Abstract
Simple SummaryLung cancer (LC) is the most common malignancy and the leading cause of cancer deaths in the world. Limitations of current screening approaches, such as substantial cost, radiation exposure, and high-false positive rates as well as increasing numbers of LC diagnoses in people without known risk factors, indicate the need for the development of new screening strategies. The aim of our study was to evaluate the utility of differential scanning calorimetry (DSC) for LC patients’ diagnosis. We found that DSC curves could be useful in differentiation of LC patients from control individuals and some changes were subtype or/and stage-dependent. Moreover, some DSC curve features correlated with patients’ overall/progression-free survival. Although the utility of the DSC technique still needs to be confirmed in a clinical setting, with further optimization and development of the classification method, this technique could provide an accurate, non-invasive, radiation-free strategy for LC screening and diagnosis.Early detection of lung cancer (LC) significantly increases the likelihood of successful treatment and improves LC survival rates. Currently, screening (mainly low-dose CT scans) is recommended for individuals at high risk. However, the recent increase in the number of LC cases unrelated to the well-known risk factors, and the high false-positive rate of low-dose CT, indicate a need to develop new, non-invasive methods for LC detection. Therefore, we evaluated the use of differential scanning calorimetry (DSC) for LC patients’ diagnosis and predicted survival. Additionally, by applying mass spectrometry, we investigated whether changes in O- and N-glycosylation of plasma proteins could be an underlying mechanism responsible for observed differences in DSC curves of LC and control subjects. Our results indicate selected DSC curve features could be useful for differentiation of LC patients from controls with some capable of distinction between subtypes and stages of LC. DSC curve features also correlate with LC patients’ overall/progression free survival. Moreover, the development of classification models combining patients’ DSC curves with selected plasma protein glycosylation levels that changed in the presence of LC could improve the sensitivity and specificity of the detection of LC. With further optimization and development of the classification method, DSC could provide an accurate, non-invasive, radiation-free strategy for LC screening and diagnosis.
Highlights
Lung cancer (LC) is the most common malignancy and the leading cause of cancer deaths in the past few decades
Evaluation of differential scanning calorimetry (DSC) curves was performed in the temperature range 45–90 ◦ C through the calculation of several summary metrics: DSC curve peak width at half height; total area under the curve; maximum peak height; temperature of the peak maximum (Tmax ); maximum excess specific heat capacity (Cp ex ) of the first peak in the region 60–66.9 ◦ C
The utility of DSC curve summary metrics and principal components (PCs) for patient classification was illustrated via receiver operating characteristic (ROC) curves calculated for each parameter individually
Summary
Lung cancer (LC) is the most common malignancy and the leading cause of cancer deaths in the past few decades. The 5-year survival rate for LC is 56%. Only 16% of cases are diagnosed at an early stage; it is estimated that more than half of people with LC die within 1 year of being diagnosed [2]. Screening (mainly using low-dose CT scans) for individuals at high risk has the potential to dramatically improve lung cancer survival rates by diagnosing the disease at an earlier stage [5]. In recent years there has been an increase in the number of LC cases unrelated to the well-known risk factors, falling outside current screening guidelines and delaying prompt diagnosis and treatment [6]
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