The CINSARC signature as a prognostic marker for clinical outcome in multiple neoplasms

CINSARC signature outperforms gold-standard TNM staging and consensus molecular subtypes for clinical outcome in stage II-III colorectal carcinoma

The outcome of stage II-III colorectal cancer (CRC) is highly variable and therapeutic choice is currently based on TNM staging with a few additional biomarkers. However, studies show that some stage III patients have a better prognosis than some stage II patients. A promising consensus molecular (CMS) classification with prognostic relevance has been developed, but it is not used in daily practice. Our team developed CINSARC, a 67-gene expression prognostic signature, whose prognostic value has been demonstrated in many cancer types. It is applicable to formalin-fixed, paraffin-embedded (FFPE) blocks using NanoString® technology. We investigated whether it could predict outcome in stage II-III CRC. We established the CINSARC classification on the TCGA retrospective cohort comprising 297 stage II-III CRC patients using RNA sequencing and on a second independent cohort comprising 169 cases using NanoString® technology. We compared its recurrence-free and overall survival prognostic value with TNM staging and CMS classification. In the TCGA cohort, we showed that CINSARC significantly splits the population of stage II-III CRC into two groups with different progression-free interval (P = 1.68 × 10^-2; HR = 1.87 [1.11-3.16]) and overall survival (P = 3.73 × 10^-3; HR = 2.45 [1.31-4.59]) and is a strong prognostic factor in multivariate analysis, outperforming TNM staging and CMS classification. We validated these results in the second cohort by applying CINSARC on FFPE samples with Nanostring® technology. CINSARC is a ready-to-use tool with a robust independent prognostic value in stage II-III CRC.

CINSARC in high-risk soft tissue sarcoma patients treated with neoadjuvant chemotherapy: Results from the ISG-STS 1001 study

BACKGROUND

The Complexity INdex in SARComas (CINSARC) is a transcriptional signature derived from the expression of 67 genes involved in mitosis control and chromosome integrity. This study aims to assess CINSARC value of in an independent series of high-risk patients with localized soft tissue sarcoma (STS) treated with preoperative chemotherapy within a prospective, randomized, phase III study (ISG-STS 1001).

PATIENTS AND METHODS

Patients with available pre-treatment samples, treated with 3 cycles of either standard (ST) preoperative or histotype-tailored (HT) chemotherapy, were scored according to CINSARC (low-risk, C1; high-risk, C2). The 10-year overall survival probability (pr-OS) according to SARCULATOR was calculated, and patients were classified accordingly (low-risk, Sarc-LR, 10-year pr-OS>60%; high-risk, Sarc-HR, 10-year pr-OS<60%). Survival functions were estimated using the Kaplan-Meier method and compared using log-rank test.

RESULTS

Eighty-six patients were included, 30 C1 and 56 C2, 49 Sarc-LR and 37 Sarc-HR. A low level of agreement between CINSARC and SARCULATOR was observed (Cohen's Kappa = 0.174). The 5-year relapse-free survival in C1 and C2 were 0.57 and 0.55 (p = 0.481); 5-year metastases-free survival 0.63 and 0.64 (p = 0.740); 5-year OS 0.80 and 0.72 (p = 0.460). The 5-year OS in C1 treated with ST and HT chemotherapy was 0.84 and 0.76 (p = 0.251) respectively; in C2 treated it was 0.72 and 0.70 (p = 0.349). The 5-year OS in Sarc-LR treated with S and HT chemotherapy was 0.80 and 0.82 (p = 0.502) respectively; in Sarc-HR it was 0.70 and 0.61 (p = 0.233).

CONCLUSIONS

Our results, although constrained by the small size of the series, suggest that CINSARC has weak prognostic power in high-risk, localized STS treated with neoadjuvant chemotherapy.

The Molecular Biology of Soft Tissue Sarcomas: Current Knowledge and Future Perspectives

Soft tissue sarcomas are malignant tumors of mesenchymal origin, encompassing a large spectrum of entities that were historically classified according to their histological characteristics. Over the last decades, molecular biology has allowed a better characterization of these tumors, leading to the incorporation of multiple molecular features in the latest classification of sarcomas as well as to molecularly-guided therapeutic strategies. This review discusses the main uses of molecular biology in current practice for the diagnosis and treatment of soft tissue sarcomas, in addition to perspectives for this rapidly evolving field of research.

Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

Osteosarcoma has a guarded prognosis. A major hurdle in developing more effective osteosarcoma therapies is the lack of disease-specific biomarkers to predict risk, prognosis, or therapeutic response. Exosomes are secreted extracellular microvesicles emerging as powerful diagnostic tools. However, their clinical application is precluded by challenges in identifying disease-associated cargo from the vastly larger background of normal exosome cargo. We developed a method using canine osteosarcoma in mouse xenografts to distinguish tumor-derived from host-response exosomal messenger RNAs (mRNAs). The model allows for the identification of canine osteosarcoma-specific gene signatures by RNA sequencing and a species-differentiating bioinformatics pipeline. An osteosarcoma-associated signature consisting of five gene transcripts (SKA2, NEU1, PAF1, PSMG2, and NOB1) was validated in dogs with spontaneous osteosarcoma by real-time quantitative reverse transcription PCR (qRT-PCR), while a machine learning model assigned dogs into healthy or disease groups. Serum/plasma exosomes were isolated from 53 dogs in distinct clinical groups ("healthy", "osteosarcoma", "other bone tumor", or "non-neoplastic disease"). Pre-treatment samples from osteosarcoma cases were used as the training set, and a validation set from post-treatment samples was used for testing, classifying as "osteosarcoma detected" or "osteosarcoma-NOT detected". Dogs in a validation set whose post-treatment samples were classified as "osteosarcoma-NOT detected" had longer remissions, up to 15 months after treatment. In conclusion, we identified a gene signature predictive of molecular remissions with potential applications in the early detection and minimal residual disease settings. These results provide proof of concept for our discovery platform and its utilization in future studies to inform cancer risk, diagnosis, prognosis, and therapeutic response.

WNT/β-Catenin Pathway in Soft Tissue Sarcomas: New Therapeutic Opportunities?

Simple Summary

The WNT/β-catenin signaling pathway is involved in fundamental processes for the proliferation and differentiation of mesenchymal stem cells. However, little is known about its relevance for mesenchymal neoplasms, such us soft tissue sarcomas (STS). Chemotherapy based on doxorubicin (DXR) still remains the standard first-line treatment for locally advanced unresectable or metastatic STS, although overall survival could not be improved by combination with other chemotherapeutics. In this sense, the development of new therapeutic approaches continues to be an unmatched goal. This review covers the most important molecular alterations of the WNT signaling pathway in STS, broadening the current knowledge about STS as well as identifying novel drug targets. Furthermore, the current therapeutic options and drug candidates to modulate WNT signaling, which are usually classified by their interaction site upstream or downstream of β-catenin, and their presumable clinical impact on STS are discussed.

Abstract

Soft tissue sarcomas (STS) are a very heterogeneous group of rare tumors, comprising more than 50 different histological subtypes that originate from mesenchymal tissue. Despite their heterogeneity, chemotherapy based on doxorubicin (DXR) has been in use for forty years now and remains the standard first-line treatment for locally advanced unresectable or metastatic STS, although overall survival could not be improved by combination with other chemotherapeutics. In this sense, the development of new therapeutic approaches continues to be a largely unmatched goal. The WNT/β-catenin signaling pathway is involved in various fundamental processes for embryogenic development, including the proliferation and differentiation of mesenchymal stem cells. Although the role of this pathway has been widely researched in neoplasms of epithelial origin, little is known about its relevance for mesenchymal neoplasms. This review covers the most important molecular alterations of the WNT signaling pathway in STS. The detection of these alterations and the understanding of their functional consequences for those pathways controlling sarcomagenesis development and progression are crucial to broaden the current knowledge about STS as well as to identify novel drug targets. In this regard, the current therapeutic options and drug candidates to modulate WNT signaling, which are usually classified by their interaction site upstream or downstream of β-catenin, and their presumable clinical impact on STS are also discussed.

Complexity index in sarcoma and genomic grade index gene signatures in rhabdomyosarcoma of pediatric and adult ages

BACKGROUND

Rhabdomyosarcoma (RMS), the most frequent soft-tissue sarcoma in childhood, shows extensive heterogeneity in histology, site and age of onset, clinical course, and prognosis. Adolescents and young adults (AYA) with RMS form a subgroup of patients whose survival lacks behind that of children while diagnosed with histologically similar tumors.

PROCEDURES

A 67-gene prognostic signature related to chromosome integrity, mitotic control, and genome complexity in sarcomas (CINSARC) is considered a powerful tool for identifying tumors with a highly metastatic potential. With this study, we investigated the prognostic value of CINSARC signature on a cohort of 48 pediatric (PEDs) and AYAs-RMS.

