Prognostic significance of promyelocytic leukemia expression in gastrointestinal stromal tumor; integrated proteomic and transcriptomic analysis

Quantitative proteomic studies addressing unmet clinical needs in sarcoma

Sarcoma is a rare and complex disease comprising over 80 malignant subtypes that is frequently characterized by poor prognosis. Challenges in clinical management include uncertainties in diagnosis and disease classification, limited prognostic and predictive biomarkers, incompletely understood disease heterogeneity among and within subtypes, lack of effective treatment options, and limited progress in identifying new drug targets and novel therapeutics. Proteomics refers to the study of the entire complement of proteins expressed in specific cells or tissues. Advances in proteomics have included the development of quantitative mass spectrometry (MS)-based technologies which enable analysis of large numbers of proteins with relatively high throughput, enabling proteomics to be studied on a scale that has not previously been possible. Cellular function is determined by the levels of various proteins and their interactions, so proteomics offers the possibility of new insights into cancer biology. Sarcoma proteomics therefore has the potential to address some of the key current challenges described above, but it is still in its infancy. This review covers key quantitative proteomic sarcoma studies with findings that pertain to clinical utility. Proteomic methodologies that have been applied to human sarcoma research are briefly described, including recent advances in MS-based proteomic technology. We highlight studies that illustrate how proteomics may aid diagnosis and improve disease classification by distinguishing sarcoma histologies and identify distinct profiles within histological subtypes which may aid understanding of disease heterogeneity. We also review studies where proteomics has been applied to identify prognostic, predictive and therapeutic biomarkers. These studies traverse a range of histological subtypes including chordoma, Ewing sarcoma, gastrointestinal stromal tumors, leiomyosarcoma, liposarcoma, malignant peripheral nerve sheath tumors, myxofibrosarcoma, rhabdomyosarcoma, synovial sarcoma, osteosarcoma, and undifferentiated pleomorphic sarcoma. Critical questions and unmet needs in sarcoma which can potentially be addressed with proteomics are outlined.

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

Background

The increasing incidence of gastrointestinal stromal tumors (GISTs) has led to the discovery of more novel prognostic markers. We aim to establish an unsupervised prognostic model for the early prediction of the prognosis of future patients with GISTs and to guide clinical treatment.

Methods

We downloaded the GISTs dataset through the cBioPortal website. We extracted clinical information and pathological information, including the microsatellite instability (MSI) score, fraction genome altered (FGA) score, tumor mutational burden (TMB), and copy number alteration burden (CNAB), of patients with GISTs. For survival analysis, we used univariate Cox regression to analyze the contribution of each factor to prognosis and calculated a hazard ratio (HR) and 95% confidence interval (95% CI). For clustering groupings, we used the t-distributed stochastic neighbor embedding (t-SNE) method for data dimensionality reduction. Subsequently, the k-means method was used for clustering analysis.

Results

A total of 395 individuals were included in the study. After dimensionality reduction with t-SNE, all patients were divided into two subgroups. Cluster 1 had worse OS than cluster 2 (HR=3.45, 95% CI, 2.22-5.56, P<0.001). The median MSI score of cluster 1 was 1.09, and the median MSI score of cluster 2 was 0.24, which were significantly different (P<0.001). The FGA score of cluster 1 was 0.28, which was higher than that of cluster 2 (P<0.001). In addition, both the TMB and CNAB of cluster 1 were higher than those of cluster 2, and the P values were less than 0.001.

Conclusion

Based on the CNA of GISTs, patients can be divided into high-risk and low-risk groups. The high-risk group had a higher MSI score, FGA score, TMB and CNAB than the low-risk group. In addition, we established a prognostic nomogram based on the CNA and clinicopathological characteristics of patients with GISTs.

Differential Expression of CADM1 in Gastrointestinal Stromal Tumors of Different Sites and with Different Gene Abnormalities

Gastrointestinal stromal tumor (GIST), the most common mesenchymal tumor of the human gastrointestinal tract, differentiating toward the interstitial cell of Cajal (ICC), arises predominantly in the stomach and small intestine. Small intestinal GISTs appear to have worse prognosis than gastric GISTs. In a pilot study of a cDNA expression chip using several GISTs, we found that Cell Adhesion Molecule 1 (CADM1), which could contribute to tumor growth and infiltration, is expressed more strongly in small intestinal GISTs than gastric GISTs. In the present study, we examined CADM1 expression in GISTs of different sites and with different gene abnormalities using a large number of gastric and small intestinal GISTs. First, immunoblotting confirmed significantly higher CADM1 expression in small intestinal GISTs with exon 11 c-kit mutation than gastric GISTs with exon 11 c-kit mutation. Real-time PCR also revealed that small intestinal GISTs with exon 11 c-kit mutation showed significantly higher CADM1 mRNA than gastric GISTs with exon 11 c-kit mutation. Although most small intestinal GISTs showed high CADM1 mRNA expression regardless of gene abnormality types, different CADM1 expression was detected between gastric GISTs with c-kit mutation and those with PDGFRA mutation. Immunohistochemistry showed that many small intestinal GISTs were CADM1-positive but most gastric GISTs CADM1-negative or -indefinite. In the normal gastric and small intestinal walls, immunoreactivity of CADM1 was detected only in nerves, but neither in gastric ICCs nor small intestinal ICCs, indicating that the high CADM1expression in small intestinal GISTs might be acquired during tumorigenesis. Different CADM1 expression between gastric and small intestinal GISTs might be related to different prognoses between them. Further functional experiments are needed to elucidate the role of CADM1 on GIST biology, and there is a possibility that targeting therapy against CADM1 has a preventive effect for tumor spreading in small intestinal GISTs.

