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.

Construction and validation of an immunoediting-based optimized neoantigen load (ioTNL) model to predict the response and prognosis of immune checkpoint therapy in various cancers.

BACKGROUND: Only a minority of patients clinically benefit from immune checkpoint therapy. Tumor clones with neoantigens have immunogenicity; therefore, they are eliminated by T-cell-mediated immune editing. Identifying neoantigen clones with the ability to induce immune elimination may better predict the clinical outcome of immunotherapy. METHODS: We developed ioTNL model, which indicates the immunoediting-based optimized tumor neoantigen load, by identifying tumor clones that could induce immune elimination. Data of more than two hundred patients from our patient pool and previously reported studies who underwent anti-PD-(L)1 therapy were collected to validate the prediction performance of ioTNL model. Clonal architectures, immune editing scores and ioTNL scores were identified. The association between the response as well as prognosis and the ioTNL were evaluated. Panel sequencing of genes from 2,469 patients within 20 cancer types was performed to profile the landscape of immunoediting. RESULTS: As expected, the ioTNL score could predict the response in patients who underwent immune checkpoint inhibitor (ICI) immunotherapy for various cancers, including non-small cell lung cancer (NSCLC; p = 0.0066), skin cutaneous melanoma (SKCM; p = 0.026) and nasopharyngeal carcinoma (NPC; p = 0.0025). Patients with a high ioTNL score demonstrated longer survival than those with a low score. We verified the ioTNL on our cohort through panel sequencing and found that the ioTNL was associated with the response (p = 0.025) and prognosis (p = 0.00082) in anti-PD-(L)1 monotherapy. In addition, we found that the immune editing score correlated with the tumor mutation burden (TMB) and the objective response rate of immunotherapy. CONCLUSIONS: Identifying neoantigen clones with the ability to induce immune elimination would better predict the efficacy of immunotherapy. We have proved that the reliable method of ioTNL can be applied to whole-exome sequencing (WES) and panel data and would have a broad application in precision diagnosis in immunotherapy.

Investigations of sequencing data and sample type on HLA class Ia typing with different computational tools.

Human leukocyte antigen (HLA) can encode the human major histocompatibility complex (MHC) proteins and play a key role in adaptive and innate immunity. Emerging clinical evidences suggest that the presentation of tumor neoantigens and neoantigen-specific T cell response associated with MHC class I molecules are of key importance to activate the adaptive immune systemin cancer immunotherapy. Therefore, accurate HLA typing is very essential for the clinical application of immunotherapy. In this study, we conducted performance evaluations of 4 widely used HLA typing tools (OptiType, Phlat, Polysolver and seq2hla) for predicting HLA class Ia genes from WES and RNA-seq data of 28 cancer patients. HLA genotyping data using PCR-SBT method was firstly obtained as the golden standard and was subsequently compared with HLA typing data by using NGS techniques. For both WES data and RNA-seq data, OptiType showed the highest accuracy for HLA-Ia typing than the other 3 programs at 2-digit and 4-digit resolution. Additionally, HLA typing accuracy from WES data was higher than from RNA-seq data (99.11% for WES data versus 96.42% for RNA-seq data). The accuracy of HLA-Ia typing by OptiType can reach 100% with the average depth of HLA gene regions >20x. Besides, the accuracy of 2-digit and 4-digit HLA-Ia typing based on control samples was higher than tumor tissues. In conclusion, OptiType by using WES data from control samples with the high average depth (>20x) of HLA gene regions can present a probably superior performance for HLA-Ia typing, enabling its application in cancer immunotherapy.

Case Report: Therapeutic Response to Chemo-Immunotherapy in an Advanced Large Cell Lung Carcinoma Patient With Low Values of Multiple Predictive Biomarkers.

Immune checkpoint inhibitors have revolutionized the treatments of lung cancers, and multiple predictive biomarkers alone or in combination help clinicians with the appropriate therapeutic selections. Recently, chemo-immunotherapy has been recommended for treating advanced non-small cell lung cancers in patients without driver mutations. However, the clinical relevance of predictive biomarkers and the treatment efficacy of chemo-immunotherapy in large cell lung carcinoma (LCLC) remain unclear. Here, we reported a rare case of LCLC with none driver gene mutations and low values of multiple predictive biomarkers. These biomarkers included a low PD-L1 expression of 5-10%, a low tumor mutational burden (TMB) of 2.5 muts/mb, a low CD8(+) tumor-infiltrating lymphocyte density of 147.91 psc/mm². After one-cycle chemotherapy, the patient progressed rapidly and then was switched to pembrolizumab combining paclitaxel plus cisplatin. Interestingly, he achieved a partial response after two cycles of chemo-immunotherapy, showing multiple lymph nodes obviously shrunk on CT scan, and other clinical symptoms were relieved when compared with the baseline findings. After five cycles of chemo-immunotherapy, this advanced patient still benefited and was changed to maintenance immunotherapy monotherapy. This case suggests that chemo-immunotherapy may provide an effective therapeutic option for those LCLC patients with low values of multiple predictive biomarkers, particularly for those who progressed from first-line classical treatments.

