Comprehensive analysis of multiple parameters associated with tumor immune microenvironment in ARID1A mutant cancers

Chromatin Remodelers Are Regulators of the Tumor Immune Microenvironment

Immune checkpoint inhibitors show remarkable responses in a wide range of cancers, yet patients develop adaptive resistance. This necessitates the identification of alternate therapies that synergize with immunotherapies. Epigenetic modifiers are potent mediators of tumor-intrinsic mechanisms and have been shown to regulate immune response genes, making them prime targets for therapeutic combinations with immune checkpoint inhibitors. Some success has been observed in early clinical studies that combined immunotherapy with agents targeting DNA methylation and histone modification; however, less is known about chromatin remodeler-targeted therapies. Here, we provide a discussion on the regulation of tumor immunogenicity by the chromatin remodeling SWI/SNF complex through multiple mechanisms associated with immunotherapy response that broadly include IFN signaling, DNA damage, mismatch repair, regulation of oncogenic programs, and polycomb-repressive complex antagonism. Context-dependent targeting of SWI/SNF subunits can elicit opportunities for synthetic lethality and reduce T-cell exhaustion. In summary, alongside the significance of SWI/SNF subunits in predicting immunotherapy outcomes, their ability to modulate the tumor immune landscape offers opportunities for therapeutic intervention.

Impact of ARID1A and TP53 mutations in pediatric refractory or relapsed mature B-Cell lymphoma treated with CAR-T cell therapy

BACKGROUND

Chimeric antigen receptor (CAR)-T cell therapy has been used to treat pediatric refractory or relapsed mature B-cell non-Hodgkin lymphoma (r/r MB-NHL) with significantly improved outcomes, but a proportion of patients display no response or experience relapse after treatment. To investigate whether tumor-intrinsic somatic genetic alterations have an impact on CAR-T cell treatment, the genetic features and treatment outcomes of 89 children with MB-NHL were analyzed.

METHODS

89 pediatric patients treated at multiple clinical centers of the China Net Childhood Lymphoma (CNCL) were included in this study. Targeted next-generation sequencing for a panel of lymphoma-related genes was performed on tumor samples. Survival rates and relapse by genetic features and clinical factors were analyzed. Survival curves were calculated using a log-rank (Mantel-Cox) test. The Wilcox sum-rank test and Fisher's exact test were applied to test for group differences.

RESULTS

A total of 89 driver genes with somatic mutations were identified. The most frequently mutated genes were TP53 (66%), ID3 (55%), and ARID1A (31%). The incidence of ARID1A mutation and co-mutation of TP53 and ARID1A was high in patients with r/r MB-NHL (P = 0.006; P = 0.018, respectively). CAR-T cell treatment significantly improved survival in r/r MB-NHL patients (P = 0.00081), but patients with ARID1A or ARID1A and TP53 co-mutation had poor survival compared to those without such mutations.

CONCLUSION

These results indicate that children with MB-NHL harboring ARID1A or TP53 and ARID1A co-mutation are insensitive to initial conventional chemotherapy and subsequent CAR-T cell treatment. Examination of ARID1A and TP53 mutation status at baseline might have prognostic value, and risk-adapted or more effective therapies should be considered for patients with these high-risk genetic alterations.

Prognostic Value of Neoantigen Load in Immune Checkpoint Inhibitor Therapy for Cancer

Immune checkpoint inhibitors (ICIs) have made great progress in the field of tumors and have become a promising direction of tumor treatment. With advancements in genomics and bioinformatics technology, it is possible to individually analyze the neoantigens produced by somatic mutations of each patient. Neoantigen load (NAL), a promising biomarker for predicting the efficacy of ICIs, has been extensively studied. This article reviews the research progress on NAL as a biomarker for predicting the anti-tumor effects of ICI. First, we provide a definition of NAL, and summarize the detection methods, and their relationship with tumor mutation burden. In addition, we describe the common genomic sources of NAL. Finally, we review the predictive value of NAL as a tumor prediction marker based on various clinical studies. This review focuses on the predictive ability of NAL’s ICI efficacy against tumors. In melanoma, lung cancer, and gynecological tumors, NAL can be considered a predictor of treatment efficacy. In contrast, the use of NAL for urinary system and liver tumors requires further research. When NAL alone is insufficient to predict efficacy, its combination with other indicators can improve prediction efficiency. Evaluating the response of predictive biomarkers before the treatment initiation is essential for guiding the clinical treatment of cancer. The predictive power of NAL has great potential; however, it needs to be based on more accurate sequencing platforms and technologies.

ARID1A Downregulation Predicts High PD-L1 Expression and Worse Clinical Outcome in Patients With Gallbladder Cancer

Background

Recent studies have confirmed that AT-rich interactive domain-containing protein 1A (ARID1A) plays a critical role in tumorigenesis, but its role in gallbladder cancer (GBC) remains unclear.

