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Abstract A070 Identifying therapeutic vulnerabilities in desmoplastic small round cell tumor through multi-omics analyses

2024· article· en· W4402266354 on OpenAlexaboutno aff
Danh D. Truong, Emre Arslan, Veena Kochat, Sandhya Krishnan, Clement Agyemang, Margarita Divenko, Roberto Cárdenas-Zúñiga, Davis R. Ingram, Rossana Lazcano, Akshay Basi, Javier A. Gomez, Hannah C. Beird, Chia-Chin Wu, Jared K. Burks, P. Andrew Futreal, Alexander J. Lazar, Ravin Ratan, Najat C. Daw, Kunal Rai, Andrea Hayes, Joseph A. Ludwig

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsnot available
Fundersnot available
KeywordsOmicsComputational biologyBiologyDesmoplastic small-round-cell tumorCancerMedicinePathologyBioinformaticsInternal medicineImmunohistochemistry

Abstract

fetched live from OpenAlex

Abstract Desmoplastic small round cell tumor (DSRCT) is a rare and usually incurable aggressive sarcoma subtype affecting both adults and young adolescents. All tumor cells harbor a pathognomonic EWS::WT1 fusion protein (FP), but FP-targeted agents are nonexistent. Less than 20% of patients survive beyond five years with standard of care. Our work showed that a subset of DSRCT cells (mostly of epithelial lineage) highly express the androgen receptor (AR). On the other hand, a separate subgroup of DSRCT cells exhibited neuroendocrine (NE) markers without AR expression. Given that all the DSRCT cells share the same FP, we explore gene expression, chromatin accessibility, and proteomic analyses to understand mechanisms that may drive epithelial and NE phenotypes and present therapeutic opportunities. We studied the transcriptomic, epigenomic, and proteomic profiles of DSRCT from nine patients with matched specimens. By gene expression, three subsets of patients were identified. Two subsets were either high in AR or NE markers, but not both. The third subset appeared to exhibit both markers – implying a hybrid phenotype or a poorly differentiated phenotype. There was strong concordance between the transcriptomic and the proteomic markers as measured by multiplex immunofluorescent imaging. In AR-positive tumor nests, AR was localized to the nucleus, suggesting downstream activation of this pathway. AR expression was correlated with pan-cytokeratin expression in the tumor nests. On the other hand, tumor nests marked by neural-specific enolase, an NE marker, demonstrated little to no expression of AR. The ATAC-seq data revealed enriched motifs associated with the DSRCT subtypes. Epithelial/AR-positive subtypes were enriched in motifs for nuclear receptors (ARE and GRE) and CCAAT-enhancer-binding proteins. The NE subtype lacked the ARE and GRE motifs but was enriched for zinc finger motifs, including WT1 and the EGR family. EMT was also associated with the NE and hybrid subtypes, such as ZEB1, SNAI1, and SNAI2 (Slug). Additionally, MEF2 transcription factors were enriched in the NE and hybrid subtypes associated with muscle and neural development. Despite the male predilection, AR heterogeneity in DSRCT was an unexpected finding that may change potential treatment options. In vitro studies of AR stimulation and inhibition demonstrated that we could promote cell proliferation and inhibit cell growth, respectively. However, the emergence of the NE subtype in prostate cancer is generally in response to AR-directed therapy, which is not yet used for DSRCT patients. The motif analysis suggests that NE is associated with enriched motifs for zinc fingers, EMT, and MEF2 transcription factors. How the NE phenotype emerges in DSRCT has yet to be discovered. Ongoing research will shed light on transcription factor binding and how targeting AR may affect the epithelial/AR and NE states in DSRCT. Citation Format: Danh Truong, Emre Arslan, Veena Kochat, Sandhya Krishnan, Clement Agyemang, Margarita Divenko, Roberto Cárdenas-Zúñiga, Davis Ingram, Rossana Lazcano, Akshay Basi, Javier Gomez, Hannah Beird, Chia-Chin Wu, Jared Burks, P. Andrew Futreal, Alexander Lazar, Ravin Ratan, Najat C. Daw, Kunal Rai, Andrea Hayes, Joseph Ludwig. Identifying therapeutic vulnerabilities in desmoplastic small round cell tumor through multi-omics analyses [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A070.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.246
GPT teacher head0.467
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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