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Sleep is regulated in a time-of-day dependent manner and profits working memory. However, the impact of the circadian timing system as well as contributions of specific sleep properties to this beneficial effect remains largely unexplored. Moreover, it is unclear to which extent inter-individual differences in sleep-wake regulation depend on circadian phase and modulate the association between sleep and working memory. Here, sleep electroencephalography (EEG) was recorded during a 40-h multiple nap protocol, and working memory performance was assessed by the n-back task 10 times before and after each scheduled nap sleep episode. Twenty-four participants were genotyped regarding a functional polymorphism in adenosine deaminase (rs73598374, 12 G/A-, 12 G/G-allele carriers), previously associated with differences in sleep-wake regulation. Our results indicate that genotype-driven differences in sleep depend on circadian phase: heterozygous participants were awake longer and slept less at the end of the biological day, while they exhibited longer non rapid eye movement (NREM) sleep and slow wave sleep concomitant with reduced power between 8–16 Hz at the end of the biological night. Slow wave sleep and NREM sleep delta EEG activity covaried positively with overall working memory performance, independent of circadian phase and genotype. Moreover, REM sleep duration benefitted working memory particularly when occurring in the early morning hours and specifically in heterozygous individuals. Even though based on a small sample size and thus requiring replication, our results suggest genotype-dependent differences in circadian sleep regulation. They further indicate that REM sleep, being under strong circadian control, boosts working memory performance according to genotype in a time-of-day dependent manner. Finally, our data provide first evidence that slow wave sleep and NREM sleep delta activity, majorly regulated by sleep homeostatic mechanisms, is linked to working memory independent of the timing of the sleep episode within the 24-h cycle.
Purpose: Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a “watch and wait” strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging.
Experimental design: We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial.
Results: A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76).
Conclusion: The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a “watch and wait” strategy.
Translational relevance: Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for “watch and wait”.