Literature DB >> 29925435

Tumor analysis: freeze-thawing cycle of triple-negative breast cancer cells alters tumor CD24/CD44 profiles and the percentage of tumor-infiltrating immune cells.

Matthieu Le Gallo1,2, Thibault de la Motte Rouge1,2, Amanda Poissonnier1,2,3, Vincent Lavoué1,2,4, Patrick Tas1,2,4, Jean Leveque1,2,4, Florence Godey1,2, Patrick Legembre5,6.   

Abstract

OBJECTIVE: The use of novel methods to characterize living tumor cells relies on well-conceived biobanks. Herein, we raised the question of whether the composition of fresh and freeze/thawed dissociated tumor samples is comparable in terms of quantitative and qualitative profiling.
RESULTS: Breast cancer is a heterogeneous disease, encompassing luminal A and B, basal/triple-negative breast cancer (TNBC), and ERBB2-like tumors. We examined living cells dissociated from TNBC and found that a classical freeze/thaw protocol leads to a marked reduction in the number of CD45-CD44LowCD24Low tumor cells. This, in turn, changed the percentage of tumor cells with certain CD44/CD24 expression patterns and changed the percentage of tumor-infiltrating immune cells. These cryopreservation-driven alterations in cellular phenotype make it impossible to compare fresh and frozen samples from the same patient directly. Moreover, the freeze/thaw process changed the transcriptomic signatures of triple-negative cancer stem cells in such a manner that hierarchical clustering no longer ranked them according to expected inter-individual differences. Overall, this study suggests that all analyses of living tumor cells should be conducted only using freshly dissociated tumors if we are to generate a robust scoring system for prognostic/predictive markers.

Entities:  

Keywords:  Frozen; Immune infiltrate; Living biobank; Triple negative breast cancer

Mesh:

Substances:

Year:  2018        PMID: 29925435      PMCID: PMC6011598          DOI: 10.1186/s13104-018-3504-5

Source DB:  PubMed          Journal:  BMC Res Notes        ISSN: 1756-0500


Introduction

Personalized medicine necessitates identification of biomarkers that are accurate, sensitive, and disease-specific. This is particularly true for cancer, which is a disease characterized by marked tumor heterogeneity. Although thousands of biomarkers have been described, few have translated successfully to the clinic. Biomarkers are crucial not only for tumor diagnosis and prognosis, but also for better stratification of patients, which reduces concerns related to over-treatment of indolent cancers and under-treatment of aggressive cancers. Among women, breast cancer is the most common cause of cancer, and the second leading cause of cancer death after lung cancer [1, 2]. Triple-negative breast cancer (TNBC) represents 15–20% of all breast cancer cases. It comprises a heterogeneous tumor subset that lacks expression of estrogen and progesterone receptors and does not overexpress HER2. As a group, TNBCs are aggressive and prognoses and clinical outcomes are poor [1]. The low percentage of tumor-infiltrating lymphocytes (TILs) [3] and accumulation of cancer stem cells (CSCs) [4] mean that TNBCs often show drug resistance, recurrence, and metastasis [5, 6]. Therefore, exhaustive characterization of tumor-infiltrating immune cells and tumor cell heterogeneity is crucial if we are to identify new prognostic and predictive biomarkers and therapeutic targets. Most, if not all, genomic and proteomic studies are performed using flash-frozen tumor tissues; hopefully, such tissues will yield transcriptomic and/or protein signatures that can be used to develop a personalized medicine approach. However, the complexity and heterogeneity of TNBC tumors mean that clinicians have yet to achieve this goal. We hypothesized that the use of novel methods to exhaustively characterize dissociated and living tumor cells may move us a step closer. For instance, multiparameter flow cytometry (i.e., cyTOF, single cell sequencing) can reveal detailed signatures that are unique to cells inside a tumor (e.g., immune, stromal, and tumor cells) and, by so-doing, identify new markers associated with relapse, and/or targets for a new generation of therapeutic drugs. Accordingly, laboratories will come to rely on well-conceived biobanks to develop dissociated cancer tissues.

Main text

Methods and patients

Patients

Patients (n = 15) were diagnosed and treated at the Centre Eugène Marquis between 2017 and 2018. None showed evidence of relapse at the time of diagnosis and none received chemotherapy, endocrine therapy, or radiation therapy prior to surgery. Treatment decisions and follow-up processes were based solely on international recommendations.

Tumor samples

Triple-negative breast tumors were collected by a pathologist after resection by a surgeon and immediately placed in RPMI medium. The dissociation process initiated within 2 h after surgical resection. Tumors were dissociated using the tumor dissociation kit (Human) (Miltenyi Biotec GmbH), which is optimized to deliver a high yield of tumor cells and TILs while preserving important cell surface epitopes. TNBC pieces were weighed and cut into small pieces (< 2 mm3), which were then treated with dissociation kit (Human) in a gentleMACS Dissociator, according to the manufacturer’s recommendations. Briefly, tumors were mechanically dissociated in the gentleMACS dissociator for 36 s and then incubated at 37 °C for 30 min under continuous rotation. Next, a cycle of mechanical–chemical–mechanical dissociation was performed and dissociated cells were resuspended in RPMI. Macroscopic pieces were removed using a Corning® cell strainer (70 μm). Tumor cells were then washed twice in RPMI (20 ml) and counted using a hemocytometer.

Flow cytometry

Tumor cells (50,000 cells) were suspended in PBS supplemented with 2% BSA, 2% FCS, and FcR block (Miltenyi Biotec GmbH) at 4 °C for 20 min. Cells were then stained for 30 min at 4 °C with anti-CD24 PE (clone ML5, BD Biosciences), anti-CD44 APC (clone G44-26, BD Biosciences), and anti-CD45 PE-Vio770 (clone 5B1, Miltenyi Biotec GmbH) antibodies. Isotypic antibodies were used as a control for each fluorochrome (obtained from the same manufacturers). Cells were then washed twice in PBS supplemented with 2% BSA and 2% FCS and resuspended in PBS. To assess cell viability, cells were incubated with 7-AAD (BD Biosciences) for 10 min prior to cytometry analysis. Data were acquired using a Novocyte cytometer (ACEA Biosciences) and analyzed using FlowJo or Novoexpress software.

Cryopreservation and storage

The freezing process was carried out using standardized freezing procedures, following the guidelines issued by the “Haute Autorité de Santé”, the government agency regulating the French healthcare system, for human tissue and cell samples biobanking. Freshly dissociated tumor cells were frozen in 1 ml of human serum albumin (HSA) Vialebex® (LFB BIOMEDICAMENTS, Les Ullis, France) supplemented with 10% DMSO (Sigma Aldrich). Each vial contained 2–5 × 106 cells (depending on the tumor dissociation yield). Briefly, freshly dissociated cell pellets were resuspended in 500 µl of pre-cooled HSA. Then, 500 µl of pre-cooled HSA containing 20% DMSO solution was added drop by drop to the cell suspension. The suspension was then homogenized and transferred to a cryotube. To ensure a standardized and controlled rate of freezing (− 1 °C/min), cryotubes were first placed in a CoolCell® LX Cell Freezing Container (BioCision) at − 80 °C. After 24 h, cells were transferred to a freezer set at − 150 °C. All freezers were monitored; no critical temperature variations were recorded during storage. For thawing, cells were placed in a water bath at 37 °C and then transferred to RPMI (40 ml) at RT to allow complete thawing. After a second wash in 20 ml of RPMI, the cells were counted using a hemocytometer. Cell viability was assessed using Trypan Blue.

Generation of mammospheres

Matched freshly dissociated and thawed tumor cells from the same patient were treated in the same way. Cells (1.5 × 106) were seeded in 2 ml of Mammocult medium (StemCell Technologies) supplemented with heparin (4 µg/ml; Stem Cell Technologies), hydrocortisone (480 ng/ml; Stem Cell Technologies), penicillin (100 units/ml), and streptomycin (100 µg/ml) (Gibco) in ultra-low binding 6-well plates (Corning). After 15 days at 37 °C/5% CO2, mammospheres were collected and passed through a cell strainer (40 µm) to separate suspended cells from mammospheres. Next, mammospheres were dissociated with trypsin/EDTA (0.05% trypsin; Gibco) for 5 min. Dissociated cells were washed twice in PBS and stained as described above. RNA extracted from mammospheres using the NucleoSpin RNA XS extraction kit (Macherey–Nagel), according to the manufacturer’s recommendations.

