Genomic Grade Index predicts postoperative clinical outcome of GIST

The spectrum and significance of secondary (co-occurring) genetic alterations in sarcomas: the hallmarks of sarcomagenesis

Bone and soft tissue tumors are generally classified into complex karyotype sarcomas versus those with recurrent genetic alterations, often in the form of gene fusions. In this review, we provide an overview of important co-occurring genomic alterations, organized by biological mechanisms and covering a spectrum of genomic alteration types: mutations (single-nucleotide variations or indels) in oncogenes or tumor suppressor genes, copy number alterations, transcriptomic signatures, genomic complexity indices (e.g. CINSARC), and complex genomic structural variants. We discuss the biological and prognostic roles of these so-called secondary or co-occurring alterations, arguing that recognition and detection of these alterations may be significant for our understanding and management of mesenchymal tumors. On a related note, we also discuss major recurrent alterations in so-called complex karyotype sarcomas. These secondary alterations are essential to sarcomagenesis via a variety of mechanisms, such as inactivation of tumor suppressors, activation of proliferative signal transduction, telomere maintenance, and aberrant regulation of epigenomic/chromatin remodeling players. The use of comprehensive genomic profiling, including targeted next-generation sequencing panels or whole-exome sequencing, may be incorporated into clinical workflows to offer more comprehensive, potentially clinically actionable information. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Application of Multi-Omics Approach in Sarcomas: A Tool for Studying Mechanism, Biomarkers, and Therapeutic Targets

Sarcomas are rare, heterogeneous mesenchymal neoplasms with various subtypes, each exhibiting unique genetic characteristics. Although studies have been conducted to improve the treatment for sarcomas, the specific development from normal somatic cells to sarcoma cells is still unclear and needs further research. The diagnosis of sarcomas depends heavily on the pathological examination, which is yet a difficult work and requires expert analysis. Advanced treatment like precise medicine optimizes the efficacy of treatment and the prognosis of sarcoma patients, yet, in sarcomas, more studies should be done to put such methods in clinical practice. The revolution of advanced technology has pushed the multi-omics approach to the front, and more could be learnt in sarcomas with such methods. Multi-omics combines the character of each omics techniques, analyzes the mechanism of tumor cells from different levels, which makes up for the shortage of single-omics, and gives us an integrated picture of bioactivities inside tumor cells. Multi-omics research of sarcomas has reached appreciable progress in recent years, leading to a better understanding of the mutation, proliferation, and metastasis of sarcomas. With the help of multi-omics approach, novel biomarkers were found, with promising effects in improving the process of diagnosis, prognosis anticipation, and treatment decision. By analyzing large amounts of biological features, subtype clustering could be done in a better precision, which may be useful in the clinical procedure. In this review, we summarized recent discoveries using multi-omics approach in sarcomas, discussed their merits and challenges, and concluded with future perspectives of the sarcoma research.

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

BACKGROUND

Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type.

MATERIALS AND METHODS

Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification.

RESULTS

A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk.

CONCLUSIONS

CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

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.

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.

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.

Systematic review of current prognostication systems for primary gastrointestinal stromal tumors

BACKGROUND

The advent of tyrosine kinase inhibitors as adjuvant therapy has revolutionized the management of GIST and emphasized the need for accurate prognostication systems. Numerous prognostication systems have been proposed for GIST but at present it remains unknown which system is superior. The present systematic review aims to summarize current prognostication systems for primary treatment-naive GIST.

METHODS

A literature review of the Pubmed and Embase databases was performed to identify all published articles in English, from the 1st January 2002 to 28th Feb 2017, reporting on clinical prognostication systems of GIST.

RESULTS

Twenty-three articles on GIST prognostication systems were included. These systems were classified as categorical systems, which stratify patients into risk groups, or continuous systems, which provide an individualized form of risk assessment. There were 16 categorical systems in total. There were 4 modifications of the National Institute of Health (NIH) system, 2 modifications of Armed Forces Institute of Pathology (AFIP) criteria and 3 modifications of Joensuu (modified NIH) criteria. Of the 7 continuous systems, there were 3 prognostic nomograms, 3 mathematical models and 1 prognostic heat/contour maps. Tumor size, location and mitotic count remain the main variables used in these systems.

CONCLUSION

Numerous prognostication systems have been proposed for the risk stratification of GISTs. The most widely used systems today are the NIH, Joensuu modified NIH, AFIP and the Memorial Sloan Kettering Cancer Center nomogram. More validation and comparison studies are required to determine the optimal prognostication system for GIST.

Gene expression identifies heterogeneity of metastatic behavior among gastrointestinal stromal tumors

BACKGROUND

Adjuvant imatinib is useful in patients with gastrointestinal stromal tumors (GIST) at high risk of recurrence. At present, the risk of recurrence is determined based on tumor size, mitotic rate, tumor site, and tumor rupture. Previous studies using various biochemical pathways identified gene expression patterns that distinguish two subsets of aggressive fibromatosis (AF), serous ovarian carcinoma (OVCA), and clear cell renal cell carcinoma (RCC). These gene sets separated soft tissue sarcomas into two groups with different probabilities of developing metastatic disease. The present study used these gene sets to identify GIST subgroups with different probabilities of developing metastatic disease.

