Meta-Analytic Framework for Sparse <i>K</i>-Means to Identify Disease Subtypes in Multiple Transcriptomic Studies

<p>Disease phenotyping by omics data has become a popular approach that potentially can lead to better personalized treatment. Identifying disease subtypes via unsupervised machine learning is the first step toward this goal. In this article, we extend a sparse <i>K</i>-means method toward a meta-analytic framework to identify novel disease subtypes when expression profiles of multiple cohorts are available. The lasso regularization and meta-analysis identify a unique set of gene features for subtype characterization. An additional pattern matching reward function guarantees consistent subtype signatures across studies. The method was evaluated by simulations and leukemia and breast cancer datasets. The identified disease subtypes from meta-analysis were characterized with improved accuracy and stability compared to single study analysis. The breast cancer model was applied to an independent METABRIC dataset and generated improved survival difference between subtypes. These results provide a basis for diagnosis and development of targeted treatments for disease subgroups. Supplementary materials for this article are available online.</p

Genome CNVs from leukocytes predicting short PSADT correlated with lower PSA-free survival.

<p>Kaplan-Meier analysis on patients predicted by LSR based on CNV of patients’ leukocytes as likely recurrent and having PSADT 4 months or less versus likely non-recurrent or recurrent but having PSADT 15 months or more (upper left). Similar survival analyses were also performed on case segregations based on Gleason’s grades (upper middle), Nomogram probability (upper right), the status of 8 fusion transcripts (lower left), or a model by combining LSR, Nomogram and fusion transcript status using LDA (lower middle), or a model by combining LSR, Nomogram, Gleason grade and fusion transcript status using LDA (lower right). Number of samples analyzed and p values are indicated.</p

LSR of genome CNV from leukocytes to predict prostate cancer recurrence with short PSADT.

<p>LSR derived from leukocyte genome CNV predicts PSADT 4 months or less. ROC analysis using LSRs derived from leukocyte CNVs as a prediction parameter (red) to predict PSADT 4 months or less, versus Nomogram (blue), Gleason’s grade (green) and the status of 8 fusion transcripts[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135982#pone.0135982.ref014" target="_blank">14</a>] (yellow). Samples were analyzed by the same procedure as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135982#pone.0135982.g003" target="_blank">Fig 3</a>. (B) Combination of LSR (L), Gleason’s grade (G), Nomogram (N) and the status of fusion transcripts (F) to predict prostate cancer recurrent PSADT 4 months or less. ROC analysis of a model combining LSR, fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by black. ROC analysis of a model combining fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by red. ROC analysis of a model combining LSR, fusion transcripts and Gleason’s grade using LDA is indicated by blue. ROC analysis of a model combining LSR, fusion transcripts and Nomogram using LDA is indicated by green. ROC analysis of a model combining LSR, Nomogram and Gleason’s grade is indicated by yellow.</p

Prediction of prostate cancer recurrence based on leukocyte LSR, Gleason, Nomogram and fusion transcript status.

<p>L-LSR; N-Nomogram; F-fusion transcript status; G-Gleason grade.</p><p>L+N+F: LDA model to combine LSR, Nomogram and fusion transcript status</p><p>L+N+G: LDA model to combine LSR, Nomogram and Gleason grade</p><p>N+F+G: LDA model to combine Nomogram, fusion transcript status and Gleason grade</p><p>L+N+F+G: LDA model to combine LSR, Nomogram, fusion transcript status and Gleason grade.</p><p>The results represent the average of the analyses on 10 random equal splits of training and testing results.</p><p>Prediction of prostate cancer recurrence based on leukocyte LSR, Gleason, Nomogram and fusion transcript status.</p

Large LSRs of genome CNVs from leukocytes correlated with lower PSA-free survival.

<p>Kaplan-Meier analysis on patients predicted by LSR based on CNV of patients’ leukocytes as likely recurrent versus likely non-recurrent (upper left). Similar survival analyses were also performed on case segregations based on Gleason’s grades (upper middle), Nomogram probability (upper right), the status of 8 fusion transcripts (lower left), or a model by combining LSR, Nomogram and fusion transcript status using LDA (lower middle), or a model by combining LSR, Nomogram, Gleason grade and fusion transcript status using LDA (lower right). Number of samples analyzed and p values are indicated.</p

LSR of genome CNV from leukocytes to predict prostate cancer recurrence.