Proteomic research in sarcomas - current status and future opportunities

Sarcomas are a rare group of mesenchymal cancers comprising over 70 different histological subtypes. For the majority of these diseases, the molecular understanding of the basis of their initiation and progression remains unclear. As such, limited clinical progress in prognosis or therapeutic regimens have been made over the past few decades. Proteomics techniques are being increasingly utilised in the field of sarcoma research. Proteomic research efforts have thus far focused on histological subtype characterisation for the improvement of biological understanding, as well as for the identification of candidate diagnostic, predictive, and prognostic biomarkers for use in clinic. However, the field itself is in its infancy, and none of these proteomic research findings have been translated into the clinic. In this review, we provide a brief overview of the proteomic strategies that have been employed in sarcoma research. We evaluate key proteomic studies concerning several rare and ultra-rare sarcoma subtypes including, gastrointestinal stromal tumours, osteosarcoma, liposarcoma, leiomyosarcoma, malignant rhabdoid tumours, Ewing sarcoma, myxofibrosarcoma, and alveolar soft part sarcoma. Consequently, we illustrate how routine implementation of proteomics within sarcoma research, integration of proteomics with other molecular profiling data, and incorporation of proteomics into clinical trial studies has the potential to propel the biological and clinical understanding of this group of complex rare cancers moving forward.

Current status of proteomics of soft tissue sarcomas

INTRODUCTION

Proteomics has been used in soft tissue sarcoma (STS) research in the attempts to improve the understanding of the disease background and develop novel clinical applications. Using various proteomics modalities, aberrant regulations of numerous intriguing proteins were identified in STSs, and the possible utilities of identified proteins as biomarkers or therapeutic targets have been explored. STS is an exceptionally diverse group of malignant diseases with highly complex molecular backgrounds and, therefore, an overview of the achievements and prospects of STS proteomics could enhance our knowledge of the possibilities and limitations of cancer proteomics. Areas covered: This review examines all STSs that have been examined using proteomics modalities, discussing unique aspects, limitations, and possible improvements of individual reports. To contribute to the current progress in cancer treatment development using novel anti-cancer drugs, proteomics plays a central role in linking cutting-edge technologies, application of proteogenomics, patient-derived cancer models, and biobanking system. Expert commentary: Therefore, proteomic-based STS research will be developed as an interdisciplinary science. STS proteomics will be further developed based on the interaction of oncologists with basic researchers in various fields, aimed at obtaining an enhanced understanding of the biology of the disease and achieving superior clinical outcomes for patients.

Recent advances in mass spectrometry-based proteomics of gastric cancer

The last decade has witnessed remarkable technological advances in mass spectrometry-based proteomics. The development of proteomics techniques has enabled the reliable analysis of complex proteomes, leading to the identification and quantification of thousands of proteins in gastric cancer cells, tissues, and sera. This quantitative information has been used to profile the anomalies in gastric cancer and provide insights into the pathogenic mechanism of the disease. In this review, we mainly focus on the advances in mass spectrometry and quantitative proteomics that were achieved in the last five years and how these up-and-coming technologies are employed to track biochemical changes in gastric cancer cells. We conclude by presenting a perspective on quantitative proteomics and its future applications in the clinic and translational gastric cancer research.

Current status of publicly available sarcoma cell lines for use in proteomic studies

Cell lines are valuable resources for proteomic studies and can be used as tools to verify the significance of proteomic findings. Here, the authors overview the current status of the publicly available sarcoma cell lines. The authors surveyed seven major cell banks and found that the diversity observed in the sarcoma cell banks was largely insufficient; sarcoma cell lines are available for only a limited histological subtype. They also observed a number of issues with the pathological diagnosis of the cell lines, limitations in their behavioral diversity, and various unmet needs. Well characterized cell lines with accurate diagnosis based on modern diagnosis criteria should be available from public cell banks. The authors conclude that additional cell lines, along with detailed genetic and pathological analyses, should be prepared and deposited in order to promote sarcoma-specific proteomic research. The authors focused on sarcoma cell lines, but their discussion can be applied to the other cancers.