The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.
more »
« less
Bayesian Hierarchical Quantile Regression with Application to Characterizing the Immune Architecture of Lung Cancer
The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell receptor (TCR) repertoire sequencing is a relatively new technology that enables monitoring of T-cells, a subset of immune cells that play a central role in modulating immune response. These immunologic relationships are complex and are governed by various distributional aspects of an individual patient's tumor profile. We propose Bayesian QUANTIle regression for hierarchical COvariates (QUANTICO) that allows simultaneous modeling of hierarchical relationships between multilevel covariates, conducts explicit variable selection, estimates quantile and patient-specific coefficient effects, to induce individualized inference. We show QUANTICO outperforms existing approaches in multiple simulation scenarios. We demonstrate the utility of QUANTICO to investigate the effect of TCR variables on immune response in a cohort of lung cancer patients. At population level, our analyses reveal the mechanistic role of T-cell proportion on the immune cell abundance, with tumor mutation burden as an important factor modulating this relationship. At a patient level, we find several outlier patients based on their quantile-specific coefficient functions, who have higher mutational rates and different smoking history.
more » « less- Award ID(s):
- 2112943
- NSF-PAR ID:
- 10378709
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 79
- Issue:
- 3
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 2474-2488
- Size(s):
- ["p. 2474-2488"]
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Introduction Immunotherapies have shown great promise, but are not effective for all tumors types and are effective in less than 3% of patients with pancreatic ductal adenocarcinomas (PDAC). To make an immune treatment that is effective for more cancer patients and those with PDAC specifically, we genetically engineered Salmonella to deliver exogenous antigens directly into the cytoplasm of tumor cells. We hypothesized that intracellular delivery of an exogenous immunization antigen would activate antigen-specific CD8 T cells and reduce tumors in immunized mice.
Methods To test this hypothesis, we administered intracellular delivering (ID) Salmonella that deliver ovalbumin as a model antigen into tumor-bearing, ovalbumin-vaccinated mice. ID Salmonella delivers antigens by autonomously lysing in cells after the induction of cell invasion.
Results We showed that the delivered ovalbumin disperses throughout the cytoplasm of cells in culture and in tumors. This delivery into the cytoplasm is essential for antigen cross-presentation. We showed that co-culture of ovalbumin-recipient cancer cells with ovalbumin-specific CD8 T cells triggered a cytotoxic T cell response. After the adoptive transfer of OT-I CD8 T cells, intracellular delivery of ovalbumin reduced tumor growth and eliminated tumors. This effect was dependent on the presence of the ovalbumin-specific T cells. Following vaccination with the exogenous antigen in mice, intracellular delivery of the antigen cleared 43% of established KPC pancreatic tumors, increased survival, and prevented tumor re-implantation.
Discussion This response in the immunosuppressive KPC model demonstrates the potential to treat tumors that do not respond to checkpoint inhibitors, and the response to re-challenge indicates that new immunity was established against intrinsic tumor antigens. In the clinic, ID Salmonella could be used to deliver a protein antigen from a childhood immunization to refocus pre-existing T cell immunity against tumors. As an off-the-shelf immunotherapy, this bacterial system has the potential to be effective in a broad range of cancer patients.
-
more » « less
Abstract The PD‐1 immune checkpoint‐based therapy has emerged as a promising therapy strategy for treating the malignant brain tumor glioblastoma (GBM). However, patient response varies in clinical trials, mainly due to the tumor heterogeneity and immunological resistance in the tumor microenvironment. To further understand how mechanistically the niche interplay and competition drive anti‐PD‐1 resistance, an in silico model is established to quantitatively describe the biological rationale of critical GBM‐immune interactions, such as tumor growth and apoptosis, T cell activation and cytotoxicity, and tumor‐associated macrophage (TAM) mediated immunosuppression. Such an in silico experimentation and predictive model, based on the in vitro microfluidic chip‐measured end‐point data and patient‐specific immunological characteristics, allows for a comprehensive and dynamic analysis of multiple TAM‐associated immunosuppression mechanisms against the anti‐PD‐1 immunotherapy. The computational model demonstrates that the TAM‐associated immunosuppression varies in severity across different GBM subtypes, which results in distinct tumor responses. The prediction results indicate that a combination therapy by co‐targeting of PD‐1 checkpoint and TAM‐associated CSF‐1R signaling can enhance the immune responses of GBM patients, especially those patients with mesenchymal GBM who are irresponsive to the single anti‐PD‐1 therapy. The development of a patient‐specific in silico
– in vitro GBM model will help navigate and personalize immunotherapies for GBM patients. -
null (Ed.)Background Despite approval of immunotherapy for a wide range of cancers, the majority of patients fail to respond to immunotherapy or relapse following initial response. These failures may be attributed to immunosuppressive mechanisms co-opted by tumor cells. However, it is challenging to use conventional methods to systematically evaluate the potential of tumor intrinsic factors to act as immune regulators in patients with cancer. Methods To identify immunosuppressive mechanisms in non-responders to cancer immunotherapy in an unbiased manner, we performed genome-wide CRISPR immune screens and integrated our results with multi-omics clinical data to evaluate the role of tumor intrinsic factors in regulating two rate-limiting steps of cancer immunotherapy, namely, T cell tumor infiltration and T cell-mediated tumor killing. Results Our studies revealed two distinct types of immune resistance regulators and demonstrated their potential as therapeutic targets to improve the efficacy of immunotherapy. Among them, PRMT1 and RIPK1 were identified as a dual immune resistance regulator and a cytotoxicity resistance regulator, respectively. Although the magnitude varied between different types of immunotherapy, genetically targeting PRMT1 and RIPK1 sensitized tumors to T-cell killing and anti-PD-1/OX40 treatment. Interestingly, a RIPK1-specific inhibitor enhanced the antitumor activity of T cell-based and anti-OX40 therapy, despite limited impact on T cell tumor infiltration. Conclusions Collectively, the data provide a rich resource of novel targets for rational immuno-oncology combinations.more » « less
-
more » « less
Abstract The perivascular niche (PVN) plays an essential role in brain tumor stem‐like cell (BTSC) fate control, tumor invasion, and therapeutic resistance. Here, a microvasculature‐on‐a‐chip system as a PVN model is used to evaluate the ex vivo dynamics of BTSCs from ten glioblastoma patients. BTSCs are found to preferentially localize in the perivascular zone, where they exhibit either the lowest motility, as in quiescent cells, or the highest motility, as in the invasive phenotype, with migration over long distance. These results indicate that PVN is a niche for BTSCs, while the microvascular tracks may serve as a path for tumor cell migration. The degree of colocalization between tumor cells and microvessels varies significantly across patients. To validate these results, single‐cell transcriptome sequencing (10 patients and 21 750 single cells in total) is performed to identify tumor cell subtypes. The colocalization coefficient is found to positively correlate with proneural (stem‐like) or mesenchymal (invasive) but not classical (proliferative) tumor cells. Furthermore, a gene signature profile including PDGFRA correlates strongly with the “homing” of tumor cells to the PVN. These findings demonstrate that the model can recapitulate in vivo tumor cell dynamics and heterogeneity, representing a new route to study patient‐specific tumor cell functions.
