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Record W7133090144

Interrogation of the Non-small Cell Lung Carcinoma Proteome Landscape Uncovers Proteotypes and an ACO2-Iron Axis associated with Tumor Aggressiveness and Patient Outcomes

2022· dissertation· W7133090144 on OpenAlexaff
Shideh Mirhadi

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsAmgen (Canada)
FundersScheme for Promotion of Academic and Research Collaboration
KeywordsProteomeAconitaseStromal cellCell growthCellGene knockdownLung cancerProteomicsCarcinomaMitochondrion
DOInot available

Abstract

fetched live from OpenAlex

Tumor engraftment in immunodeficient mice is an independent indicator of poor prognosis in early-stage non-small cell lung cancer (NSCLC). We hypothesized that a shared molecular mechanism among the engrafting tumors allow for engraftability. To develop biological and clinically meaningful insights, this work aimed to first identify and verify the molecular mechanism responsible for engraftment by comparing the proteome of engrafting (XG) and non-engrafting (Non-XG) primary NSCLC tumors and second to annotate the scope of molecular heterogeneity that exists within the more aggressive XG tumors through proteome classification of NSCLC patient-derived xenografts (PDXs). Proteome comparison of XG and Non-XG primary tumors revealed a signature of 12 metabolism proteins that predict engraftment. Low level expression of mitochondrial aconitase (ACO2) protein was the strongest single predictor of engraftment. Consistently, knockdown of ACO2 in normal lung fibroblasts increased the cell proliferation and colony formation. While ectopic expression of ACO2 in H226 NSCLC cells inhibited cell proliferation in vitro and tumor growth in vivo. ACO2’s proliferative role was associated with modulation of iron homeostasis by controlling the delivery of mitochondrial iron-sulphur clusters to cytosolic aconitase through CISD1. Insufficient cytosolic iron sulphur clusters maintenance results in an iron starvation response, leading to increased iron, iron toxicity and consequent cell death. The data suggest a model wherein lower iron level provides the appropriate signal for the stromal cells to support the growth and engraftment of tumor cells. The scope of heterogeneity among the engraftable subset was also assessed by tumor proteome profiling of PDXs. This resolved the known major histological subtypes, and 3 proteome subtypes (proteotypes) within lung adenocarcinoma and 2 in squamous cell carcinoma were further identified. These identified proteotypes were associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features including a higher acute phase response (APR) in the proteotype with the worst survival. As many APR proteins have important roles in reducing the iron environment, the possibility of APR involvement in the lower iron observed in the more aggressive XG tumors is discussed in the final chapter of the thesis.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.247
Teacher spread0.242 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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