A prognostic profile of hypoxia-induced genes for localised high-grade soft tissue sarcoma

Integration of pre-treatment computational radiomics, deep radiomics, and transcriptomics enhances soft-tissue sarcoma patient prognosis

Our objective was to capture subgroups of soft-tissue sarcoma (STS) using handcraft and deep radiomics approaches to understand their relationship with histopathology, gene-expression profiles, and metastatic relapse-free survival (MFS). We included all consecutive adults with newly diagnosed locally advanced STS (N = 225, 120 men, median age: 62 years) managed at our sarcoma reference center between 2008 and 2020, with contrast-enhanced baseline MRI. After MRI postprocessing, segmentation, and reproducibility assessment, 175 handcrafted radiomics features (h-RFs) were calculated. Convolutional autoencoder neural network (CAE) and half-supervised CAE (HSCAE) were trained in repeated cross-validation on representative contrast-enhanced slices to extract 1024 deep radiomics features (d-RFs). Gene-expression levels were calculated following RNA sequencing (RNAseq) of 110 untreated samples from the same cohort. Unsupervised classifications based on h-RFs, CAE, HSCAE, and RNAseq were built. The h-RFs, CAE, and HSCAE grouping were not associated with the transcriptomics groups but with prognostic radiological features known to correlate with lower survivals and higher grade and SARCULATOR groups (a validated prognostic clinical-histological nomogram). HSCAE and h-RF groups were also associated with MFS in multivariable Cox regressions. Combining HSCAE and transcriptomics groups significantly improved the prognostic performances compared to each group alone, according to the concordance index. The combined radiomic-transcriptomic group with worse MFS was characterized by the up-regulation of 707 genes and 292 genesets related to inflammation, hypoxia, apoptosis, and cell differentiation. Overall, subgroups of STS identified on pre-treatment MRI using handcrafted and deep radiomics were associated with meaningful clinical, histological, and radiological characteristics, and could strengthen the prognostic value of transcriptomics signatures.

Technical development and validation of a clinically applicable microenvironment classifier as a biomarker of tumour hypoxia for soft tissue sarcoma

BACKGROUND

Soft tissue sarcomas (STS) are rare, heterogeneous tumours and biomarkers are needed to inform management. We previously derived a prognostic tumour microenvironment classifier (24-gene hypoxia signature). Here, we developed/validated an assay for clinical application.

METHODS

Technical performance of targeted assays (Taqman low-density array, nanoString) was compared in 28 prospectively collected formalin-fixed, paraffin-embedded (FFPE) biopsies. The nanoString assay was biologically validated by comparing to HIF-1α/CAIX immunohistochemistry (IHC) in clinical samples. The Manchester (n = 165) and VORTEX Phase III trial (n = 203) cohorts were used for clinical validation. The primary outcome was overall survival (OS).

RESULTS

Both assays demonstrated excellent reproducibility. The nanoString assay detected upregulation of the 24-gene signature under hypoxia in vitro, and 16/24 hypoxia genes were upregulated in tumours with high CAIX expression in vivo. Patients with hypoxia-high tumours had worse OS in the Manchester (HR 3.05, 95% CI 1.54-5.19, P = 0.0005) and VORTEX (HR 2.13, 95% CI 1.19-3.77, P = 0.009) cohorts. In the combined cohort, it was independently prognostic for OS (HR 2.24, 95% CI 1.42-3.53, P = 0.00096) and associated with worse local recurrence-free survival (HR 2.17, 95% CI 1.01-4.68, P = 0.04).

CONCLUSIONS

This study comprehensively validates a microenvironment classifier befitting FFPE STS biopsies. Future uses include: (1) selecting high-risk patients for perioperative chemotherapy; and (2) biomarker-driven trials of hypoxia-targeted therapies.

Immunologic constant of rejection signature is prognostic in soft-tissue sarcoma and refines the CINSARC signature

BACKGROUND

Soft-tissue sarcomas (STSs) are heterogeneous and aggressive tumors, with high metastatic risk. The immunologic constant of rejection (ICR) 20-gene signature is a signature of cytotoxic immune response. We hypothesized that ICR might improve the prognostic assessment of early-stage STS.

METHODS

We retrospectively applied ICR to 1455 non-metastatic STS and searched for correlations between ICR classes and clinicopathological and biological variables, including metastasis-free survival (MFS).

RESULTS

Thirty-four per cent of tumors were classified as ICR1, 27% ICR2, 24% ICR3, and 15% ICR4. These classes were associated with patients' age, pathological type, and tumor depth, and an enrichment from ICR1 to ICR4 of quantitative/qualitative scores of immune response. ICR1 class was associated with a 59% increased risk of metastatic relapse when compared with ICR2-4 class. In multivariate analysis, ICR classification remained associated with MFS, as well as pathological type and Complexity Index in Sarcomas (CINSARC) classification, suggesting independent prognostic value. A prognostic clinicogenomic model, including the three variables, was built in a learning set (n=339) and validated in an independent set (n=339), showing greater prognostic precision than each variable alone or in doublet. Finally, connectivity mapping analysis identified drug classes potentially able to reverse the expression profile of poor-prognosis tumors, such as chemotherapy and targeted therapies.

