Tumor specific gene expression profiles in human leiomyosarcoma: an evaluation of intratumor heterogeneity

Habitat escalated adaptive therapy (HEAT): a phase 2 trial utilizing radiomic habitat-directed and genomic-adjusted radiation dose (GARD) optimization for high-grade soft tissue sarcoma

BACKGROUND

Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS.

METHODS

Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies.

DISCUSSION

This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS.

TRIAL REGISTRATION

NCT05301283.

TRIAL STATUS

The trial started recruitment on March 17, 2022.

The Future of Radiation Oncology in Soft Tissue Sarcoma

Radiotherapy (RT) is an important component of the treatment of soft tissue sarcomas (STS) and has been traditionally incorporated with a homogenous approach despite the reality that STS displays a known heterogeneity in clinicopathologic features and treatment outcomes. In this article, we explore the principle components of personalized medicine, including genomics, radiomics, and treatment response, along with their impact on the future of radiation therapy for STS. We propose a shift in the treatment paradigm for STS from a one-size-fits-all technique to one that implements the tenets of personalized medicine and includes the framework for a potential clinical trial technique in this heterogeneous disease.

Tumour endothelial marker 1/endosialin-mediated targeting of human sarcoma

BACKGROUND

Tumour endothelial marker 1 (TEM1/endosialin/CD248) is a tumour-restricted cell-surface protein expressed by human sarcomas. We previously developed a high-affinity human single-chain variable fragment (scFv)-Fc fusion protein (78Fc) against TEM1 and demonstrated its specific binding to human and mouse TEM1.

PATIENT AND METHODS

Clinical sarcoma specimens were collected between 2000 and 2015 at the Hospital of the University of Pennsylvania, as approved by the institutional review board and processed by standard formalin-fixed paraffin embedded techniques. We analysed TEM1 expression in 19 human sarcoma subtypes (n = 203 specimens) and eight human sarcoma-cell lines. Near-infrared (NIR) imaging of tumour-bearing mice was used to validate 78Fc binding to TEM1⁺ sarcoma in vivo. Finally, we tested an immunotoxin conjugate of anti-TEM1 78Fc with saporin (78Fc-Sap) for its therapeutic efficacy against human sarcoma in vitro and in vivo.

RESULTS

TEM1 expression was identified by immunohistochemistry in 96% of human sarcomas, of which 81% expressed TEM1 both on tumour cells and the tumour vasculature. NIR imaging revealed specific in vivo targeting of labelled 78Fc to TEM1⁺ sarcoma xenografts. Importantly, 78Fc-Sap was effective in killing in vitro TEM1⁺ sarcoma cells and eliminated human sarcoma xenografts without apparent toxicity in vivo.

CONCLUSION

TEM1 is an important therapeutic target for human sarcoma, and the high-affinity TEM1-specific scFv fusion protein 78Fc is suitable for further clinical development for therapeutic applications in sarcoma.

Oncopig Soft-Tissue Sarcomas Recapitulate Key Transcriptional Features of Human Sarcomas

Human soft-tissue sarcomas (STS) are rare mesenchymal tumors with a 5-year survival rate of 50%, highlighting the need for further STS research. Research has been hampered by limited human sarcoma cell line availability and the large number of STS subtypes, making development of STS cell lines and animal models representative of the diverse human STS subtypes critical. Pigs represent ideal human disease models due to their similar size, anatomy, metabolism, and genetics compared to humans. The Oncopig encodes inducible KRAS^G12D and TP53^R167H transgenes, allowing for STS modeling in a spatial and temporal manner. This study utilized Oncopig STS cell line (fibroblast) and tumor (leiomyosarcoma) RNA-seq data to compare Oncopig and human STS expression profiles. Altered expression of 3,360 and 7,652 genes was identified in Oncopig STS cell lines and leiomyosarcomas, respectively. Transcriptional hallmarks of human STS were observed in Oncopig STS, including altered TP53 signaling, Wnt signaling activation, and evidence of epigenetic reprogramming. Furthermore, master regulators of Oncopig STS expression were identified, including FOSL1, which was previously identified as a potential human STS therapeutic target. These results demonstrate the Oncopig STS model’s ability to mimic human STS transcriptional profiles, providing a valuable resource for sarcoma research and cell line development.

