To analyze cell cycle phase distribution, a series of CCSs were created as described in Methods (Fig. 1A, Additional file 1). The CCS masterset, 252 genes that express preferentially in cycling cells and in a cell cycle-regulated manner, represents the entire cell cycle and is henceforth denoted as CCS_cycling. Eighteen CCS subsets, each composed of genes whose expressions peak at a specific stage of the cell cycle, represent the phases of the cell cycle and are denoted using the subscript naming convention of CCS_phase. For example, the CCS subsets for the G1 phase are expressed as CCS_G1, for the G2-M phase as CCS_G2-M, and so on.

Solid tumors are composed of various proportions of cycling and non-cycling cells 9, and cell cycle phase distributions can be assessed as per total cells or as per cycling cells. Since microarray measurements are the net expression of all cells in the sample, the data is generally per total cells. To obtain data per cycling cells from a given microarray dataset (Fig. 1B, total gene dataset), a subdataset is created by extracting the expression values of CCS_cycling genes (Fig. 1B, cycling gene dataset). Then, both the total and the cycling gene datasets undergo quantile normalization which gives the same expression value distribution for each sample 10. In the total gene dataset, normalization is done on all genes. On the other hand, in the cycling gene dataset, normalization is done only on the cycling genes. Because genes in the CCS_cycling preferentially express in cycling cells, the influence of non-cycling cells would be limited for the cycling gene dataset. Scores for each CCS are calculated for both datasets. CCS_cycling and CCS_phase scores for the total gene dataset could index the proportion of cycling cells and of cells at the designated cell cycle phase per total cells, respectively. Similarly, CCS_phase scores for the cycling gene dataset could index the proportion of cells at the cell cycle phase per cycling cells. CCS_cycling scores for the cycling gene dataset could index the proportion of cycling cells per cycling cells and thus would show constant values.

In the preliminary analysis of the Whitfiled et al. cell cycle dataset 11, CCS indexed cell cycle phase distribution as expected (Additional file 2). To confirm that the CCS method is valid for independent datasets, a cell cycle dataset of synchronized HCT116 cells was prepared and analyzed. As shown in Fig. 2A, similar heat map patterns were observed for the total and the cycling gene datasets. Differences in the CCS_cycling scores for both the total and the cycling gene datasets were slight in the situation where most cells were expected to be in the cell cycle. Peaks in the CCS_phase scores shifted according to cell cycle progression (Fig. 2A, DMSO 0–10 h), and peaks ceased around the M phase in cells treated with the mitosis inhibitor nocodazole (Fig. 2A, Ncz 7–10 h), consistent with DNA flow cytometry measurements (Fig. 2B). The CCS method was able to index cell cycle phase distribution even for an independent cell cycle dataset derived from a different cell line and a different platform.

Solid tumors are not solely composed of cycling cells but contain various numbers of non-cycling cells 9. Theoretically, changes in the proportion of cycling cells in the sample are expected to evenly change the proportion of cells in all cell cycle phases. To examine the influence of changes in the proportion of cycling cells on CCS scores, analysis was conducted on the Fournier et al. dataset 12 of profiles of human mammary epithelial cells (HMECs) cultured in leucine-rich extra cellular matrix. In this system, HMECs grow exponentially and then enter a quiescent state 1213. As shown in Fig. 2C, CCS_cycling and CCS_phase scores for the total gene dataset uniformly decreased as the HMECs transitioned from cycling (day 3) to non-cycling state (day 7) (Fig. 2C, upper panel). According to the DNA flow cytometry estimation in the original report, the S phase and G2+M phase fraction size decreased from 15% ± 5.1 (day 5) to 5.5% ± 0.5 (day 7), and from 12% ± 1.1 (day 5) to 7% ± 2.5 (day 7), respectively (day 3 data was not available) 12. On the other hand, the G0+G1 phase fraction size increased from 73% ± 6.3 (day 5) to 86% ± 4.6 (day 7). Due to the inability of DNA flow cytometry to distinguish cells in G0 from cells in G1, decisive conclusions cannot be made. However, from two situations in which 1) 3D cultured HMECs gradually underwent growth arrest and 2) CCS_G1 scores decreased at day 7, this increase can be regarded as an increase in the number of cells at the G0 phase as well as a decrease in the number of cells at the G1 phase. To our surprise, the heat map for the cycling gene dataset showed increasing CCS_G1 scores towards day 7 (Fig. 2C, lower panel). This increase in CCS_G1 scores could be due to the G1 phase prolongation which is known to occur under G0-inducing conditions, such as serum starvation and development 1415. For further confirmation, we analyzed the Cam et al. dataset 16 of profiles of growing and serum starved T98 breast cancer cells. Similar to the results for HMECs, a uniform decrease in CCS_cycling and CCS_phase scores for the total gene dataset was observed in serum-starved cells (Fig. 2D, upper panel). In addition, an increase in CCS_G1 scores for the cycling gene dataset was observed (Fig. 2D, lower panel), indicating prolongation of the G1 phase. Taken together, these results suggested that changes in the proportion of cycling cells in the sample can be presented as uniform changes in CCS_cycling and CCS_phase scores for the total gene dataset.

