Matching Items (4)
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- All Subjects: T cells
- Creators: Lake, Douglas
- Resource Type: Text
Description
Osteosarcoma is the most common bone cancer in children and adolescents. Patients with metastatic osteosarcoma are typically refractory to treatment. Numerous lines of evidence suggest that cytotoxic T-lymphocytes (CTL) limit the development of metastatic osteosarcoma. I have investigated the role of Programmed Death Receptor-1 (PD-1) in limiting the efficacy of immune mediated control of metastatic osteosarcoma. I show that human metastatic, but not primary, osteosarcoma tumors express the ligand for PD-1 (PD-L1) and that tumor infiltrating CTL express PD-1, suggesting this pathway may limit CTL control of metastatic osteosarcoma in patients. PD-L1 is also expressed on the K7M2 osteosarcoma tumor cell line that establishes metastases in mice, and PD-1 is expressed on tumor infiltrating CTL during disease progression. Blockade of PD-1/PD-L1 interactions dramatically improves the function of osteosarcoma-reactive CTL in vitro and in vivo, and results in decreased tumor burden and increased survival in the K7M2 mouse model of metastatic osteosarcoma. My results suggest that blockade of PD-1/PD-L1 interactions in patients with metastatic osteosarcoma should be pursued as a therapeutic strategy. However, PD-1/PD-L1 blockade treated mice still succumb to disease due to selection of PD-L1 mAb resistant tumor cells via up-regulation of other co-inhibitory T cell receptors. Combinational α-CTLA-4 and α-PD-L1 blockade treated mice were able to completely eradicate metastatic osteosarcoma, and generate immunity to disease. These results suggest that blockade of PD-1/PD-L1 interactions in patients with metastatic osteosarcoma, although improves survival, may lead to tumor resistance, requiring combinational immunotherapies to combat and eradicate disease.
ContributorsLussier, Danielle (Author) / Blattman, Joseph N. (Thesis advisor) / Anderson, Karen (Committee member) / Goldstein, Elliott (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cancer researchers have traditionally used a handful of markers to understand the origin of tumors and to predict therapeutic response. Additionally, performing machine learning activities on disparate data sources of varying quality is fraught with inherent bias. The Caris Life Sciences Molecular Database (CMD) is an immense resource for discovery as it contains over 215,000 molecular profiles of tumors with consistently gathered clinical grade molecular data along with immense amounts of clinical outcomes data. This resource was leveraged to generate two artificial intelligence algorithms aiding in diagnosis and one for therapy selection.
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
ContributorsAbraham, Jim (Author) / Spetzler, David (Thesis advisor) / Frasch, Wayne (Thesis advisor) / Lake, Douglas (Committee member) / Compton, Carolyn (Committee member) / Arizona State University (Publisher)
Created2020
Description
Adoptive transfer of T cells engineered to express synthetic antigen-specific T cell receptors (TCRs) has provocative therapeutic applications for treating cancer. However, expressing these synthetic TCRs in a CD4+ T cell line is a challenge. The CD4+ Jurkat T cell line expresses endogenous TCRs that compete for space, accessory proteins, and proliferative signaling, and there is the potential for mixed dimer formation between the α and β chains of the endogenous receptor and that of the synthetic cancer-specific TCRs. To prevent hybridization between the receptors and to ensure the binding affinity measured with flow cytometry analysis is between the tetramer and the TCR construct, a CRISPR-Cas9 gene editing pipeline was developed. The guide RNAs (gRNAs) within the complex were designed to target the constant region of the α and β chains, as they are conserved between TCR clonotypes. To minimize further interference and confer cytotoxic capabilities, gRNAs were designed to target the CD4 coreceptor, and the CD8 coreceptor was delivered in a mammalian expression vector. Further, Golden Gate cloning methods were validated in integrating the gRNAs into a CRISPR-compatible mammalian expression vector. These constructs were transfected via electroporation into CD4+ Jurkat T cells to create a CD8+ knockout TCR Jurkat cell line for broadly applicable uses in T cell immunotherapies.
ContributorsHirneise, Gabrielle Rachel (Author) / Anderson, Karen (Thesis advisor) / Mason, Hugh (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2020
Description
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the causative pathogen of Coronavirus Disease 2019 (COVID-19). Successful vaccination aims to elicit neutralizing antibodies (NAbs) which inhibit viral infection. Traditional NAb quantification methods (neutralization assays) are labor-intensive and expensive, with limited practicality for routine use (e.g. monitoring vaccination response). Thus, a rapid (10-minute) lateral flow assay (LFA) for quantification of SARS-CoV-2 NAbs was developed. Using the NAb LFA, an 18-month longitudinal study assessing monthly NAb titers was conducted in a cohort of over 500 COVID-19 mRNA vaccine recipients. Three NAb response groups were identified: vaccine strong responders (VSRs), moderate responders (VMRs), and poor responders (VPRs). VSRs generated high and durable NAb titers. VMRs initially generated high NAb titers but showed more rapid waning with time post-vaccination. Finally, VPRs rarely generated NAb titers ≥1:160, even after 3rd dose. Although strong humoral responses correlate with vaccine effectiveness, viral-specific CD4+ and CD8+ T cells are critical for long-term protection. Discordant phenotypes of viral-specific CD8+ and CD4+CXCR5+ T follicular helper (cTfh) cells have recently been associated with differential NAb responses. The second portion of this dissertation was to investigate whether/how SARS-CoV-2 T cell responses differ in individuals with impaired NAb titers following mRNA vaccination. Thus, phenotypic and functional characterization of T cell activation across NAb response groups was conducted. It was hypothesized that VPRs would exhibit discordant SARS-CoV-2 T cell activation and altered cTfh phenotypes. Peripheral blood mononuclear cells were isolated from VPRs, VMRs, VSRs, naturally infected, and normal donors. SARS-CoV-2 responsive T cells were characterized using in vitro activation induced marker assays, multicolor flow cytometry, and multiplex cytokine analysis. Further, CXCR5+ cTfh were examined for chemokine receptor expression (CCR6 and CXCR3). Results demonstrated that despite differential NAb responses, activation of SARS-CoV-2 responsive CD4+ and CD8+ T cells was comparable across NAb groups. However, double-positive CD4+CD8+, CD8low, and activated CD4+CXCR5+CCR6-CXCR3+ (Tfh1-like) T cells were expanded in VPRs compared to VMR and VSRs. Interestingly, a unique population of CD8+CXCR5+ T cells was also expanded in VPRs. These novel findings may aid in identification of individuals with impaired or altered immune responses to COVID-19 mRNA vaccination.
ContributorsRoeder, Alexa Jordan (Author) / Lake, Douglas (Thesis advisor) / McFadden, Grant (Committee member) / Borges Florsheim, Esther (Committee member) / Chang, Yung (Committee member) / Rahman, Masmudur (Committee member) / Arizona State University (Publisher)
Created2023