Heterogeneity of Metastatic Melanoma: Correlation of MITF With Its Transcriptional Targets MLSN1, PEDF, HMB-45, and MART-1

OBJECTIVES

Histologic and molecular heterogeneity is well recognized in malignant melanoma; however, the diversity of expression of new and classic melanoma markers has not been correlated in serial sections of metastases.

METHODS

We examined and correlated the expression of microphthalmia transcription factor (MITF) with its transcriptional targets, including melastatin (MLSN1/TRPM1), pigment epithelium-derived factor (SERPINF1/PEDF), SILV/PMEL17/GP100 (human melanoma black 45 [HMB-45]), and melanoma antigen recognized by T cells 1 (MART-1)/MLANA, in 13 melanoma metastases in lymph nodes of 13 patients. The expression levels and patterns of marker expression were recorded by a semiquantitative, 4-point ordinal reactivity method.

RESULTS

Our results showed a consistently robust and diffuse expression of MITF protein in 12 (92%) of 13 metastatic tumors compared with variable expression of MLSN1 (46%) messenger RNA or PEDF (75%), HMB-45 (54%), and MART-1 (46%) proteins.

CONCLUSIONS

Overall, in melanoma lymph node metastases, MITF protein expression was not tightly correlated with its gene targets. Moreover, the immunoreactivity for MITF, compared with MART-1 and HMB-45, was retained, supporting immunohistochemical detection of MITF as a more sensitive method of detecting metastatic melanoma.

Three-dimensional mRNA measurements reveal minimal regional heterogeneity in esophageal squamous cell carcinoma

The classic tumor clonal evolution theory postulates that cancers change over time to produce unique molecular subclones within a parent neoplasm, presumably including regional differences in gene expression. More recently, however, this notion has been challenged by studies showing that tumors maintain a relatively stable transcript profile. To examine these competing hypotheses, we microdissected discrete subregions containing approximately 3000 to 8000 cells (500 to 1500 μm in diameter) from ex vivo esophageal squamous cell carcinoma (ESCC) specimens and analyzed transcriptomes throughout three-dimensional tumor space. Overall mRNA profiles were highly similar in all 59 intratumor comparisons, in distinct contrast to the markedly different global expression patterns observed in other dissected cell populations. For example, normal esophageal basal cells contained 1918 and 624 differentially expressed genes at a greater than twofold level (95% confidence level of <5% false positives), compared with normal differentiated esophageal cells and ESCC, respectively. In contrast, intratumor regions had only zero to four gene changes at a greater than twofold level, with most tumor comparisons showing none. The present data indicate that, when analyzed using a standard array-based method at this level of histological resolution, ESCC contains little regional mRNA heterogeneity.

Integrated proteomics and genomics analysis reveals a novel mesenchymal to epithelial reverting transition in leiomyosarcoma through regulation of slug

