Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning

We define cell morphodynamics as the cell’s time dependent morphology. It could be called the cell’s shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell magnetorotation, with the aid of cell embedded magnetic nanoparticles, our method allows each cell to move freely in 3 dimensions, with a rapid following of cell deformations in all 3-dimensions, so as to identify and classify a cell by its dynamic morphology. Using object recognition and machine learning algorithms, we continuously measure the real-time shape dynamics of each cell, where from we successfully resolve the inherent broad heterogeneity of the morphological phenotypes found in a given cancer cell population. In three illustrative experiments we have achieved clustering, differentiation, and identification of cells from (A) two distinct cell lines, (B) cells having gone through the epithelial-to-mesenchymal transition, and (C) cells differing only by their motility. This microfluidic method may enable a fast screening and identification of invasive cells, e.g., metastatic cancer cells, even in the absence of biomarkers, thus providing a rapid diagnostics and assessment protocol for effective personalized cancer therapy.

Mitochondrial DNA and MitomiR Variations in Pancreatic Cancer: Potential Diagnostic and Prognostic Biomarkers

Pancreatic cancer is an aggressive disease with poor prognosis. Only about 15–20% of patients diagnosed with pancreatic cancer can undergo surgical resection, while the remaining 80% are diagnosed with locally advanced or metastatic pancreatic ductal adenocarcinoma (PDAC). In these cases, chemotherapy and radiotherapy only confer marginal survival benefit. Recent progress has been made in understanding the pathobiology of pancreatic cancer, with a particular effort in discovering new diagnostic and prognostic biomarkers, novel therapeutic targets, and biomarkers that can predict response to chemo- and/or radiotherapy. Mitochondria have become a focus in pancreatic cancer research due to their roles as powerhouses of the cell, important subcellular biosynthetic factories, and crucial determinants of cell survival and response to chemotherapy. Changes in the mitochondrial genome (mtDNA) have been implicated in chemoresistance and metastatic progression in some cancer types. There is also growing evidence that changes in microRNAs that regulate the expression of mtDNA-encoded mitochondrial proteins (mitomiRs) or nuclear-encoded mitochondrial proteins (mitochondria-related miRs) could serve as diagnostic and prognostic cancer biomarkers. This review discusses the current knowledge on the clinical significance of changes of mtDNA, mitomiRs, and mitochondria-related miRs in pancreatic cancer and their potential role as predictors of cancer risk, as diagnostic and prognostic biomarkers, and as molecular targets for personalized cancer therapy.

Targeting Akt in cancer for precision therapy

Biomarkers-guided precision therapeutics has revolutionized the clinical development and administration of molecular-targeted anticancer agents. Tailored precision cancer therapy exhibits better response rate compared to unselective treatment. Protein kinases have critical roles in cell signaling, metabolism, proliferation, survival and migration. Aberrant activation of protein kinases is critical for tumor growth and progression. Hence, protein kinases are key targets for molecular targeted cancer therapy. The serine/threonine kinase Akt is frequently activated in various types of cancer. Activation of Akt promotes tumor progression and drug resistance. Since the first Akt inhibitor was reported in 2000, many Akt inhibitors have been developed and evaluated in either early or late stage of clinical trials, which take advantage of liquid biopsy and genomic or molecular profiling to realize personalized cancer therapy. Two inhibitors, capivasertib and ipatasertib, are being tested in phase III clinical trials for cancer therapy. Here, we highlight recent progress of Akt signaling pathway, review the up-to-date data from clinical studies of Akt inhibitors and discuss the potential biomarkers that may help personalized treatment of cancer with Akt inhibitors. In addition, we also discuss how Akt may confer the vulnerability of cancer cells to some kinds of anticancer agents.

Tumor-Targeted Fluorescent Proteinoid Nanocapsules Encapsulating Synergistic Drugs for Personalized Cancer Therapy

Personalized cancer treatment based on specific mutations offers targeted therapy and is preferred over “standard” chemotherapy. Proteinoid polymers produced by thermal step-growth polymerization of amino acids may form nanocapsules (NCs) that encapsulate drugs overcoming miscibility problems and allowing passive targeted delivery with reduced side effects. The arginine-glycine-glutamic acid (RGD) sequence is known for its preferential attraction to αvβ3 integrin, which is highly expressed on neovascular endothelial cells that support tumor growth. Here, tumor-targeted RGD-based proteinoid NCs entrapping a synergistic combination of Palbociclib (Pal) and Alpelisib (Alp) were synthesized by self-assembly to induce the reduction of tumor cell growth in different types of cancers. The diameters of the hollow and drug encapsulating poly(RGD) NCs were 34 ± 5 and 22 ± 3 nm, respectively; thereby, their drug targeted efficiency is due to both passive and active targeting. The encapsulation yield of Pal and Alp was 70 and 90%, respectively. In vitro experiments with A549, MCF7 and HCT116 human cancer cells demonstrate a synergistic effect of Pal and Alp, controlled release and dose dependence. Preliminary results in a 3D tumor spheroid model with cells derived from patient-derived xenografts of colon cancer illustrate disassembly of spheroids, indicating that the NCs have therapeutic potential.

Machine learning in oncology—Perspectives in patient-reported outcome research

Background Increasing data volumes in oncology pose new challenges for data analysis. Machine learning, a branch of artificial intelligence, can identify patterns even in very large and less structured datasets. Objective This article provides an overview of the possible applications for machine learning in oncology. Furthermore, the potential of machine learning in patient-reported outcome (PRO) research is discussed. Materials and methods We conducted a selective literature search (PubMed, MEDLINE, IEEE Xplore) and discuss current research. Results There are three primary applications for machine learning in oncology: (1) cancer detection or classification; (2) overall survival prediction or risk assessment; and (3) supporting therapy decision-making and prediction of treatment response. Generally, machine learning approaches in oncology PRO research are scarce and few studies integrate PRO data into machine learning models. Discussion Machine learning is a promising area of oncology, but few models have been transferred into clinical practice. The promise of personalized cancer therapy and shared decision-making through machine learning has yet to be realized. As an equally important emerging research area in oncology, PROs should also be incorporated into machine learning approaches. To gather the data necessary for this, broad implementation of PRO assessments in clinical practice, as well as the harmonization of existing datasets, is suggested.