The authors have declared that no competing interests exist.
Conceived and designed the experiments: Conception and design: EC PH FLCK. Development of methodology: EC PH FLCK. Study supervision: PH FLCK. Performed the experiments: MP GD NR FGS. Analyzed the data: EC MW GB FLCK. Contributed reagents/materials/analysis tools: EC MP GD NR FGS FLCK. Wrote the paper: EC GB PH FLCK.
The quality of tissue samples and extracted mRNA is a major source of variability in tumor transcriptome analysis using genome-wide expression microarrays. During and immediately after surgical tumor resection, tissues are exposed to metabolic, biochemical and physical stresses characterized as “warm ischemia”. Current practice advocates cryopreservation of biosamples within 30 minutes of resection, but this recommendation has not been systematically validated by measurements of mRNA decay over time. Using Illumina HumanHT-12 v3 Expression BeadChips, providing a genome-wide coverage of over 24,000 genes, we have analyzed gene expression variation in samples of 3 hepatocellular carcinomas (HCC) and 3 lung carcinomas (LC) cryopreserved at times up to 2 hours after resection. RNA Integrity Numbers (RIN) revealed no significant deterioration of mRNA up to 2 hours after resection. Genome-wide transcriptome analysis detected non-significant gene expression variations of −3.5%/hr (95% CI: −7.0%/hr to 0.1%/hr; p = 0.054). In LC, no consistent gene expression pattern was detected in relation with warm ischemia. In HCC, a signature of 6 up-regulated genes (
Whole-genome expression profiling using microarrays has proven to be a powerful and reliable tool for classifying tumors and identifying predictors of therapeutic responses
While protocol standardization and reliable performance of genome-wide expression microarrays now generate high quality and consistent data across platforms
Using two types of tumors, hepatocellular carcinoma (HCC) and non-small cell lung carcinomas (LC), we have investigated (i) whether delaying tissue snap-freezing after surgery has a significant impact on RNA integrity and genome-wide expression profiling analysis and if so, (ii) whether it is possible to establish a transcriptomic signature related to
The non-normalized fluorescent signals (AVG_Signal) have been generated by the Illumina Genome Studio V2010.2 for the 3 HepatoCellular Carcinomas (HCC) and the 3 Lung Carcinomas (LC ) samples taken at the center and at the periphery of the tumors and maintained at room temperature and then frozen in liquid nitrogen at different times: 5 minutes (t5, reference time), 15 minutes (t15), 30 minutes (t30) and 120 minutes (t120).
Patient | Tumor pathology | Delay to cryopreservation (minutes) | Site | RIN Number | Detected Genes at p<0.01 | p95/p05 | Scatter plot r2 |
4 | HCC | 5 | Central | 8 | 9396 | 21.65 | |
4 | HCC | 5 | Peripheral | 7.4 | 9521 | 17.60 | 0.9763 |
4 | HCC | 15 | Central | 7.6 | 9175 | 21.22 | |
4 | HCC | 15 | Peripheral | 7.8 | 9486 | 20.77 | 0.9661 |
4 | HCC | 30 | Central | 7.9 | 9583 | 17.25 | |
4 | HCC | 30 | Peripheral | 7.8 | 9184 | 15.78 | 0.9824 |
4 | HCC | 120 | Central | 7.6 | 9171 | 13.83 | |
4 | HCC | 120 | Peripheral | 7.3 | 9319 | 11.88 | 0.9803 |
5 | HCC | 5 | Central | 7.2 | 10154 | 13.45 | |
5 | HCC | 5 | Peripheral | 6.7 | 9841 | 12.76 | 0.9749 |
5 | HCC | 15 | Central | 7.1 | 9627 | 11.42 | |
5 | HCC | 15 | Peripheral | 7.7 | 10120 | 16.52 | 0.8487 |
5 | HCC | 30 | Central | 7.6 | 10178 | 15.69 | |
5 | HCC | 30 | Peripheral | 7.3 | 10072 | 14.03 | 0.9792 |
5 | HCC | 120 | Central | 7.8 | 9677 | 11.75 | |
