CoChat
Sign Up
Cited by 251Sarcoma Diagnosis and TreatmentCancer Immunotherapy and BiomarkersBone Tumor Diagnosis and Treatments

The DOI matches its Crossref record; 59 of the 60 references it cites were found in a registry or an index.

Immuno-genomic landscape of osteosarcoma

Open access
Wu, Chia-Chin, Beird, Hannah C., Andrew Livingston, J., Advani, Shailesh, Mitra, Akash, Cao, Shaolong, Reuben, Alexandre, Ingram, Davis, Wang, Wei-Lien, Ju, Zhenlin, Hong Leung, Cheuk, Lin, Heather, Zheng, Youyun, Roszik, Jason, Wang, Wenyi, Patel, Shreyaskumar, Benjamin, Robert S., Somaiah, Neeta, Conley, Anthony P., Mills, Gordon B., Hwu, Patrick, Gorlick, Richard, Lazar, Alexander, Daw, Najat C., Lewis, Valerae, Futreal, P. Andrew·2020·Nature Communications
DOI 10.1038/s41467-020-14646-wPMC PMC7035358
Open this paper in CoChat — free accountOpen PDF

Abstract

Limited clinical activity has been seen in osteosarcoma (OS) patients treated with immune checkpoint inhibitors (ICI). To gain insights into the immunogenic potential of these tumors, we conducted whole genome, RNA, and T-cell receptor sequencing, immunohistochemistry and reverse phase protein array profiling (RPPA) on OS specimens from 48 pediatric and adult patients with primary, relapsed, and metastatic OS. Median immune infiltrate level was lower than in other tumor types where ICI are effective, with concomitant low T-cell receptor clonalities. Neoantigen expression in OS was lacking and significantly associated with high levels of nonsense-mediated decay (NMD). Samples with low immune infiltrate had higher number of deleted genes while those with high immune infiltrate expressed higher levels of adaptive resistance pathways. PARP2 expression levels were significantly negatively associated with the immune infiltrate. Together, these data reveal multiple immunosuppressive features of OS and suggest immunotherapeutic opportunities in OS patients.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-020-14646-w

