Genome and Transcriptome Biomarkers of Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.

METHODS

In this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib's efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.

RESULTS

We developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.

CONCLUSION

The GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

Autophagy unrelated transcriptional mechanisms of hydroxychloroquine resistance revealed by integrated multi-omics of evolved cancer cells

Hydroxychloroquine (HCQ) and chloroquine are repurposed drugs known to disrupt autophagy, a molecular recycling pathway essential for tumor cell survival, chemotherapeutic resistance, and stemness. We pursued a multi-omic strategy in OVCAR3 ovarian cancer and CCL218 colorectal cancer cells. Two genome-scale screens were performed. In the forward genetic screen, cell populations were passaged for 15 drug pulse-chases with HCQ or vehicle control. Evolved cells were collected and processed for bulk RNA-seq, exome-seq, and single-cell RNA-seq (scRNA-seq). In the reverse genetic screen, a pooled CRISPR-Cas9 library was used in cells over three pulse-chases of HCQ or vehicle control treatments. HCQ evolved cells displayed remarkably few mutational differences, but substantial transcriptional differences. Transcriptomes revealed multiple pathways associated with resistance to HCQ, including upregulation of glycolysis, exocytosis, and chromosome condensation/segregation, or downregulation of translation and apoptosis. The Cas9 screen identified only one autophagy gene. Chromosome condensation and segregation were confirmed to be disrupted by HCQ in live cells and organelle-free in vitro extracts. Transcriptional plasticity was the primary mechanism by which cells evolved resistance to HCQ. Neither autophagy nor the lysosome were substantive hits. Our analysis may serve as a model for how to better position repurposed drugs in oncology.

Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer

This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78-0.92), 0.76 (95% CI 0.68-0.84), and 0.89 (95% CI 0.84-0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86-1.00), 0.79 (95% CI 0.68-0.91), and 0.96 (95% CI 0.90-1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72-0.96), 0.75 (95% CI 0.62-0.87), and 0.86 (95% CI 0.75-0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.

Plasma versus Tissue Tumor Mutational Burden as Biomarkers of Durvalumab plus Tremelimumab Response in Patients with Metastatic Colorectal Cancer in the CO.26 Trial

PURPOSE

Tissue-derived tumor mutation burden (TMB) of ≥10 mutations/Mb is a histology-agnostic biomarker for the immune checkpoint inhibitor (ICI) pembrolizumab. However, the dataset in which this was validated lacked colorectal cancers (CRC), and there is limited evidence for immunotherapy benefits in CRC using this threshold.

PATIENTS AND METHODS

CO.26 was a randomized phase II study of 180 patients, comparing durvalumab and tremelimumab (D + T, n = 119 patients) versus best supportive care (BSC; n = 61 patients). ctDNA sequencing was available for 168 patients (n = 118 D + T; n = 50), of whom 165 had evaluable plasma TMB (pTMB). Tissue sequencing was available for 108 patients. Optimal thresholds for stratifying patients based on OS were determined using a minimal P value approach. This report includes the final OS analysis.

RESULTS

Tissue TMB ≥10 mutations/Mb was not predictive of benefit from D + T compared with BSC in microsatellite stable (MSS) metastatic CRC [HR, 0.71 (95% CI, 0.28-1.80); P = 0.47]. No tissue TMB threshold could identify a high TMB group that benefited from ICI. By contrast, plasma TMB (pTMB) ≥28 mutations/Mb was predictive of benefit from D + T [HR, 0.34 (95% CI, 0.13-0.85); P = 0.022], as was clonal pTMB ≥10.6 mutations/Mb [HR, 0.10 (95% CI, 0.014-0.79); P = 0.029] and subclonal pTMB ≥25.9/Mb [HR, 0.20 (95% CI, 0.061-0.69); P = 0.010]. Higher pTMB was associated with length of time on cytotoxic agents (P = 0.021) and prior anti-EGFR exposure (P = 2.44 × 10-06).

CONCLUSIONS

pTMB derived from either clonal or subclonal mutations may identify a group likely to benefit from immunotherapy, although validation is required. Tissue TMB provided no predictive utility for immunotherapy in this trial.

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features

Background

Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction.

Methods

We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay.

Results

Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity.

Conclusion

The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.

