IntroductionLung cancer survival remains poor in the western world due to late presentation in most cases, leading to difficulty of treatment in these advanced and metastatic patients. Therefore, the development of a robust biomarker for prognosis and to monitor treatment response and relapse would be of great benefit. The use of Alu repeats and DNA Integrity Index has been shown to hold both diagnostic and prognostic value, and as it is obtained from the plasma of patients, it can serve as a non-invasive tool for routine monitoring. This study evaluates the efficiency of this technique in malignant lung cancer patients.MethodsPlasma samples were collected from 48 patients, consisting of 29 lung cancer patients and 19 non-cancer controls. Alu repeat ratio and confounders were measured.ResultsObservations showed a higher Alu repeat ratio amongst the cancer group compared to controls (p=0.035), mean Alu ratio 0.38 (range 0.01-0.93) and 0.22 (0.007-0.44) respectively, ROC curve analysis AUC 0.61 (p=0.22). Analysis by staging was more promising, whereby a higher DNA Integrity Index was seen in advanced cases compared to both early stage and controls, p<0.0001; AUC: 0.92 (P=0.0002) and p=0.0006, AUC – 0.88 (p=0.0007) respectively, however no significant difference was observed in the early stage compared to controls. Short term survival data also showed a DNA Integrity Index of >0.5 to be associated with poorer overall survival p=0.03.ConclusionThe results of this study show a potential use of Alu repeats ratios for prognostic purposes in the advanced setting for lung cancer patients.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow.

Deregulation of glycolysis is common in non-small cell lung cancer (NSCLC). Hexokinase (HK) enzymes catalyze the phosphoryl-group-transfer in glucose metabolism. There are a very few studies that have begun to reveal the connections between glucose metabolism and splicing programs. Unlike HK2 gene, which is expressed as a single transcript, there are several transcripts of the HK1 gene due to alternative splicing. However, the functional differential roles of HK1 isoforms in glucose metabolism and tumor progression are still elusive. Here, we show that primary NSCLC patient tumor cells metabolically differ from the normal lung epithelium where they display predominant expression of one of the HK1 transcripts, hexokinase1b (HK1b). We utilized CRISPR-Cas9 system to selectively target specific HK1b isoform in NSCLC and show that silencing HK1b in NSCLC cells inhibits tumorigenesis through diminishing glycolysis and proliferation. Our findings constitute the first demonstration of the first biochemical distinction between the HK1 splice variants. Finally, HK1b deletion sensitizes NSCLC cells to standard-of-care, cisplatin treatment, and the combination therapy synergistically increases both apoptotic cell death by cisplatin and autophagic cell death by increased formation of LC3-II associated autophagic vesicles and myelinoid bodies. Notably, loss of HK1b leads to cellular DNA damage, further combination with cisplatin therapy showed significantly increased levels of DNA damage. Importantly, we showed that glycolysis and cisplatin resistance can be restored by adding-back HK1b in HK1b knock-out cells. Our findings reveal that targeting HK1b isoform alone or in combination with cisplatin may represent a novel strategy for NSCLC patients.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally,  to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.

Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally,  to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community.