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Cancer-specific immune evasion and substantial heterogeneity within cancer types provide evidence for personalized immunotherapy.

ABSTRACT: The immune response against cancer is orchestrated by various parameters and site-dependent specificities have been poorly investigated. In our analyses of ten different cancer types, we describe elevated infiltration by regulatory T cells as the most common feature, while other lymphocyte subsets and also expression of immune-regulatory molecules on tumor-infiltrating lymphocytes showed site-specific variation. Multiparametric analyses of these data identified similarities of renal and liver or lung with head and neck cancer. Co-expression of immune-inhibitory ligands on tumor cells was most frequent in colorectal, lung and ovarian cancer. Genes related to antigen presentation were frequently dysregulated in liver and lung cancer. Expression of co-inhibitory molecules on tumor-infiltrating T cells accumulated in advanced stages while T-cell abundance was related to enhanced expression of genes related to antigen presentation. Our results promote evaluation of cancer-specific or even personalized immunotherapeutic combinations to overcome primary or secondary resistance as major limitation of immune-checkpoint inhibition.

SUBMITTER: Thelen M

PROVIDER: S-EPMC8208982 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Project description:BackgroundAnthracycline agents can cause cardiotoxicity. We used multivariable risk prediction models to identify a subset of patients with breast cancer at high risk of cardiotoxicity, for whom the harms of anthracycline chemotherapy may balance or exceed the benefits.Patients and methodsA clinical prediction model for anthracycline cardiotoxicity was created in 967 patients with human epidermal growth factor receptor-negative breast cancer treated with doxorubicin in the ECOG-ACRIN study E5103. Cardiotoxicity was defined as left ventricular ejection fraction (LVEF) decline of ≥ 10% to < 50% and/or a centrally adjudicated clinical heart failure diagnosis. Patient-specific incremental absolute benefit of anthracyclines (compared with non-anthracycline taxane chemotherapy) was estimated using the PREDICT model to assess breast cancer mortality risk.ResultsOf the 967 women who initiated therapy, 51 (5.3%) developed cardiotoxicity (12 with clinical heart failure). In a multivariate model, increasing age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.01-1.08), higher body mass index (OR, 1.06; 95% CI, 1.02-1.10), and lower baseline LVEF (OR, 0.93; 95% CI, 0.89-0.98) at baseline were significantly associated with cardiotoxicity. The concordance statistic of the risk model was 0.70 (95% CI, 0.63-0.77). In patients with low anticipated treatment benefit (n = 176) from the addition of anthracycline (< 2% absolute risk difference of breast cancer mortality at 10 years), 16 (9%) of 176 had a > 10% risk of cardiotoxicity and 61 (35%) of 176 had a 5% to 10% risk of cardiotoxicity at 1 year.ConclusionOlder age, higher body mass index, and lower baseline LVEF were associated with increased risk of cardiotoxicity. We identified a subgroup with low predicted absolute benefit of anthracyclines but with high predicted risk of cardiotoxicity. Additional studies are needed incorporating long-term cardiac outcomes and cardiotoxicity model external validation prior to implementation in routine clinical practice.

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Cancer-specific immune evasion and substantial heterogeneity within cancer types provide evidence for personalized immunotherapy.

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