Common human cancers have been found to be frequently associated with somatic mutations in dominant and recessive oncogenes including the ras and p53 genes and often produce mutant oncogene proteins that are uniquely present in the patient's cancer but not in his/her normal cells. These tumor specific proteins could form the basis for highly tumor specific cellular immunotherapy which targets an epitope that is present in each cancer cell and is fundamental to the maintenance of the malignant phenotype. It is now known that cytotoxic T lymphocytes (CTL) detect target cells for killing by recognizing short peptide fragments of endogenous proteins which are presented to them by class I MHC molecules on the surface of the target cell. The target proteins therefore do not have to be normally expressed on the cell surface. We have developed effective methods for induction of mutant oncogene- specific CTL in animals, and have detected such responses in humans, and shown that we can induce them with peptide vaccination. In this project, we will: 1) Test the immunological efficacy of individualized, mutant p53- specific, peptide-pulsed autologous dendritic cells (DC) with concurrent IL12 in a Phase II clinical trial of adjuvant immunotherapy for small cell lung cancer patients who achieve a good response to standard therapy. 2) Immunologically and molecularly characterize mature DC from patients with SCLC on this clinical trial before and after chemotherapy. We have preliminary data which shows that DC are functionally defective in patients with cancer, but are fully functional when grown in vitro growth conditions of DC precursors for future clinical vaccine trials, and will help elucidate the mechanism of this DC dysfunction. 3) Characterize dendritic cells genetically engineered to express T-cell epitopes as an alternative to peptide pulsing for autologous cell vaccines. 4) Analyze SCLC tumors from these patients for acquired defects in the machinery of antigen presentation, particularly beta2 microglobulin. The ultimate goal of this work is to better understand human cellular immune responses to mutant oncogene products in small cell lung cancer and to develop effective clinical translational therapies, particularly in the minimal residual disease setting.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
3P50CA070907-03S1
Application #
6296131
Study Section
Project Start
1998-09-01
Project End
1999-08-31
Budget Start
1998-10-01
Budget End
1999-09-30
Support Year
3
Fiscal Year
1998
Total Cost
Indirect Cost
City
Dallas
State
TX
Country
United States
Zip Code
75390
Sinicropi-Yao, Sara L; Amann, Joseph M; Lopez, David Lopez Y et al. (2018) Co-Expression Analysis Reveals Mechanisms Underlying the Varied Roles of NOTCH1 in NSCLC. J Thorac Oncol :
Le, Xiuning; Puri, Sonam; Negrao, Marcelo V et al. (2018) Landscape of EGFR-Dependent and -Independent Resistance Mechanisms to Osimertinib and Continuation Therapy Beyond Progression in EGFR-Mutant NSCLC. Clin Cancer Res 24:6195-6203
Wang, Shidan; Chen, Alyssa; Yang, Lin et al. (2018) Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci Rep 8:10393
Gomez, Daniel Richard; Byers, Lauren Averett; Nilsson, Monique et al. (2018) Integrative proteomic and transcriptomic analysis provides evidence for TrkB (NTRK2) as a therapeutic target in combination with tyrosine kinase inhibitors for non-small cell lung cancer. Oncotarget 9:14268-14284
Parra, Edwin R; Villalobos, Pamela; Mino, Barbara et al. (2018) Comparison of Different Antibody Clones for Immunohistochemistry Detection of Programmed Cell Death Ligand 1 (PD-L1) on Non-Small Cell Lung Carcinoma. Appl Immunohistochem Mol Morphol 26:83-93
Yamauchi, Mitsuo; Barker, Thomas H; Gibbons, Don L et al. (2018) The fibrotic tumor stroma. J Clin Invest 128:16-25
Ma, Junsheng; Hobbs, Brian P; Stingo, Francesco C (2018) Integrating genomic signatures for treatment selection with Bayesian predictive failure time models. Stat Methods Med Res 27:2093-2113
Yi, Faliu; Yang, Lin; Wang, Shidan et al. (2018) Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC Bioinformatics 19:64
Song, Kai; Bi, Jia-Hao; Qiu, Zhe-Wei et al. (2018) A quantitative method for assessing smoke associated molecular damage in lung cancers. Transl Lung Cancer Res 7:439-449
Ji, Xuemei; Bossé, Yohan; Landi, Maria Teresa et al. (2018) Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat Commun 9:3221

Showing the most recent 10 out of 1059 publications