The current treatment of leiomyosarcoma (LMS), the most common form of sarcoma, involves ineffective systemic therapy that is based on a trial and error approach and on the assumption that tumors called """"""""LMS"""""""" form a homogenous group. Recent advances in other forms of cancer therapy have relied on recognizing targetable pathways that are active in subsets of cases and the use of drugs on those cases only. Currently, no such appreciation exists for LMS, and we propose that an effective treatment for LMS should be based on a rational, molecular sub-classification of these tumors. In a continuation of previous work, we will focus efforts our efforts on the study of the molecular subtypes in LMS that we recently discovered. In the first Aim we will establish molecular signatures for large numbers of LMS cases with detailed clinical follow-up from surgical pathology archives. To this end, we will use a novel technique (3SEQ) that we developed to allow gene expression profiling on mRNA isolated from formalin-fixed paraffin-embedded tissue (FFPET) through next generation sequencing. Using this approach, we will confirm and extend our initial molecular characterization of LMS subtypes. Furthermore we will determine the prognostic significance of the molecular subtypes, and identify subtype-specific oncogenic pathways.
We aim at discovering predictors for the response to commonly used chemotherapeutic drugs, and will identify molecular prognosticators for the development of metastases. 3SEQ will also be used to analyze undifferentiated pleomorphic sarcoma (UPS, aka MFH) to determine to which extent the molecular subtypes now recognized in LMS can be identified in this tumor.
In Aim 2 we will identify the genetic events that are unique to each subtype and that could represent potential therapeutic targets and additional diagnostic markers. To achieve this, we will perform paired-end whole transcriptome sequencing (RNA-Seq) on a representative set of LMS cases to identify the single nucleotide variants and fusion transcripts that are unique to each LMS subtype. These studies will lead to a better understanding of the molecular events that drive LMS oncogenesis, and will improve the ability of clinicians to better diagnose LMS. As an ultimate goal, this work will prognosticate LMS behavior in individual patients and could lead to a more rational choice of treatment options for this disease.
The majority of leiomyosarcomas do not respond to existing chemotherapy regimens. Any improvement of leiomyosarcoma treatment relies on the identification of molecular subsets within this tumor type that differ in clinical behavior and that may display a differential response to treatment. In this grant we will develop a clinically robust classifier of molecular subtypes of leiomyosarcoma, improve outcome prediction, and identify novel potential treatment options for LMS.
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