Biology subjects

Lewensohn, R.

Publications and source records attributed to Lewensohn, R..

3 recordsLinked to original sources

bioRxiv2021

SRF-CLICAL: an approach for patient risk stratification using random forest models

An important part of good clinical care is identifying which patients have a high likelihood of experiencing adverse outcomes. Similarly, due to the significant impact cancer treatment can have on a patients quality of life, it is also important to properly identify which patients are likely to benefit from more aggressive treatment options. As such, models for predictive risk stratification can be extremely useful in clinical decision making. In this paper, we present, Survival Random Forest-Clinical Categorization Algorithm (SRF-CLICAL), a new method for patient risk stratification using random forests for survival, regression and classification. As a proof of concept, we demonstrate this method on two different cohorts of cancer patients.

bioinformatics↗

bioRxiv2021

Multiplexed electrokinetic sensor for detection and therapy monitoring of extracellular vesicles from liquid biopsies of non-small-cell lung cancer patients

Liquid biopsies based on extracellular vesicle (EV) protein profiles represent a promising tool for treatment monitoring of tumors, including non-small-cell lung cancers (NSCLC). In this study, we present the development of an electrokinetic sensor for multiplexed surface protein profiling of EVs and analysis of clinical samples. The method detects the difference in the streaming current obtained as a result of EV binding to the inner surface of a functionalized microcapillary, thereby estimating the expression level of a surface marker. Using multiple microchannels functionalized with different antibodies in a parallel fluidic connection, we first demonstrate the capacity for simultaneous detection of multiple surface markers in small EVs (sEVs) from NSCLC cells. To investigate the prospects of liquid biopsies based on EVs, we then apply the method to profile sEVs isolated from the pleural effusion (PE) fluids of three NSCLC adenocarcinoma patients with different genomic alterations (ALK-fusion, KRAS and EGFR) and applied treatments (chemotherapy, EGFR or ALK tyrosine kinase inhibitors). These vesicles were targeted against CD9 tetraspanin, as well as EGFR and PD-L1, two markers of interest in NSCLC. The electrokinetic signals showed detection of these markers on sEVs yet highlighting distinct interpatient differences, e.g., increased EGFR levels in sEVs from a patient with EGFR mutation as compared to an ALK-mutant one. The sensors also detected differences in PD-L1 expressions, in line with those measured by complementary methods. The analysis of sEVs from a patient prior and post crizotinib treatment also revealed a significant increase in the expression of some markers, e.g. EGFR and PD-L1. The obtained results hold promise for the application of the method for tumor treatment monitoring based on sEVs from liquid biopsies.

bioengineering↗

bioRxiv2021

High throughput imaging of nanoscale extracellular vesicles by scanning electron microscopy for accurate size-based profiling and morphological analysis.

Nanoscale extracellular vesicle (EVs) have been found to play a key role in intercellular communication, offering opportunities for both diagnostics and therapeutics. However, lying below the diffraction limit and also being highly heterogeneous in their size, morphology and abundance, these vesicles pose significant challenges for their physical characterization. Here, we present a direct visual approach for their accurate morphological and size-based profiling by using scanning electron microscopy (SEM). To achieve that, we methodically examined various process steps and developed a protocol to improve the throughput, conformity and image quality while preserving the shape of EVs. The investigation was performed with small EVs (sEVs) isolated from a non-small cell lung cancer (NSCLC) cell line H1975 as well as from a human serum, and the results were compared with those obtained from nanoparticle tracking analysis (NTA). While the comparison of the sEV size distributions showed good agreement between the two methods for large sEVs (diameter >70 nm), the microscopy based approach showed a better capacity for analyses on smaller vesicles, with higher sEV counts compared to NTA. In addition, we demonstrated the possibility of identifying non-EV particles based on size and morphological features. The study also showed process steps that can generate artifacts bearing resemblance with sEVs. The results therefore present a simple way to use a widely available microscopy tool for accurate and high throughput physical characterization of EVs.

bioengineering↗