Transcriptome Deconvolution Reveals Absence of Cancer Cell Expression Signature in Immune Checkpoint Blockade Response

Immune checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some patients with cancer. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer- and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic expression signatures of ICB response conserved across tumor types, suggesting that cancer cells lack tissue-agnostic transcriptomic features of ICB response.

SIGNIFICANCE

Our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB response, which has implications on the development of improved ICB diagnostics and treatments.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Cytotoxic T lymphocyte (CTL) and terminal exhausted T lymphocyte (ETL) activities crucially influence immune checkpoint inhibitor (ICI) response. Despite this, the efficacy of ETL and CTL transcriptomic signatures for response prediction remains limited. Investigating this across the TCGA and publicly available single-cell cohorts, we find a strong positive correlation between ETL and CTL expression signatures in most cancers. We hence posited that their limited predictability arises due to their mutually canceling effects on ICI response. Thus, we developed DETACH, a computational method to identify a gene set whose expression pinpoints to a subset of melanoma patients where the CTL and ETL correlation is low. DETACH enhances CTL's prediction accuracy, outperforming existing signatures. DETACH signature genes activity also demonstrates a positive correlation with lymphocyte infiltration and the prevalence of reactive T cells in the tumor microenvironment (TME), advancing our understanding of the CTL cell state within the TME.

Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis

BACKGROUND

Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear.

METHODS

ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR.

RESULTS

Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers.

CONCLUSIONS

Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.

TLR5 agonist in combination with anti-PD-1 treatment enhances anti-tumor effect through M1/M2 macrophage polarization shift and CD8+ T cell priming

Immune checkpoint inhibitors have revolutionized anti-tumor therapy, notably improving treatment responses in various tumors. However, many patients remain non-responsive and do not experience benefits. Given that Toll-like receptors (TLRs) can counteract tumor immune tolerance by stimulating both innate and adaptive immune responses, TLR agonists are being explored as potential immune adjuvants for cancer treatment. In this study, we assessed the potential of enhancing the efficacy of immune checkpoint inhibitors by activating innate immunity with a TLR5 agonist. In a mouse tumor model, combination therapy with TLR5 agonist and anti-PD-1 significantly inhibited tumor growth. The TLR5 agonist shifted the balance from M2-like to M1-like macrophages and upregulated the expression of co-stimulatory molecules in macrophages. Furthermore, TLR5 agonist promoted the activation and tumor infiltration of CD8+ T cells. As a result, the TLR5 agonist augmented the anti-tumor efficacy of anti-PD-1, suggesting its potential in modulating the tumor microenvironment to enhance the anti-tumor response. Our findings point toward the possibility of optimizing immune checkpoint inhibitor therapy using TLR5 agonists.

Exploration of Germline Correlates and Risk of Immune-Related Adverse Events in Advanced Cancer Patients Treated with Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) are increasingly used in the treatment of many tumor types, and durable responses can be observed in select populations. However, patients may exhibit significant immune-related adverse events (irAEs) that may lead to morbidity. There is limited information on whether the presence of specific germline mutations may highlight those at elevated risk of irAEs. We evaluated 117 patients with metastatic solid tumors or hematologic malignancies who underwent genomic analysis through the ongoing Personalized OncoGenomics (POG) program at BC Cancer and received an ICI during their treatment history. Charts were reviewed for irAEs. Whole genome sequencing of a fresh biopsy and matched normal specimens (blood) was performed at the time of POG enrollment. Notably, we found that MHC class I alleles in the HLA-B27 family, which have been previously associated with autoimmune conditions, were associated with grade 3 hepatitis and pneumonitis (q = 0.007) in patients treated with combination PD-1/PD-L1 and CTLA-4 inhibitors, and PD-1 inhibitors in combination with IDO-1 inhibitors. These data highlight that some patients may have a genetic predisposition to developing irAEs.