METHODS

We utilized these three gene sets, hierarchical clustering, and Kaplan-Meier analysis, to examine 60 primary resected GIST samples using Agilent chip expression profiling.

RESULTS

Hierarchical clustering using both the combined and individual AF-, OVCA-, and RCC- gene sets identified differences in probabilities of developing metastatic disease between the clusters defined by the first branch point of the clustering dendrograms (p = 0.029 for the combined gene set, p = 0.003 for the AF-gene set, p < 0.001 for the OVCA-gene set, and p = 0.003 for the RCC-gene set).

CONCLUSIONS

Hierarchical clustering using these gene sets identified at least two subsets of GIST with distinct clinical behavior and risk of metastatic disease. The use of gene expression analysis along with other known prognostic factors may better predict the long-term outcome following surgery, and thus restrict the use of adjuvant therapy to high-risk GIST, and reduce heterogeneity among groups in clinical trials of new drugs.

Decreasing iron-related indexes without anaemia in a patient with genetic haemochromatosis

Present case report refers to a 48-year-old man with genetic haemochromatosis (C282Y mut/mut) diagnosed at the age of 26. After aggressive iron depleting regimen carried out up to normalization of iron-related indexes, he received a maintenance regimen based on regular phlebotomies for about 20 years. In 2014, a marked reduction of both serum ferritin and transferrin saturation percent, without concomitant anaemia, was noted on two different occasions at 5-month interval. An obscure occult GI bleeding was suspected, but both upper and lower GI tract endoscopy were negative for abnormal findings, as also was a detailed abdominal US scan. The persistence of low iron-related indexes prompted the physicians to perform a videocapsule endoscopy, which showed an ulcerative bleeding lesion in the small bowel, not confirmed however by both anterograde and retrograde double-balloon enteroscopy. Further MRI and PET allowed the identification of a 3.5 cm large lesion, located outside the small bowel wall, suspected to be a gastrointestinal stromal tumour (GIST). A further laparoscopic procedure allowed the resection of 10 cm of midileum, which included the mass, fully consistent with GIST at pathology.

PDL1 expression is an independent prognostic factor in localized GIST

Gastrointestinal stromal tumors (GIST) are the most frequently occurring digestive sarcomas. The prognosis of localized GIST is heterogeneous, notably for patients with an Armed Forces Institute of Pathology (AFIP) intermediate or high risk of relapse. Despite imatinib effectiveness, it is crucial to develop therapies able to overcome the resistance mechanisms. The immune system represents an attractive prognostic and therapeutic target. The Programmed cell Death 1 (PD1)/programmed cell death ligand 1 (PDL1) pathway is a key inhibitor of the immune response; recently, anti-PD1 and anti-PDL1 drugs showed very promising results in patients with solid tumors. However, PDL1 expression has never been studied in GIST. Our objective was to analyze PDL1 expression in a large series of clinical samples. We analyzed mRNA expression data of 139 operated imatinib-untreated localized GIST profiled using DNA microarrays and searched for correlations with histoclinical features including postoperative metastatic relapse. PDL1 expression was heterogeneous across tumors and was higher in AFIP low-risk than in high-risk samples, and in samples without than with metastatic relapse. PDL1 expression was associated with immunity-related parameters such as T-cell-specific and CD8⁺ T-cell-specific gene expression signatures and probabilities of activation of interferon α (IFNα), IFNγ, and tumor necrosis factor α (TNFα) pathways, suggesting positive correlation with a cytotoxic T-cell response. In multivariate analysis, the PDL1-low group was associated with a higher metastatic risk independently of the AFIP classification and the KIT mutational status. In conclusion, PDL1 expression refines the prediction of metastatic relapse in localized GIST and might improve our ability to better tailor adjuvant imatinib. In the metastatic setting, PDL1 expression might guide the use of PDL1 inhibitors, alone or associated with tyrosine kinase inhibitors.

Gastrointestinal stromal tumors: risk assessment and adjuvant therapy

Adjuvant imatinib prolongs recurrence-free survival and probably overall survival of patients who have undergone surgery for gastrointestinal stromal tumor (GIST). Estimation of the risk of recurrence with a prognostication tool and tumor mutation analysis is essential before imatinib initiation, because approximately 60% of patients with GIST with operable tumor are cured by surgery alone and some mutated tyrosine kinases are insensitive to imatinib. Adjuvant imatinib is usually administered for 3 years at the dose of 400 mg once daily. Early detection of tumors that recur despite adjuvant therapy with longitudinal imaging of the abdomen is likely beneficial.