Multi-omics and single cell characterization of cancer immunosenescence landscape.

Cellular senescence (CS) is closely related to tumor progression. However, the studies about CS genes across human cancers have not explored the relationship between cancer senescence signature and telomere length. Additionally, single-cell analyses have not revealed the evolutionary trends of malignant cells and immune cells at the CS level. We defined a CS-associated signature, called "senescence signature", and found that patients with higher senescence signature had worse prognosis. Higher senescence signature was related to older age, higher genomic instability, longer telomeres, increased lymphocytic infiltration, higher pro-tumor immune infiltrates (Treg cells and MDSCs), and could predict responses to immune checkpoint inhibitor therapy. Single-cell analysis further reveals malignant cells and immune cells share a consistent evolutionary trend at the CS level. MAPK signaling pathway and apoptotic processes may play a key role in CS, and senescence signature may effectively predict sensitivity of MEK1/2 inhibitors, ERK1/2 inhibitors and BCL-2 family inhibitors. We also developed a new CS prediction model of cancer survival and established a portal website to apply this model ( https://bio-pub.shinyapps.io/cs_nomo/ ).

Multi-omics analyses reveal spatial heterogeneity in primary and metastatic oesophageal squamous cell carcinoma.

BACKGROUND: Biopsies obtained from primary oesophageal squamous cell carcinoma (ESCC) guide diagnosis and treatment. However, spatial intra-tumoral heterogeneity (ITH) influences biopsy-derived information and patient responsiveness to therapy. Here, we aimed to elucidate the spatial ITH of ESCC and matched lymph node metastasis (LNmet ). METHODS: Primary tumour superficial (PTsup ), deep (PTdeep ) and LNmet subregions of patients with locally advanced resectable ESCC were evaluated using whole-exome sequencing (WES), whole-transcriptome sequencing and spatially resolved digital spatial profiling (DSP). To validate the findings, immunohistochemistry was conducted and a single-cell transcriptomic dataset was analysed. RESULTS: WES revealed 15.72%, 5.02% and 32.00% unique mutations in PTsup , PTdeep and LNmet , respectively. Copy number alterations and phylogenetic trees showed spatial ITH among subregions both within and among patients. Driver mutations had a mixed intra-tumoral clonal status among subregions. Transcriptome data showed distinct differentially expressed genes among subregions. LNmet exhibited elevated expression of immunomodulatory genes and enriched immune cells, particularly when compared with PTsup (all P < .05). DSP revealed orthogonal support of bulk transcriptome results, with differences in protein and immune cell abundance between subregions in a spatial context. The integrative analysis of multi-omics data revealed complex heterogeneity in mRNA/protein levels and immune cell abundance within each subregion. CONCLUSIONS: This study comprehensively characterised spatial ITH in ESCC, and the findings highlight the clinical significance of unbiased molecular classification based on multi-omics data and their potential to improve the understanding and management of ESCC. The current practices for tissue sampling are insufficient for guiding precision medicine for ESCC, and routine profiling of PTdeep and/or LNmet should be systematically performed to obtain a more comprehensive understanding of ESCC and better inform treatment decisions.

Characterization of the tumor microenvironment and identification of spatially predictive biomarkers associated with beneficial neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). However, 20-40% of patients with LARC show little to no response to nCRT. Thus, comprehensively understanding the tumor microenvironment (TME), which might influence therapeutic efficacy, and identifying robust predictive biomarkers is urgently needed. Pre-treatment tumor biopsy specimens from patients with LARC were evaluated in detail through digital spatial profiling (DSP), public RNA sequencing datasets, and multiplex immunofluorescence (mIF). DSP analysis revealed distinct characteristics of the tumor stroma compared to the normal stroma and tumor compartments. We identified high levels of human leukocyte antigen-DR/major histocompatibility complex class II (HLA-DR/MHC-II) in the tumor compartment and B cells in the stroma as potential spatial predictors of nCRT efficacy in the Discovery cohort. Public datasets validated their predictive capacity for clinical outcomes. Using mIF in an independent nCRT cohort and/or the total cohort, we validated that a high density of HLA-DR/MHC-II+ cells in the tumor and CD20 + B cells in the stroma was associated with nCRT efficacy (all p ≤ 0.021). Spatial profiling successfully characterized the LARC TME and identified robust biomarkers with the potential to accurately predict nCRT response. These findings have important implications for individualized therapy.