Microarray analysis

RNA quality was assessed using an RNA6000 nano chip (Agilent). For each condition (fresh or freeze/thawed), 9 ng of RNA was reverse transcribed using the Ovation PicoSL WTA System V2 (Nugen, Leek, The Netherlands). Fragmented cDNAs were hybridized to GeneChip Human Gene 2.0 ST microarrays (Affymetrix), which were scanned by a GeneChip Scanner 3000 7G (Affymetrix). Raw data and quality-control metrics were generated using Expression Console software (Affymetrix). Probes were mapped using Brainarray V23 CDF files (http://brainarray.mbni.med.umich.edu/) and normalized by robust multi-array averaging with R software. Statistical analyses were performed using the limma R package; genes showing a twofold change in expression and a P value of 0.05 were considered significant. Gene Ontology terms enrichment analyses were performed using the ToppGene Suite (Chen J, Bardes EE, Aronow BJ, Jegga AG 2009. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Research 10.1093/nar/gkp427). Hierarchical clustering was performed using Morpheus Matrix visualization and analysis software (https://software.broadinstitute.org/morpheus/).

Accession numbers

Raw and normalized data have been deposited to the GEO database accession ID GSE114359.

Results

We raised the question of whether the composition of fresh and freeze/thawed samples from the same patients was sufficiently similar in terms of quantitative and qualitative profiling and asked whether both could be used to investigate TILs (CD45-positive cells) and the phenotype (CD45negCD24LowCD44High) of CSCs. To address this, we used multiparameter flow cytometry to compare the phenotypes of 15 freshly dissociated TNBCs and their matched cryopreserved counterparts. Briefly, to minimize mortality due to sample processing time, the pathologist collected samples in RPMI medium and the dissociation process was started within a maximum of 120 min after surgical resection. Fresh TNBCs were dissociated using the Miltenyi Tumor dissociation kit (Human); 0.8 million cells were analyzed. In parallel, we cultured 1.5 × 106 cells in serum-free medium in low-attachment plates to isolate spheroids, which are enriched in CSCs (see “Methods and patients” section). The remaining cells were frozen using a standardized procedure following the French “Haute Authorité de Santé” guidelines to biobanks and kept for 2 weeks in a freezer at − 150 °C. The cells were then thawed and analyzed immediately by flow cytometry; spheroids were also generated. Total RNA was isolated from fresh and thawed spheroids and the transcriptomic signatures compared (see Additional file 1: Figure S1). Cell viability was assessed by 7-AAD staining. The results showed that the freezing/thawing procedure for dissociated TNBC cells yielded 25.4% dead cells, with five dissociated tumor samples showing a drop in absolute numbers of 10% or less, and four tumors showing more than 50% dead cells (Fig. 1a) after thawing. The reasons for this were unknown since all samples were treated in a similar manner. Strikingly, most of the cells that died following the freezing/thawing process originated from the CD45− population (Fig. 1b), which in turn impacted the percentage of tumor-infiltrating leukocytes (i.e., CD45+ cells; Fig. 1b). Of note, the percentage of tumor cells with a CD45−CD24LowCD44Low phenotype fell markedly after thawing (Fig. 1c, d). Two hypotheses may explain these losses: either these cells are highly sensitive to freezing/thawing, or they are reprogrammed to exhibit a different CD24/CD44 phenotype. The percentage of dead cells observed in Fig. 1a and the short delay between thawing and immunophenotyping are not compatible with the latter hypothesis, strongly suggesting that CD45−CD24LowCD44Low cells do not tolerate the freeze/thaw process well. Loss of this population resulted mainly in artificial enrichment of the CD24HighCD44Low cell population and, albeit to a lesser extent, the CD24LowCD44High and CD24HighCD44High populations (Fig. 1d). Because the CD24LowCD44High lineage was enriched after the freeze/thaw cycle, and this cell subset seems to correspond to stem/progenitor cancer cells [5, 6], we next wondered whether the transcriptomic signature of these enriched CSCs varied after the freezing/thawing process. To compare the transcriptome of CSCs derived from fresh and frozen samples, we used stringent cell culture procedures to culture spheroid cells. Compared with the bulk of dissociated tumor cells, spheroids were enriched for CD24LowCD44High cells (Fig. 2a). Next, we examined freshly dissociated TNBC cells from three different TNBC patients, along with the cells from the same patients frozen immediately after dissociation and cryopreserved for 15 days before thawing. Both sets of cells were cultured for 15 days in serum-free medium in low-adherence plates to select CSCs (CD44HighCD24Low) and the transcriptomic signatures were compared (Fig. 2b). Unexpectedly, unsupervised hierarchical clustering of these tumor cells ranked tumors according to whether they had been subjected to freeze/thaw treatment and not according to their inter-individual differences; this shows that freeze/thawing the cells had a deleterious effect in their transcriptomic signatures. Gene Ontology terms enrichment analysis revealed that the freeze/thaw process drove deregulation of 274 genes (-fold change > 2, P < 0.05); most of these were associated to epithelial to mesenchymal transition (EMT), tissues morphogenesis and cell junction and adhesion (Fig. 2c). Of these, 224 genes (82%) in freeze/thaw spheroids were significantly down-regulated when compared with those in fresh spheroids( Table 1). Because these genes play a role in organization of cell junctions and are lost during freeze/thaw of CSCs, we suggest that cryopreservation either spares breast cancer stem cells showing the most dedifferentiated mesenchymal profiles or favors EMT reprogramming (Fig. 2c and Additional file 2: Table S1).
Fig. 1

Flow cytometry analysis of freshly dissociated and frozen tumor samples. a Left panels viability of fresh (upper) or freeze/thawed (lower) tumor cells was assessed using the non-permeant fluorescent DNA marker 7-ADD. The gate shows the percentage of living cells. Right panels percentage of dead cells observed after thawing 15 TNBCs. b Leucocytes and non-immune cells (mainly tumor cells) within the dissociated tumor cell population were analyzed by flow cytometry after staining with an anti-CD45 mAb. The percentage of CD45+ (leukocytes) and CD45− (tumor cells) before and after freezing is depicted. Statistical differences were evaluated using the Mann–Whitney U test. c Two examples of fresh and freeze/thawed dissociated tumor cells. Expression of CD44 and CD24 by CD45− tumor cells was evaluated. d Comparison of the proportion of CD45− tumor cells showing CD24 and CD44 before and after the freeze/thaw process. P values were calculated using a paired Student’s t test (n = 15). Density plot showing the percentage of living tumor cells expressing CD24 and CD44 before and after cryopreservation

Fig. 2

Transcriptomic signatures of fresh and freeze/thawed CSCs. a Flow cytometry analysis of freshly dissociated tumor cells and spheroids derived from the tumor after 15 days of culture in low serum/low adherence conditions. The dot plot is representative of data obtained from all 15 tumors. b Heatmap showing RNA expression by spheroids derived from fresh or frozen tumor cells from three different patients. Hierarchical clustering was performed on Robust Multi-Array Average (rma)-normalized expression data using the One minus Pearson’s correlation method. c Overall, 274 genes showed differential expression in spheroids isolated from fresh and frozen tumors. Network representation of GO terms (biological process) significantly enriched in the 224 genes downregulated in freeze/thawed mammospheres as compared to fresh ones. Similar go terms were spatially clustered and colors represent corrected p-values

Table 1

Transcriptomic comparison between fresh TNBC cells and their freeze/thawed counterparts