Leiomyosarcoma is one of the most common mesenchymal tumors. Proteomics profiling analysis by reverse-phase protein lysate array surprisingly revealed that expression of the epithelial marker E-cadherin (encoded by CDH1) was significantly elevated in a subset of leiomyosarcomas. In contrast, E-cadherin was rarely expressed in the gastrointestinal stromal tumors, another major mesenchymal tumor type. We further sought to 1) validate this finding, 2) determine whether there is a mesenchymal to epithelial reverting transition (MErT) in leiomyosarcoma, and if so 3) elucidate the regulatory mechanism responsible for this MErT. Our data showed that the epithelial cell markers E-cadherin, epithelial membrane antigen, cytokeratin AE1/AE3, and pan-cytokeratin were often detected immunohistochemically in leiomyosarcoma tumor cells on tissue microarray. Interestingly, the E-cadherin protein expression was correlated with better survival in leiomyosarcoma patients. Whole genome microarray was used for transcriptomics analysis, and the epithelial gene expression signature was also associated with better survival. Bioinformatics analysis of transcriptome data showed an inverse correlation between E-cadherin and E-cadherin repressor Slug (SNAI2) expression in leiomyosarcoma, and this inverse correlation was validated on tissue microarray by immunohistochemical staining of E-cadherin and Slug. Knockdown of Slug expression in SK-LMS-1 leiomyosarcoma cells by siRNA significantly increased E-cadherin; decreased the mesenchymal markers vimentin and N-cadherin (encoded by CDH2); and significantly decreased cell proliferation, invasion, and migration. An increase in Slug expression by pCMV6-XL5-Slug transfection decreased E-cadherin and increased vimentin and N-cadherin. Thus, MErT, which is mediated through regulation of Slug, is a clinically significant phenotype in leiomyosarcoma.

Comparative microarray data analysis for the expression of genes in the pathway of glioma

Our present work focuses on the set of genes, which are involved in primary brain tumors - the glioma pathway. These gliomas are mostly malignant (cancerous) in nature and are difficult to be cured and that's why they attract the attention of all the workers. To understand the relative functionality of these genes, we analyzed the expression pattern of all genes, using gene expression data, at genomic level, and then to check their universality in all other cancers, we compared their expression levels and patterns in all other types of cancers by using gene expression graphs, and observed their expression levels in all these cancers, whether they are over or under expressed. We found that every gene has its own unique expression pattern and level and on that basis it can be classified. We also found that oncogenes and tumor suppressor genes that were involved in the glioma pathway were showing similar expression patterns in other cancers too but their expression level is low.

Translating gene expression into clinical care: sarcomas as a paradigm

Whereas most solid tumors are characterized by considerable genetic instability and molecular heterogeneity, sarcomas include many subtypes with very specific underlying molecular events driving oncogenesis. Gene expression profiling and other modern techniques have consequently had particular success in identifying the critical biologic pathways active in specific sarcomas, yielding insights which can be translated into useful diagnostic biomarkers. Public availability of data sets and new sequencing-based technologies will accelerate this process. Molecular studies have also identified oncogenic pathways of particular importance in sarcomas which can be targeted by investigational drugs. Examples include histone deacetylases in translocation-associated sarcomas of young adults, Akt/mammalian target of rapamycin in pleomorphic sarcomas, and macrophage colony-stimulating factor in tenosynovial giant cell tumor. Despite challenges in organization and accrual, future clinical trials of sarcomas need to be designed that take into account specific molecular subtypes as distinct diseases.

Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling

Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the 'muscle-enriched' cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P<0.005). Expression of the five markers was associated with improved disease-specific survival in a multivariate Cox regression analysis (P<0.04). In this analysis that combined gene expression profiling, aCGH and IHC, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers.

Standardizing evaluation of sarcoma proliferation- higher Ki-67 expression in the tumor periphery than the center

Soft tissue sarcomas often present as large and histopathologically heterogenous tumors. Proliferation has repeatedly been identified as a prognostic factor and immunostaining for Ki-67 represents the most commonly used proliferation marker. There is, however, a lack of consensus on how to evaluate Ki-67 staining regarding optimal cut-off levels, selection of tumor areas, and the number of tumor cells to evaluate. We assessed the impact of targeting peripheral versus central tumor areas using tissue microarray-based staining for Ki-67 throughout the tumor diameter in 25 leiomyosarcomas. In 18/25 tumors, Ki-67 expression was higher in the tumor periphery. If 10% staining tumor nuclei was used as cut-off and the maximal Ki-67 staining section in the tumor periphery was considered, 21/25 tumors would have been classified as highly proliferative compared to 14/25 if the tumor center had been analyzed. Similar results were obtained also when higher cut-off levels were used and if the mean expression rather than the maximal expression was considered and the differences were neither caused by necrosis nor by hypoxia (assessed as HIF-1alpha expression). Our findings suggest that the determination of proliferation in soft tissue sarcomas should be standardized for clinical application of Ki-67 as a prognostic marker.

