Dataset Information

Alternative splicing of Apoptosis Stimulating Protein of TP53-2 (ASPP2) results in an oncogenic isoform promoting migration and therapy resistance in soft tissue sarcoma (STS).

ABSTRACT:

Background

Metastatic soft tissue sarcoma (STS) are a heterogeneous group of malignancies which are not curable with chemotherapy alone. Therefore, understanding the molecular mechanisms of sarcomagenesis and therapy resistance remains a critical clinical need. ASPP2 is a tumor suppressor, that functions through both p53-dependent and p53-independent mechanisms. We recently described a dominant-negative ASPP2 isoform (ASPP2κ), that is overexpressed in human leukemias to promote therapy resistance. However, ASPP2κ has never been studied in STS. MATERIALS AND METHODS: Expression of ASPP2κ was quantified in human rhabdomyosarcoma tumors using immunohistochemistry and qRT-PCR from formalin-fixed paraffin-embedded (FFPE) and snap-frozen tissue. To study the functional role of ASPP2κ in rhabdomyosarcoma, isogenic cell lines were generated by lentiviral transduction with short RNA hairpins to silence ASPP2κ expression. These engineered cell lines were used to assess the consequences of ASPP2κ silencing on cellular proliferation, migration and sensitivity to damage-induced apoptosis. Statistical analyses were performed using Student's t-test and 2-way ANOVA.

Results

We found elevated ASPP2κ mRNA in different soft tissue sarcoma cell lines, representing five different sarcoma sub-entities. We found that ASSP2κ mRNA expression levels were induced in these cell lines by cell-stress. Importantly, we found that the median ASPP2κ expression level was higher in human rhabdomyosarcoma in comparison to a pool of tumor-free tissue. Moreover, ASPP2κ levels were elevated in patient tumor samples versus adjacent tumor-free tissue within individual patients. Using isogenic cell line models with silenced ASPP2κ expression, we found that suppression of ASPP2κ enhanced chemotherapy-induced apoptosis and attenuated cellular proliferation.

Conclusion

Detection of oncogenic ASPP2κ in human sarcoma provides new insights into sarcoma tumor biology. Our data supports the notion that ASPP2κ promotes sarcomagenesis and resistance to therapy. These observations provide the rationale for further evaluation of ASPP2κ as an oncogenic driver as well as a prognostic tool and potential therapeutic target in STS.

SUBMITTER: Tsintari V

PROVIDER: S-EPMC9250729 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundApoptosis-stimulating Protein of TP53-2 (ASPP2) is a tumor suppressor enhancing TP53-mediated apoptosis via binding to the TP53 core domain. TP53 mutations found in cancers disrupt ASPP2 binding, arguing for an important role of ASPP2 in TP53-mediated tumor suppression. We now identify an oncogenic splicing variant, ASPP2κ, with high prevalence in acute leukemia.MethodsAn mRNA screen to detect ASPP2 splicing variants was performed and ASPP2κ was validated using isoform-specific PCR approaches. Translation into a genuine protein isoform was evaluated after establishing epitope-specific antibodies. For functional studies cell models with forced expression of ASPP2κ or isoform-specific ASPP2κ-interference were created to evaluate proliferative, apoptotic and oncogenic characteristics of ASPP2κ.FindingsExon skipping generates a premature stop codon, leading to a truncated C-terminus, omitting the TP53-binding sites. ASPP2κ translates into a dominant-negative protein variant impairing TP53-dependent induction of apoptosis. ASPP2κ is expressed in CD34+ leukemic progenitor cells and functional studies argue for a role in early oncogenesis, resulting in perturbed proliferation and impaired induction of apoptosis, mitotic failure and chromosomal instability (CIN) - similar to TP53 mutations. Importantly, as expression of ASPP2κ is stress-inducible it defines a novel class of dynamic oncogenes not represented by genomic mutations.InterpretationOur data demonstrates that ASPP2κ plays a distinctive role as an antiapoptotic regulator of the TP53 checkpoint, rendering cells to a more aggressive phenotype as evidenced by proliferation and apoptosis rates - and ASPP2κ expression results in acquisition of genomic mutations, a first initiating step in leukemogenesis. We provide proof-of-concept to establish ASPP2κ as a clinically relevant biomarker and a target for molecule-defined therapy. FUND: Unrestricted grant support from the Wilhelm Sander Foundation for Cancer Research, the IZKF Program of the Medical Faculty Tübingen, the Brigitte Schlieben-Lange Program and the Margarete von Wrangell Program of the State Ministry Baden-Wuerttemberg for Science, Research and Arts and the Athene Program of the excellence initiative of the Eberhard-Karls University, Tübingen.

Project description:BackgroundLack of using a validated algorithm to select patients is a source of selection bias in oncology studies using administrative claims. The objective of this study to evaluate published algorithms to identify patients with soft tissue sarcoma (STS) in administrative claims and to evaluate new algorithms to improved performance.MethodsTwo cancer populations including STS cases and non-STS controls were selected from the MarketScan Explorys Linked Claims-Electronic Medical Record (EMR) Database between January 1, 2000 and July 31, 2018. Eligible cases had a diagnosis on a clinical record for STS in the EMR while controls had no evidence of STS on any EMR records. Both cases and controls were enrolled in administrative claims during a period of observation and were aged???18 years. A split sample was used to test and validate algorithms using data from administrative claims. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated for 14 algorithms. Prior literature validating algorithms in administrative claims across other cancer types report both sensitivity and specificity ranging from as low as 73% to as high as 95%. This was used as a benchmark for defining algorithm success.ResultsThere were 784 STS cases and 249,062 non-STS cancer controls eligible for analysis. Requiring at least two claims with an ICD-CM diagnosis code for STS achieved a sensitivity of 67% but had a specificity of 72%. Algorithms that required NCCN-recommended systemic treatment for STS improved the specificity to over 90% but dropped the sensitivity to below 20%. Other combinations of diagnostic tests, symptoms, and procedures did not improve performance.ConclusionsThe algorithms tested in this study sample did not achieve sufficient performance and suggest the ability to accurately identify the STS population in administrative data is problematic. Difficulties are likely due to the origin of STS in a variety of locations, the non-specific symptoms of STS, and the common diagnostic tests recommended to diagnose the disease. Future research applying machine learning to examine timing and patterns of variables that comprise the diagnostic process may further investigate the ability to accurately identify STS cases in claims databases.