ProbeIdGene namelogFCFCtP valueAdj. P valBDESCENTREZIDENSEMBLBT133_freshBT134_freshBT138_freshBT133_thawedBT134_thawedBT138_thawed
10216_atPRG42.977.812.570.040.85− 4.41Proteoglycan 410216.00ENSG000001166905.183.845.888.735.419.64
664613_atMIR544A2.465.484.280.000.85− 4.29MicroRNA 544a664613.00ENSG000002075874.132.412.944.706.146.01
767568_atSNORD113-82.425.352.560.040.85− 4.41Small nucleolar RNA, C/D box 113-8767568.00ENSG000002003673.963.093.014.145.277.91
407000_atMIR218-12.314.952.710.030.85− 4.40MicroRNA 218-1407000.00ENSG000002077324.053.623.134.195.867.67
102723604_atLOC1027236042.144.423.040.020.85− 4.37Uncharacterized LOC102723604102723604.00ENSG000002589285.043.475.055.376.987.63
105369519_atLOC1053695192.084.222.520.040.85− 4.42Uncharacterized LOC105369519105369519.00NA2.963.413.463.785.097.19
2633_atGBP12.064.172.620.040.85− 4.41Guanylate binding protein 12633.00ENSG000001172285.853.163.906.796.985.32
26822_atSNORD14A2.054.133.540.010.85− 4.33Small nucleolar RNA, C/D box 14A26822.00ENSG000002720345.953.944.026.687.056.30
767579_atSNORD114-31.993.972.870.030.85− 4.38Small nucleolar RNA, C/D box 114-3767579.00ENSG000002018394.052.302.545.675.513.68
342908_atZNF4041.783.444.010.010.85− 4.30Zinc finger protein 404342908.00ENSG000001762225.565.294.227.186.187.07
107983995_atNA1.653.143.440.010.85− 4.34NANANA3.953.773.775.114.746.58
767606_atSNORD114-261.643.113.430.010.85− 4.34Small nucleolar RNA, C/D box 114-26767606.00ENSG000002004133.982.442.475.024.204.59
664612_atMIR5391.572.983.380.010.85− 4.34MicroRNA 539664612.00ENSG000002025607.157.177.227.778.849.66
54518_atAPBB1IP1.572.972.520.040.85− 4.42Amyloid beta precursor protein binding family B member 1 interacting protein54518.00ENSG000000774206.123.544.646.296.316.39
100033820_atSNORD116-281.532.882.460.050.85− 4.42Small nucleolar RNA, C/D box 116-28100033820.00ENSG000002781234.082.253.174.174.155.75
8404_atSPARCL11.482.782.650.040.85− 4.40SPARC like 18404.00ENSG000001525836.894.745.717.737.096.94
100289635_atZNF6051.452.733.650.010.85− 4.32Zinc finger protein 605100289635.00ENSG000001964586.094.605.457.066.566.86
105376805_atLOC1053768051.412.663.090.020.85− 4.36Uncharacterized LOC105376805105376805.00ENSG000002381424.474.243.964.626.276.01
100128002_atLOC1001280021.412.662.520.040.85− 4.42Uncharacterized LOC100128002100128002.00NA3.363.723.034.186.054.11
158131_atOR1Q11.362.572.490.050.85− 4.42Olfactory receptor family 1 subfamily Q member 1158131.00ENSG000001652023.882.843.535.663.695.00
100616251_atMIR15871.362.572.600.040.85− 4.41MicroRNA 1587100616251.00ENSG000002639724.554.604.255.025.447.03
106481206_atRNU6-94P1.312.472.590.040.85− 4.41RNA, U6 small nuclear 94, pseudogene106481206.00NA3.973.613.315.373.825.62
57562_atCEP1261.302.464.040.010.85− 4.30Centrosomal protein 12657562.00ENSG000001103184.803.934.716.195.705.45
5170_atPDPK11.292.442.470.050.85− 4.423-Phosphoinositide dependent protein kinase 15170.00ENSG000001409924.173.493.676.095.044.07
26787_atSNORD611.282.433.600.010.85− 4.33Small nucleolar RNA, C/D box 6126787.00ENSG000002069792.632.183.343.574.134.28
26770_atSNORD791.232.352.910.030.85− 4.38Small nucleolar RNA, C/D box 7926770.00NA5.334.325.016.875.945.55
90499_atFAM95A1.222.322.490.050.85− 4.42Family with sequence similarity 95 member A90499.00NA3.933.533.654.234.496.04
100126355_atMIR365A1.202.302.600.040.85− 4.41MicroRNA 365a100126355.00ENSG000001991305.263.964.056.124.975.78
441728_atLOC4417281.202.293.290.020.85− 4.35Golgin-like pseudogene441728.00NA3.293.393.154.383.825.21
5228_atPGF1.192.282.780.030.85− 4.39Placental growth factor5228.00ENSG000001196303.933.805.044.975.995.36
619564_atSNORD721.172.243.080.020.85− 4.36Small nucleolar RNA, C/D box 72619564.00ENSG000002122962.291.801.892.943.952.60
168667_atBMPER1.152.215.790.000.85− 4.24BMP binding endothelial regulator168667.00ENSG000001646194.904.784.606.055.646.02
64853_atAIDA1.152.212.510.050.85− 4.42Axin interactor, dorsalization associated64853.00ENSG000001860634.412.583.684.844.634.64
100462981_atMTRNR2L21.152.212.640.040.85− 4.40MT-RNR2-like 2100462981.00ENSG000002690282.952.613.153.893.314.95
100379296_atRNY4P131.142.203.050.020.85− 4.37RNA, Ro-associated Y4 pseudogene 13100379296.00NA3.423.023.474.064.025.24
100873920_atNHS-AS11.122.174.520.000.85− 4.28NHS antisense RNA 1100873920.00ENSG000002300203.292.943.063.814.574.27
340527_atNHSL21.112.163.270.020.85− 4.35NHS like 2340527.00ENSG000002041314.914.014.045.865.055.38
7857_atSCG21.102.153.010.020.85− 4.37Secretogranin II7857.00ENSG000001719514.113.414.485.644.834.84
503582_atARGFX1.102.142.840.030.85− 4.39Arginine-fifty homeobox503582.00ENSG000001861032.982.692.913.903.244.74
574494_atMIR521-11.102.142.910.030.85− 4.38MicroRNA 521-1574494.00ENSG000002076343.233.033.583.924.035.17
26022_atTMEM981.082.124.330.000.85− 4.29Transmembrane protein 9826022.00ENSG000000060424.103.423.554.734.575.03
343521_atTCTEX1D41.082.115.330.000.85− 4.25Tctex1 domain containing 4343521.00ENSG000001883964.984.864.695.836.195.73
4919_atROR11.072.102.780.030.85− 4.39Receptor tyrosine kinase like orphan receptor 14919.00ENSG000001854834.274.195.126.245.255.30
440910_atLOC4409101.072.095.580.000.85− 4.24Uncharacterized LOC440910440910.00ENSG000002314314.113.733.775.054.784.98
105373893_atLOC1053738931.062.093.870.010.85− 4.31Uncharacterized LOC105373893105373893.00NA2.543.113.033.604.363.91
105375249_atLOC1053752491.062.082.920.030.85− 4.38Uncharacterized LOC105375249105375249.00NA3.062.602.853.124.334.23
84973_atSNHG71.032.052.970.020.85− 4.37Small nucleolar RNA host gene 784973.00ENSG000002330164.844.294.025.624.825.81
400728_atFAM87B1.032.043.330.020.85− 4.35Family with sequence similarity 87 member B400728.00ENSG000001777574.654.114.015.484.765.63
283685_atGOLGA6L21.032.042.490.050.85− 4.42Golgin A6 family-like 2283685.00ENSG000001744504.224.064.064.524.846.07
9376_atSLC22A81.012.023.320.020.85− 4.35Solute carrier family 22 member 89376.00ENSG000001494523.303.433.294.093.975.00
9787_atDLGAP5− 1.00− 2.00− 2.660.040.85− 4.40DLG associated protein 59787.00ENSG000001267875.915.434.604.594.403.95
5709_atPSMD3− 1.00− 2.00− 3.120.020.85− 4.36Proteasome 26S subunit, non-ATPase 35709.00ENSG000001083446.236.907.035.886.055.23
5154_atPDGFA− 1.00− 2.00− 3.420.010.85− 4.34Platelet derived growth factor subunit A5154.00ENSG000001974615.016.025.814.544.604.69
163050_atZNF564− 1.00− 2.00− 3.740.010.85− 4.32Zinc finger protein 564163050.00ENSG000002497095.696.306.204.675.305.22
7405_atUVRAG− 1.00− 2.01− 2.590.040.85− 4.41UV radiation resistance associated7405.00ENSG000001983826.497.017.356.446.175.23
283624_atLINC00641− 1.01− 2.01− 3.150.020.85− 4.36Long intergenic non-protein coding RNA 641283624.00ENSG000002584415.996.256.365.415.634.55