Breaking NGF-TrkA immunosuppression in melanoma sensitizes immunotherapy for durable memory T cell protection

Melanoma cells, deriving from neuroectodermal melanocytes, may exploit the nervous system's immune privilege for growth. Here we show that nerve growth factor (NGF) has both melanoma cell intrinsic and extrinsic immunosuppressive functions. Autocrine NGF engages tropomyosin receptor kinase A (TrkA) on melanoma cells to desensitize interferon γ signaling, leading to T and natural killer cell exclusion. In effector T cells that upregulate surface TrkA expression upon T cell receptor activation, paracrine NGF dampens T cell receptor signaling and effector function. Inhibiting NGF, either through genetic modification or with the tropomyosin receptor kinase inhibitor larotrectinib, renders melanomas susceptible to immune checkpoint blockade therapy and fosters long-term immunity by activating memory T cells with low affinity. These results identify the NGF-TrkA axis as an important suppressor of anti-tumor immunity and suggest larotrectinib might be repurposed for immune sensitization. Moreover, by enlisting low-affinity T cells, anti-NGF reduces acquired resistance to immune checkpoint blockade and prevents melanoma recurrence.

The role of ARL4C in predicting prognosis and immunotherapy drug susceptibility in pan-cancer analysis

Background: ARLs, which are a class of small GTP-binding proteins, play a crucial role in facilitating tumor tumorigenesis and development. ARL4C, a vital member of the ARLs family, has been implicated in the progression of tumors, metastatic dissemination, and development of resistance to therapeutic drugs. Nevertheless, the precise functional mechanisms of ARL4C concerning tumor prognosis and immunotherapy drug susceptibility remain elusive. Methods: By combining the GTEx and TCGA databases, the presence of ARL4C was examined in 33 various types of cancer. Immunohistochemistry and immunofluorescence staining techniques were utilized to confirm the expression of ARL4C in particular tumor tissues. Furthermore, the ESTIMATE algorithm and TIMER2.0 database were utilized to analyze the tumor microenvironment and immune infiltration associated with ARL4C. The TISCH platform facilitated the utilization of single-cell RNA-seq datasets for further analysis. ARL4C-related immune escape was investigated using the TISMO tool. Lastly, drug sensitivity analysis was conducted to assess the sensitivity of different types of tumors to compounds based on the varying levels of ARL4C expression. Results: The study found that ARL4C was highly expressed in 23 different types of cancer. Moreover, the presence of high ARL4C expression was found to be associated with a poor prognosis in BLCA, COAD, KIRP, LGG, and UCEC. Notably, ARL4C was also expressed in immune cells, and its high expression was found to be correlated with cancer immune activation. Most importantly, the drug sensitivity analysis revealed a positive correlation between ARL4C expression and the heightened sensitivity of tumors to Staurosporine, Midostaurin, and Nelarabine. Conclusion: The findings from our study indicate that the expression level of ARL4C may exert an influence on cancer development, prognosis, and susceptibility to immunotherapy drugs. In addition, the involvement of ARL4C in the tumor immune microenvironment has expanded the concept of ARL4C-targeted immunotherapy.

A pan-cancer clinical platform to predict immunotherapy outcomes and prioritize immuno-oncology combinations in early-phase trials

BACKGROUND

Immunotherapy is effective, but current biomarkers for patient selection have proven modest sensitivity. Here, we developed VIGex, an optimized gene signature based on the expression level of 12 genes involved in immune response with RNA sequencing.

METHODS

We implemented VIGex using the nCounter platform (Nanostring) on a large clinical cohort encompassing 909 tumor samples across 45 tumor types. VIGex was developed as a continuous variable, with cutoffs selected to detect three main categories (hot, intermediate-cold and cold) based on the different inflammatory status of the tumor microenvironment.

FINDINGS

Hot tumors had the highest VIGex scores and exhibited an increased abundance of tumor-infiltrating lymphocytes as compared with the intermediate-cold and cold. VIGex scores varied depending on tumor origin and anatomic site of metastases, with liver metastases showing an immunosuppressive tumor microenvironment. The predictive power of VIGex-Hot was observed in a cohort of 98 refractory solid tumor from patients treated in early-phase immunotherapy trials and its clinical performance was confirmed through an extensive metanalysis across 13 clinically annotated gene expression datasets from 877 patients treated with immunotherapy agents. Last, we generated a pan-cancer biomarker platform that integrates VIGex categories with the expression levels of immunotherapy targets under development in early-phase clinical trials.

CONCLUSIONS

Our results support the clinical utility of VIGex as a tool to aid clinicians for patient selection and personalized immunotherapy interventions.

FUNDING

BBVA Foundation; 202-2021 Division of Medical Oncology and Hematology Fellowship award; Princess Margaret Cancer Center.

