Comparative Transcriptome Analysis Quantifies Immune Cell Transcript Levels, Metastatic Progression, and Survival in Osteosarcoma

Abstract

Overall survival of patients with osteosarcoma (OS) has improved little in the past three decades, and better models for study are needed. OS is common in large dog breeds and is genetically inducible in mice, making the disease ideal for comparative genomic analyses across species. Understanding the level of conservation of intertumor transcriptional variation across species and how it is associated with progression to metastasis will enable us to more efficiently develop effective strategies to manage OS and to improve therapy. In this study, transcriptional profiles of OS tumors and cell lines derived from humans (n = 49), mice (n = 103), and dogs (n = 34) were generated using RNA sequencing. Conserved intertumor transcriptional variation was present in tumor sets from all three species and comprised gene clusters associated with cell cycle and mitosis and with the presence or absence of immune cells. Further, we developed a novel gene cluster expression summary score (GCESS) to quantify intertumor transcriptional variation and demonstrated that these GCESS values associated with patient outcome. Human OS tumors with GCESS values suggesting decreased immune cell presence were associated with metastasis and poor survival. We validated these results in an independent human OS tumor cohort and in 15 different tumor data sets obtained from The Cancer Genome Atlas. Our results suggest that quantification of immune cell absence and tumor cell proliferation may better inform therapeutic decisions and improve overall survival for OS patients.Significance: This study offers new tools to quantify tumor heterogeneity in osteosarcoma, identifying potentially useful prognostic biomarkers for metastatic progression and survival in patients. Cancer Res; 78(2); 326-37. ©2017 AACR.

Fig 1. OS transcriptome profiles across 3 species are more similar to each other than to other types of human tumors

Pairwise Pearson correlations were calculated between (A) Human (HOS1) (B) Dog (DOS) and (C) Mouse (MOS) OS data sets, publicly available OS datasets (HOS2 and HOS3) and sets of 25 tumors from the TCGA representing a wide range of tumor types using 12,062 genes common in all 3 species.

Fig 2. OS transcriptome profiles show common inter-tumor transcriptional variation across human, mouse, and dog samples

A) FPKM values derived from OS tumors and cell line data (indicated by black bars below heatmaps) were log transformed and mean centered within each species. Invariant genes were then removed leaving (i) human (n=9,190), (ii) mouse (n=8,051), and (iii) dog (n=8,003) genes which were used for unsupervised average linkage clustering. Transcripts with increased levels are shown in yellow while transcripts with decreased levels are shown in blue. Transcript level clusters with correlation > 0.60 and containing ≥ 60 genes were systematically identified and these clusters are visualized with a numbered black bar to the right of each of the heatmaps. Lists of each gene in each cluster are provided as Supplementary Table 4. Gene clusters observed in more than one species are surrounded by colored boxes and given a reference number. The “Cell Cycle” conserved transcript cluster is shown in red (HOS-4,MOS-8,DOS-3). The “Immune-1” transcript cluster is shown in green (HOS-1,MOS-4,DOS-4). The “Immune-2” transcript cluster is shown in Purple (HOS- 8,MOS-1,DOS-5). A cluster composed of muscle transcripts only present in Human and Mouse data is shown in Blue (HOS-3, MOS-2). B) Blow up the conserved Cell Cycle clusters (HOS-4, MOS-4 and DOS-3) showing the location of representative genes. C) Blow up of the Immune-1 (HOS-1,MOS-4,DOS-4) and Immune-2 (HOS-8,MOS-1 and DOS-5) regions showing the location of representative genes. D) Venn diagrams showing the number of overlapping genes observed to be commonly present in both datasets. Fishers Exact Test results indicated that the observed overlap is highly unlikely to occur by random chance. Highly significant FET (p < 10E-10) are marked with ***.

Fig 3. Gene Cluster Expression Summary Scores (GCESS) represent the relative amount of transcript present for each cluster of genes and identify correlation between high cell cycle or low immune cell GCESSs and poor survival

A) Overview of analyses method. (i) The log2-transformed and mean-centered values for each gene in a cluster are summed to generate a single score (GCESS value) for each sample. Samples with high relative expression levels have large positive GCESSs while samples with low relative levels have large negative GCESSs. (ii.) The GCESS scores can be used to separate the tumors into groups based on GCESS score Quartiles. (iii.) The GCESS Quartile based groups can then be examined for associations with outcome using Kaplin Meier Analyses. GCESSs for human (tumors, normal bone, OS cell lines), mouse (tumors, OS cell lines), and dog (tumors, osteoblasts, OS cell lines) samples were calculated and Violin Plots were generated for (B) the “cell cycle” cluster, (C) the “immune-1” cluster, and (D) the “immune-2” cluster. Samples were ranked by GCESS and divided into quartiles groups (Q1 = lowest GCESSs, Q4 = highest GCESSs). KM analyses were performed, using human (n=35) and dog (n=19) samples for which survival data was known, to determine correlations between low/high GCESSs and survival. There were 17 (out of 35) death events in the human data (HOS2), and 19 (out of 19) death events in the dog data. (B) A significant association with shorter time to death was observed with a high cell cycle GCESS in the dog cohort, and a strong trend was also present in the human data. (C) In the human data, low Immune-1 GCESS was significantly associated with a worse survival and (D) a strong trend was present between low Immune-2 and worse survival

Fig 4. Replication of association between high cell cycle GCESS or low immune GCESS and poor survival outcomes using array data from independent cohort

Following a similar strategy to the one used for the RNA-Seq data, 14 strong and highly correlated clusters were identified in the A) GSE21257 dataset. Gene cluster overlap analyses comparing clusters derived from human array and human RNA-Seq data sets identified four clusters that corresponded to the conserved RNA-Seq clusters. The B) Cell cycle cluster is labeled in red, the C) Immune-1 cluster is labeled in green and the D) Immune-2 cluster is labeled in purple. Fisher Exact to assess the likelihood of observing the overlap by random chance indicated that the enrichment was highly significant (p < 1E-10) for the Cell Cycle and Immune clusters. KM analyses using GCESS groups using the approach outlined in Figure 3a showed that high levels of B) Cell cycle transcripts were associated with worse outcomes and significantly increased likelihood of tumor metastasis. Low levels of (C) Immune-1 and (D) Immune-2 transcripts were associated with significantly worse survival and significantly increased likelihood of tumor metastasis (E) Metastatic Samples have lower Immune-2 transcript levels in mouse and human samples. MOS-1 GCESS values were lower in tumors from mice where metastatic lesions were observed during necropsy (p <0.05). Tumors from Human patients with metastases present at diagnoses showed a trend towards lower Immune-2 GCESS scores in both the RNA- SEQ data as well as the array data and this trend became increasingly significant in the array data when tumors where metastases were observed in the patient within one year (p < 0.001) or at any point (p < 0.0001) were included with the patients with metastases at diagnosis.