Gene expression profiling in sarcomas

Sarcomas are a heterogeneous group of malignant mesenchymal tumors of difficult classification. There is considerable variability in both histological appearance and responsiveness to therapy. Their overall poor clinical prognosis is reflected by the fact that >65% of patients suffering retroperitoneal soft tissue sarcoma die within 5 years [Heslin MJ, et al. Prognostic factors associated with long-term survival for retroperitoneal sarcoma: implications for management. J Clin Oncol 1997;15(8):2832-9]. A greater understanding of the biology of sarcomas is needed in order to increase the potential for identifying new therapeutic targets and strategies. Microarray analysis permits a global approach to gene expression analysis of thousands of genes at the same time and has proven to be useful for further molecular characterization of tumor tissue and cell lines. This article provides a comprehensive review of possible new biomarkers identified in gene expression studies of sarcomas. These markers give new insight into the pathogenesis of sarcomas, such as malignant fibrous histiocytoma [Lee YF, et al. Molecular classification of synovial sarcomas, leiomyosarcomas and malignant fibrous histiocytomas by gene expression profiling. Br J Cancer 2003;88(4):510-5], allow a further subclassifcation of tumors like calponin-positive and calponin-negative leiomyosarcoma, or may help to predict treatment responsiveness and prognosis in patients based on an individual gene expression pattern. In some studies candidate targets for possible new treatment strategies were identified. For instance newly identified markers such as ERBB2 [Allander SV, et al. Expression profiling of synovial sarcoma by cDNA microarrays: association of ERBB2, IGFBP2, and ELF3 with epithelial differentiation. Am J Pathol 2002;161(5):1587-95] and EGFR [Nielsen TO, et al. Molecular characterization of soft tissue tumours: a gene expression study. Lancet 2002;359(9314):1301-7] might lead to the possible therapeutic use of Trastuzumab, Gefitinib or Cetuximab in synovial sarcoma, comparable to the use of tyrosine kinase inhibitor STI (Gleevec) that is the standard treatment today of CD117-positive gastrointestinal stromal tumors.

Adult human sarcomas. II. Medical oncology

Human sarcoma cells can be killed by radio- and chemotherapy, but tumor cells acquiring resistance frequently kill the patient. A keen understanding of the intracellular course of oncogenic cascades leads to the discovery of small molecular inhibitors of the involved phosphorylated kinases. Targeted therapy complements chemotherapy. Oncogene silencing is feasible by small interfering RNA. The restoration of some of the mutated or deleted tumor-suppressor genes (p53, Rb, PTEN, hSNF, INK/ARF and WT) by demethylation or reacetylation of their histones has been accomplished. Genetically engineered or naturally oncolytic viruses selectively lyse tumors and leave healthy tissues intact. Adeno- or retroviral vectors deliver genes of immunological costimulators, tumor antigens, chemo- or cytokines and/or tumor-suppressor proteins into tumor (sarcoma) cells. Suicide gene delivery results in apoptosis induction. Genes of enzymes that target prodrugs as their substrates render tumor cells highly susceptible to chemotherapy, with the prodrug to be targeted intracellularly. It will be combinations of sophisticated surgical removal of the nonencapsulated and locally invasive primary sarcomas, advanced forms of radiotherapy to the involved sites and immunotherapy with sarcoma vaccines that will cure primary sarcomas. Adoptive immunotherapy with immune lymphocytes will be operational in metastatic disease only when populations of regulatory T cells are controlled. Targeted therapy with small molecular inhibitors of oncogene cascades, the driving forces of sarcoma cells, alteration of the tumor stroma from a supportive to a tumor-hostile environment, reactivation or replacement of wild-type tumor-suppressor genes, and radio-chemotherapy (with much reduced toxicity) will eventually accomplish the cure of metastatic sarcomas.