CONCLUSION

ICR signature is independently associated with postoperative MFS in early-stage STS, independently from other prognostic features, including CINSARC. We built a robust prognostic clinicogenomic model integrating ICR, CINSARC, and pathological type, and suggested differential vulnerability of each prognostic group to different systemic therapies.

A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma

Simple Summary

Soft tissue sarcomas (STS) still lack effective clinical stratification and prognostic models. The aim of this study is to establish a reliable prognostic gene signature in STS. Using 189 STS samples from the TCGA database, a four-gene signature (including DHRS3, JRK, TARDBP and TTC3) and nomograms that can be used to predict the overall survival and relapse free survival of STS patients was developed. The predictive ability for metastasis free survival was externally verified in the GEO cohort. We demonstrated that the novel gene signature provides an attractive platform for risk stratification and prognosis prediction of STS patients, which is of great importance for individualized clinical treatment and long-term management of patients with this rare and severe disease.

Abstract

Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

Proteomic profiling of soft tissue sarcomas with SWATH mass spectrometry

Soft tissue sarcomas (STS) are a group of rare and heterogeneous cancers. While large-scale genomic and epigenomic profiling of STS have been undertaken, proteomic analysis has thus far been limited. Here we utilise sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) for proteomic profiling of formalin fixed paraffin embedded (FFPE) specimens from a cohort of STS patients (n = 36) across four histological subtypes (leiomyosarcoma, synovial sarcoma, undifferentiated pleomorphic sarcoma and dedifferentiated liposarcoma). We quantified 2951 proteins across all cases and show that there is a significant enrichment of gene sets associated with smooth muscle contraction in leiomyosarcoma, RNA splicing regulation in synovial sarcoma and leukocyte activation in undifferentiated pleomorphic sarcoma. We further identified a subgroup of STS cases that have a distinct expression profile in a panel of proteins, with worse survival outcomes when compared to the rest of the cohort. Our study highlights the value of comprehensive proteomic characterisation as a means to identify histotype-specific STS profiles that describe key biological pathways of clinical and therapeutic relevance; as well as for discovering new prognostic biomarkers in this group of rare and difficult-to-treat diseases.

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Proteomic analysis of FFPE specimens from four soft tissue sarcoma histological subtypes was performed by SWATH MS.

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Subtype-specific biological pathways and protein-protein interaction networks were identified.

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A panel of proteins associated with sarcoma patient survival outcome was defined.

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A subset of proteins identified by SWATH MS was validated by orthogonal immunohistochemical analysis.

Predictive and prognostic transcriptomic biomarkers in soft tissue sarcomas

Soft tissue sarcomas (STS) are rare and heterogeneous tumours comprising over 80 different histological subtypes. Treatment options remain limited in advanced STS with high rates of recurrence following resection of localised disease. Prognostication in clinical practice relies predominantly on histological grading systems as well as sarcoma nomograms. Rapid developments in gene expression profiling technologies presented opportunities for applications in sarcoma. Molecular profiling of sarcomas has improved our understanding of the cancer biology of these rare cancers and identified potential novel therapeutic targets. In particular, transcriptomic signatures could play a role in risk classification in sarcoma to aid prognostication. Unlike other solid and haematological malignancies, transcriptomic signatures have not yet reached routine clinical use in sarcomas. Herein, we evaluate early developments in gene expression profiling in sarcomas that laid the foundations for transcriptomic signature development. We discuss the development and clinical evaluation of key transcriptomic biomarker signatures in sarcomas, including Complexity INdex in SARComas (CINSARC), Genomic Grade Index, and hypoxia-associated signatures. Prospective validation of these transcriptomic signatures is required, and prospective trials are in progress to evaluate reliability for clinical application. We anticipate that integration of these gene expression signatures alongside existing prognosticators and other Omics methodologies, including proteomics and DNA methylation analysis, could improve the identification of 'high-risk' patients who would benefit from more aggressive or selective treatment strategies. Moving forward, the incorporation of these transcriptomic prognostication signatures in clinical practice will undoubtedly advance precision medicine in the routine clinical management of sarcoma patients.

Integrating tumor hypoxic stress in novel and more adaptable strategies for cancer immunotherapy

Immunotherapy is poised to become an increasingly utilized therapy in the treatment of cancer. However, several abnormalities in the tumor microenvironment (TME) that can thwart the efficacy of immunotherapies have been established. Microenvironmental hypoxia is a determining factor in shaping aggressiveness, metastatic potential and treatment resistance of solid tumors. The characterization of this phenomenon could prove beneficial for determining a patient's treatment path and for the introduction of novel targetable factors that can enhance therapeutic outcome. Indeed, the ablation of hypoxia has the potential to sensitize tumors to immunotherapy by metabolically remodeling their microenvironment. In this review, we discuss the intrinsic contributions of hypoxia to cellular plasticity, heterogeneity, stemness and genetic instability in the context of immune escape. In addition, we will shed light on how managing hypoxia can ameliorate response to immunotherapy and how integrating hypoxia gene signatures could play a role in this pursuit.

