Linking stages of non-rapid-eye-movement sleep to the spectral EEG markers of the drives for sleep and wake

The conventional staging classification reduces all patterns of sleep polysomnogram signals to a small number of yes-or-no variables labeled wake or a stage of sleep (e.g., W, N1, N2, N3, and R for wake, the 1st, 2nd and 3rd stages of non-rapid-eye-movement sleep, and rapid-eye-movement sleep, respectively). However, the neurobiological underpinnings of such stages remained to be elucidated. We tried to evaluate their link to scores on the 1st and 2nd principal components of the EEG spectrum (1PCS and 2PCS), the markers of two major groups of promoters/inhibitors of sleep/wakefulness delineated as the drives for sleep and wake, respectively. On two occasions, polysomnographic records were obtained from 69 university students during 50-min afternoon naps, and 30-s stage epochs were assigned to 1PCS and 2PCS. Results suggested two-dimensionality of the structure of individual differences in amounts of stages. Amount of N1 loaded exclusively on one of two dimensions associated with 1PCS, amounts of W and N2 loaded exclusively on another dimension associated with 2PCS, and amount of N3 equally loaded on both dimensions. Scores demonstrated stability within each stage, but a drastic change in just one of two scores occurred during transitions from one stage to another on the way from wakefulness to deeper sleep (e.g., 2PCS changed from >0 to <0 during transition W→N1, 1PCS changed from <0 to >0 during transition N1→N2). Therefore, the transitions between stages observed during short naps might be linked to rapid changes in the reciprocal interactions between the promoters/inhibitors of sleep/wakefulness.

Implementation of Mass Cytometry for Immunoprofiling of Patients with Solid Tumors

Monitoring immune responses to solid cancers may be a better prognostic tool than conventional staging criteria, and it can also serve as an important criterion for the selection of individualized therapy. Multiparametric phenotyping by mass cytometry extended possibilities for immunoprofiling. However, careful optimization of each step of such method is necessary for obtaining reliable results. Also, with respect to procedure length and costs, sample preparation, staining, and storage should be optimized. Here, we designed a panel of 31 antibodies which allows for identification of several subpopulations of lymphoid and myeloid cells in a solid tumor and peripheral blood simultaneously. For sample preparation, disaggregation of tumor tissue with two different collagenases combined with DNase I was compared, and removal of dead or tumor cells by magnetic separation was evaluated. Two possible procedures of barcoding for single-tube staining of several samples were examined. While the palladium-based barcoding affected the stability of several antigens, the staining with two differently labeled CD45 antibodies was suitable for cells isolated from a patient’s blood and tumor. The storage of samples in the intercalation solution for up to two weeks did not influence results of the analysis, which allowed the measurement of samples collected within this interval on the same day. This procedure optimized on samples from patients with head and neck squamous cell carcinoma enabled identification of various immune cells including rare subpopulations.

A Prognostic Gene Signature Expressed in Primary Cutaneous Melanoma: Synergism With Conventional Staging

Background Current clinico-pathological American Joint Committee on Cancer (AJCC) staging of primary cutaneous melanoma is limited in its ability to determine clinical outcome, and complementary biomarkers are not available for routine prognostic assessment. We therefore adapted a gene signature, previously identified in fresh-frozen (FF) melanomas and adjacent stroma, to formalin-fixed paraffin-embedded (FFPE) melanomas. The aim was to develop a gene expression profiling (GEP) score to define patient survival probability at the time of first diagnosis. Methods Expression of 11 FF melanoma signature genes was quantified by reverse transcription polymerase chain reaction in an FFPE melanoma training cohort (n = 125), corresponding to the combined FF melanoma training and validation cohorts. The resulting GEP score was validated technically and clinically in an independent FFPE melanoma cohort (n = 211). All statistical tests were two-sided. Results We identified a prognostic eight-gene signature in the tumor area (tumor and adjacent tissue) of AJCC stage I–III melanomas. A signature-based GEP score correlated with melanoma-specific survival (MSS; Kaplan-Meier analysis: P &lt; .0001) was independent of tumor stage (multivariable regression analysis: P = .0032) and stroma content (&lt;5%–90%) and complemented conventional AJCC staging (receiver operating characteristic curve analysis: area under the curve = 0.91). In the clinical validation cohort, the GEP score remained statistically significant (P = .0131) in a multivariable analysis accounting for conventional staging. The GEP score was technically robust (reproducibility: 93%; n = 84) and clinically useful, as a binary as well as a continuous score, in predicting stage-specific patient MSS. Conclusions The GEP score is a clinically significant prognostic tool, contributes additional information regarding the MSS of melanoma patients, and complements conventional staging.