Gene expression in epithelial ovarian cancer: a study of intratumor heterogeneity

The aim of this study was to investigate the intratumor heterogeneity of gene expression profiles in epithelial ovarian cancer (EOC). This was done to evaluate whether sampling of a single macrodissected tissue sample from each EOC case would bias the data and result in, eg, prognostic studies based on gene expression microarray experiments. From nine EOCs removed at Odense University Hospital, Denmark, three tumor samples of 200-300 mg each were taken with greatest possible mutual distance. The samples were immediately flash frozen. A parallel section was taken for histopathologic comparison. RNA was extracted from the tissue samples. Five micrograms of each RNA sample was used for labeling. The fragmented biotin-labeled complementary RNA was hybridized to Affymetrix GeneChip Human Genome U133 plus 2.0 arrays, and scanning was performed on the GeneArray scanner 3000 (Affymetrix, Santa Clara, CA). Data were evaluated using hierarchical clustering and intraclass correlation coefficient (ICC) from reliability analysis. All evaluation methods revealed low intratumor heterogeneity. Intratumor ICCs ranged from 0.888 to 0.978. In contrast, "between-tumor" ICC was 0.549 indicating much lower intra- than intertumor heterogeneity. Due to a low degree of intratumor variation, we conclude that it is sufficiently accurate in a clinical setup to use single, macrodissected tumor samples in the evaluation of gene expression in EOCs.

Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas

Gastrointestinal stromal tumor (GIST) has emerged as a clinically distinct type of sarcoma with frequent overexpression and mutation of the c-Kit oncogene and a favorable response to imatinib mesylate [also known as STI571 (Gleevec)] therapy. However, a significant diagnostic challenge remains in the differentiation of GIST from leiomyosarcomas (LMSs). To improve on the diagnostic evaluation and to complement the immunohistochemical evaluation of these tumors, we performed a whole-genome gene expression study on 68 well characterized tumor samples. Using bioinformatic approaches, we devised a two-gene relative expression classifier that distinguishes between GIST and LMS with an accuracy of 99.3% on the microarray samples and an estimated accuracy of 97.8% on future cases. We validated this classifier by using RT-PCR on 20 samples in the microarray study and on an additional 19 independent samples, with 100% accuracy. Thus, our two-gene relative expression classifier is a highly accurate diagnostic method to distinguish between GIST and LMS and has the potential to be rapidly implemented in a clinical setting. The success of this classifier is likely due to two general traits, namely that the classifier is independent of data normalization and that it uses as simple an approach as possible to achieve this independence to avoid overfitting. We expect that the use of simple marker pairs that exhibit these traits will be of significant clinical use in a variety of contexts.

Early effects of imatinib mesylate on the expression of insulin-like growth factor binding protein-3 and positron emission tomography in patients with gastrointestinal stromal tumor

BACKGROUND

Imatinib has demonstrated marked clinical efficacy against gastrointestinal stromal tumor (GIST). Microarray technology, real-time polymerase chain reaction (PCR) validation, and fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging were used to study the early molecular effects of imatinib antitumor activity in GIST.

METHODS

After exposure of sensitive and resistant sarcoma cell lines to imatinib for 24 to 48 hours, the changes in gene expression were evaluated using a 1146 unique pathway array with Western blot validation. Real-time PCR was used to confirm changes in gene expression in human GIST samples (preimatinib biopsy and postimatinib surgical specimen after 3-7 days of therapy). FDG-PET was performed to correlate radiographic findings with the effects of imatinib on gene expression in GIST.