3931_atLCAT− 1.01− 2.01− 5.270.000.85− 4.25Lecithin-cholesterol acyltransferase3931.00ENSG000002133985.495.735.804.834.714.45
1318_atSLC31A2− 1.01− 2.01− 2.960.020.85− 4.38Solute carrier family 31 member 21318.00ENSG000001368675.636.516.835.565.175.21
4771_atNF2− 1.01− 2.02− 2.700.030.85− 4.40Neurofibromin 24771.00ENSG000001865755.546.075.835.225.184.00
54947_atLPCAT2− 1.01− 2.02− 2.510.050.85− 4.42Lysophosphatidylcholine acyltransferase 254947.00ENSG000000872535.566.637.045.195.345.67
1978_atEIF4EBP1− 1.01− 2.02− 2.980.020.85− 4.37Eukaryotic translation initiation factor 4E binding protein 11978.00ENSG000001878405.946.916.675.685.795.01
407043_atMIR7-1− 1.02− 2.02− 3.210.020.85− 4.36MicroRNA 7-1407043.00ENSG000002841794.775.465.674.543.864.46
8893_atEIF2B5− 1.02− 2.03− 2.440.050.85− 4.42Eukaryotic translation initiation factor 2B subunit epsilon8893.00ENSG000001451916.737.466.766.486.285.13
205564_atSENP5− 1.02− 2.03− 2.450.050.85− 4.42SUMO1/sentrin specific peptidase 5205564.00ENSG000001192316.726.757.156.536.064.98
2305_atFOXM1− 1.02− 2.03− 2.840.030.85− 4.39Forkhead box M12305.00ENSG000001112065.615.605.154.644.963.71
113263_atGLCCI1− 1.02− 2.03− 2.650.040.85− 4.40Glucocorticoid induced 1113263.00ENSG000001064156.156.576.536.115.414.66
2030_atSLC29A1− 1.03− 2.04− 2.600.040.85− 4.41Solute carrier family 29 member 1 (Augustine blood group)2030.00ENSG000001127595.226.506.645.025.065.20
6660_atSOX5− 1.03− 2.04− 4.880.000.85− 4.27SRY-box 56660.00ENSG000001345324.323.944.543.133.333.24
144100_atPLEKHA7− 1.03− 2.04− 2.570.040.85− 4.41Pleckstrin homology domain containing A7144100.00ENSG000001666893.855.384.813.673.853.44
23248_atRPRD2− 1.03− 2.04− 2.450.050.85− 4.42Regulation of nuclear pre-mRNA domain containing 223248.00ENSG000001631256.947.367.596.806.625.38
26273_atFBXO3− 1.03− 2.05− 3.220.020.85− 4.35F-box protein 326273.00ENSG000001104295.286.106.405.064.694.94
57037_atANKMY2− 1.03− 2.05− 2.840.030.85− 4.39Ankyrin repeat and MYND domain containing 257037.00ENSG000001065245.456.125.335.194.554.06
9701_atPPP6R2− 1.04− 2.05− 2.880.030.85− 4.38Protein phosphatase 6 regulatory subunit 29701.00ENSG000001002395.035.515.224.644.563.46
10564_atARFGEF2− 1.04− 2.05− 2.460.050.85− 4.42ADP ribosylation factor guanine nucleotide exchange factor 210564.00ENSG000001241987.107.707.106.756.665.37
79005_atSCNM1− 1.04− 2.06− 3.370.010.85− 4.34Sodium channel modifier 179005.00ENSG000001631564.534.805.404.273.813.54
6558_atSLC12A2− 1.04− 2.06− 2.880.030.85− 4.38Solute carrier family 12 member 26558.00ENSG000000646517.478.247.427.156.776.09
377677_atCA13− 1.05− 2.06− 3.020.020.85− 4.37Carbonic anhydrase 13377677.00ENSG000001850153.824.884.773.823.323.19
10678_atB3GNT2− 1.05− 2.07− 5.230.000.85− 4.25UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 210678.00ENSG000001703406.957.177.396.295.946.15
6641_atSNTB1− 1.05− 2.07− 2.910.030.85− 4.38syntrophin beta 16641.00ENSG000001721646.237.027.185.996.095.19
51050_atPI15− 1.06− 2.08− 3.280.020.85− 4.35Peptidase inhibitor 1551050.00ENSG000001375584.703.743.812.772.963.35
222068_atTMED4− 1.06− 2.08− 2.620.040.85− 4.41Transmembrane p24 trafficking protein 4222068.00ENSG000001586046.226.926.846.175.824.81
1719_atDHFR− 1.06− 2.08− 2.820.030.85− 4.39Dihydrofolate reductase1719.00ENSG000002287164.725.345.884.734.223.81
51203_atNUSAP1− 1.06− 2.09− 2.700.030.85− 4.40Nucleolar and spindle associated protein 151203.00ENSG000001378046.155.525.254.535.233.97
6217_atRPS16− 1.06− 2.09− 2.750.030.85− 4.39Ribosomal protein S166217.00ENSG000001051938.539.009.288.507.947.17
28960_atDCPS− 1.07− 2.10− 2.510.050.85− 4.42Decapping enzyme, scavenger28960.00ENSG000001100635.106.585.594.755.064.25
9929_atJOSD1− 1.07− 2.10− 2.520.040.85− 4.42Josephin domain containing 19929.00ENSG000001002216.817.417.056.935.645.48
54534_atMRPL50− 1.07− 2.10− 2.900.030.85− 4.38Mitochondrial ribosomal protein L5054534.00ENSG000001368975.556.486.675.085.614.80
51706_atCYB5R1− 1.08− 2.11− 2.670.040.85− 4.40Cytochrome b5 reductase 151706.00ENSG000001593485.425.826.364.625.514.24
6615_atSNAI1− 1.08− 2.11− 4.820.000.85− 4.27Snail family transcriptional repressor 16615.00ENSG000001242165.555.855.804.364.624.99
1063_atCENPF− 1.08− 2.12− 3.030.020.85− 4.37Centromere protein F1063.00ENSG000001177247.156.276.935.826.155.12
5832_atALDH18A1− 1.08− 2.12− 2.730.030.85− 4.40Aldehyde dehydrogenase 18 family member A15832.00ENSG000000595736.947.267.206.516.485.16
116092_atDNTTIP1− 1.09− 2.12− 2.570.040.85− 4.41Deoxynucleotidyltransferase terminal interacting protein 1116092.00ENSG000001014575.896.686.595.615.824.47
6241_atRRM2− 1.10− 2.14− 2.830.030.85− 4.39Ribonucleotide reductase regulatory subunit M26241.00ENSG000001718487.056.785.625.475.245.44
54764_atZRANB1− 1.10− 2.14− 3.010.020.85− 4.37Zinc finger RANBP2-type containing 154764.00ENSG000000199957.418.667.867.186.916.54
50_atACO2− 1.10− 2.14− 2.430.050.85− 4.43Aconitase 250.00ENSG000001004124.956.145.364.015.183.97
84546_atSNORD35B− 1.10− 2.15− 2.560.040.85− 4.41Small nucleolar RNA, C/D box 35B84546.00ENSG000002005305.695.746.574.425.714.55
677845_atSNORA79− 1.11− 2.15− 2.500.050.85− 4.42Small nucleolar RNA, H/ACA box 79677845.00ENSG000002213034.996.705.514.764.704.42
54700_atRRN3− 1.11− 2.16− 2.520.040.85− 4.42RRN3 homolog, RNA polymerase I transcription factor54700.00ENSG00000085721, ENSG000002784945.795.485.915.614.194.05
56907_atSPIRE1− 1.11− 2.17− 3.210.020.85− 4.35Spire type actin nucleation factor 156907.00ENSG000001342786.987.967.506.636.635.83
1739_atDLG1− 1.11− 2.17− 3.420.010.85− 4.34Discs large MAGUK scaffold protein 11739.00ENSG000000757116.807.237.186.516.015.36
9991_atPTBP3− 1.12− 2.17− 2.960.020.85− 4.38Polypyrimidine tract binding protein 39991.00ENSG000001193148.299.038.697.977.886.81
54512_atEXOSC4− 1.12− 2.17− 2.890.030.85− 4.38Exosome component 454512.00ENSG000001788966.257.396.945.576.315.35
56910_atSTARD7− 1.12− 2.17− 2.790.030.85− 4.39StAR related lipid transfer domain containing 756910.00ENSG000000840908.178.429.007.597.986.66
5894_atRAF1− 1.12− 2.17− 2.620.040.85− 4.41Raf-1 proto-oncogene, serine/threonine kinase5894.00ENSG000001321557.087.187.556.446.795.22
3099_atHK2− 1.12− 2.18− 2.800.030.85− 4.39Hexokinase 23099.00ENSG000001593996.527.977.186.286.315.72
1017_atCDK2− 1.13− 2.18− 2.600.040.85− 4.41Cyclin dependent kinase 21017.00ENSG000001233746.477.677.785.956.735.86
9388_atLIPG− 1.13− 2.19− 2.820.030.85− 4.39Lipase G, endothelial type9388.00ENSG000001016704.666.095.534.624.323.94
100128398_atLOC100128398− 1.14− 2.20− 2.820.030.85− 4.39Uncharacterized LOC100128398100128398.00ENSG000001765934.704.735.994.234.133.65
8714_atABCC3− 1.14− 2.21− 2.450.050.85− 4.42ATP binding cassette subfamily C member 38714.00ENSG000001088466.347.367.006.036.394.86
64866_atCDCP1− 1.15− 2.22− 2.460.050.85− 4.42CUB domain containing protein 164866.00ENSG000001638147.318.237.107.006.625.57
