VIVA1: a more invasive subclone of MDA-MB-134VI invasive lobular carcinoma cells with increased metastatic potential in xenograft models
Bibliographic record
Abstract
BACKGROUND: Invasive lobular carcinoma (ILC) is the second most common type of breast cancer. As few tools exist to study ILC metastasis, we isolated ILC cells with increased invasive properties to establish a spontaneously metastasising xenograft model. METHODS: MDA-MB-134VI ILC cells were placed in transwells for 7 days. Migrated cells were isolated and expanded to create the VIVA1 cell line. VIVA1 cells were compared to parental MDA-MB-134VI cells in vitro for ILC marker expression and relative proliferative and invasive ability. An intraductally injected orthotopic xenograft model was used to assess primary and metastatic tumour growth in vivo. RESULTS: Similar to MDA-MB-134VI, VIVA1 cells retained expression of oestrogen receptor (ER) and lacked expression of E-cadherin, however showed increased invasion in vitro. Following intraductal injection, VIVA1 and MDA-MB-134VI cells had similar primary tumour growth and survival kinetics. However, macrometastases were apparent in 7/10 VIVA1-injected animals. Cells from a primary orthotopic tumour (VIVA-LIG43) were isolated and showed similar proliferative rates but were also more invasive than parental cells. Upon re-injection intraductally, VIVA-LIG43 cells had more rapid tumour growth with similar metastatic incidence and location. CONCLUSIONS: We generated a new orthotopic spontaneously metastasising xenograft model for ER+ ILC amenable for the study of ILC metastasis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".