5 | HCC | 120 | Peripheral | 7.1 | 10250 | 17.37 | 0.874 |
6 | HCC | 5 | Central | 6.9 | 10994 | 17.49 | |
6 | HCC | 5 | Peripheral | 6.8 | 10437 | 16.32 | 0.9795 |
6 | HCC | 15 | Central | 7 | 10407 | 16.88 | |
6 | HCC | 15 | Peripheral | 6.9 | 10750 | 16.41 | 0.9604 |
6 | HCC | 30 | Central | 6.6 | 10732 | 16.13 | |
6 | HCC | 30 | Peripheral | 7.1 | 10262 | 15.93 | 0.9801 |
6 | HCC | 120 | Central | 6.9 | 10392 | 17.12 | |
6 | HCC | 120 | Peripheral | 6.5 | 10393 | 16.70 | 0.8507 |
2 | LC | 5 | Central | 6.7 | 11775 | 23.07 | |
2 | LC | 5 | Peripheral | 5.4 | 11576 | 17.89 | 0.9599 |
2 | LC | 15 | Central | 6 | 11952 | 24.63 | |
2 | LC | 15 | Peripheral | 5.2 | 11551 | 18.41 | 0.9573 |
2 | LC | 30 | Central | 6.8 | 11497 | 19.48 | |
2 | LC | 30 | Peripheral | 7.5 | 12078 | 20.67 | 0.9697 |
2 | LC | 120 | Central | 6.2 | 12036 | 21.07 | |
2 | LC | 120 | Peripheral | 6.2 | 12300 | 16.84 | 0.9738 |
5 | LC | 5 | Central | 6 | 11591 | 21.70 | |
5 | LC | 5 | Peripheral | 6.2 | 11061 | 24.18 | 0.9593 |
5 | LC | 15 | Central | 6.1 | 11004 | 21.78 | |
5 | LC | 15 | Peripheral | 6 | 11150 | 24.02 | 0.9832 |
5 | LC | 30 | Central | 5.7 | 10054 | 14.88 | |
5 | LC | 30 | Peripheral | 6.2 | 10696 | 15.49 | 0.9644 |
5 | LC | 120 | Central | 6.6 | 11078 | 17.06 | |
5 | LC | 120 | Peripheral | 6.2 | 11144 | 19.20 | 0.9801 |
6 | LC | 5 | Central | 6.9 | 10521 | 14.18 | |
6 | LC | 5 | Peripheral | 6.8 | 10413 | 17.40 | 0.9652 |
6 | LC | 15 | Central | ND | 10519 | 9.51 | |
6 | LC | 15 | Peripheral | 5.8 | 10556 | 15.29 | 0.9631 |
6 | LC | 30 | Central | 6.6 | 10511 | 19.14 | |
6 | LC | 30 | Peripheral | 5.9 | 10688 | 17.85 | 0.9791 |
6 | LC | 120 | Central | 6.4 | 11090 | 17.74 | |
6 | LC | 120 | Peripheral | 7.8 | 10095 | 10.39 | 0.9603 |
Type of tumor and delay to tumor freezing are shown. RNA integrity is evaluated through the RIN number. The ratio of centiles P95/P05 reflects the overall strength of the signal compared to the background. The Pearson correlation coefficient (r2) shows the correlation between log-expression levels of the central and peripheral samples, for each tumor and each time to cryopreservation.
HCC: HepatoCellular Carcinoma.
LC: Lung Carcinoma.
ND: Not Determined.
There was mild evidence for a slow decline in expression level by 3.5%/hr (95% CI −7%/hr to 0.1%/hr; p = 0.054) in relation with delay to cryopreservation (
Rate of change (%/hr) | 95%CI | p value | ||
All probes | ||||
All samples | −3.5 | −7.0 to 0.1 | 0,054 | |
HCC | −2.3 | −7 to 2.3 | 0,33 | |
LC | −4.6 | −9.8 to 0.6 | 0,09 | |
Central tumor | −3.5 | −8.6 to 1.6 | 0,09 | |
Peripheral tumor | −3.4 | −8.2 to 1.4 | 0,17 | |
Probes with the lowest 5% of geometric mean expression | ||||
All samples | −1.7 | −3 to 0.4 | 0,009 | |
HCC | −0.8 | −2.6 to 0.9 | 0,35 | |
LC | −2.5 | −4.3 to −0.8 | 0,004 | |
Probes with the highest 5% of geometric mean expression | ||||
All samples | −8 | −16.2 to 0.1 | 0,054 | |
HCC | −5.9 | −15 to 3.8 | 0,23 | |
LC | −10.2 | −23 to 2.9 | 0,13 | |
Probes in warm ischemia genes | ||||
All samples | −4.7 | 13 to 3.6 | 0,27 | |
HCC | 7.3 | −18 to 2.9 | 0,16 | |
LC | 2.2 | −16 to 11 | 0,75 | |
Probes in HCC genes | ||||
HCC | −8,5 | −24 to 7.2 | 0,29 |
Over-all rate of expression changes for all probes in all samples combined, in LC and HCC samples and in peripheral and central samples are estimated as percent-change per hour. Expression levels changes are also estimated for different sets of probes (lowest and highest 5% of geometric mean expression, probes in warm ischemia genes and probes in HCC genes).
