Abstract 2874: Understanding small cell lung cancer metastasis using circulating tumor cell (CTC)-derived tumor explant (CDX) models
Bibliographic record
Abstract
Abstract Small cell lung cancer (SCLC) accounts for about 13% of all malignant lung tumors and is characterized by an extremely low survival rate and widespread early dissemination. Over two-thirds of SCLC patients are diagnosed with metastases in multiple organs; typically liver, lymph nodes and brain. The number and site of secondary lesions at diagnosis correlates with unfavorable disease outcome. Studying metastasis in SCLC is extremely challenging, as metastatic tumor samples are rarely obtained from these patients. Our laboratory has developed a patient-faithful biobank of >45 SCLC circulating tumor cell (CTC)-derived explants (CDX) models with which to study SCLC1. We implemented a resection protocol using these models to study the metastatic process in SCLC by interrogating the genetic and phenotypic components of metastatic cells using combinations of tissue pathology and next generation sequencing methods. Upon resection of the subcutaneous tumor, we detect the presence of overt macrometastatic lesions in multiple models, which in most cases retain matched organ tropism to that observed in the patient. Whereas all models so far tested disseminated to the lungs, the ATOH1 subtye2 models CDX17, CDX17P, CDX25 and CDX30P predominantly grew in the liver, and CDX3 and CDX3P (ASCL1 subtype3) preferentially colonized the brain despite having a much slower growth. CDX SCLC cells can be isolated from subcutaneous tumors and orthotopically implanted, recapitulating the same tropism. Finally, CDX cells labelled ex vivo can be re-implanted directly into the bloodstream or into the brain parenchyma giving rise to SCLC secondary tumors, providing us with an excellent tool to monitor and study several steps of the metastatic cascade, including extravasation and brain colonization. We report for the first time, brain tropic mouse models of SCLC. Molecular studies are underway to investigate the underlying genetic determinants and mechanisms underpinning metastatic spread of SCLC via transcriptomic studies of matched subcutaneous and metastatic tissues and CTCs in CDX. In parallel, CDX3P is being used to identify markers of pre-metastatic disease and brain colonization at the single cell level to identify novel therapeutic strategies and biomarkers that may benefit the significant number of SCLC patients who present with brain lesions. 1Hodgkinson et al., Nature Medicine 20, 897-903 (2014) 2Simpson et al., Nature Cancer 1, pages437-451(2020) Citation Format: Maria Peiris-Pagès, Mitchell Revill, Derrick Morgan, Stewart Brown, Melanie Galvin, Lynsey Priest, Mathew Carter, Sheila K. Singh, Kristopher Frese, Fiona Blackhall, Kathryn Simpson, Caroline Dive. Understanding small cell lung cancer metastasis using circulating tumor cell (CTC)-derived tumor explant (CDX) models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2874.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".