2673_atGFPT1− 1.15− 2.23− 2.860.030.85− 4.38Glutamine--fructose-6-phosphate transaminase 12673.00ENSG000001983807.658.487.997.487.056.13
6376_atCX3CL1− 1.16− 2.23− 2.480.050.85− 4.42C-X3-C motif chemokine ligand 16376.00ENSG000000062105.366.556.824.745.714.81
116113_atFOXP4− 1.16− 2.24− 3.510.010.85− 4.33Forkhead box P4116113.00ENSG000001371665.236.445.954.534.844.78
203413_atCT83− 1.16− 2.24− 2.490.050.85− 4.42Cancer/testis antigen 83203413.00ENSG000002040194.956.725.254.504.484.45
8503_atPIK3R3− 1.17− 2.25− 2.520.040.85− 4.42Phosphoinositide-3-kinase regulatory subunit 38503.00ENSG00000117461, ENSG000002781396.137.365.605.485.174.93
22992_atKDM2A− 1.17− 2.25− 2.510.040.85− 4.42Lysine demethylase 2A22992.00ENSG000001731208.138.428.277.767.526.03
55707_atNECAP2− 1.18− 2.26− 2.770.030.85− 4.39NECAP endocytosis associated 255707.00ENSG000001571915.936.967.075.216.145.08
55227_atLRRC1− 1.18− 2.26− 2.740.030.85− 4.39Leucine rich repeat containing 155227.00ENSG000001372693.605.284.363.443.213.05
8694_atDGAT1− 1.19− 2.28− 2.630.040.85− 4.40Diacylglycerol O-acyltransferase 18694.00ENSG000001850005.466.597.055.265.544.74
64225_atATL2− 1.19− 2.28− 3.390.010.85− 4.34Atlastin GTPase 264225.00ENSG000001197875.736.756.415.315.424.61
6301_atSARS− 1.19− 2.28− 2.620.040.85− 4.41Seryl-tRNA synthetase6301.00ENSG000000316986.586.907.306.156.304.76
6536_atSLC6A9− 1.19− 2.28− 3.920.010.85− 4.31Solute carrier family 6 member 96536.00ENSG000001965176.256.417.045.305.045.79
4494_atMT1F− 1.19− 2.29− 2.610.040.85− 4.41Metallothionein 1F4494.00ENSG000001984177.007.657.476.326.975.25
54069_atMIS18A− 1.20− 2.29− 3.110.020.85− 4.36MIS18 kinetochore protein A54069.00ENSG000001590556.467.266.786.345.505.06
139231_atFAM199X− 1.20− 2.29− 4.940.000.85− 4.26Family with sequence similarity 199, X-linked139231.00ENSG000001235756.306.706.835.735.355.14
81502_atHM13− 1.20− 2.30− 2.640.040.85− 4.40Histocompatibility minor 1381502.00ENSG000001012946.546.817.206.046.244.66
81875_atISG20L2− 1.20− 2.30− 2.490.050.85− 4.42Interferon stimulated exonuclease gene 20 like 281875.00ENSG000001433196.636.707.716.036.464.94
10040_atTOM1L1− 1.22− 2.32− 2.980.020.85− 4.37Target of myb1 like 1 membrane trafficking protein10040.00ENSG000001411986.217.285.965.745.084.99
440138_atALG11− 1.22− 2.33− 2.470.050.85− 4.42ALG11, alpha-1,2-mannosyltransferase440138.00ENSG000002537106.856.967.146.246.434.62
57669_atEPB41L5− 1.22− 2.33− 3.050.020.85− 4.37Erythrocyte membrane protein band 4.1 like 557669.00ENSG000001151095.946.756.665.735.484.48
2120_atETV6− 1.23− 2.34− 2.750.030.85− 4.39ETS variant 62120.00ENSG000001390836.387.667.036.266.065.07
85461_atTANC1− 1.24− 2.36− 2.810.030.85− 4.39Tetratricopeptide repeat, ankyrin repeat and coiled-coil containing 185461.00ENSG000001151836.367.376.295.865.764.69
127544_atRNF19B− 1.24− 2.36− 3.130.020.85− 4.36Ring finger protein 19B127544.00ENSG000001165145.776.766.275.325.444.33
8351_atHIST1H3D− 1.24− 2.36− 2.710.030.85− 4.40Histone cluster 1 H3 family member d8351.00ENSG000001974096.546.617.436.394.835.65
2762_atGMDS− 1.25− 2.38− 2.970.020.85− 4.37GDP-mannose 4,6-dehydratase2762.00ENSG000001126995.646.196.755.175.434.22
55298_atRNF121− 1.25− 2.39− 2.520.040.85− 4.42Ring finger protein 12155298.00ENSG000001375224.685.175.444.284.492.76
6809_atSTX3− 1.26− 2.39− 3.230.020.85− 4.35Syntaxin 36809.00ENSG000001669006.036.897.185.465.924.94
51514_atDTL− 1.26− 2.39− 3.240.020.85− 4.35Denticleless E3 ubiquitin protein ligase homolog51514.00ENSG000001434766.445.085.584.274.834.22
1903_atS1PR3− 1.26− 2.40− 3.110.020.85− 4.36Sphingosine-1-phosphate receptor 31903.00ENSG000002136945.475.966.545.204.954.04
100288069_atLOC100288069− 1.27− 2.40− 3.580.010.85− 4.33Uncharacterized LOC100288069100288069.00NA5.035.985.564.543.694.55
221079_atARL5B− 1.27− 2.41− 2.860.030.85− 4.38ADP ribosylation factor like GTPase 5B221079.00ENSG000001659976.618.077.896.466.535.78
72_atACTG2− 1.27− 2.41− 3.350.010.85− 4.35Actin, gamma 2, smooth muscle, enteric72.00ENSG000001630173.764.443.102.362.872.25
23636_atNUP62− 1.27− 2.41− 2.970.020.85− 4.37Nucleoporin 6223636.00ENSG000002130245.996.147.015.405.584.35
57154_atSMURF1− 1.28− 2.43− 2.970.020.85− 4.37SMAD specific E3 ubiquitin protein ligase 157154.00ENSG00000198742, ENSG000002841265.856.765.715.325.084.08
9055_atPRC1− 1.28− 2.43− 4.050.010.85− 4.30Protein regulator of cytokinesis 19055.00ENSG000001989017.497.997.116.276.645.83
201475_atRAB12− 1.28− 2.43− 2.880.030.85− 4.38RAB12, member RAS oncogene family201475.00ENSG000002064187.798.277.746.987.295.68
11123_atRCAN3− 1.30− 2.46− 2.970.020.85− 4.37RCAN family member 311123.00ENSG000001176025.677.236.705.675.104.94
10276_atNET1− 1.30− 2.46− 3.190.020.85− 4.36Neuroepithelial cell transforming 110276.00ENSG000001738486.537.726.785.856.145.14
79065_atATG9A− 1.30− 2.47− 3.050.020.85− 4.37Autophagy related 9A79065.00ENSG000001989256.347.117.465.946.124.93
890_atCCNA2− 1.31− 2.47− 2.500.050.85− 4.42cyclin A2890.00ENSG000001453866.916.725.525.405.634.20
100506658_atOCLN− 1.31− 2.48− 3.250.020.85− 4.35occludin100506658.00ENSG00000197822, ENSG000002738143.765.055.213.443.423.24
5209_atPFKFB3− 1.31− 2.49− 2.460.050.85− 4.426-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 35209.00ENSG000001705254.955.486.214.445.013.25
51361_atHOOK1− 1.31− 2.49− 2.650.040.85− 4.40Hook microtubule tethering protein 151361.00ENSG000001347093.254.785.062.773.043.34
701_atBUB1B− 1.32− 2.50− 5.790.000.85− 4.24BUB1 mitotic checkpoint serine/threonine kinase B701.00ENSG000001569706.466.405.915.014.705.10
85406_atDNAJC14− 1.33− 2.52− 3.140.020.85− 4.36DnaJ heat shock protein family (Hsp40) member C1485406.00ENSG000001353925.916.246.275.195.383.85
6907_atTBL1X− 1.34− 2.53− 3.730.010.85− 4.32Transducin beta like 1 X-linked6907.00ENSG000001018494.835.794.974.303.903.38
3728_atJUP− 1.35− 2.54− 2.690.040.85− 4.40Junction plakoglobin3728.00ENSG000001738015.397.246.884.825.415.25
7153_atTOP2A− 1.35− 2.55− 2.470.050.85− 4.42DNA topoisomerase II alpha7153.00ENSG000001317478.136.987.446.456.965.08
56888_atKCMF1− 1.35− 2.56− 2.570.040.85− 4.41Potassium channel modulatory factor 156888.00ENSG000001764077.458.108.397.406.945.54
112616_atCMTM7− 1.35− 2.56− 2.580.040.85− 4.41CKLF like MARVEL transmembrane domain containing 7112616.00ENSG000001535515.317.096.284.465.584.57
4318_atMMP9− 1.36− 2.57− 2.860.030.85− 4.38Matrix metallopeptidase 94318.00ENSG000001009855.696.617.445.565.324.77
57535_atKIAA1324− 1.36− 2.57− 3.510.010.85− 4.33KIAA132457535.00ENSG000001162995.176.655.854.344.754.49
57162_atPELI1− 1.38− 2.60− 2.820.030.85− 4.39Pellino E3 ubiquitin protein ligase 157162.00ENSG000001973295.327.196.585.085.224.64
9654_atTTLL4− 1.38− 2.60− 2.480.050.85− 4.42Tubulin tyrosine ligase like 49654.00ENSG000001359126.287.678.296.196.455.46