A total of 9,191 of 48,783 probes (18.8%) passed the probe filtering criteria and were hence eligible for testing for changes in expression. At the 5% FDR level, the ANOVA model identified 12 genes in HCC whose expression varied with delay to cryopreservation (6 up-regulated:
The BRB-ArrayTools v4.2 time course analysis model was applied to whole-genome expression microarray data (HCC and LC samples) to identify significant individual deregulated genes over harvesting time. No significant deregulated genes in LC were observed. Individual log-expression profiles (
Dendrogram for clustering experiments was created using centred correlation and average linkage method. Length of nodes corresponds to correlation between samples. HCC4_5P: HCC from patient 4 taken at the periphery of the tumor and maintained at room temperature and then frozen in liquid nitrogen at t5 (min).
Scatter plots for each tumor pair at t5 (HCC_5_AVG_Signal and LC_5_AVG_Signal on the X Axis) versus harvested tumor pairs at t15, t30 and t120 (on the Y Axis) were generated on a logarithmic scale. Genes showing greater than 2-fold change relative to the t5 sample from the same tumor were highlighted.
While the expression of several genes have been reported to be consistently deregulated at the early stage after surgery, the time-course analysis of individuals genes performed with our sample series did not reveal any of those genes. Therefore, we analyzed independently 21 genes in our dataset (
In addition, taking advantage of the publicly available Liverome database, which provides a large collection of well-curated HCC gene expression signatures (
The Neyman’s test revealed that the rates of gene expression changes for the 21 selected ischemic genes and 34 HCC specific genes tended to the extremes of the distribution among the full panel of genes. Both the log-linear trends and the quadratic terms were significantly extreme (Ischemia genes: Linear effect beta = 0.94, SE = 0.18, p = 3.0E-7; Quadratic effect beta = 0.66, SE = 0.18, p = 3.0E-4; HCC genes: Linear effect beta = 1.16, SE = 0.14, p = 1.9E-16; Quadratic effect beta = 1.35, SE = 0.14, p = 1.9E-21). However, strikingly, after normalizing the estimated parameters by their standard errors, the re-calculated ranks were completely consistent with a uniform distribution among all genes (Ischemia genes: Linear effect beta = 0.12, SE = 0.18, p = 0.53, Quadratic effect beta = −0.26, SE = 0.18, p = 0.16; HCC genes: Linear effect beta = 0.13, SE = 0.14, p = 0.36, Quadratic effect beta = −0.07, SE = 0.14, p = 0.60), suggesting that those genes could be systematically be prone to highly variable gene expression changes between and within samples.
