Abstract 44: Characterization and targeting of a temporal micro-metastatic signature in human brain metastases
Bibliographic record
Abstract
Abstract Metastases to the brain (BM) are the most common neoplasms to affect the adult central nervous system, arising in 40% of cancer patients and at a rate 10 times greater than primary brain tumors. Despite the prevalence and poor survival rates, therapeutic strategies for BM remain limited, and a substantial cause is the lack of proper preclinical models available to interrogate the intricacies of BM development. Previous work in our lab utilized BM samples from patient-derived lung-to-brain metastases to successfully establish clinically relevant in vivo models of BM. Here we further characterized the cells responsible for BM, termed brain metastasis initiation cells (BMIC). Patient-derived BMICs were injected via intracranial (BT), intracardiac (IC) and intrathoracic (IT) routes into NOD/SCID mice and re-isolated at different stages of metastatic progression. We isolated cells from primary lung tumors (LT), cells injected via intra-thoracic route that crossed the blood brain barrier to seed the brain forming micro-metastases (BMIT), and cells injected via intracardiac route that seeded large macro-metastases (BMIC). Through RNA sequencing we determined cells from BMIT (micro-metastasis stage) to retain a vastly different genetic profile compared to BMICs isolated at other stages of metastasis. Several of these genes belonged to pathways implicated in autonomic central nervous system neoplasms and neural development. Through connectivity mapping of BMIT profiles we discovered drugs that could inhibit the micro-metatsasis signature, and further in vitro validation revealed apomorphine to reduce BMIC sphere formation and proliferation. In vivo treatment with apomorphine blocked both micro- and macro-metastatic stages of our BMIC model. Of 5 BMIT genes identified to be specifically targeted by apomorphine, KIF16B, TESK2 and SEPW1 were shown to have significant value when applied as an independent prognostic signature in a cohort of lung adenocarcinoma patients. Future work will further validate the efficacy of apomorphine in targeting BMICs in primary lung cancer patient samples. With this work we present a possible new avenue for therapeutic targeting toward the prevention of BM development, where it is anticipated to transform a uniformly fatal disease into one that is eminently more treatable. Citation Format: Mohini Singh, Chitra Venugopal, Tomas Tokar, Nicole McFarlane, Minomi Subhapanditha, David Bakhshinyan, Maleeha Qazi, Parvez Vora, Neil Savage, Naresh K. Murty, Igor Jurisica, Sheila K. Singh. Characterization and targeting of a temporal micro-metastatic signature in human brain metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 44.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".