Combined Use of the Neutrophil-Lymphocyte and Platelet-Lymphocyte Ratios as a Prognostic Predictor in Patients with Operable Soft Tissue Sarcoma

Background: Preoperative neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are associated with poor prognosis in soft tissue sarcoma (STS). The aim of the present study is to determine whether the combination of NLR and PLR (CNP) can better predict patient survival after resection for STS.

Methods: We included 310 STS patients in this retrospective study. Preoperative CNP was calculated as follows: patients with both elevated NLR (>2.51) and PLR (>191.1) were given a score of 2; patients showing an increase in one or neither were allocated a score of 1 or 0, respectively.

Results: Cut-off values of 2.51 and 191.1 were defined as elevated NLR and PLR, respectively. Elevated CNP was significantly associated with older age (P=0.034), larger tumor size (P=0.025), deeper tumor location (P=0.044), higher tumor grade (P=0.028), a more advanced stage according to the American Joint Committee on Cancer (AJCC) (P=0.005), shorter overall survival (OS) (P=0.000) and shorter disease-free survival (DFS) (P=0.000). Multivariate analysis indicated CNP but not NLR or PLR to be an independent prognostic factor for OS and DFS (P=0.000 and P=0.001, respectively).

Conclusions: Preoperative CNP is associated with tumor progression and can be considered an independent marker of postoperative survival in patients with STS.

The hypoxia marker CAIX is prognostic in the UK phase III VorteX-Biobank cohort: an important resource for translational research in soft tissue sarcoma

BACKGROUND

Despite high metastasis rates, adjuvant/neoadjuvant systemic therapy for localised soft tissue sarcoma (STS) is not used routinely. Progress requires tailoring therapy to features of tumour biology, which need exploration in well-documented cohorts. Hypoxia has been linked to metastasis in STS and is targetable. This study evaluated hypoxia prognostic markers in the phase III adjuvant radiotherapy VorteX trial.

METHODS

Formalin-fixed paraffin-embedded tumour biopsies, fresh tumour/normal tissue and blood were collected before radiotherapy. Immunohistochemistry for HIF-1α, CAIX and GLUT1 was performed on tissue microarrays and assessed by two scorers (one pathologist). Prognostic analysis of disease-free survival (DFS) used Kaplan-Meier and Cox regression.

RESULTS

Biobank and outcome data were available for 203 out of 216 randomised patients. High CAIX expression was associated with worse DFS (hazard ratio 2.28, 95% confidence interval: 1.44-3.59, P<0.001). Hypoxia-inducible factor-1α and GLUT1 were not prognostic. Carbonic anhydrase IX remained prognostic in multivariable analysis.

CONCLUSIONS

The VorteX-Biobank contains tissue with linked outcome data and is an important resource for research. This study confirms hypoxia is linked to poor prognosis in STS and suggests that CAIX may be the best known marker. However, overlap between single marker positivity was poor and future work will develop an STS hypoxia gene signature to account for tumour heterogeneity.

Validation of a hypoxia related gene signature in multiple soft tissue sarcoma cohorts

PURPOSE

There is a need for adjuvant/neo-adjuvant treatment strategies to prevent metastatic relapse in soft tissue sarcoma (STS). Tumor hypoxia is associated with a high-risk of metastasis and is potentially targetable. This study aimed to derive and validate a hypoxia mRNA signature for STS for future biomarker-driven trials of hypoxia targeted therapy.

MATERIALS AND METHODS

RNA sequencing was used to identify seed genes induced by hypoxia in seven STS cell lines. Primary tumors in a training cohort (French training) were clustered into two phenotypes by seed gene expression and a de novo hypoxia signature derived. Prognostic significance of the de novo signature was evaluated in the training and two independent validation (French validation and The Cancer Genome Atlas) cohorts.

RESULTS

37 genes were up-regulated by hypoxia in all seven cell lines, and a 24-gene signature was derived. The high-hypoxia phenotype defined by the signature was enriched for well-established hypoxia genes reported in the literature. The signature was prognostic in univariable analysis, and in multivariable analysis in the training (n = 183, HR 2.16, P = 0.0054) and two independent validation (n = 127, HR 3.06, P = 0.0019; n = 258, HR 2.05, P = 0.0098) cohorts. Combining information from the de novo hypoxia signature and a genome instability signature significantly improved prognostication. Transcriptomic analyses showed high-hypoxia tumors had more genome instability and lower immune scores.

CONCLUSIONS

A 24-gene STS-specific hypoxia signature may be useful for prognostication and identifying patients for hypoxia-targeted therapy in clinical trials.