RESULTS

CINSARC resulted not significantly correlated with age, suggesting other determinants to be responsible for that difference in survival. It remained a significant prognostic variable in both the groups of PEDs and AYAs. Also, genomic grade index signature was tested on the same cohort and showed very similar results with CINSARC.

CONCLUSIONS

Our study showed that CINSARC correlated with outcome in RMS patients and may be potentially considered a tool to predict outcome, and so stratify RMS patients.

[New data on the molecular biology of soft tissue sarcoma]

Sarcoma consists in a group of rare malignant tumours of mesenchymal origin characterized by their vast clinical, pathological and biological heterogeneity. The pathological diagnosis of sarcoma relies classically of the differentiation features of tumour cells, with dozens of different tumour subtypes described in the last international classifications. Over the last decades, the advances in the development of new techniques of molecular biology have led to a major complexification of sarcoma classification, with the identification of multiple and specific molecular alterations that have led to significant changes for patients diagnostic, prognostic and therapeutic management. This review aims at giving an overview on the current knowledge of the molecular biology of soft tissue sarcoma, and emphasizes on their consequences for the daily management of patients.

The CINSARC signature predicts the clinical outcome in patients with Luminal B breast cancer

CINSARC, a multigene expression signature originally developed in sarcomas, was shown to have prognostic impact in various cancers. We tested the prognostic value for disease-free survival (DFS) of CINSARC in a series of 6035 early-stage invasive primary breast cancers. CINSARC had independent prognostic value in the Luminal B subtype and not in the other subtypes. In Luminal B patients receiving adjuvant endocrine therapy but no chemotherapy, CINSARC identified patients with different 5-year DFS (90% [95%CI 86-95] in low-risk vs. 79% [95%CI 75-84] in high-risk, p = 1.04E-02). Luminal B CINSARC high-risk tumors were predicted to be less sensitive to endocrine therapy and CDK4/6 inhibitors, but more vulnerable to homologous recombination targeting and immunotherapy. We concluded that CINSARC adds prognostic information to that of clinicopathological features in Luminal B breast cancers, which might improve patients' stratification and better orient adjuvant treatment. Moreover, it identifies potential therapeutic avenues in this aggressive molecular subtype.

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.

Molecular prognostication of uterine smooth muscle neoplasms: From CGH array to CINSARC signature and beyond

Uterine leiomyoma and leiomyosarcoma are located at the ends of the spectrum of smooth muscle lesions. Leiomyosarcoma belongs to the complex genomic sarcomas characterized by complex karyotypes. In contrast, leiomyoma, has a low level of chromosomal complexity. The analysis of genomic profiles of uterine smooth muscle tumors shows that genomic complexity, which is an expression of chromosomal instability, correlates with the metastatic potential and malignity of tumors: the more genetically complex a smooth muscle tumor is, the more malignant is its progression. In uterine tumors with uncertain malignant potential, the assessment of genomic index by CGH array, that is, counting the genomic complexity of a tumor, allows tumors with a risk of recurrence such as leiomyosarcomas to be distinguished from benign tumors like leiomyomas. The prognosis of leiomyosarcoma is poor and the most powerful prognostic factor so far is stage, as the histologic grade is not informative. In the quest to find efficient molecular prognostic factors, the transcriptomic signature CINSARC Nanocind, a mirror of chromosomic complexity and instability, outperforms stage, in both overall and recurrence-free survival. Genomic index and the CINSARC signature will contribute to improving diagnoses, therapeutic strategies, and randomization in future clinical trials. The biological understanding of the links between the CINSARC signature and metastatic mechanisms may lead to the development of new drugs. Furthermore, ctDNA is a promising new technique to detect residual disease and early recurrence.