The mammalian cell cycle is a highly regulated and conserved process 17. To investigate whether CCS derived from human datasets can be used to closely related species, the Yamamoto et al. dataset 18, cell cycle profiles (G0 to S) of NIH3T3 mouse fibroblasts, was analyzed. The heat map showed changes in the proportion of cycling cells (Additional file 3: upper panel) as well as cell cycle progression from G1 to S phase (Additional file 3: lower panel), as quiescent cells (FGF 0 h) re-enter the cell cycle, progress through G1 phase and enter S phase (FGF 12 h). These results showed that the human CCS created in this study can be applied for the analysis of mouse datasets.

The CCS method was applied to the Herschkowitz et al. dataset 19 which contains 122 profiles of 13 different mouse mammary carcinoma models and normal samples. The authors reported that some models developed similar tumors (homogeneous models) of gene expression and histological phenotype while other models showed heterogeneity (heterogeneous models) and gave "randomness of the molecular basis of tumor initiation" as the reason for the heterogeneity. As shown in Fig. 3A, CCS_cycling and CCS_phase scores for the total gene dataset for the normal samples were consistently very low, while scores for tumors were varying degrees higher, indicating variation in the proportion of cycling cells. It is reasonable that heterogeneous models show variation in CCS_cycling and CCS_phase scores. However, variation was also seen in each homogeneous model, although Tag models had a tendency towards higher scores and the Neu model had a tendency towards lower scores. In contrast, CCS_phase scores for the cycling gene dataset were similar within the same homogeneous models, except in the Myc model (Fig. 3A, lower panel). To illustrate this in detail, CCS_phase scores of several models for both datasets were plotted as shown in Fig. 3B. It can be seen that each model has a specific cell cycle phase distribution. High CCS_G1 and low CCS_S-G2-M scores were seen in the Neu model. The opposite pattern was seen in one of the Tag models. The Myc model showed two different cell cycle phase distributions (Additional file 4) and the reason is not clear. However, because Myc has been reported to induce genomic instability and to contribute to tumorigenesis through a dominant mutator effect 20, additional oncogenic events may have been induced. In all cases, plots for the total gene dataset were vertically shifted in varying degrees which would be due to the influence of non-cycling cells, as presented in HMECs and T98 cells. On the other hand, plots for the cycling gene dataset showed minimal variation in alignment. These results indicated two findings: (i) the cell cycle phase distribution reflects the oncogenic events in tumors, and (ii) the cell cycle phase distribution can be better indexed when the influence of non-cycling cells is taken into account. The advantage of the CCS method can be underscored considering that the current cell cycle phase estimation methods relying on one or few measurements are not sufficient to depict cell cycle phase distribution or to distinguish non-cycling cells.

The CCS method was applied to the Ivshina et al. dataset 21 from a panel of 249 human breast cancers. The heat map for the total gene dataset showed various CCS_cycling scores, indicative of variations in the proportion of cycling cells in the sample (Fig. 4A, upper panel). The CCS_phase scores were not uniformly changed in some patients, suggesting that cell cycle phase distributions were also altered. The heat map for the cycling gene dataset displayed a rolling wave pattern (Fig. 4A, lower panel). Patients with high CCS_cycling scores for the total gene dataset had high CCS_S-G2-M and low CCS_G1 scores for the cycling gene dataset, but several exceptions existed (Fig. 4A), reminding the influence of non-cycling cells found in the analysis of mouse tumor models. Clinical annotations were available for this dataset and so the relevance between CCS scores and patient prognosis were tested. Patients were dichotomized by the median of each CCS score and then the risk differences between the two groups for disease free survival (DFS) were assessed using log-rank test and Cox univariate analysis (Fig. 4B). The CCS_cycling score for the total gene dataset was significantly predictive of poor prognosis (Hazard ratio [HR] = 1.98, p = 0.00134) (Fig. 4B and Fig. 4C, CCS_cycling), consistent with the common view that a larger number of cycling cells correlates with worse clinical outcome. The CCS_S-G2-M and several CCS_G1 scores for the total gene dataset were also predictive of poor prognosis. On the other hand, CCS_G1 scores for the cycling gene dataset had an adverse prognostic power and gave the highest prognostic value among the tests (HR = 0.41, p = 0.0000367) (Fig. 4B and Fig. 4C, CCS_G1).

To exclude the possibility of dataset specificity, the CCS method was also applied to the Langerød et al. dataset 22 from a panel of 80 breast cancers. Similar results were obtained (Additional file 5). For the total gene dataset, variations in CCS_cycling scores and non-uniform changes in CCS_phase scores in some patients were observed. Patients with high CCS_cycling scores for the total gene dataset had high CCS_S-G2-M and low CCS_G1 scores for the cycling gene dataset with some exceptions. CCS_G1 scores for the cycling gene dataset were predictive for DFS as with the Ivshina et al. dataset and gave the highest prognostic value (HR = 0.41, p = 0.00553) (Additional file 5). Taken together, these results indicated that: (i) variations in the proportion of cycling cells exist among tumors, (ii) the proportion of cycling cells correlated to the cell cycle phase distribution per cycling cells with several exceptions, and (iii) the cell cycle phase distribution per cycling cells better associated with patients' prognosis.