RESULTS

In all, 55 genes demonstrated a > or = 2-fold change after imatinib treatment of the GIST882 cells. Among these genes there was up-regulation of insulin-like growth factor binding protein-3 (IGFBP-3), a protein that modulates proliferation and apoptosis. Western blot analysis confirmed the increase of IGFBP-3 only in imatinib-sensitive GIST882 cells. Up to a 7-fold induction (49% mean increase; P = .08) of IGFBP-3 mRNA was found in tumor samples from patients with low residual FDG uptake, whereas there was an up to 12-fold reduction (-102% mean decrease; P = .03) in IGFBP-3 in those patients with high residual FDG uptake after imatinib therapy.

CONCLUSIONS

In the current study, imatinib appears to regulate numerous genes and specifically induces IGFBP-3 in GIST cells and tumor samples. IGFBP-3 levels also were found to be inversely correlated with residual FDG uptake in GIST patients early in imatinib therapy. These initial observations suggest that IGFBP-3 is an important early marker of antitumor activity of imatinib in GIST.

Unsupervised analysis uncovers changes in histopathologic diagnosis in supervised genomic studies

Human gastrointestinal stromal tumors (GIST) have recently emerged as a distinct mesenchymal tumor type that has a unique phenotype characterized by a gain of function mutations in c-kit. In contrast, leiomyosarcomas (LMS) of the gastrointestinal tract or retroperitoneum, which were previously classified together with GISTs as gastrointestinal sarcomas, have much less frequent mutations of c-kit. We performed microarray analyses to gain a comprehensive understanding of the difference between the two types of soft-tissue sarcomas at the level of gene expression. Microarray experiments were performed on 30 GISTs and 30 LMSs that were collected at the time of surgical resection. These tumors were categorized based on the histopathologic diagnosis recorded in our institutional database. Prior to our search for genes that are differentially expressed between these two types of cancers, we first carried out an unsupervised analysis using multidimensional scaling (MDS) to determine whether the two groups have marked overall differences in gene expression. Initially, the MDS did not reveal a good separation between the two groups. We then re-reviewed the histopathology of these tumors and realized that some of the cases included in our study were acquired 10 years ago when the diagnosis of gastrointestinal sarcoma was made according to histopathologic criteria alone without immunohistochemistry for c-kit. An experienced pathologist reviewed all of the specimens and this revealed that a number of the GIST cases were classified as LMS in the clinical database. Correction of the histopathologic diagnosis and relabeling of the samples resulted in a much more pronounced separation of GIST and LMS in the MDS analysis. This study underscores the need to re-review histopathology as reclassification occurs. While updating the clinical database may be desired, this is usually impractical. For molecular studies that use archival samples, it is critical to have the archival samples re-reviewed by a pathologist. Further, unsupervised analysis often proves to be a critical quality control step in identifying structural problems that may exist. Finally, MDS analysis further supports that GIST is a distinct type of sarcoma.

Transit tumor retrieval preserves RNA fidelity and obviates snap-freezing

Genetic expression profiling is enabling investigators to discover new diagnostic and possibly therapeutic pathways in sarcoma biology. To draw substantial conclusions from these molecular analyses, adequate tissue samples must be accrued. Beyond cohort size, the most variable and limiting aspect of doing gene expression analyses on fresh human tissue is the preservation of labile ribonucleic acids extracted from clinical specimens. We have developed a novel retrieval protocol that is readily amenable to the clinical constraints placed on surgeons and pathologists that minimizes variables that can corrupt ribonucleic acid fidelity. We evaluate critically genomic message integrity of mesenchymal tumors derived from transcontinental inter-institutional collaboration. Intact total ribonucleic acid was isolated and assessed for quality and quantity. Ribosomal RNA integrity was quantified using a bioanalyzer. Ribonucleic acid from 42 mesenchymal tumors was isolated and quantified, with selected samples amplified. The mean ribosomal ratios for collaborative institutions ranged from 1.0 to 1.18. Samples remained at 4 degrees C before processing from 1 to 17 days. Tumors stabilized using this protocol retained total ribonucleic acid integrity suitable for amplification and genomic expression analysis regardless of the institutional source or preprocessing duration, enabling a potential consortium of investigators to collaborate in the expression profiling of sarcomas.