A Global and Integrated Analysis of CINSARC-Associated Genetic Defects

The Complexity Index in Sarcomas (CINSARC) signature is a transcriptomic marker that identifies high-risk soft-tissue sarcomas and is associated with high metastatic potential. During the last decade, CINSARC has been successfully developed and validated and is currently being assessed in two prospective phase III clinical trials for stratification of therapy. Although the link between CINSARC expression and tumor aggressiveness is well established, questions remain about how CINSARC genes are regulated. In this study, we leveraged a The Cancer Genome Atlas multiomics study on sarcomas with complex genetics to appraise the association between CINSARC profile, genomic features, and two potential regulation mechanisms, DNA methylation and miRNA expression. CINSARC expression was associated with an increase of ploidy, intratumor heterogeneity, copy-number alteration, altered expression of 37 miRNAs, and a decrease of DNA methylation. These genetic changes are not independent, but rather act together to promote or repress CINSARC expression. These findings depict new insights into CINSARC regulation. SIGNIFICANCE: These findings demonstrate that CINSARC is associated with a variety of genomic aberrations that contribute to higher risk for metastasis and may serve as a prognostic factor in sarcomas and beyond.

Kinome expression profiling to target new therapeutic avenues in multiple myeloma

Multiple myeloma (MM) account for approximately 10% of hematological malignancies and is the second most common hematological disorder. Kinases inhibitors are widely used and their efficiency for the treatment of cancers has been demonstrated. Here, in order to identify kinases of potential therapeutic interest for the treatment of MM, we investigated the prognostic impact of the kinome expression profile in large cohorts of patients. We identified 36 kinome-related genes significantly linked with a prognostic value to MM, and built a kinome index based on their expression. The Kinome Index (KI) is linked to prognosis, proliferation, differentiation, and relapse in MM. We then tested inhibitors targeting seven of the identified protein kinas-es (PBK, SRPK1, CDC7-DBF4, MELK, CHK1, PLK4, MPS1/TTK) in human myeloma cell lines. All tested inhibitors significantly reduced the viability of myeloma cell lines, and we confirmed the potential clinical interest of three of them on primary myeloma cells from patients. In addition, we demonstrated their ability to potentialize the toxicity of conventional treatments, including Melphalan and Lenalidomide. This highlights their potential beneficial effect in myeloma therapy. Three kinases inhibitors (CHK1i, MELKi and PBKi) overcome resistance to Lenalidomide, while CHK1, PBK and DBF4 inhibitors re-sensitize Melphalan resistant cell line to this conventional therapeutic agent. Altogether, we demonstrate that kinase inhibitors could be of therapeutic interest especially in high-risk myeloma patients defined by the KI. CHEK1, MELK, PLK4, SRPK1, CDC7-DBF4, MPS1/TTK and PBK inhibitors could represent new treatment options either alone or in combination with Melphalan or IMiD for refractory/relapsing myeloma patients.

Standard treatment and emerging drugs for managing synovial sarcoma: adult's and pediatric oncologist perspective

INTRODUCTION

in this review we discuss the standard of care for both pediatric and adult synovial sarcoma (SS), the prognostic differences between them, and the treatments available for localized and advanced diseases. We also overview the biology and the recent drugs under consideration in clinical trials on SS. Areas covered: we focus on new targeted therapies being investigated for advanced SS, especially anti-angiogenic drugs, and immunotherapy. We review all the published data and ongoing trials dedicated to SS or to soft tissue sarcoma in general, paying particular attention to the results obtained in SS patients. Expert opinion: we expect new treatment strategies to become available for SS in the near future. The ongoing and published trials on targeted therapies and immunotherapy mainly concern adult patients, but the somatic biology of pediatric SS has some similarities as in adult disease. A stronger cooperation between adult and pediatric oncologists in recent years has led to a more shared effort to find new treatment strategies for advanced SS patients, regardless of their age.

CINSARC signature as a prognostic marker for clinical outcome in sarcomas and beyond

Prognostication is a key issue for sarcoma patients' care as it triggers the therapeutic approach including chemotherapy, which is still not standard for localized patients. Current prognostic evaluation, based on the FNCLCC grading system, has recently been improved by the CINSARC signature outperforming histology-based grading system by identifying high-risk patients in every grade, even in those considered as low. CINSARC is an expression-based signature related to mitosis and chromosome integrity with prognostic value in a wide range of cancers additional to sarcoma. First developed with frozen material, CINSARC is now coupled with NanoString technology allowing evaluation from FFPE blocks used in clinical practice. Consequently, CINSARC is currently evaluated in clinical trials with a dual objective of demonstrating the benefit of chemotherapy in sarcoma patients and testing its response prediction. Considering its overarching value in oncology, its development is welcome in any cancers where the prognostication needs to be improved.

Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas

Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequencing data could be a valuable new tool to understand differences between and within entities. Here we used machine learning analysis to identify novel diagnostic and prognostic markers and therapeutic targets for soft tissue sarcomas. Gene expression data was used from the Cancer Genome Atlas, the Genotype-Tissue Expression project and the French Sarcoma Group. We identified three groups of tumors that overlap in their molecular profiles as seen with unsupervised t-Distributed Stochastic Neighbor Embedding clustering and a deep neural network. The three groups corresponded to subtypes that are morphologically overlapping. Using a random forest algorithm, we identified novel diagnostic markers for soft tissue sarcoma that distinguished between synovial sarcoma and MPNST, and that we validated using qRT-PCR in an independent series. Next, we identified prognostic genes that are strong predictors of disease outcome when used in a k-nearest neighbor algorithm. The prognostic genes were further validated in expression data from the French Sarcoma Group. One of these, HMMR, was validated in an independent series of leiomyosarcomas using immunohistochemistry on tissue micro array as a prognostic gene for disease-free interval. Furthermore, reconstruction of regulatory networks combined with data from the Connectivity Map showed, amongst others, that HDAC inhibitors could be a potential effective therapy for multiple soft tissue sarcoma subtypes. A viability assay with two HDAC inhibitors confirmed that both leiomyosarcoma and synovial sarcoma are sensitive to HDAC inhibition. In this study we identified novel diagnostic markers, prognostic markers and therapeutic leads from multiple soft tissue sarcoma gene expression datasets. Thus, machine learning algorithms are powerful new tools to improve our understanding of rare tumor entities.

Soft-tissue sarcomas are a group of rare cancers that can be challenging to diagnose and treat. The morphology of the different soft-tissue sarcoma subtypes can overlap and the prognosis differs significantly between, and also within, the different subtypes. Moreover, targeted therapies are often not available. In this study we used transcriptome sequencing data from The Cancer Genome Atlas, containing 206 soft-tissue sarcoma samples which we analyzed using different machine learning algorithms to gain novel insights. When possible, we verified our findings in independent datasets or in cell lines. First, we found that both synovial sarcomas and malignant peripheral nerve sheath tumors show the largest overlap with normal tissue derived from the nervous system. This link with neural differentiation for synovial sarcoma was not well established until now. Second, genes were identified whose expression could be used to differentiate between the different soft-tissue sarcomas where the morphology overlaps. Third, novel prognostic genes were identified for separate subtypes. One gene, HMMR, which we found as a strong prognostic gene for leiomyosarcoma, was verified with immunohistochemistry on samples from our archives. Last, using a network analysis new potential therapies were identified. HDAC inhibitors were identified as a potential strong therapy for sarcomas, including leiomyosarcomas, which we verified in cell lines.

Prognostication in Mesenchymal Tumors: Can We Improve?

Prognostication in mesenchymal tumors can be challenging. They exhibit diverse, and sometimes overlapping, histologic features that are not always predictive of their true behavior. This article highlights examples of both traditional and emerging sarcoma biomarkers.

Asynchronous and pathological windows of implantation: two causes of recurrent implantation failure

STUDY QUESTION

Is endometrial recurrent implantation failure (RIF) only a matter of an asynchronous (displaced) window of implantation (WOI), or could it also be a pathological (disrupted) WOI?

SUMMARY ANSWER

Our predictive results demonstrate that both displaced and disrupted WOIs exist and can present independently or together in the same RIF patient.

WHAT IS KNOWN ALREADY

Since 2002, many gene expression signatures associated with endometrial receptivity and RIF have been described. Endometrial transcriptomics prediction has been applied to the human WOI in two previous studies. One study describes endometrial RIF to be the result of a temporal displacement of the WOI. The other indicates that endometrial RIF can also result from a molecularly disrupted WOI without temporal displacement.

STUDY DESIGN, SIZE, DURATION

Retrospective analysis was undertaken to compare WOI endometrial transcriptomics predictions in controls (n = 72) and RIF patients (n = 43). RIF was clinically designated by the absence of implantation after four or more transfers of high quality embryos or after the placement of 10 or more embryos in multiple transfers. Endometrial tissue samples were collected from LH + 5 to LH + 8. We compared the two molecular causes of RIF to signatures currently described in the literature. We propose a new transcriptomic RIF taxonomy to fill the gap between the two hypotheses and to guide the development of clinical detection and determination of both types of RIF.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Utilizing 115 gene expression profiles, two different predictive designs were developed: one considering RIF versus controls removing menstrual cycle timing, called the disrupted or pathological model, and another stratifying the WOI in transcriptomic profiles related to timing for predicting displacements. The predictive value of each model was compared between all signatures selected. We propose a new genomic approach that distinguishes between both types of RIF in the same sample cohort.