Phase I trial of KN046, a novel bispecific antibody targeting PD-L1 and CTLA-4 in patients with advanced solid tumors.

BACKGROUND: KN046 is a novel bispecific antibody targeting programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). This multicenter phase I trial investigated the safety, tolerability, pharmacokinetics (PK), and efficacy of KN046 in patients with advanced solid tumors. METHODS: Patients who failed standard treatment were included. KN046 was administered at doses of 1, 3, and 5 mg/kg every 2 weeks (Q2W), 5 mg/kg every 3 weeks (Q3W), and 300 mg Q3W based on the modified toxicity probability interval method in the dose-escalation phase; the recommended dose was used in the expansion phase. Primary objectives were maximum tolerated dose (MTD) and recommended phase II dose (RP2D) in escalation and preliminary efficacy in expansion. Secondary objectives included PK, pharmacodynamics, safety, and tolerability of KN046. We also explored biomarkers based on PD-L1 expression, multiplex immunofluorescence (mIF) staining, and RNAseq-derived nCounter platform. RESULTS: Totally, 100 eligible patients were enrolled, including 59 with nasopharyngeal carcinoma (NPC), 36 with epidermal growth factor receptor (EGFR)-mutated non-small-cell lung cancer (NSCLC), and those with other advanced solid tumors. The most common treatment-related adverse events (TRAEs) were rash (33.0%), pruritus (31.0%), and fatigue (20.0%). Grade ≥3 TRAEs were observed in 14.0% of participants. No dose-limiting toxicity occurred in the dose-escalation phase, and the MTD was not reached. The RP2D was determined as 5 mg/kg Q2W according to the pharmacokinetic-pharmacodynamic model, the preliminary exposure-response analysis, and the overall safety profile. Among 88 efficacy-evaluable participants, the objective response rate (ORR) was 12.5%, and the median duration of response was 16.6 months. In the NPC subgroup, the ORR was 15.4%, and the median overall survival (OS) was 24.7 (95% CI 16.3 to not estimable) months. In the EGFR-mutant NSCLC subgroup, the ORR was 6.3%. mIF analysis results showed patients with high CD8 expression showed longer median OS (27.1 vs 9.2 months, p=0.02); better prognosis was observed in patients with high CD8 and PD-L1 expression. CONCLUSIONS: KN046 was well tolerated and showed promising antitumor efficacy in advanced solid tumors, especially in patients with NPC. The combination of both CD8 and PD-L1 expression improved the prediction of KN046 response. TRIAL REGISTRATION NUMBERS: NCT03733951 .

Pan-cancer analyses of synonymous mutations based on tissue-specific codon optimality.

Codon optimality has been demonstrated to be an important determinant of mRNA stability and expression levels in multiple model organisms and human cell lines. However, tissue-specific codon optimality has not been developed to investigate how codon optimality is usually perturbed by somatic synonymous mutations in human cancers. Here, we determined tissue-specific codon optimality in 29 human tissues based on mRNA expression data from the Genotype-Tissue Expression project. We found that optimal codons were associated with differentiation, whereas non-optimal codons were correlated with proliferation. Furthermore, codons biased toward differentiation displayed greater tissue specificity in codon optimality, and the tissue specificity of codon optimality was primarily present in amino acids with high degeneracy of the genetic code. By applying tissue-specific codon optimality to somatic synonymous mutations in 8532 tumor samples across 24 cancer types and to those in 416 normal cells across six human tissues, we found that synonymous mutations frequently increased optimal codons in tumor cells and cancer-related genes (e.g., genes involved in cell cycle). Furthermore, an elevated frequency of optimal codon gain was found to promote tumor cell proliferation in three cancer types characterized by DNA damage repair deficiency and could act as a prognostic biomarker for patients with triple-negative breast cancer. In summary, this study profiled tissue-specific codon optimality in human tissues, revealed alterations in codon optimality caused by synonymous mutations in human cancers, and highlighted the non-negligible role of optimal codon gain in tumorigenesis and therapeutics.