LEVEL OF EVIDENCE

Diagnostic study, Level III-3 (no consistently applied gold standard). See the Guidelines for Authors for a complete description of levels of evidence.

A molecular map of mesenchymal tumors

A comprehensive study of the gene expression profile of 96 mesenchymal tumors identifies molecular fingerprints for most tumors in this group.

Background

Bone and soft tissue tumors represent a diverse group of neoplasms thought to derive from cells of the mesenchyme or neural crest. Histological diagnosis is challenging due to the poor or heterogenous differentiation of many tumors, resulting in uncertainty over prognosis and appropriate therapy.

Results

We have undertaken a broad and comprehensive study of the gene expression profile of 96 tumors with representatives of all mesenchymal tissues, including several problem diagnostic groups. Using machine learning methods adapted to this problem we identify molecular fingerprints for most tumors, which are pathognomonic (decisive) and biologically revealing.

Conclusion

We demonstrate the utility of gene expression profiles and machine learning for a complex clinical problem, and identify putative origins for certain mesenchymal tumors.

In silico microdissection of microarray data from heterogeneous cell populations

Background

Very few analytical approaches have been reported to resolve the variability in microarray measurements stemming from sample heterogeneity. For example, tissue samples used in cancer studies are usually contaminated with the surrounding or infiltrating cell types. This heterogeneity in the sample preparation hinders further statistical analysis, significantly so if different samples contain different proportions of these cell types. Thus, sample heterogeneity can result in the identification of differentially expressed genes that may be unrelated to the biological question being studied. Similarly, irrelevant gene combinations can be discovered in the case of gene expression based classification.

Results

We propose a computational framework for removing the effects of sample heterogeneity by "microdissecting" microarray data in silico. The computational method provides estimates of the expression values of the pure (non-heterogeneous) cell samples. The inversion of the sample heterogeneity can be facilitated by providing accurate estimates of the mixing percentages of different cell types in each measurement. For those cases where no such information is available, we develop an optimization-based method for joint estimation of the mixing percentages and the expression values of the pure cell samples. We also consider the problem of selecting the correct number of cell types.

Conclusion

The efficiency of the proposed methods is illustrated by applying them to a carefully controlled cDNA microarray data obtained from heterogeneous samples. The results demonstrate that the methods are capable of reconstructing both the sample and cell type specific expression values from heterogeneous mixtures and that the mixing percentages of different cell types can also be estimated. Furthermore, a general purpose model selection method can be used to select the correct number of cell types.

Role of gene expression arrays in sarcomas

Microarray technology allows the rapid analysis of expression of thousands of genes in a sample. Gene expression profiles are likely characteristic of subtypes of sarcomas and may be useful in diagnosis and classification of this heterogeneous group of tumors. Gene expression may also be useful prognostically with respect to the natural history and response to therapy of these tumors.

Retrieval yield of total and messenger RNA in mesenchymal tissue ex vivo

Mesenchymal neoplasms are a heterogeneous group of tumors comprising more than 200 benign entities and approximately 100 sarcomas. Large intraobserver and interobserver variability mandates improvements in diagnostic criteria. Gene expression microarrays are one tool in an evolving field of technology that permits the screening of tissue for massive amounts of information regarding its genetic composition. Such information may aid clinicians to diagnose and treat sarcomas. Complementary deoxyribonucleic acid microarrays, although very promising, are limited by the fact that messenger ribonucleic, the genetic messenger that permits deoxyribonucleic acid to encode for proteins and is the element retrieved from tumor samples ex vivo, is highly unstable, degrading quite readily. We found that even with optimal retrieval times and processing, total ribonucleic acid extraction from tumor tissue ex vivo is retrieved in adequate amounts to avoid amplification in 23% to 55% (mean 36%) of specimens. The percentage of high-grade tumors that yielded sufficient total ribonucleic acid was significantly higher than low grade and benign tumors. When adequate retrieval is achieved, the quantity and quality of messenger ribonucleic acid is robust. Surgeons, pathologists, and clinical intermediaries must be aware of issues surrounding messenger ribonucleic acid retrieval from surgical specimens to optimize collection.