493856_atCISD2− 1.39− 2.61− 4.510.000.85− 4.28CDGSH iron sulfur domain 2493856.00ENSG000001453546.537.527.376.005.655.61
1857_atDVL3− 1.39− 2.62− 2.900.030.85− 4.38Dishevelled segment polarity protein 31857.00ENSG000001612027.038.077.796.776.665.30
102723739_atLOC102723739− 1.39− 2.63− 2.660.040.85− 4.40Uncharacterized LOC102723739102723739.00NA3.144.095.292.792.802.75
91452_atACBD5− 1.40− 2.63− 2.440.050.85− 4.42Acyl-CoA binding domain containing 591452.00ENSG000001078975.597.025.875.245.353.71
79622_atSNRNP25− 1.41− 2.66− 3.190.020.85− 4.36Small nuclear ribonucleoprotein U11/U12 subunit 2579622.00ENSG000001619815.446.736.204.545.394.20
200634_atKRTCAP3− 1.41− 2.66− 2.790.030.85− 4.39Keratinocyte associated protein 3200634.00ENSG000001579925.967.906.935.595.845.12
6533_atSLC6A6− 1.41− 2.66− 2.610.040.85− 4.41Solute carrier family 6 member 66533.00ENSG000001313896.447.158.295.876.505.28
3419_atIDH3A− 1.41− 2.67− 2.560.040.85− 4.41Isocitrate dehydrogenase 3 (NAD(+)) alpha3419.00ENSG000001664116.507.786.686.245.954.54
85236_atHIST1H2BK− 1.42− 2.67− 2.840.030.85− 4.39Histone cluster 1 H2B family member k85236.00ENSG000001979038.267.618.777.177.395.83
4133_atMAP2− 1.42− 2.68− 2.670.040.85− 4.40Microtubule associated protein 24133.00ENSG000000780185.207.305.834.864.764.45
10809_atSTARD10− 1.42− 2.68− 2.820.030.85− 4.39StAR related lipid transfer domain containing 1010809.00ENSG000002145305.747.206.044.204.945.57
440278_atCATSPER2P1− 1.43− 2.70− 3.430.010.85− 4.34Cation channel sperm associated 2 pseudogene 1440278.00ENSG000002057714.415.584.784.173.163.15
8985_atPLOD3− 1.46− 2.74− 3.090.020.85− 4.36Procollagen-lysine,2-oxoglutarate 5-dioxygenase 38985.00ENSG000001063977.178.098.066.926.635.41
7978_atMTERF1− 1.46− 2.74− 3.130.020.85− 4.36Mitochondrial transcription termination factor 17978.00ENSG000001279895.755.785.994.983.314.87
146223_atCMTM4− 1.47− 2.78− 4.220.010.85− 4.29CKLF like MARVEL transmembrane domain containing 4146223.00ENSG000001837236.797.296.905.746.004.82
64426_atSUDS3− 1.48− 2.78− 3.910.010.85− 4.31SDS3 homolog, SIN3A corepressor complex component64426.00ENSG000001117075.365.856.044.524.773.53
57530_atCGN− 1.48− 2.79− 2.470.050.85− 4.42Cingulin57530.00ENSG000001433753.805.945.362.964.093.61
6271_atS100A1− 1.48− 2.79− 2.430.050.85− 4.42S100 calcium binding protein A16271.00ENSG000001606784.997.067.094.484.995.24
375035_atSFT2D2− 1.49− 2.81− 2.720.030.85− 4.40SFT2 domain containing 2375035.00ENSG000002130648.028.639.947.048.037.05
158586_atZXDB− 1.50− 2.82− 2.980.020.85− 4.37Zinc finger, X-linked, duplicated B158586.00ENSG000001984556.617.356.906.505.244.62
9368_atSLC9A3R1− 1.50− 2.82− 3.060.020.85− 4.37SLC9A3 regulator 19368.00ENSG000001090626.518.267.595.916.445.52
6398_atSECTM1− 1.50− 2.83− 2.510.040.85− 4.42Secreted and transmembrane 16398.00ENSG000001415745.806.888.255.565.255.61
1717_atDHCR7− 1.51− 2.84− 2.910.030.85− 4.387-Dehydrocholesterol reductase1717.00ENSG000001728936.168.017.106.155.505.10
7097_atTLR2− 1.51− 2.84− 3.050.020.85− 4.37Toll like receptor 27097.00ENSG000001374625.326.656.313.745.134.89
94039_atZNF101− 1.53− 2.90− 2.440.050.85− 4.42Zinc finger protein 10194039.00ENSG000001818964.286.825.073.943.993.64
22974_atTPX2− 1.54− 2.91− 3.850.010.85− 4.31TPX2, microtubule nucleation factor22974.00ENSG000000883256.776.866.445.595.594.27
1838_atDTNB− 1.55− 2.92− 3.700.010.85− 4.32Dystrobrevin beta1838.00ENSG000001381014.935.766.594.314.174.17
51176_atLEF1− 1.55− 2.92− 3.590.010.85− 4.33Lymphoid enhancer binding factor 151176.00ENSG000001387954.604.505.544.012.713.28
10207_atPATJ− 1.55− 2.94− 3.540.010.85− 4.33PATJ, crumbs cell polarity complex component10207.00ENSG000001328495.447.036.295.144.594.38
29028_atATAD2− 1.56− 2.95− 3.110.020.85− 4.36ATPase family, AAA domain containing 229028.00ENSG000001568026.596.846.375.915.263.94
4301_atAFDN− 1.58− 2.99− 3.080.020.85− 4.37Afadin, adherens junction formation factor4301.00ENSG000001303967.288.547.966.926.745.38
3838_atKPNA2− 1.58− 2.99− 2.620.040.85− 4.41Karyopherin subunit alpha 23838.00ENSG000001824817.537.887.857.196.544.78
79837_atPIP4K2C− 1.58− 2.99− 2.430.050.85− 4.42Phosphatidylinositol-5-phosphate 4-kinase type 2 gamma79837.00ENSG000001669085.027.636.734.545.184.91
3691_atITGB4− 1.59− 3.01− 3.590.010.85− 4.33Integrin subunit beta 43691.00ENSG000001324704.896.446.023.864.644.08
8342_atHIST1H2BM− 1.59− 3.02− 7.740.000.85− 4.21Histone cluster 1 H2B family member m8342.00ENSG000002737036.086.156.494.884.554.50
1317_atSLC31A1− 1.60− 3.03− 2.620.040.85− 4.41Solute carrier family 31 member 11317.00ENSG000001368687.618.978.407.027.625.54
360019_atKRT18P10− 1.61− 3.05− 3.190.020.85− 4.36Keratin 18 pseudogene 10360019.00NA3.495.093.332.022.322.73
388564_atTMEM238− 1.62− 3.07− 2.910.030.85− 4.38Transmembrane protein 238388564.00ENSG000002334935.096.527.125.184.364.34
4316_atMMP7− 1.62− 3.07− 2.790.030.85− 4.39Matrix metallopeptidase 74316.00ENSG000001376736.908.396.736.196.154.82
27350_atAPOBEC3C− 1.62− 3.08− 3.590.010.85− 4.33Apolipoprotein B mRNA editing enzyme catalytic subunit 3C27350.00ENSG000002445096.786.837.466.075.664.47
26578_atOSTF1− 1.62− 3.08− 2.590.040.85− 4.41Osteoclast stimulating factor 126578.00ENSG000001349966.817.327.156.366.064.00
8424_atBBOX1− 1.62− 3.08− 2.430.050.85− 4.43Gamma-Butyrobetaine hydroxylase 18424.00ENSG000001291513.194.735.982.953.102.97
56990_atCDC42SE2− 1.63− 3.10− 2.830.030.85− 4.39CDC42 small effector 256990.00ENSG000001589856.366.436.685.775.283.52
27095_atTRAPPC3− 1.64− 3.11− 2.680.040.85− 4.40Trafficking protein particle complex 327095.00ENSG000000541167.438.629.347.397.215.87
1672_atDEFB1− 1.64− 3.12− 2.760.030.85− 4.39Defensin beta 11672.00ENSG000001648256.438.537.295.206.465.67
7045_atTGFBI− 1.65− 3.13− 2.510.040.85− 4.42Transforming growth factor beta induced7045.00ENSG000001207088.867.387.936.927.225.10
105374310_atLOC105374310− 1.65− 3.15− 3.110.020.85− 4.36Uncharacterized LOC105374310105374310.00NA4.906.396.914.274.804.16
1836_atSLC26A2− 1.67− 3.17− 2.900.030.85− 4.38Solute carrier family 26 member 21836.00ENSG000001558506.097.646.785.935.414.17
196_atAHR− 1.68− 3.21− 3.620.010.85− 4.33Aryl hydrocarbon receptor196.00ENSG000001065468.489.968.807.667.756.77
4233_atMET− 1.70− 3.24− 2.820.030.85− 4.39MET proto-oncogene, receptor tyrosine kinase4233.00ENSG000001059766.256.905.635.724.393.58
10652_atYKT6− 1.70− 3.24− 3.350.010.85− 4.34YKT6 v-SNARE homolog10652.00ENSG000001066366.297.037.095.775.514.03
5768_atQSOX1− 1.70− 3.25− 2.530.040.85− 4.41Quiescin sulfhydryl oxidase 15768.00ENSG000001162605.277.667.065.275.404.22
93474_atZNF670− 1.72− 3.30− 5.970.000.85− 4.24Zinc finger protein 67093474.00ENSG000002774624.164.704.863.292.652.61
7020_atTFAP2A− 1.74− 3.34− 2.580.040.85− 4.41Transcription factor AP-2 alpha7020.00ENSG000001372035.646.838.415.115.465.09