Liverome dataset | Experimental dataset | Conclusions | |||||||
Reported results | |||||||||
Gene symbol | Entrez Gene ID | Gene name | Frequency(studies) | Up-regulated expression | Down-regulated expression | Expression reported with a p-value | Rate of expression change (%/h) | Up or down expression | Consistant results between datasets |
A2M | 2 | alpha-2-macroglobulin | 5 | <0.005 (9) | −0,062217 | Yes | |||
ADH4 | 127 | alcohol dehydrogenase 4 (class II), pi polypeptide | 4 | <0.005 (9) | 0.0236812 |
No | |||
ALB | 213 | albumin | 5 |
<0.005 (9) | 0.04337605 |
No | |||
APOA1 | 335 | apolipoprotein A-I | 4 |
<0.005 (9) | −0,2482746 | Yes | |||
BHMT | 635 | betaine–homocysteine S-methyltransferase | 5 | <0.005 (9) | −0,3139206 | Yes | |||
COL1A2 | 1278 | collagen, type I, alpha 2 | 4 | <0.005 (9) | −0.2465753 |
No | |||
CYP3A4 | 1576 | cytochrome P450, family 3, subfamily A, polypeptide 4 | 4 |
<0.005 (9) | 0,4847241 | No | |||
DUSP1 | 1843 | dual specificity phosphatase 1 | 5 |
<0.005 (9) | −0,1140679 | No | |||
ECHS1 | 1892 | enoyl CoA hydratase, short chain, 1, mitochondrial | 6 |
<0.005 (9) | 0,2454375 | No | |||
FAM36A | 116228 | family with sequence similarity 36, member A | 4 |
<0.005 (9) | 0,267976 | No | |||
FGB | 2244 | fibrinogen beta chain | 4 | <0.005 (9) | −0.1665138 |
Yes | |||
FGG | 2266 | fibrinogen gamma chain | 4 | <0.005 (9) | −0.015657767 |
Yes | |||
FN1 | 2335 | fibronectin 1 | 4 | <0.005 (9) | −0.0767727 |
No | |||
GMFG | 9535 | glia maturation factor, gamma | 4 | <0.005 (9) | 0,100325 | Yes | |||
GPC3 | 2719 | glypican 3 | 7 | <0.005 (9) | 0,3639491 | Yes | |||
HAMP | 57817 | hepcidin antimicrobial peptide | 4 |
<0.005 (9) | 0,7744631 | No | |||
HGFAC | 3083 | HGF activator | 4 | 0,5554259 | No | ||||
HPD | 3242 | 4-hydroxyphenylpyruvate dioxygenase | 4 | 0,4381038 | No | ||||
HSD17B6 | 8630 | hydroxysteroid (17-beta) dehydrogenase 6 homolog (mouse) | 4 | <0.005 (9) | −0,3196788 | Yes | |||
IGFBP3 | 3486 | insulin-like growth factor binding protein 3 | 4 | <0.005 (9) | −0.4585644 |
Yes | |||
LCN2 | 3934 | lipocalin 2 | 4 | <0.005 (9) | 0,279305 | Yes | |||
MT1F | 4494 | metallothionein 1F | 4 |
−0,4286072 | Yes | ||||
MT2A | 4502 | metallothionein 2A | 5 |
<0.005 (9) | −1,311318 | Yes | |||
MT3 | 4504 | metallothionein 3 | 4 | −0,0167001 | Yes | ||||
PCK1 | 5105 | phosphoenolpyruvate carboxykinase 1 (soluble) | 4 | 0.0363959 |
No | ||||
PLG | 5340 | plasminogen | 5 | <0.005 (9) | 0,0354232 | No | |||
PRPSAP1 | 5635 | phosphoribosyl pyrophosphate synthetase-associated protein 1 | 4 | <0.005 (9) | −0,0122151 | No | |||
RHOB | 388 | ras homolog gene family, member B | 4 |
<0.005 (9) | 0,0256628 | No | |||
SAA2 | 6289 | serum amyloid A2 | 4 | <0.005 (9) | 0,0021512 | No | |||
SLC22A1 | 6580 | solute carrier family 22 (organic cation transporter), member 1 | 6 |
<0.005 (9) | −0.21260725 |
Yes | |||
SPARC | 6678 | secreted protein, acidic, cysteine-rich (osteonectin) | 7 | <0.005 (9) | −0,1714643 | No | |||
TDO2 | 6999 | tryptophan 2,3-dioxygenase | 5 | <0.005 (9) | −0.0018556 |
Yes | |||
TUBA1B | 10376 | tubulin, alpha 1b | 4 | −0,0030352 | No | ||||
UBD | 10537 | ubiquitin D | 7 | <0.005 (9) | 0,5806394 | Yes |
Expression trend of 34 HCC specific genes reported as deregulated in more than 4 studies in the public Liverome database was compared to experimental expression trend.
average rate of expression from different Illumina probes.
discrepancies between Liverome studies results.
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Our study aimed at evaluating the effects of time to cryopreservation on both RNA integrity and gene expression variations by analyzing three hepatocellular carcinomas and three lung carcinomas that were snap-frozen at different times after surgical resection.