Differential gene expression in leiomyosarcoma

BACKGROUND

Leiomyosarcomas (LMS) are a common subtype of soft tissue sarcoma. The molecular causes of the disease remain unclear.

METHODS

In the current study, gene expression in LMS, leiomyomas, and normal myometrium was examined. RNA was prepared and gene expression was determined using microarray analysis arrays containing approximately 12,000 known genes and 48,000 expressed sequence tags (ESTs).

RESULTS

A number of genes were found to be differentially expressed in these sample sets, and six genes including cyclin-dependent kinase inhibitor 2A, diaphanous (Drosophila homolog) 3, doublecortin, calpain 6, interleukin-17B, and proteolipid 1 were found to be overexpressed in LMS compared with normal myometrium and 18 other tissues. Sets of genes were identified whose expression could be used to cluster samples with either LMS, leiomyomas, or normal myometrium.

CONCLUSIONS

The authors concluded that differences in gene expression can be detected between LMS and leiomyomas, normal myometrium, and other tissues, and that these changes in gene expression may yield clues with regard to the pathophysiology of leiomyosarcoma.

Gene expression analysis of human soft tissue leiomyosarcomas

Leiomyosarcoma of the somatic soft tissue is a rare malignant mesenchymal neoplasm that metastasizes to other organs in a subset of cases. Much remains to be learned about the mechanisms underlying the development of aggressive behavior of this tumor. It has been difficult to predict the clinical behavior of leiomyosarcomas using the morphology-based grading system, even though tumor size and histological grade have correlated with biologic behavior in some studies. In this study we analyzed the gene expression patterns of 35 samples of mesenchymal origin, including 11 cases of leiomyosarcomas of different histological grades arising in soft tissue and the retroperitoneum, using the Affymetrix U133a chips, which contain more than 22,000 genes and expression sequence tags (ESTs). We identified a set of genes whose expression was commonly altered in all leiomyosarcoma samples. In addition, we identified specific gene expression patterns in several subsets of the tumor. We used these alterations of gene expression to subclassify the leiomyosarcomas into 3 groups. Interestingly, the grouping of these samples correlated well with tumor differentiation and clinical aggressiveness. The analysis identified 92 genes that distinguish low-grade, well-differentiated leiomyosarcomas from less well-differentiated, high-grade, and metastatic leiomyosarcoma. Thesse alterations of gene expression appear to be correlated with the clinical behavior and histological grade of the tumor. The striking differences in terms of gene expression pattern among leiomyosarcomas of different differentiation status and clinical aggressiveness imply that several genetic abnormalities are responsible for the genesis and progression of this tumor.

Data extraction from composite oligonucleotide microarrays

Microarray or DNA chip technology is revolutionizing biology by empowering researchers in the collection of broad-scope gene information. It is well known that microarray-based measurements exhibit a substantial amount of variability due to a number of possible sources, ranging from hybridization conditions to image capture and analysis. In order to make reliable inferences and carry out quantitative analysis with microarray data, it is generally advisable to have more than one measurement of each gene. The availability of both between-array and within-array replicate measurements is essential for this purpose. Although statistical considerations call for increasing the number of replicates of both types, the latter is particularly challenging in practice due to a number of limiting factors, especially for in-house spotting facilities. We propose a novel approach to design so-called composite microarrays, which allow more replicates to be obtained without increasing the number of printed spots.