11221_atDUSP10− 1.74− 3.34− 3.520.010.85− 4.33Dual specificity phosphatase 1011221.00ENSG000001435077.277.368.156.186.534.85
56900_atTMEM167B− 1.75− 3.36− 2.510.040.85− 4.42Transmembrane protein 167B56900.00ENSG000002157176.057.347.335.785.983.71
5557_atPRIM1− 1.77− 3.42− 3.720.010.85− 4.32DNA primase subunit 15557.00ENSG000001980565.696.345.895.243.853.52
5795_atPTPRJ− 1.78− 3.45− 2.690.040.85− 4.40Protein tyrosine phosphatase, receptor type J5795.00ENSG000001491775.398.036.454.695.314.51
79022_atTMEM106C− 1.79− 3.46− 2.530.040.85− 4.41Transmembrane protein 106C79022.00ENSG000001342915.417.666.775.505.253.73
481_atATP1B1− 1.80− 3.47− 2.470.050.85− 4.42ATPase Na+/K+ transporting subunit beta 1481.00ENSG000001431537.6410.318.186.697.586.48
105376171_atLOC105376171− 1.81− 3.51− 3.930.010.85− 4.31Uncharacterized LOC105376171105376171.00ENSG000002315215.556.066.775.094.153.71
283987_atHID1− 1.84− 3.59− 3.150.020.85− 4.36HID1 domain containing283987.00ENSG000001678615.147.506.414.344.844.34
1942_atEFNA1− 1.85− 3.59− 3.360.010.85− 4.34Ephrin A11942.00ENSG000001692423.735.035.962.893.023.27
84844_atPHF5A− 1.85− 3.60− 2.790.030.85− 4.39PHD finger protein 5A84844.00ENSG000001004104.916.557.504.703.974.75
10653_atSPINT2− 1.86− 3.63− 2.560.040.85− 4.41Serine peptidase inhibitor, Kunitz type 210653.00ENSG000001676425.077.846.954.085.095.10
100313770_atMIR548K− 1.87− 3.66− 2.880.030.85− 4.38MicroRNA 548k100313770.00ENSG000002213333.674.796.363.003.163.05
83481_atEPPK1− 1.90− 3.73− 2.960.020.85− 4.38Epiplakin 183481.00ENSG000002611503.925.276.603.383.373.34
55971_atBAIAP2L1− 1.90− 3.74− 2.680.040.85− 4.40BAI1 associated protein 2 like 155971.00ENSG000000064535.127.776.234.145.254.03
3675_atITGA3− 1.91− 3.75− 2.690.030.85− 4.40Integrin subunit alpha 33675.00ENSG000000058846.847.817.056.016.253.72
1545_atCYP1B1− 1.95− 3.86− 2.710.030.85− 4.40Cytochrome P450 family 1 subfamily B member 11545.00ENSG000001380617.768.419.927.137.545.58
57153_atSLC44A2− 1.96− 3.88− 2.620.040.85− 4.41Solute carrier family 44 member 257153.00ENSG000001293536.658.628.636.296.934.82
11009_atIL24− 1.96− 3.90− 2.950.020.85− 4.38Interleukin 2411009.00ENSG000001628926.617.427.134.366.634.28
6272_atSORT1− 1.99− 3.97− 2.780.030.85− 4.39Sortilin 16272.00ENSG000001342435.517.427.935.075.704.12
6692_atSPINT1− 2.00− 4.01− 3.350.010.85− 4.35Serine peptidase inhibitor, Kunitz type 16692.00ENSG000001661455.618.076.524.844.674.68
129642_atMBOAT2− 2.01− 4.03− 3.800.010.85− 4.32Membrane bound O-acyltransferase domain containing 2129642.00ENSG000001437976.457.146.805.335.433.59
1829_atDSG2− 2.01− 4.04− 3.590.010.85− 4.33Desmoglein 21829.00ENSG000000466046.138.006.214.675.294.34
92421_atCHMP4C− 2.07− 4.19− 2.760.030.85− 4.39Charged multivesicular body protein 4C92421.00ENSG000001646954.827.956.364.174.224.54
219970_atGLYATL2− 2.07− 4.19− 5.740.000.85− 4.24Glycine-N-acyltransferase like 2219970.00ENSG000001566896.746.556.575.304.443.92
1366_atCLDN7− 2.10− 4.30− 3.200.020.85− 4.36Claudin 71366.00ENSG000001818855.768.287.544.965.604.71
55041_atPLEKHB2− 2.12− 4.34− 2.520.040.85− 4.42Pleckstrin homology domain containing B255041.00ENSG000001157625.987.537.365.855.633.04
337875_atHIST2H2BA− 2.16− 4.47− 3.170.020.85− 4.36Histone cluster 2 H2B family member a (pseudogene)337875.00NA5.746.897.365.175.173.17
6768_atST14− 2.18− 4.53− 2.650.040.85− 4.40Suppression of tumorigenicity 146768.00ENSG000001494184.107.396.613.594.003.98
8140_atSLC7A5− 2.20− 4.58− 2.990.020.85− 4.37Solute carrier family 7 member 58140.00ENSG000001032576.759.188.085.846.744.84
1824_atDSC2− 2.20− 4.61− 3.510.010.85− 4.33Desmocollin 21824.00ENSG000001347557.209.008.686.436.775.07
200058_atFLJ23867− 2.20− 4.61− 3.110.020.85− 4.36Uncharacterized protein FLJ23867200058.00NA6.249.218.005.635.625.59
51330_atTNFRSF12A− 2.28− 4.86− 2.710.030.85− 4.40TNF receptor superfamily member 12A51330.00ENSG000000063276.988.5310.215.907.165.82
2517_atFUCA1− 2.31− 4.97− 2.640.040.85− 4.40alpha-L-Fucosidase 12517.00ENSG000001791635.267.768.625.335.194.17
1832_atDSP− 2.34− 5.06− 6.250.000.85− 4.23Desmoplakin1832.00ENSG000000966967.048.218.175.745.565.11
1500_atCTNND1− 2.38− 5.22− 2.780.030.85− 4.39Catenin delta 11500.00ENSG000001985616.609.088.825.747.014.60
55959_atSULF2− 2.39− 5.24− 3.210.020.85− 4.36Sulfatase 255959.00ENSG000001965626.438.466.994.716.153.84
102723517_atLOC102723517− 2.42− 5.34− 2.870.030.85− 4.38Uncharacterized LOC102723517102723517.00ENSG000002348997.569.8310.726.947.686.25
79679_atVTCN1− 2.42− 5.36− 2.750.030.85− 4.39V-set domain containing T-cell activation inhibitor 179679.00ENSG000001342584.116.817.683.763.663.92
50848_atF11R− 2.45− 5.45− 2.560.040.85− 4.41F11 receptor50848.00ENSG000001587695.118.907.364.605.424.00
4071_atTM4SF1− 2.46− 5.50− 2.700.030.85− 4.40Transmembrane 4 L six family member 14071.00ENSG000001699085.879.247.985.715.824.19
999_atCDH1− 2.46− 5.50− 3.130.020.85− 4.36Cadherin 1999.00ENSG000000390686.219.336.904.975.324.78
2568_atGABRP− 2.51− 5.70− 2.920.030.85− 4.38gamma-Aminobutyric acid type A receptor pi subunit2568.00ENSG000000947555.838.828.484.596.094.90
54845_atESRP1− 2.56− 5.91− 2.580.040.85− 4.41Epithelial splicing regulatory protein 154845.00ENSG000001044133.978.066.653.274.063.66
3655_atITGA6− 2.58− 5.97− 2.730.030.85− 4.40Integrin subunit alpha 63655.00ENSG000000914095.879.568.764.935.845.69
3854_atKRT6B− 2.61− 6.11− 2.620.040.85− 4.41Keratin 6B3854.00ENSG000001854799.029.609.428.237.644.34
3694_atITGB6− 2.63− 6.18− 5.730.000.85− 4.24Integrin subunit beta 63694.00ENSG000001152217.218.247.044.285.594.73
27075_atTSPAN13− 2.75− 6.73− 2.700.030.85− 4.40Tetraspanin 1327075.00ENSG000001065375.269.388.104.394.755.35
654319_atSNORA5A− 2.76− 6.79− 3.190.020.85− 4.36Small nucleolar RNA, H/ACA box 5A654319.00ENSG000002068383.566.906.283.322.692.45
8842_atPROM1− 2.76− 6.79− 2.560.040.85− 4.41Prominin 18842.00ENSG000000070625.989.887.294.826.173.86
102723505_atLINC02095− 2.86− 7.25− 2.670.040.85− 4.40Long intergenic non-protein coding RNA 2095102723505.00ENSG000002286395.178.978.944.905.434.17
114569_atMAL2− 3.05− 8.28− 3.350.010.85− 4.34mal, T-cell differentiation protein 2 (gene/pseudogene)114569.00ENSG000001476767.9510.239.526.327.654.58
938_atCD24P4− 3.29− 9.81− 3.130.020.85− 4.36CD24 molecule pseudogene 4938.00NA6.487.897.123.496.161.96
105376425_atLOC105376425− 3.46-11.03− 3.400.010.85− 4.34Uncharacterized LOC105376425105376425.00NA6.449.616.905.204.682.68
8364_atHIST1H4C− 3.63-12.39− 3.250.020.85− 4.35Histone cluster 1 H4 family member c8364.00ENSG000001970616.785.039.384.502.753.05
260436_atFDCSP− 4.42-21.46− 2.810.030.85− 4.39Follicular dendritic cell secreted protein260436.00ENSG000001816178.0811.3211.194.139.034.16
6279_atS100A8− 4.79-27.66− 3.930.010.85− ρ4.31S100 calcium binding protein A86279.00ENSG000001435465.669.8210.423.963.564.01