MAIN RESULTS AND THE ROLE OF CHANCE

From the 16 signatures analysed, we clearly predicted two causes of RIF-both a displaced WOI and an on-time but pathologically disrupted WOI. A high predictive value related to WOI profiles associated with menstrual cycle timing was found in most of the signatures. Specifically, 69% of the signatures analysed presented an accuracy higher than expected by chance in a range from 0.87 to 0.97. Displacements and disruptions were not molecularly independent, as some signatures were moderately associated with both causes. The gene and functional comparison between signatures revealed that they were not similar, although we did find functions in common and a cluster of moderate functional concordance between some of the signatures that predicted displacements (the highest Cohen's Kappa index were between 0.55 and 0.62 depending on the functional database). We propose a new transcriptomic RIF taxonomy to fill the gap between these prior studies and to establish methodology for detecting and distinguishing both types of RIF in clinical practice. Our findings indicate these two phenotypes could present independently or together in the same RIF patient. RIF patients designated by clinical criteria have been stratified transcriptomically as 18.6% with only a displaced WOI, 53.5% with a displaced and pathological WOI, 23.3% with only a disrupted WOI, and 4.7% could be a clinical RIF with non-endometrial origin. The new RIF transcriptomic taxonomy avoids menstrual cycle timing as a confounding variable that should be controlled for, distinguishing clearly between a disrupted and a displaced WOI for precision medicine in RIF.

LIMITATIONS REASONS FOR CAUTION

The main objective of this study was to use transcriptomics to detect both RIF causes and to understand the role of transcriptomic signatures in these phenotypes. The predictive value in absolute terms for each signature was not indicative in these prediction designs; instead, the comparison between signatures was most important for prediction capability in the same sample cohort for both RIF causes. Clinical follow up of the RIF taxonomies proposed has not been analysed in this study, so further prospective clinical studies are necessary to determine the prevalence and penetrance of these phenotypes.

WIDER IMPLICATIONS OF THE FINDINGS

The main insight from this study is a new understanding of RIF taxonomy. Understanding how to classify RIF patients to distinguish clinically between a patient who could benefit from a personalized embryo transfer day and a patient with a disrupted WOI will enable identification and stratification for the research and development of new treatments. In addition, we demonstrate that basic research designs in endometrial transcriptomics cause masking of the study variable by the menstrual cycle timing.

STUDY FUNDING/COMPETING INTEREST(S)

This research has been funded by IVI-RMA; the authors do not have any competing interests.

Genomic complexity in pediatric synovial sarcomas (Synobio study): the European pediatric soft tissue sarcoma group (EpSSG) experience

A genomic index (GI) tool using array comparative genomic hybridization (aCGH) on tumor cells has emerged as independent prognostic factor associated with the risk of metastatic relapse in synovial sarcoma (SS). The aim was to assess GI in pediatric patients with SS, to determine its value as a prognostic factor. All pediatric/adolescent/young adults' (<25 years) with localized SS prospectively included in the European EpSSG-NRSTS05 protocol with a contributive aCGH were selected. Definition of GI was A² /C, where A is the total number of alterations (segmental gains and losses) and C is the number of involved chromosomes on aCGH results. GI₁ group corresponds to cases with no copy number alterations (flat profile, GI = 0) and GI₂ group cases with at least one or more copy number alterations (rearranged profile; GI ≥ 1). Samples were available from 61 patients. The median age of the cohort was 13 years (range: 4-24). Overall, 55.7% were GI₁ group, and 44.3% GI₂ . After a median follow-up of 62 months (range: 0.1-112), 10 tumor events occurred and five patients died. Respectively, for GI₁ versus GI₂ groups, five-year event-free survival (EFS) was 93.8 ± 4.2% versus 64.9 ± 10.1% (P < 0.006) and five-year Metastatic-Free Survival (MFS) 93.8 ± 4.2% versus 72.9 ± 9.5% (P < 0.04). In multivariate analysis, GI status as adjusted for IRS group, patient age, site, and tumor size remain independent prognostic for EFS with a relative risk (RR) of 6.4 [1.3-31.9] (P < 0.01) and RR for MFS is 4.8 [0.9-25.7] (P < 0.05). Genomic complexity evaluated through GI may explain the metastatic behavior of pediatric SS.