<p>(A) LSR derived from leukocyte genome CNV predicts prostate cancer recurrence. Receiver operating curve (ROC) analyses using LSRs derived from leukocyte CNVs as prediction parameter (red) to predict prostate cancer recurrence, versus Nomogram (blue), Gleason’s grade (green) and the status of 8 fusion transcripts[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135982#pone.0135982.ref014" target="_blank">14</a>] (yellow). The samples were equally split randomly into training and testing sets 10 times. The ROC analysis represents the results from the most representative split. (B) Combination of LSR (L), Gleason’s grade (G), Nomogram (N) and the status of fusion transcripts (F) to predict prostate cancer recurrence. ROC analysis of a model combining LSR, fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by black. ROC analysis of a model combining fusion transcripts, Nomogram and Gleason’s grade using LDA is indicated by red. ROC analysis of a model combining LSR, fusion transcripts and Gleason’s grade using LDA is indicated by blue. ROC analysis of a model combining LSR, fusion transcripts and Nomogram using LDA is indicated by green. ROC analysis of a model combining LSR, Nomogram and Gleason’s grade is indicated by yellow. Similar random splits of training and testing data sets were performed as of (A).</p

Large size ratio (LSR) of CNVs from leukocytes from prostate cancer patients are correlated with aggressive behavior of prostate cancer.

<p>(A) Schematic diagram of LSR model of leukocyte CNV. (B) LSRs from leukocytes are associated with aggressive prostate cancer recurrence behavior. Upper panel: Correlation of LSRs from leukocyte genomes with prostate cancers that were recurrent; Lower panel: Correlation of LSRs from leukocyte genomes with prostate cancers that were non-recurrent 90 months after radical prostatectomy. (C) LSRs from leukocytes are associated with short PSADT. Upper panel: Correlation of LSRs from leukocyte genomes with prostate cancers that had recurrent serum prostate specific antigen doubling time (PSADT) 4 months or less; Lower panel: Correlation of LSRs from leukocyte genomes with prostate cancers that were not recurrent or recurrent but having PSADT 15 months or more.</p

Copy number variations (CNV) in blood and prostate cancer from prostate cancer patients.

<p>(A) Histogram of frequency of amplification (red) or deletion (blue) of genome sequences of leukocytes (upper panel, n = 273) from prostate cancer patients. (B) Manhattan plots of p-values in association with prostate cancer recurrence of each gene CNV from leukocytes.</p

Exercise mitigates sleep-loss-induced changes in glucose tolerance, mitochondrial function, sarcoplasmic protein synthesis, and diurnal rhythms

OBJECTIVE: Sleep loss has emerged as a risk factor for the development of impaired glucose tolerance. The mechanisms underpinning this observation are unknown; however, both mitochondrial dysfunction and circadian misalignment have been proposed. Because exercise improves glucose tolerance and mitochondrial function, and alters circadian rhythms, we investigated whether exercise may counteract the effects induced by inadequate sleep. METHODS: To minimize between-group differences of baseline characteristics, 24 healthy young males were allocated into one of the three experimental groups: a Normal Sleep (NS) group (8 h time in bed (TIB) per night, for five nights), a Sleep Restriction (SR) group (4 h TIB per night, for five nights), and a Sleep Restriction and Exercise group (SR+EX) (4 h TIB per night, for five nights and three high-intensity interval exercise (HIIE) sessions). Glucose tolerance, mitochondrial respiratory function, sarcoplasmic protein synthesis (SarcPS), and diurnal measures of peripheral skin temperature were assessed pre- and post-intervention. RESULTS: We report that the SR group had reduced glucose tolerance post-intervention (mean change ± SD, P value, SR glucose AUC: 149 ± 115 A.U., P = 0.002), which was also associated with reductions in mitochondrial respiratory function (SR: -15.9 ± 12.4 pmol O2.s-1.mg-1, P = 0.001), a lower rate of SarcPS (FSR%/day SR: 1.11 ± 0.25%, P < 0.001), and reduced amplitude of diurnal rhythms. These effects were not observed when incorporating three sessions of HIIE during this period (SR+EX: glucose AUC 67 ± 57, P = 0.239, mitochondrial respiratory function: 0.6 ± 11.8 pmol O2.s-1.mg-1, P = 0.997, and SarcPS (FSR%/day): 1.77 ± 0.22%, P = 0.971). CONCLUSIONS: A five-night period of sleep restriction leads to reductions in mitochondrial respiratory function, SarcPS, and amplitude of skin temperature diurnal rhythms, with a concurrent reduction in glucose tolerance. We provide novel data demonstrating that these same detrimental effects are not observed when HIIE is performed during the period of sleep restriction. These data therefore provide evidence in support of the use of HIIE as an intervention to mitigate the detrimental physiological effects of sleep loss