Abstract 1751: MSC-1 is a first-in-class humanized monoclonal antibody that modulates the tumor microenvironment by inhibiting a novel cancer immunotherapy target, LIF
Bibliographic record
Abstract
Abstract Leukemia Inhibitory Factor (LIF) is a member of the IL-6 family of cytokines and is involved in many physiological and pathological processes including the promotion of an immunosuppressive environment to support embryo implantation, down-regulation of autoimmune processes and the regulation of stem cell homeostasis and differentiation. In cancer, LIF is hypothesized to have a complex role tumor development and progression, creating an immunosuppressive tumor microenvironment as well as promoting the activity of cancer initiating cells (CICs). LIF is highly expressed in a subset of tumors across multiple tumor types, e.g. glioblastoma multiforme (GBM), non-small cell lung cancer (NSCLC), colon, ovarian, prostate, and pancreatic cancer, and correlates with poor prognosis. Given the pleiotropic role LIF is hypothesized to play in cancer, inhibition of LIF represents an exciting new therapeutic concept sitting at the intersection of two key therapeutic approaches in oncology: blockade of tumor evasion of the immune system and blockade of tumor growth via inhibition of CICs. We have identified and developed MSC-1, a first-in-class humanized IgG1 monoclonal antibody that is a potent and selective inhibitor of LIF. MSC-1 cross reacts with mouse and non-human primate LIF and inhibits LIF signaling by blocking the recruitment of gp130 to the LIF-LIFR-gp130 signaling complex. The efficacy of MSC-1 was evaluated in multiple mouse tumor models and the mechanism of action investigated. LIF inhibition with MSC-1 or shRNAs reduced tumor growth in multiple syngeneic tumor models (NSCLC, ovarian and colon), and clear target engagement was shown for MSC-1. Investigations into the mechanism of action identified that inhibition of LIF with MSC-1 reprogrammed the tumor microenvironment by decreasing immunosuppressive M2 macrophages and increasing the number of intratumoral NK cells and total/activated T cells. MSC-1 also decreased immunosuppressive M2 macrophages in an orthotopic GBM xenograft model and human GBM organotypic tumor slices in an ex vivo model. Similarly, immunosuppressive macrophage genes were decreased when monocytes were co-cultured with supernatants from a GBM cell line in which LIF expression had been knocked-down. Given the effects of MSC-1 on intratumoral immune cells, we hypothesized that MSC-1 could be effectively combined with checkpoint inhibitors and we are currently evaluating MSC-1/checkpoint inhibitor combination therapy. Taken together, these findings form the basis of a robust therapeutic hypothesis, whereby MSC-1 treatment will lead to clinical activity in multiple cancer indications. A Phase I dose-escalation and expansion study of MSC-1 is planned to initiate early 2018 in patients with advanced solid tumors that will incorporate target engagement and PD biomarkers, as well as safety and efficacy endpoints. Citation Format: Angus Sinclair, Robin Hallett, Patricia Giblin, Isabel Huber-Ruano, Judit Anido, Naimish Pandya, Kimberly Hoffman, Ada Sala, Monica Pascual, Vanessa Chigancas, Swetha Raman, Johan Fransson, Jean-Philippe Julien, Robert Wasserman, Jeanne Magram, Joan Seoane. MSC-1 is a first-in-class humanized monoclonal antibody that modulates the tumor microenvironment by inhibiting a novel cancer immunotherapy target, LIF [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 1751.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".