The Ludwig Institute for Cancer Research Melbourne Melanoma Cell Line Panel
Bibliographic record
Abstract
We established a range of melanoma cell lines from patient material (Table 1) termed Ludwig-Melbourne-Melanoma (LM-MEL-) followed by a unique number (method: Anaka et al., 2012). These lines have been human leucocyte antigen (HLA)-typed, their mutational status for common mutated genes in melanoma determined using the Sequenom MelCarta panel, and their transcriptomes profiled. All lines are tested for mycoplasma, and ethical approval for research purposes has been granted by the Austin Health Human Research Ethics Committee (HREC). Therapeutic options for advanced stage melanoma are limited, and despite the recent success with inhibitors of mutant v-raf murine sarcoma viral oncogene homolog B1 (BRAF) activity, long-lasting responses remain rare (Chapman et al., 2011). Immunotherapy may help overcome treatment failure, and there has been some success in the clinic with immunotherapy agents (Hodi et al., 2010). Knowledge of the antigen presenting major histocompatibility complex (MHC) class I HLA-types and antigenic proteins expressed by model systems is crucial for the preclinical testing of immunological interventions such as therapeutic cancer vaccines. Cancer-testis antigens (CTAg) and differentiation-antigens [mainly regulated by the microphtalmia-associated transcription factor (MITF)], represent two of the most studied families of potential cancer vaccine targets in melanoma (Caballero and Chen, 2009); and show various degrees of tissue-restriction and immunogenicity. The expression levels of some of these ‘immune-targetable’ genes premelanosome protein (PMEL), melan-A(MLANA), tyrosinase (TYR) and members of the melanoma antigen family (MAGE) in the LM-MEL cell lines are shown as an example in Figure 1. The complete gene-expression data and available additional clinical data of the here presented cell line panel are accessible online at ArrayExpress (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1496/) and in Table S1. All cell lines are available upon request for a small handling fee or on a collaborative basis and slides for IHC from matched patient tumors and matched PBMCs/sera are accessible for some lines in limited quantities. AB is supported by a fellowship from the Cure Cancer Australia Foundation. JC and AB are supported by a grant from the Melanoma Research Alliance (MRA) and JC is supported by a practitioner fellowship from the Nation Health and Medical Research Council (NHMRC). This research was supported in part by the Cancer Council Victoria (CCV), the Austin Medical Research Foundation (AHMRF) and Operational Infrastructure Support Program Funding of the Victorian State Government. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.026 |
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".