Bibliographic record
Abstract
Abstract Studies have shown that tissue specific T cells exist in the peripheral T cell repertoire in an ‘ignorant’ or naïve state. Using our mouse models, we have shown that tissue specific T cells can be activated in a variety of ways to induce autoimmunity or tumor immunity. We have examined different strategies to promote anti-tumor responses including vaccination, DC stimulation and transfer of tumor specific T cell populations. Several new studies have found the following results. 1) Peptide pulsed DC vaccines, are superior to peptide vaccines. 2) Transfer of tissue specific T cells have improved efficacy when stimulated with DCs pulsed with antigen in mouse models. 3) Human tumor infiltrating lymphocyte (TIL) cultures stimulated with DCs and anti-CD3 show noteable shift in various markers, supporting the possibility of improved effector function in vivo. Furthermore, lessons can be learned from chronic viral infection models, since the presence of sustained tumor antigens may mimic some of the same evasion strategies. With this rationale, we have shown that IL-7 can ‘cure’ mice of chronic viral infections as well as promote anti-tumor immunity. Further recent studies have shown that NK cells inhibit CD8 responses and importantly, chronic viral infections cannot be established in the absence of NK cells. References 1. Ohashi PS, Oehen S, Burki K, Pircher H, Ohashi CT, Odermatt B, Malissen B, Zinkernagel R, Hengartner H. 1991. Ablation of “tolerance” and induction of diabetes by virus infection in viral antigen transgenic mice. Cell 65: 305-17 2. Garza KM, Chan SM, Suri R, Nguyen LT, Odermatt B, Schoenberger SP, Ohashi PS. 2000. Role of antigen presenting cells in mediating tolerance and autoimmunity. Journal of Experimental Medicine 191: 2021-7 3. Millar DG, Garza KM, Odermatt B, Elford AR, Ono N, Li Z, Ohashi PS. 2003. Hsp70 promotes antigen-presenting cell function and converts T-cell tolerance to autoimmunity in vivo. Nat.Med. 9: 1469-70 4. Speiser DE, Miranda R, Zakarian A, Bachmann MF, McKall-Faienza K, Odermatt B, Hanahan D, Zinkernagel RM, Ohashi PS. 1997. Self Antigens Expressed by Solid Tumors Do Not Efficiently Stimulate Naive or Activated T Cells: Implications for Immunotherapy. Journal of Experimental Medicine 186: 645-53 5. Pellegrini M, Calzascia T, Elford AR, Shahinian A, Lin AE, Dissanayake D, Dhanji S, Nguyen LT, Gronski MA, Morre M, Assouline B, Lahl K, Sparwasser T, Ohashi PS, Mak TW. 2009. Adjuvant IL-7 antagonizes multiple cellular and molecular inhibitory networks to enhance immunotherapies. Nat.Med. 15: 528-36 6. Pellegrini M, Calzascia T, Toe JG, Preston SP, Lin AE, Elford AR, Shahinian A, Lang PA, Lang KS, Morre M, Assouline B, Lahl K, Sparwasser T, Tedder TF, Paik JH, DePinho RA, Basta S, Ohashi PS, Mak TW. 2011. IL-7 engages multiple mechanisms to overcome chronic viral infection and limit organ pathology. Cell 144: 601-13 7. Lang PA, Lang KS, Xu HC, Grusdat M, Parish IA, Recher M, Elford AR, Dhanji S, Shaabani N, Tran CW, Dissanayake D, Rahbar R, Ghazarian M, Brüstle A, Fine J, Chen P, Weaver CT, Klose C, Diefenbach A, Häussinger D, Carlyle JR, Kaech SM, Mak TW, Ohashi PS. (2011) Natural killer cell activation enhances immune pathology and promotes chronic infection by limiting CD8+ T-cell immunity. Proc Natl Acad Sci U S A. 2011 Dec 13. [Epub ahead of print] Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY32-03. doi:1538-7445.AM2012-SY32-03
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".