Methods

In total, 224 patients from Zhongshan Hospital were recruited for this retrospective study. The clinicopathological and baseline characteristics of the patients were collected. Bioinformatics analysis was performed to reveal variations in genes and signaling pathways, and ARID1A and PD-L1 expression and the number of PD1+ tumor-infiltrating lymphocytes (TILs) were measured by immunohistochemical staining.

Results

ARID1A expression was negatively correlated with overall survival in patients with GBC, and multivariate analysis identified ARID1A as an independent prognostic factor for overall survival. A heatmap and gene set enrichment analysis suggested that cytotoxic T lymphocyte signatures and immune-related signaling pathways were downregulated in ARID1A low tumors. Subsequent immunohistochemical staining confirmed that ARID1A expression was negatively correlated with PD-L1 expression and PD1+ TILs in the tumor microenvironment. The Kaplan–Meier analysis suggested that high ARID1A expression combined with low PD-L1 expression or low PD1+ TIL counts is associated with the best prognosis in patients with GBC.

Conclusion

ARID1A inactivation can lead to a worse prognosis in patients with GBC, potentially by mediating immune evasion through the PD1/PD-L1 pathway.

Clinical and molecular parameters associated to pneumonitis development in non-small-cell lung cancer patients receiving chemoimmunotherapy from NADIM trial

BACKGROUND

Pneumonitis (Pn) is one of the main immune-related adverse effects, having a special importance in lung cancer, since they share affected tissue. Despite its clinical relevance, Pn development remains an unpredictable treatment adverse effect, whose mechanisms are mainly unknown, being even more obscure when it is associated to chemoimmunotherapy.

METHODS

In order to identify parameters associated to treatment related Pn, we analyzed clinical variables and molecular parameters from 46 patients with potentially resectable stage IIIA non-small-cell lung cancer treated with neoadjuvant chemoimmunotherapy included in the NADIM clinical trial (NCT03081689). Pn was defined as clinical or radiographic evidence of lung inflammation without alternative diagnoses, from treatment initiation to 180 days.

RESULTS

Among 46 patients, 12 developed Pn (26.1%). Sex, age, smoking status, packs-year, histological subtype, clinical or pathological response, progression-free survival, overall survival and number of nivolumab cycles, were not associated to Pn development. Regarding molecular parameters at diagnosis, Pn development was not associated to programmed death ligand 1, TPS, T cell receptor repertoire parameters, or tumor mutational burden. However, patients who developed Pn had statistically significant lower blood median levels of platelet to monocyte ratio (p=0.012) and teratocarcinoma-derived growth factor 1 (p=0.013; area under the curve (AUC) 0.801), but higher median percentages of natural killers (NKs) (p=0.019; AUC 0.786), monocytes (p=0.017; AUC 0.791), MSP (p=0.006; AUC 0.838), PARN (p=0.017; AUC 0.790), and E-Cadherin (p=0.022; AUC 0.788). In addition, the immune scenario of Pn after neoadjuvant treatment involves: high levels of neutrophils and NK cells, but low levels of B and T cells in peripheral blood; increased clonality of intratumoral T cells; and elevated plasma levels of several growth factors (EGF, HGF, VEGF, ANG-1, PDGF, NGF, and NT4) and inflammatory cytokines (MIF, CCL16, neutrophil gelatinase-associated lipocalin, BMP-4, and u-PAR).

CONCLUSIONS

Although statistically underpowered, our results shed light on the possible mechanisms behind Pn development, involving innate and adaptative immunity, and open the possibility to predict patients at high risk. If confirmed, this may allow the personalization of both, the surveillance strategy and the therapeutic approaches to manage Pn in patients receiving chemoimmunotherapy.

Effect and biomarker of immune checkpoint blockade therapy for ARID1A deficiency cancers

The AT-rich interaction domain 1A (ARID1A) are frequently mutates across a broad spectrum of cancers. The majority of ARID1A mutations are inactivating mutations and lead to loss expression of the ARID1A protein. To date, clinical applicable targeted cancer therapy based on ARID1A mutational status has not been described. With increasing number of studies reported that the ARID1A deficiency may be a novel predictive biomarker for immune checkpoint blockade (ICB) treatment. ARID1A deficiency would compromise mismatch repair pathway and increase the number of tumor-infiltrating lymphocytes, tumor mutation burden and expression of programmed cell death ligand 1 (PD-L1) in some cancers, which would suggested cooperate with ICB treatment. In this review, we summarize the relationship between ARID1A deficiency and ICB treatment including potential mechanisms, potential therapeutic combination, and the biomarker value of ARID1A deficiency.