No deterioration of the RINs was observed with increasing harvest time, even after 120 minutes. This, combined with the quality controls of the arrays, indicates that stress induced by delay to cryopreservation on surgically resected liver and lung tumors had limited impact on both integrity of extracted RNAs and microarray performance. These results corroborate previous observations by Strand
RNA decay is a tightly controlled and one of the key processes that control the steady-state level of gene expression. Our findings in conjunction with some other studies prove that RNA degradation in lung
Even though RNA degradation within the first hour following surgical resection and before snap-freezing is limited, the process of collection and, specifically the lag time between resection and cryopreservation represent a complex form of metabolic and micro-structural stresses underlying a condition often defined as “warm ischemia”. This complex process may induce significant changes in the level of expression of particular genes. Identification of such changes is an important concern because they may bias the interpretation of transcriptomic data on resected tumor samples. We have attempted to identify a gene expression signature characteristic of the warm-ischemia. Taken together, the time-course analysis of genome-wide data from HCC and LC did not reveal any consistent group of genes, even considering genes involved in inflammatory and immune responses and in cellular growth that were previously reported to be altered during the period between tumor resection and snap-freezing
In contrast to LC, our study in HCC identified 12 genes, representing less than 0.05% of all genes tested, with differential expression in relation to delayed time to freezing. This small number and low proportion of genes is consistent with observations by others in other tissues exposed to warm ischemia
Overall, our observations emphasize the importance of identifying tissue-specific genes deregulated following surgical resection, in order to avoid misinterpreting changes in expression induced by warm ischemia as pathologically significant changes.
Altogether, our findings and previous studies suggest that the effects of warm ischemia induced by the time that elapsed between surgical resection and snap-freezing are minimal within the first two hour post-resection, and that any significant changes in expression induced by warm ischemia are likely to be tissue-specific rather than a systematic expression profiling signature common in all tissue types.
In this study we have only considered the effect of time that elapsed between surgical resection and snap-freezing on RNA integrity and genome-wide expression profiling in two cancer sites, each including only 3 distinct cases. Larger series of different cancer sites, various time points of delay to cryopreservation and several other parameters in tissue handling protocols that may affect RNA integrity (
Tissue specimens were obtained during surgery, according to procedures established by the Tumor Bank of the hospital of Caen, France. Information leaflets were given to the patients regarding use of their biological samples for research. Patients were invited to contact a representative of the tumor bank if they wished to refuse this use. In our series no refusal was recorded. The study was approved by the local ethics committee (Comité de Protection des Personnes Nord Ouest III) on 7th March 2009. This project (IARC reference 09–13) was cleared after ethical review by the IARC (International Agency for Research on Cancer) Institutional Review Board on 29th September 2009. The data were analyzed anonymously.
This study was conducted on 2 types of tumor, hepatocellular carcinoma (HCC) and lung carcinoma (LC), selected for their incidence, their vascularization and their cellular homogeneity. For each tumor type, three patients were selected (HCC4, HCC5, HCC6, LC2, LC5 and LC6). HCC is the most common liver cancer and surgery is the standard treatment. Poorly to moderately differentiated HCC tumors, with large axis superior or equal to 3 cm, were selected. Lung carcinoma is one of the most frequent cancers in France. Moderately differentiated squamous cell carcinoma tumors, with large axis superior to 3 cm, were selected. Age at diagnosis of patients with HCC was [60–80 years] and [55–76 years] for patients with LC.
Each tumor was processed by a pathologist within the operating theatre directly after resection and divided into several samples measuring approximately 0.125 cm3 (0.5×0.5×0.5 cm). For each experimental condition, a sample was extracted from the center and from the periphery of the tumor, and each of these was placed in a cryotube, maintained at room temperature and then frozen in liquid nitrogen at different times: 5 minutes (t5, reference time), 15 minutes (t15), 30 minutes (t30) and 120 minutes (t120). The 48 tumor samples (24 HCC samples from 3 patients and 8 experimental conditions and 24 LC samples from 3 patients and the same 8 experimental conditions) were then stored at −80°C until extraction and analysis. The details of samples and experimental conditions are summarized in
For each sample, RNA extraction was performed using the NucleoSpin RNA II kit (Macherey-Nagel) according to the manufacturer’s instructions. RNA concentration and RNA purity were evaluated with the Nanodrop® (Thermo Scientific). RNA integrity and quantification were characterized by measuring the 28 s/18 s rRNA ratio and RIN (RNA Integrity Number) using the Agilent 2100 bioanalyzer instrument and the RNA 6000 Nano kit. The RIN software classifies the integrity of eukaryotic total RNAs on a scale of 1 to 10, from most to least degraded.
