Outcome prediction in patients with localized soft tissue sarcoma: which tool is the best?

Outcome prediction in patients with localized soft tissue sarcoma: which tool is the best? Soft tissue sarcomas (STS) are rare malignancies of mesenchymal origin comprising ∼1% of all adult cancers. According to the 2013 updated World Health Organization (WHO) classification STS represents a highly heterogeneous tumor entity of more than 50 subtypes showing very distinct histologic, molecular and certainly clinical characteristics [1]. Surgery is the mainstay of treatment of localized disease, however, around 50% of patients experience metastatic recurrence during the course of their disease. This disease stage is still characterized by an unfavorable prognosis and a median overall survival of ∼12–15 months [2, 3]. Due to conflicting data, adjuvant chemotherapy is not a standard treatment in STS in adulthood [4], but may be offered to the cohort of high-risk patients—tumor size >5 cm, deep localization, G2/3—after detailed patient information and shared-decision making. However, in spite of numerous studies, we are still missing strong prognostic markers to predict patients’ outcome. The classic pathologic grading system for STS based on the Federation Francaise des Centres de Lutte Contre le Cancer (FNCLCC) is still the most accepted tool to predict metastatic relapse in localized STS patients at an early stage [5]. The FNCLCC grading system differentiates three malignancy grades based on the evaluation of the main pathologic parameters necrosis, differentiation and the mitotic index per 10 HPFs (high-power field). Another comparable three-grade system has been developed by the National Cancer Institute but has the same limitations as the FNCLCC grading system in daily practice, both being not fully applicable in all pathologic STS subtypes. In the era of next generation sequencing, gene expression profiling, and epigenomic studies there are high expectations that deeper insights into tumor biology may help us to better predict clinical outcome in localized STS patients and, thus, to ease the decision whether adjuvant treatment will be beneficial in a specific case or not. In this respect, probably the best studied gene expression signature is the CINSARC signature developed and validated in 2010 by the French Sarcoma Group in a cohort of 310 STS patients [6]. CINSARC defines ‘high-risk’ and ‘low-risk’ patients based on a high or low number of genomic alterations in a 67 gene expression signature. Although other signatures do exist, CINSARC is probably the most widely studied multigene signature in STS. The CINSARC signature has also been validated in terms of response to neoadjuvant chemotherapy [7]. However, is CINSARC the best predictive signature or may other signatures from more frequent tumor types be more informative also in STS? The Genomic Grade Index (GGI) has been developed in breast cancer by comparing the genomic profile of grade 3 versus grade 1 tumors. With 108 genes being analyzed the GGI analyzes roughly the twofold number of genes compared with the ones being analyzed in CINSARC. In analogy, the GGI groups tumors into ‘GGI-high’ and ‘GGI-low’ with high versus low risks of recurrence and improves the prognostic classification of grade 2 breast cancer [8]. As both breast cancer and STS are based on similar major morphologic criteria, such as mitotic count and differentiation, Bertucci et al. hypothesized in their paper entitled ‘The GGI predicts post-operative clinical outcome in patients with soft tissue sarcoma’ that the GGI may improve the prognostic classification of STS [9]. For that purpose, gene expression data of 678 STS patients—more than twice of the number of patients than being tested for the CINSARC validation—were analyzed using DNA microarrays and RNA sequencing. In summary, the authors could demonstrate that the GGI is an independent predictor for metastatic relapse. Moreover, Bertucci et al. have certainly carried out the largest study of gene expression profiles in STS as of today. When applying the GGI to the STS samples, 41% were classified as ‘GGI-low’ and 59% as ‘GGI-high’. The ‘GGI-high’ group was more associated with poor prognostic features than the ‘GGI-low’ group, such as pathologic grade 3, undifferentiated sarcomas, and a complex genetic karyotype. The 5-year metastasis-free survival was 53% in the ‘GGI-high’ group versus 78% in the ‘GGI-low’ group. In fact, the GGI-based classification stratified the patients with pathologic grade 1–2 and those with pathologic grade 3 into two classes with different 5-year metastasis-free survival. As in breast cancer, the GGI has the potential to improve pathologic grading in STS to facilitate therapeutic stratification of individual cases. One major limitation of both the CINCARC and the GGI signature is that they have been established on RNA extracts from fresh frozen tissue which in daily practice is available only in a very small minority of cases. A prerequisite to translate the GGI signature into clinical routine will be the establishment of a test based on formalin-fixed paraffin-embedded samples. In summary, Bertucci et al. could demonstrate that the GGI classifying localized STS patients after surgery outperforms the prognostic performance of the conventional FNCLCC grading system. The GGI provides independent prognostic information for metastasis-free survival in multivariate analysis complementary to CINSARC multigene signature. Therefore, the GGI could help to identify patients who may really benefit from adjuvant chemotherapy and prevent others from receiving an insufficient and toxic treatment. The relatively large number of genes included in the GGI may hamper the application into clinical routine. No doubt, further validation in prospective clinical trials of adjuvant chemotherapy in STS patients is warranted. Funding None declared. Disclosure The authors have declared no conflict of interest. References 1 Fletcher CDM, Bridge JA, Hogendoorn P, Mertens F. WHO Classification of Tumours of Soft Tissue and Bone  ( IARC WHO Classification of Tumours), 4th edition, 2013. 2 Clark MA, Fisher C, Judson I et al.   Soft-tissue sarcomas in adults. N Engl J Med  2005; 353( 7): 701– 711. Google Scholar CrossRef Search ADS PubMed  3 Blay JY, van Glabbeke M, Verweij J et al.   Advanced soft-tissue sarcoma: a disease that is potentially curable for a subset of patients treated with chemotherapy. Eur J Cancer  2003; 39( 1): 64– 69. Google Scholar CrossRef Search ADS PubMed  4 ESMO/European Sarcoma Network Working Group. Soft tissue and visceral sarcomas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol  2014; 25( Suppl 3): iii102– iii112. CrossRef Search ADS PubMed  5 Coindre JM, Terrier P, Guillou L et al.   Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas: a study of 1240 patients from the French Federation of Cancer Centers Sarcoma Group. Cancer  2001; 91( 10): 1914– 1926. Google Scholar CrossRef Search ADS PubMed  6 Chibon F, Lagarde P, Salas S et al.   Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nat Med  2010; 16( 7): 781– 787. Google Scholar CrossRef Search ADS PubMed  7 Bertucci F, Finetti P, Sabatier R, Birnbaum D. The CINSARC signature: prognostic and predictive of response to chemotherapy? Cell Cycle  2010; 9( 19): 4025– 4027. Google Scholar CrossRef Search ADS PubMed  8 Metzger-Filho O, Catteau A, Michiels S et al.   Genomic Grade Index (GGI): feasibility in routine practice and impact on treatment decisions in early breast cancer. PLoS One  2013; 8( 8): e66848. Google Scholar CrossRef Search ADS PubMed  9 Bertucci F, de Nonneville A, Finetti P et al.   The Genomic Grade Index predicts postoperative clinical outcome in patients with soft-tissue sarcoma. Ann Oncol  2018; 29( 2): 459– 465. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For Permissions, please email: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Oncology Oxford University Press