Activation of peroxisome proliferation-activated receptor-γ inhibits transforming growth factor-ß1-induced airway smooth muscle cell proliferation by suppressing Smad-miR-21 signaling.

The aims of the current study were to examine the signaling mechanisms for transforming growth factor-ß1 (TGF-ß1)-induced rat airway smooth muscle cell (ASMC) proliferation and to determine the effect of activation of peroxisome proliferation-activated receptor-γ (PPAR-γ) on TGF-ß1-induced rat ASMC proliferation and its underlying mechanisms. TGF-ß1 upregulated microRNA 21 (miR-21) expression by activating Smad2/3, and this in turn downregulated forkhead box O1 (FOXO1) mRNA expression. In addition, TGF-ß1-Smad-miR-21 signaling also downregulated phosphatase and tensin homolog deleted on chromosome ten (PTEN) expression and thus de-repressed the PI3K-Akt pathway. Depletion of PTEN reduced the nuclear FOXO1 protein level without affecting its mRNA level. Inhibition of the PI3K-Akt pathway or proteasome function reversed PTEN knockdown-induced nuclear FOXO1 protein reduction. Our study further showed that loss of FOXO1 increased cyclin D1 expression, leading to rat ASMC proliferation. Preincubation of rat ASMCs with pioglitazone, a PPAR-γ activator, blocked TGF-ß1-induced activation of Smad2/3 and its downstream targets changes of miR-21, PTEN, Akt, FOXO1, and cyclin D1, resulting in the inhibition of rat ASMC proliferation. Our study suggests that the activation of PPAR-γ inhibits rat ASMC proliferation by suppressing Smad-miR-21 signaling and therefore has a potential value in the prevention and treatment of asthma by negatively modulating airway remodeling.

Activation of AMPK α2 inhibits airway smooth muscle cells proliferation.

The aims of the present study were to examine the effect of adenosine monophosphate-activated protein kinase (AMPK) activation on airway smooth muscle cells (ASMCs) proliferation and to address its potential mechanisms. Platelet derived growth factor (PDGF) activated phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway, and this in turn up-regulated S-phase kinase-associated protein 2 (Skp2) and consequently reduced cyclin dependent kinase inhibitor 1B (p27) leading to ASMCs proliferation. Pre-incubation of cells with metformin, an AMPK activator, blocked PDGF-induced activation of mTOR and its downstream targets changes of Skp2 and p27 without changing Akt phosphorylation and inhibited ASMCs proliferation. Transfection of ASMCs with AMPK α2-specific small interfering RNA (siRNA) reversed the effect of metformin on mTOR phosphorylation, Skp2 and p27 protein expression and cell proliferation. Our study suggests that activation of AMPK, particularly AMPK α2, negatively regulates mTOR activity to suppress ASMCs proliferation and therefore has a potential value in the prevention and treatment of asthma by negatively modulating airway remodeling.

Activation of AMPK inhibits PDGF-induced pulmonary arterial smooth muscle cells proliferation and its potential mechanisms.

The aims of the present study were to examine signaling mechanisms for PDGF-induced pulmonary arterial smooth muscle cells (PASMC) proliferation and to determine the effect of AMPK activation on PDGF-induced PASMC proliferation and its underlying mechanisms. PDGF activated PI3K/Akt/mTOR signaling pathway, and this in turn up-regulated Skp2 and consequently reduced p27 leading to PASMC proliferation. Prior incubation of PASMC with metformin induced a dramatic AMPK activation and significantly blocked PDGF-induced cell proliferation. PASMC lacking AMPKα2 were resistant to the inhibitory effect of metformin on PDGF-induced cell proliferation. Metformin did not affect Akt activation but blocked mTOR phosphorylation in response to PDGF; these were accompanied by the reversion of Skp2 up-regulation and p27 reduction. Our study suggests that the activation of AMPK negatively regulates mTOR activity to suppress PASMC proliferation and therefore has a potential value in the prevention and treatment of pulmonary hypertension by negatively modulating pulmonary vascular remodeling.