Genome-wide gene expression profiling analysis was performed on Illumina HumanHT-12 v3 Expression BeadChips, providing a coverage of more than 24,000 annotated genes (48,783 probes corresponding to 1 to 3 probes per gene) including well characterized genes and splice variants. Candidate probe sequences included on the HumanHT-12 v3 Expression BeadChip derive from the National Center for Biotechnology Information Reference Sequence (NCBI) RefSeq (Build 36.2, Rel 22) and the UniGene (Build 199) databases. Using the Illumina TotalPrep RNA Amplification Kit (Ambion®), 500 ng of extracted RNAs were converted to cDNAs and subsequent biotin labeled single-stranded cRNAs. The distribution of homogeneous
RNA quality was tested for association with time to cryopreservation in two ways, before the data were normalized. First, the continuous RIN values were tested for association with time by linear regression. Second, the number of genes detected in each sample was analyzed by Poisson regression. Detection of an RNA by a probe was defined by significantly (p<0.01) higher intensity than both the gene- and sample-specific mean of negative control probes. Array and array position were included as random effects, patient ID, center versus peripheral source, RIN and tumor type as fixed effects in each of these regressions.
The ratio of centiles P95/P05 calculated for each sample prior to normalisation reflects the overall strength of the signal compared to the background. We considered ratios above 10 to be acceptable.
Bead-set standard deviations were observed to be approximately proportional to mean expression levels for each probe, suggesting the data should be log-transformed. The log-transformed data appeared to be homoskedastic.
For each sample, scatter plots were generated to compare the t5 tissue to the corresponding tissue frozen after delay (t15, t30 and t120), using the log scale.
We also calculated the Pearson correlation coefficient between log-expression levels of the central and peripheral samples, for each tumor and each time to cryopreservation.
We first tested for a trend in expression over all genes. The transformed expression levels were averaged with inverse-variance weighting to obtain a minimum variance estimate of the mean log expression level (log of the geometric mean) over all probes.
The data were modelled via a random-intercept linear mixed regression model, with over-all rates of change estimated as percent-change per hour. To attempt to detect any non-linear behaviour, a term quadratic in time was tested. The data were also stratified by tumor type, to examine if there were detectable differences between tumor sites, and by peripheral versus central origin of the sample. In order to investigate whether expression changes were more pronounced in genes with higher or lower levels of expression, the above analyses were repeated after restricting to those genes in the highest or lowest 5% of geometric mean expression across all time points.
Regression analysis of time course expression data for individual genes was performed using BRB-ArrayTools software v4.2 developed by Dr Richard Simon and BRB-ArrayTools Development Team. Data were log-transformed and quantile normalized without background subtraction as described above, but with the exclusion of any probe showing excess dispersion (defined by more than 80% of individual probe values differing from the median by more than 1.5-fold). The BRB-ArrayTools time course analysis model fits the same quadratic model as used over-all, with null hypothesis that both linear and quadratic terms were zero. Genes for which this hypothesis was rejected were identified. The tests were performed at a false discovery rate (FDR) threshold of 0.05
Unsupervised hierarchical clustering of samples was performed using both Genome Studio V2010.2 and BRB-ArrayTools software v4.2.
Twenty-one genes that have been previously reported as deregulated at an early stage after surgery were specifically selected for further analysis in the experimental dataset
Finally, in order to examine whether some reported biologically significant HCC genes could also be found deregulated in a context of warm ischemia associated with tumor freezing delay, we restricted our analysis of the rates of expression changes in 34 HCC genes reported to be deregulated in more than 4 studies in the public Liverome database
We thank Pr Christian Chabannon and Tumorothèque Caen Basse Normandie for providing the tissue samples. We also thank Dr Maimuna Mendy, Dr Fabienne Lesueur and Dr James McKay for their support and useful discussions.