The HCT116 colorectal cancer cell line (ATCC) was grown in McCoy's 5A medium modified (Sigma-Aldrich) with 10% FBS (JBS) and maintained at 37°C and 5% CO₂. Synchronous culture was obtained by incubating cells for 19 h in 2 mM of thymidine, followed by a 9-h incubation in normal medium and a second 16-h incubation in thymidine (2 mM). Cells were washed with normal medium followed by treatment with DMSO for 0, 2, 4, 6, 7, 8, 9, and 10 h as a control or 0.1 mg/ml nocodazole (Sigma-Aldrich) for 7, 8, 9, and 10 h. Cells were stained with propidium iodide and analyzed with DNA flow cytometry.

Total RNA was reverse transcribed, labeled, and hybridized to Human Genome U133 Plus 2.0 arrays (Affymetrix) according to the manufacturer's instructions. The expression value for each probe was calculated using the GC-RMA algorithm. The microarray data were deposited in the GEO database (GEO number: GSE14103).

Two datasets were used to create the CCS. First, the Whitfield et al. dataset 11 of 47 profiles of synchronized Hela S3 cells for 0–46 h time points (1-h intervals) after release of double thymidine block was analyzed to identify genes which express in a cell cycle-regulated manner. Raw signal intensities from the Cy5 and Cy3 channels were quantile normalized for each sample. Cy5/Cy3 ratios were log-transformed and quantile normalized across the arrays. Resulting values were smoothened using a moving average with a window size of 3 and were standardized by Z-transformation. Then, Fourier transformations were applied to each probe for 1-40-h periods in 15-min increments to identify periodicity and phase offset. Fourier transformation magnitudes for the known 51 cell cycle-regulated genes (listed in Whitfield et al. 11) demonstrated a peak at the 14.75-h periodicity (Additional file 6). Thus, probes were selected using the criterion of

Z-score(P_i) > 1.96

where P_i is the Fourier transformation magnitude of the 14.75-h periodicity for probe i, i = 1,..., 44,160. The analysis yielded a list of 1,633 periodically expressed probes representing 976 genes. Second, the Bar-Joseph et al. dataset 35 of 17 profiles of synchronized primary human foreskin fibroblasts (FFs) for 0–32 h time points (2-h intervals) after release of double thymidine block and 2 profiles of serum starved FFs was investigated to identify genes which preferentially express in cycling cells. Serum starved cells are known to exit the cell cycle phase and to enter the non-cycling G0 phase 14, thus probes, whose expression is constantly higher throughout the cell cycle compared with non-cycling cells, were selected by the criterion

max(e_ij) < min(e_ik)

where e_ij is the expression value for probe i of serum-starved FFs sample j, j = 1, 2, and e_ik is the expression value for probe i of the synchronized FFs sample k, k = 1,..., 17. This yielded 2,304 out of 22,277 probes representing 1,779 genes. Then, from the intersection, a list of 335 probes representing 252 genes was obtained. These genes which preferentially express in cycling cells and in a cell cycle-regulated manner compose the CCS masterset (CCS_cycling). A number of well-known proliferation markers such as Ki67, geminin, TOP2A, aurora A, and PCNA 1234532 were included in this signature, while some cell cycle-regulated genes such as p21 and cyclin G1 whose expression can be up-regulated in non-cycling cells 3637 were not. Lastly, according to their phase offsets, probes for CCS_cycling were assigned to 18 CCS subsets (CCS_phase) which correspond to a 360° cell cycle evenly divided into 20° increments, so that each CCS subset contains at least 3 genes. Because some genes were represented by multiple probes, the same genes may appear in different CCS subsets. The CCS gene list is shown in Additional file 1.

The given microarray dataset was used as the total gene dataset. The cycling gene dataset was created by extracting the expression values for CCS_cycling constituents from the total gene dataset. Both total and cycling gene datasets then underwent the following steps independently to give CCS scores. Expression values were log-transformed, quantile normalized to achieve the same expression value distribution for each sample, and standardized with Z-transformation across the samples. The Z-scores of the probes for each CCS genes were averaged for each sample and used as the CCS scores. To obtain robust scores, each CCS_phase score was adjusted by averaging with the neighboring CCS scores twice for a total of two cell cycle rounds. Heat maps were created by "Java Treeview" 38. In the analysis of the mouse tumor model dataset, gene ID mapping was done using human-mouse orthology information from HomoloGene 39. In the analysis of human breast cancer datasets, patients were ordered by peak in CCS_phase scores for the cycling gene dataset.

Patients were dichotomized by the median of each CCS score. To assess the risk difference between two groups for DFS, Kaplan-Meier survival analysis, log-rank test and Cox univariate analysis were conducted using R "survival" package.