BT133, BT134 and BT138 corresponds to three different TNBC patients

Flow cytometry analysis of freshly dissociated and frozen tumor samples. a Left panels viability of fresh (upper) or freeze/thawed (lower) tumor cells was assessed using the non-permeant fluorescent DNA marker 7-ADD. The gate shows the percentage of living cells. Right panels percentage of dead cells observed after thawing 15 TNBCs. b Leucocytes and non-immune cells (mainly tumor cells) within the dissociated tumor cell population were analyzed by flow cytometry after staining with an anti-CD45 mAb. The percentage of CD45+ (leukocytes) and CD45− (tumor cells) before and after freezing is depicted. Statistical differences were evaluated using the Mann–Whitney U test. c Two examples of fresh and freeze/thawed dissociated tumor cells. Expression of CD44 and CD24 by CD45tumor cells was evaluated. d Comparison of the proportion of CD45tumor cells showing CD24 and CD44 before and after the freeze/thaw process. P values were calculated using a paired Student’s t test (n = 15). Density plot showing the percentage of living tumor cells expressing CD24 and CD44 before and after cryopreservation Transcriptomic signatures of fresh and freeze/thawed CSCs. a Flow cytometry analysis of freshly dissociated tumor cells and spheroids derived from the tumor after 15 days of culture in low serum/low adherence conditions. The dot plot is representative of data obtained from all 15 tumors. b Heatmap showing RNA expression by spheroids derived from fresh or frozen tumor cells from three different patients. Hierarchical clustering was performed on Robust Multi-Array Average (rma)-normalized expression data using the One minus Pearson’s correlation method. c Overall, 274 genes showed differential expression in spheroids isolated from fresh and frozen tumors. Network representation of GO terms (biological process) significantly enriched in the 224 genes downregulated in freeze/thawed mammospheres as compared to fresh ones. Similar go terms were spatially clustered and colors represent corrected p-values Transcriptomic comparison between fresh TNBC cells and their freeze/thawed counterparts BT133, BT134 and BT138 corresponds to three different TNBC patients

Discussion

Although one study did examine the impact of cryopreservation on gametes and embryos, this study appears to be the first to examine phenotypic changes caused by freeze/thawing living cells isolated from cancer patients [7]. Although molecular oncology has opened new avenues to classifying human cancers from a molecular standpoint, a number of issues associated with heterogeneous genomic platforms limit their ability to identifying signatures capable of predicting biological behavior and/or identifying new molecular targets for more effective and less toxic therapeutic interventions [8]. We identified a novel issue using data meta-analysis based on profiling of fresh and frozen tumor samples; our findings suggest that it will be difficult to identify robust signatures transposable to patients if freeze/thawed dissociated cancer tissues are used. We strongly discourage the use of frozen dissociated tumor sample for accurate cell population characterization, as thawing creates a disequilibrium in the proportion of immune and tumor cell subpopulations. In addition, because the transcriptomic signatures of mammospheres derived from frozen/thawed samples are not representative of those of initial CSCs, this study rules out their use for identification of new prognostic and/or therapeutic targets. We strongly suggest that future studies involving dissociated tumor cells should be conducted using fresh tumors only; in parallel, tumor biobanks should develop validate methods of freezing living cells isolated from resected tumors that preserves tumor heterogeneity. In this way, we may be able to generate robust scoring systems for prognostic, predictive, and therapeutic markers. Such a system is an unmet clinical need with respect to patients with TNBC.

Limitations

Although, the freeze–thaw shock seems mainly to affect immune cells, a more detailed analysis would be required to further investigate whether some minor immune subsets could be affected by this stress and thereby, could also alter the conclusions drawn from analyses performed using freeze/thaw living cells. Additional file 1: Figure S1. Flowchart showing the TNBC dissociation and spheroid protocols. Additional file 2: Table S1. Gene ontology enrichment associated with the freeze/thaw process in TNBCs.
  8 in total

Review 1.  Analysis of oocyte physiology to improve cryopreservation procedures.

Authors:  David K Gardner; Courtney B Sheehan; Laura Rienzi; Mandy Katz-Jaffe; Mark G Larman
Journal:  Theriogenology       Date:  2006-10-17       Impact factor: 2.740

Review 2.  Metastatic stem cells: sources, niches, and vital pathways.

Authors:  Thordur Oskarsson; Eduard Batlle; Joan Massagué
Journal:  Cell Stem Cell       Date:  2014-03-06       Impact factor: 24.633

3.  Prospective identification of tumorigenic breast cancer cells.

Authors:  Muhammad Al-Hajj; Max S Wicha; Adalberto Benito-Hernandez; Sean J Morrison; Michael F Clarke
Journal:  Proc Natl Acad Sci U S A       Date:  2003-03-10       Impact factor: 11.205

Review 4.  Unbiased vs. biased approaches to the identification of cancer signatures: the case of lung cancer.

Authors:  Fabrizio Bianchi; Francesco Nicassio; Pier Paolo Di Fiore
Journal:  Cell Cycle       Date:  2008-01-14       Impact factor: 4.534

5.  ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome.

Authors:  Christophe Ginestier; Min Hee Hur; Emmanuelle Charafe-Jauffret; Florence Monville; Julie Dutcher; Marty Brown; Jocelyne Jacquemier; Patrice Viens; Celina G Kleer; Suling Liu; Anne Schott; Dan Hayes; Daniel Birnbaum; Max S Wicha; Gabriela Dontu
Journal:  Cell Stem Cell       Date:  2007-11       Impact factor: 24.633

6.  Annual Report to the Nation on the Status of Cancer, part I: National cancer statistics.

Authors:  Kathleen A Cronin; Andrew J Lake; Susan Scott; Recinda L Sherman; Anne-Michelle Noone; Nadia Howlader; S Jane Henley; Robert N Anderson; Albert U Firth; Jiemin Ma; Betsy A Kohler; Ahmedin Jemal
Journal:  Cancer       Date:  2018-05-22       Impact factor: 6.860

7.  Annual Report to the Nation on the Status of Cancer, part II: Recent changes in prostate cancer trends and disease characteristics.

Authors:  Serban Negoita; Eric J Feuer; Angela Mariotto; Kathleen A Cronin; Valentina I Petkov; Sarah K Hussey; Vicki Benard; S Jane Henley; Robert N Anderson; Stacey Fedewa; Recinda L Sherman; Betsy A Kohler; Barbara J Dearmon; Andrew J Lake; Jiemin Ma; Lisa C Richardson; Ahmedin Jemal; Lynne Penberthy
Journal:  Cancer       Date:  2018-05-22       Impact factor: 6.860

8.  Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study.

Authors:  H Raza Ali; Leon Chlon; Paul D P Pharoah; Florian Markowetz; Carlos Caldas
Journal:  PLoS Med       Date:  2016-12-13       Impact factor: 11.069

  8 in total
  2 in total

1.  Examining Peripheral and Tumor Cellular Immunome in Patients With Cancer.

Authors:  Eda K Holl; Victoria N Frazier; Karenia Landa; Georgia M Beasley; E Shelley Hwang; Smita K Nair
Journal:  Front Immunol       Date:  2019-07-31       Impact factor: 7.561

2.  The Extracellular Matrix Influences Ovarian Carcinoma Cells' Sensitivity to Cisplatinum: A First Step towards Personalized Medicine.

Authors:  Andrea Balduit; Chiara Agostinis; Alessandro Mangogna; Veronica Maggi; Gabriella Zito; Federico Romano; Andrea Romano; Rita Ceccherini; Gabriele Grassi; Serena Bonin; Deborah Bonazza; Fabrizio Zanconati; Giuseppe Ricci; Roberta Bulla
Journal:  Cancers (Basel)       Date:  2020-05-07       Impact factor: 6.639

  2 in total

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