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Clinical associations with somatic alterations in OS. a, b Kaplan-Meier survival analysis (disease-free survival for a; overall survival for b) of mutation signature 8 scores which describe the contribution of mutation signature 8 to the point mutation profile. The cohort was separated into tertiles based on signature 8 scores. Group 1 (denoted as T1) is the first tertile with lowest mutation signature 8 scores. Groups 2 and 3 (denoted as T2 and T3 respctively) are the next 2 tertiles with higher mutation signature 8 scores. c Hierarchical clustering heatmap of rearrangements classification pattern identified in our cohort along with clinical features: age of diagnosis (<18 years, 18– < 50 years, and >50 years), tumor specimen type (primary, local recurrence, metastasis), and vital status (dead, alive). Structural rearrangements were classified based on their type: deletion (del), tandem duplication (tds), inversion (inv), and interchromosomal translocation (trans), and size, and clustered (denoted as c_) versus non-clustered events. d Boxplot showing the ratio of clustered rearrangements within associated with chromothriptic regions as compared across three groups of specimens based on age of diagnosis (<18 years, 18–<50 years, and >50 years). Then significance values (P-values) of the comparisons are from the Wilcoxon rank sum test are shown in asterisks. e Pearson correlation between normalized telomere length and number of copy number segments of samples. f Pearson correlation between normalized telomere length and ATRX gene expression level (in log2 scale) of samples. Samples with both ATRX and TP53 alterations, ATRX alterations alone, and TP53 alterations alone were respectively marked as red, blue, and green color.
Fig. 1 Clinical associations with somatic alterations in OS. a, b Kaplan-Meier survival analysis (disease-free survival for a; overall survival for b) of mutation signature 8 scores which describe the contribution of mutation signature 8 to the point mutation profile. The cohort was separated into tertiles based on signature 8 scores. Group 1 (denoted as T1) is the first tertile with lowest mutation signature 8 scores. Groups 2 and 3 (denoted as T2 and T3 respctively) are the next 2 tertiles with higher mutation signature 8 scores. c Hierarchical clustering heatmap of rearrangements classification pattern identified in our cohort along with clinical features: age of diagnosis (<18 years, 18– < 50 years, and >50 years), tumor specimen type (primary, local recurrence, metastasis), and vital status (dead, alive). Structural rearrangements were classified based on their type: deletion (del), tandem duplication (tds), inversion (inv), and interchromosomal translocation (trans), and size, and clustered (denoted as c_) versus non-clustered events. d Boxplot showing the ratio of clustered rearrangements within associated with chromothriptic regions as compared across three groups of specimens based on age of diagnosis (<18 years, 18–<50 years, and >50 years). Then significance values (P-values) of the comparisons are from the Wilcoxon rank sum test are shown in asterisks. e Pearson correlation between normalized telomere length and number of copy number segments of samples. f Pearson correlation between normalized telomere length and ATRX gene expression level (in log2 scale) of samples. Samples with both ATRX and TP53 alterations, ATRX alterations alone, and TP53 alterations alone were respectively marked as red, blue, and green color.CC BY · PubMed Central
Fig. 2 Expressed neoantigens and rearrangements. a Stacked bar chart for number of nonsynonymous mutations and neoantigens detected by WGS and capture-based RNASeq. (expressed neoantigen: nonsynonymous mutations found in both WGS and capture-based RNASeq that are predicted to be a neoantigen; expressed non-neoantigen: nonsynonymous mutations found in both WGS and capture-based RNASeq that are not predicted to be a neoantigen; unexpressed: found in WGS only). b Overlapping rearrangements between WGS and RNASeq based rearrangements where both partners are within coding regions (unexpressed: found in WGS only; expressed:found in both WGS and RNASeq).
Fig. 2 Expressed neoantigens and rearrangements. a Stacked bar chart for number of nonsynonymous mutations and neoantigens detected by WGS and capture-based RNASeq. (expressed neoantigen: nonsynonymous mutations found in both WGS and capture-based RNASeq that are predicted to be a neoantigen; expressed non-neoantigen: nonsynonymous mutations found in both WGS and capture-based RNASeq that are not predicted to be a neoantigen; unexpressed: found in WGS only). b Overlapping rearrangements between WGS and RNASeq based rearrangements where both partners are within coding regions (unexpressed: found in WGS only; expressed:found in both WGS and RNASeq).CC BY · PubMed Central
Fig. 3 Immune profiling. a ESTIMATE immune scores in TCGA tumor types, our OS cohort (MDACC.OS), the OS cohort from ICGC (BOCA-UK.OS), the OS cohort from TARGET (TARGET.OS), and from four patients who underwent combination CTLA-4 blockade and PD-L1 blockade but exhibited no objective responses (Immunotherapy.OS). Note that TCGA-THYM is derived from the thymus made up mostly of lymphocytes and would have inflated scores. b. Unsupervised hierarchical clustering based on ssGSEA enrichment scores of each immune gene list from Charoentong et al. The three predominant clusters are referred to as “C1” and “C2”, and “C3” immune infiltrate groups. c Boxplot of the ESTIMATE immune scores of samples across the three immune clusters C1, C2, and C3. The significances of the comparisons were from the Wilcoxon rank sum test are shown in asterisks. d The geometric mean expression of GZMA and PRF1 genes (cytolytic score) shown as a boxplot across the three immune clusters C1, C2, and C3. The significances of the comparisons were from the Wilcoxon rank sum test. e Immunosuppressive pathways that are significantly deregulated between immune clusters C1 and C3. The significance (FDR) of deregulation were from the GSEA analysis. f Fold change and significance of expression difference between C1 and C3 for key genes from pathways in e: CD274(PD-L1), CTLA4, IDO1, IFNG, IFNGR1, and IL6 (these genes were not present in the gene lists used to define these immune groups). The linear mode in the limma R package was used to determine significance.
Fig. 3 Immune profiling. a ESTIMATE immune scores in TCGA tumor types, our OS cohort (MDACC.OS), the OS cohort from ICGC (BOCA-UK.OS), the OS cohort from TARGET (TARGET.OS), and from four patients who underwent combination CTLA-4 blockade and PD-L1 blockade but exhibited no objective responses (Immunotherapy.OS). Note that TCGA-THYM is derived from the thymus made up mostly of lymphocytes and would have inflated scores. b. Unsupervised hierarchical clustering based on ssGSEA enrichment scores of each immune gene list from Charoentong et al. The three predominant clusters are referred to as “C1” and “C2”, and “C3” immune infiltrate groups. c Boxplot of the ESTIMATE immune scores of samples across the three immune clusters C1, C2, and C3. The significances of the comparisons were from the Wilcoxon rank sum test are shown in asterisks. d The geometric mean expression of GZMA and PRF1 genes (cytolytic score) shown as a boxplot across the three immune clusters C1, C2, and C3. The significances of the comparisons were from the Wilcoxon rank sum test. e Immunosuppressive pathways that are significantly deregulated between immune clusters C1 and C3. The significance (FDR) of deregulation were from the GSEA analysis. f Fold change and significance of expression difference between C1 and C3 for key genes from pathways in e: CD274(PD-L1), CTLA4, IDO1, IFNG, IFNGR1, and IL6 (these genes were not present in the gene lists used to define these immune groups). The linear mode in the limma R package was used to determine significance.CC BY · PubMed Central
Fig. 4 Copy number alterations and immune score associations. a, b Pearson correlation of the number of deleted genes and immune scores generated from ESTIMATE for our OS cohort (a) and the TARGET cohort (b). c Boxplot of PARP2 copy number levels (in log2 level) in each of the immune infiltrate groups C1, C2, and C3 (as decribed in Fig. 3b). Wilcoxon rank sum test was used to determine significance. d Boxplot of PARP2 gene expression levels (in log2 level) in each of the immune infiltrate groups C1, C2, and C3 (as decribed in Fig. 3b). Wilcoxon rank sum test was used to determine significance.
Fig. 4 Copy number alterations and immune score associations. a, b Pearson correlation of the number of deleted genes and immune scores generated from ESTIMATE for our OS cohort (a) and the TARGET cohort (b). c Boxplot of PARP2 copy number levels (in log2 level) in each of the immune infiltrate groups C1, C2, and C3 (as decribed in Fig. 3b). Wilcoxon rank sum test was used to determine significance. d Boxplot of PARP2 gene expression levels (in log2 level) in each of the immune infiltrate groups C1, C2, and C3 (as decribed in Fig. 3b). Wilcoxon rank sum test was used to determine significance.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 60 references

