Usage Information
Abstract
Immunotherapy has emerged as a promising approach to treat cancer. However, partial responses across multiple clinical trials support the significance of characterizing intertumor and intratumor heterogeneity to achieve better clinical results and as potential tools in selecting patients for different types of cancer immunotherapies. Yet, the type of heterogeneity that informs clinical outcome and patient selection has not been fully explored. In particular, the lack of characterization of immune response–related genes in cancer cells hinders the further development of metrics to select and optimize immunotherapy. Therefore, we analyzed single-cell RNA-Seq data from lung adenocarcinoma patients and cell lines to characterize the intratumor heterogeneity of immune response–related genes and demonstrated their potential impact on the efficacy of immunotherapy. We discovered that IFN-γ signaling pathway genes are heterogeneously expressed and coregulated with other genes in single cancer cells, including MHC class II (MHCII) genes. The downregulation of genes in IFN-γ signaling pathways in cell lines corresponds to an acquired resistance phenotype. Moreover, analysis of 2 groups of tumor-restricted antigens, namely neoantigens and cancer testis antigens, revealed heterogeneity in their expression in single cells. These analyses provide a rationale for applying multiantigen combinatorial therapies to prevent tumor escape and establish a basis for future development of prognostic metrics based on intratumor heterogeneity.
Authors
Ke-Yue Ma, Alexandra A. Schonnesen, Amy Brock, Carla Van Den Berg, S. Gail Eckhardt, Zhihua Liu, Ning Jiang
Usage data is cumulative from February 2025 through February 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 993 | 199 |
| 229 | 45 | |
| Figure | 321 | 1 |
| Supplemental data | 248 | 17 |
| Citation downloads | 94 | 0 |
| Totals | 1,885 | 262 |
| Total Views | 2,147 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
