to quantify and differentiate the complex physical gestures of cancerous and non-cancerous cells. Three different classifier models, Support Vector Machine (SVM), Random Forest Tree (RFT), and Naïve Bayes Classifier (NBC) were trained with the known dataset using machine learning algorithms. The performances of the classifiers were compared for accuracy, precision, and recall measurements using five-fold cross-validation technique.

RESULTS

All the classifier models detected the cancer cells with an average accuracy of at least 82%. The NBC performed the best among the three classifiers in terms of Precision (0.91), Recall (0.9), and F₁-score (0.89) for the existing dataset.

CONCLUSIONS

This paper presents a standalone system built on machine learning techniques for cancer screening based on cell gestures. The system offers rapid, efficient, and novel identification of hGBM brain tumor cells and can be extended to define single cell analysis metrics for many other types of tumor cells.

Functionalization of nanotextured substrates for enhanced identification of metastatic breast cancer cells

Metastasis is the major cause of low survival rates among cancer patients. Once cancer cells metastasize, it is extremely difficult to contain the disease. We report on a nanotextured platform for enhanced detection of metastatic cells. We captured metastatic (MDA-MDB-231) and non-metastatic (MCF-7) breast cancer cells on anti-EGFR aptamer modified plane and nanotextured substrates. Metastatic cells were seen to change their morphology at higher rates when captured on nanotextured substrates than on plane substrates. Analysis showed statistically different morphological behaviors of metastatic cells that were very pronounced on the nanotextured substrates. Several distance matrices were calculated to quantify the dissimilarity of cell shape change. Nanotexturing increased the dissimilarity of the metastatic cells and as a result the contrast between metastatic and non-metastatic cells increased. Jaccard distance measurements found that the shape change ratio of the non-metastatic and metastatic cells was enhanced from 1:1.01 to 1:1.81, going from plane to nanotextured substrates. The shape change ratio of the non-metastatic to metastatic cells improved from 1:1.48 to 1:2.19 for the Hausdorff distance and from 1:1.87 to 1:4.69 for the Mahalanobis distance after introducing nanotexture. Distance matrix analysis showed that nanotexture increased the shape change ratios of non-metastatic and metastatic cells. Hence, the detectability of metastatic cells increased. These calculated matrices provided clear and explicit measures to discriminate single cells for their metastatic state on functional nanotextured substrates.