A Novel Computational Framework for Predicting the Survival of Cancer Patients With PD-1/PD-L1 Checkpoint Blockade Therapy.

Background: Immune checkpoint inhibitors (ICIs) induce durable responses, but only a minority of patients achieve clinical benefits. The development of gene expression profiling of tumor transcriptomes has enabled identifying prognostic gene expression signatures and patient selection with targeted therapies. Methods: Immune exclusion score (IES) was built by elastic net-penalized Cox proportional hazards (PHs) model in the discovery cohort and validated via four independent cohorts. The survival differences between the two groups were compared using Kaplan-Meier analysis. Both GO and KEGG analyses were performed for functional annotation. CIBERSORTx was also performed to estimate the relative proportion of immune-cell types. Results: A fifteen-genes immune exclusion score (IES) was developed in the discovery cohort of 65 patients treated with anti-PD-(L)1 therapy. The ROC efficiencies of 1- and 3- year prognosis were 0.842 and 0.82, respectively. Patients with low IES showed a longer PFS (p=0.003) and better response rate (ORR: 43.8% vs 18.2%, p=0.03). We found that patients with low IES enriched with high expression of immune eliminated cell genes, such as CD8+ T cells, CD4+ T cells, NK cells and B cells. IES was positively correlated with other immune exclusion signatures. Furthermore, IES was successfully validated in four independent cohorts (Riaz's SKCM, Liu's SKCM, Nathanson's SKCM and Braun's ccRCC, n = 367). IES was also negatively correlated with T cell-inflamed signature and independent of TMB. Conclusions: This novel IES model encompassing immune-related biomarkers might serve as a promising tool for the prognostic prediction of immunotherapy.

Low intratumor heterogeneity correlates with increased response to PD-1 blockade in renal cell carcinoma.

BACKGROUND: Intratumor heterogeneity (ITH) has been shown to be inversely associated with immune infiltration in several cancers including clear cell renal cell carcinoma (ccRCC), but it remains unclear whether ITH is associated with response to immunotherapy (e.g. PD-1 blockade) in ccRCC. METHODS: We quantified ITH using mutant-allele tumor heterogeneity, investigated the association of ITH with immune parameters in patients with ccRCC (n = 336) as well as those with papillary RCC (pRCC, n = 280) from The Cancer Genome Atlas, and validations were conducted in patients with ccRCC from an independent cohort (n = 152). The relationship between ITH and response to anti-PD-1 immunotherapy was explored in patients with metastatic ccRCC from a clinical trial of anti-PD-1 therapy (n = 35), and validated in three equal-size simulated data sets (n = 60) generated by random sampling with replacement based on this clinical trial cohort. RESULTS: In ccRCC, low ITH was associated with better survival, more reductions in tumor burden, and clinical benefit of anti-PD-1 immunotherapy through modulating immune activity involving more neoantigens, elevated expression of HLA class I genes, and higher abundance of dendritic cells. Furthermore, we found that the association between the level of ITH and response to PD-1 blockade was independent of the mutation status of PBRM1 and that integrating both factors performed better than the individual predictors in predicting the benefit of anti-PD-1 immunotherapy in ccRCC patients. In pRCC, increased immune activity was also observed in low- versus high-ITH tumors, including higher neoantigen counts, increased abundance of monocytes, and decreased expression of PD-L1 and PD-L2. CONCLUSIONS: ITH may be helpful in the identification of patients who could benefit from PD-1 blockade in ccRCC, and even in pRCC where no genomic metrics has been found to correlate with response to immune checkpoint inhibitors.