Outcome prediction in patients with localized soft tissue sarcoma: which tool is the best?

Loading next page...
 
/lp/ou_press/outcome-prediction-in-patients-with-localized-soft-tissue-sarcoma-02UFPLyd7j
Publisher
Oxford University Press
Copyright
© The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
ISSN
0923-7534
eISSN
1569-8041
D.O.I.
10.1093/annonc/mdx733
Publisher site
See Article on Publisher Site

Abstract

Soft tissue sarcomas (STS) are rare malignancies of mesenchymal origin comprising ∼1% of all adult cancers. According to the 2013 updated World Health Organization (WHO) classification STS represents a highly heterogeneous tumor entity of more than 50 subtypes showing very distinct histologic, molecular and certainly clinical characteristics [1]. Surgery is the mainstay of treatment of localized disease, however, around 50% of patients experience metastatic recurrence during the course of their disease. This disease stage is still characterized by an unfavorable prognosis and a median overall survival of ∼12–15 months [2, 3]. Due to conflicting data, adjuvant chemotherapy is not a standard treatment in STS in adulthood [4], but may be offered to the cohort of high-risk patients—tumor size >5 cm, deep localization, G2/3—after detailed patient information and shared-decision making. However, in spite of numerous studies, we are still missing strong prognostic markers to predict patients’ outcome. The classic pathologic grading system for STS based on the Federation Francaise des Centres de Lutte Contre le Cancer (FNCLCC) is still the most accepted tool to predict metastatic relapse in localized STS patients at an early stage [5]. The FNCLCC grading system differentiates three malignancy grades based on the evaluation of the main pathologic parameters necrosis, differentiation and the mitotic index per 10 HPFs (high-power field). Another comparable three-grade system has been developed by the National Cancer Institute but has the same limitations as the FNCLCC grading system in daily practice, both being not fully applicable in all pathologic STS subtypes. In the era of next generation sequencing, gene expression profiling, and epigenomic studies there are high expectations that deeper insights into tumor biology may help us to better predict clinical outcome in localized STS patients and, thus, to ease the decision whether adjuvant treatment will be beneficial in a specific case or not. In this respect, probably the best studied gene expression signature is the CINSARC signature developed and validated in 2010 by the French Sarcoma Group in a cohort of 310 STS patients [6]. CINSARC defines ‘high-risk’ and ‘low-risk’ patients based on a high or low number of genomic alterations in a 67 gene expression signature. Although other signatures do exist, CINSARC is probably the most widely studied multigene signature in STS. The CINSARC signature has also been validated in terms of response to neoadjuvant chemotherapy [7]. However, is CINSARC the best predictive signature or may other signatures from more frequent tumor types be more informative also in STS? The Genomic Grade Index (GGI) has been developed in breast cancer by comparing the genomic profile of grade 3 versus grade 1 tumors. With 108 genes being analyzed the GGI analyzes roughly the twofold number of genes compared with the ones being analyzed in CINSARC. In analogy, the GGI groups tumors into ‘GGI-high’ and ‘GGI-low’ with high versus low risks of recurrence and improves the prognostic classification of grade 2 breast cancer [8]. As both breast cancer and STS are based on similar major morphologic criteria, such as mitotic count and differentiation, Bertucci et al. hypothesized in their paper entitled ‘The GGI predicts post-operative clinical outcome in patients with soft tissue sarcoma’ that the GGI may improve the prognostic classification of STS [9]. For that purpose, gene expression data of 678 STS patients—more than twice of the number of patients than being tested for the CINSARC validation—were analyzed using DNA microarrays and RNA sequencing. In summary, the authors could demonstrate that the GGI is