Research works printed but not linked: 1

  1. Dentro, S. C. et al. Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types. Preprint at: https://www.biorxiv.org/content/10.1101/312041v4 (2018). · DOI

Cited by

Sorted by citations

Items 101–200 of 251

Related

See all

Metadata

Authors
Wu, C.-C., Beird, H. C., Andrew Livingston, J., Advani, S., Mitra, A., Cao, S., Reuben, A., Ingram, D., Wang, W.-L., Ju, Z., Hong Leung, C., Lin, H., Zheng, Y., Roszik, J., Wang, W., Patel, S., Benjamin, R. S., Somaiah, N., Conley, A. P., Mills, G. B., Hwu, P., Gorlick, R., Lazar, A., Daw, N. C., Lewis, V., & Futreal, P. A.
Affiliations
The University of Texas MD Anderson Cancer Center (US)
Corresponding author
Futreal, P. Andrew
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-02-21
Volume
11
Issue
1
Pages
1008-1008
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-14646-w
PMC
PMC7035358
OpenAlex
W3006860236
Cited by
251
References
60
Citations by year
2026: 27 · 2025: 39 · 2024: 44 · 2023: 30 · 2022: 44 · 2021: 48 · 2020: 19
FWCI
19.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • A Shelter for Cancer Families
  • Cancer Prevention and Research Institute of Texas (R120501)
  • QuadW Foundation
Topics
  • Health Sciences › Medicine › Oncology › Sarcoma Diagnosis and Treatment
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Bone Tumor Diagnosis and Treatments
Keywords
osteosarcoma, tumor immune microenvironment, immune checkpoint inhibitors, T-cell receptor clonality, neoantigens, nonsense-mediated mRNA decay, immune cell infiltration, PARP2, immunosuppression, pediatric osteosarcoma, immunotherapy
MeSH terms
Whole Genome Sequencing, RNA-Seq, Adolescent, Adult, Aged, Aged, 80 and over, Bone Neoplasms, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Mutation, Receptors, Antigen, T-Cell, Osteosarcoma, Cohort Studies, Immunogenetic Phenomena, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review