an independent predictor for metastatic relapse. Moreover, Bertucci et al. have certainly carried out the largest study of gene expression profiles in STS as of today. When applying the GGI to the STS samples, 41% were classified as ‘GGI-low’ and 59% as ‘GGI-high’. The ‘GGI-high’ group was more associated with poor prognostic features than the ‘GGI-low’ group, such as pathologic grade 3, undifferentiated sarcomas, and a complex genetic karyotype. The 5-year metastasis-free survival was 53% in the ‘GGI-high’ group versus 78% in the ‘GGI-low’ group. In fact, the GGI-based classification stratified the patients with pathologic grade 1–2 and those with pathologic grade 3 into two classes with different 5-year metastasis-free survival. As in breast cancer, the GGI has the potential to improve pathologic grading in STS to facilitate therapeutic stratification of individual cases. One major limitation of both the CINCARC and the GGI signature is that they have been established on RNA extracts from fresh frozen tissue which in daily practice is available only in a very small minority of cases. A prerequisite to translate the GGI signature into clinical routine will be the establishment of a test based on formalin-fixed paraffin-embedded samples. In summary, Bertucci et al. could demonstrate that the GGI classifying localized STS patients after surgery outperforms the prognostic performance of the conventional FNCLCC grading system. The GGI provides independent prognostic information for metastasis-free survival in multivariate analysis complementary to CINSARC multigene signature. Therefore, the GGI could help to identify patients who may really benefit from adjuvant chemotherapy and prevent others from receiving an insufficient and toxic treatment. The relatively large number of genes included in the GGI may hamper the application into clinical routine. No doubt, further validation in prospective clinical trials of adjuvant chemotherapy in STS patients is warranted. Funding None declared. Disclosure The authors have declared no conflict of interest. References 1 Fletcher CDM, Bridge JA, Hogendoorn P, Mertens F. WHO Classification of Tumours of Soft Tissue and Bone  ( IARC WHO Classification of Tumours), 4th edition, 2013. 2 Clark MA, Fisher C, Judson I et al.   Soft-tissue sarcomas in adults. N Engl J Med  2005; 353( 7): 701– 711. Google Scholar CrossRef Search ADS PubMed  3 Blay JY, van Glabbeke M, Verweij J et al.   Advanced soft-tissue sarcoma: a disease that is potentially curable for a subset of patients treated with chemotherapy. Eur J Cancer  2003; 39( 1): 64– 69. Google Scholar CrossRef Search ADS PubMed  4 ESMO/European Sarcoma Network Working Group. Soft tissue and visceral sarcomas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol  2014; 25( Suppl 3): iii102– iii112. CrossRef Search ADS PubMed  5 Coindre JM, Terrier P, Guillou L et al.   Predictive value of grade for metastasis development in the main histologic types of adult soft tissue sarcomas: a study of 1240 patients from the French Federation of Cancer Centers Sarcoma Group. Cancer  2001; 91( 10): 1914– 1926. Google Scholar CrossRef Search ADS PubMed  6 Chibon F, Lagarde P, Salas S et al.   Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nat Med  2010; 16( 7): 781– 787. Google Scholar CrossRef Search ADS PubMed  7 Bertucci F, Finetti P, Sabatier R, Birnbaum D. The CINSARC signature: prognostic and predictive of response to chemotherapy? Cell Cycle  2010; 9( 19): 4025– 4027. Google Scholar CrossRef Search ADS PubMed  8 Metzger-Filho O, Catteau A, Michiels S et al.   Genomic Grade Index (GGI): feasibility in routine practice and impact on treatment decisions in early breast cancer. PLoS One  2013; 8( 8): e66848. Google Scholar CrossRef Search ADS PubMed  9 Bertucci F, de Nonneville A, Finetti P et al.   The Genomic Grade Index predicts postoperative clinical outcome in patients with soft-tissue sarcoma. Ann Oncol  2018; 29( 2): 459– 465. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Journal

Annals of OncologyOxford University Press

Published: Feb 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off