Pan-cancer analysis of NFE2L2 mutations identifies a subset of lung cancers with distinct genomic and improved immunotherapy outcomes

BACKGROUND

Mutations in the KEAP1-NFE2L2 signaling pathway were linked to increased tumorigenesis and aggressiveness. Interestingly, not all hotspot mutations on NFE2L2 were damaging; some even were activating. However, there was conflicting evidence about the association between NFE2L2 mutation and Nrf2-activating mutation and responsiveness to immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and other multiple cancers.

METHODS

The study with the largest sample size (n = 49,533) explored the landscape of NFE2L2 mutations and their impact response/resistance to ICIs using public cohorts. In addition, the in-house WXPH cohort was used to validate the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC.

RESULTS

In two pan-cancer cohorts, Nrf2-activating mutation was associated with higher TMB value compared to wild-type. We identified a significant association between Nrf2-activating mutation and shorter overall survival in pan-cancer patients and NSCLC patients but not in those undergoing ICIs treatment. Similar findings were obtained in cancer patients carrying the NFE2L2 mutation. Furthermore, in NSCLC and other cancer cohorts, patients with NFE2L2 mutation demonstrated more objective responses to ICIs than patients with wild type. Our in-house WXPH cohort further confirmed the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC. Lastly, decreased inflammatory signaling pathways and immune-depleted immunological microenvironments were enriched in Nrf2-activating mutation patients with NSCLC.

CONCLUSIONS

Our study found that patients with Nrf2-activating mutation had improved immunotherapy outcomes than patients with wild type in NSCLC and other tumor cohorts, implying that Nrf2-activating mutation defined a distinct subset of pan-cancers and might have implications as a biomarker for guiding ICI treatment, especially NSCLC.

Immunologic constant of rejection as a predictive biomarker of immune checkpoint inhibitors efficacy in non-small cell lung cancer

BACKGROUND

Anti-PD1/PDL1 immune checkpoint inhibitors (ICI) transformed the prognosis of patients with advanced non-small cell lung cancer (NSCLC). However, the response rate remains disappointing and toxicity may be life-threatening, making urgent identification of biomarkers predictive for efficacy. Immunologic Constant of Rejection signature (ICR) is a 20-gene expression signature of cytotoxic immune response with prognostic value in some solid cancers. Our objective was to assess its predictive value for benefit from anti-PD1/PDL1 in patients with advanced NSCLC.

METHODS

We retrospectively profiled 44 primary tumors derived from NSCLC patients treated with ICI as single-agent in at least the second-line metastatic setting. Transcriptomic analysis was performed using the nCounter® analysis system and the PanCancer Immune Profiling Panel. We then pooled our data with clinico-biological data from four public gene expression data sets, leading to a total of 162 NSCLC patients treated with single-agent anti-PD1/PDL1. ICR was applied to all samples and correlation was searched between ICR classes and the Durable Clinical Benefit (DCB), defined as stable disease or objective response according to RECIST 1.1 for a minimum of 6 months after the start of ICI.

RESULTS

The DCB rate was 29%; 22% of samples were classified as ICR1, 30% ICR2, 22% ICR3, and 26% ICR4. These classes were not associated with the clinico-pathological variables, but showed enrichment from ICR1 to ICR4 in quantitative/qualitative markers of immune response. ICR2-4 class was associated with a 5.65-fold DCB rate when compared with ICR1 class. In multivariate analysis, ICR classification remained associated with DCB, independently from PDL1 expression and other predictive immune signatures. By contrast, it was not associated with disease-free survival in 556 NSCLC TCGA patients untreated with ICI.

CONCLUSION

The 20-gene ICR signature was independently associated with benefit from anti-PD1/PDL1 ICI in patients with advanced NSCLC. Validation in larger retrospective and prospective series is warranted.

Chemokine expression predicts T cell-inflammation and improved survival with checkpoint inhibition across solid cancers

Immune checkpoint inhibitors (ICI) are highly effective in specific cancers where canonical markers of antitumor immunity are used for patient selection. Improved predictors of T cell-inflammation are needed to identify ICI-responsive tumor subsets in additional cancer types. We investigated associations of a 4-chemokine expression signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) with metrics of antitumor immunity across tumor types. Across cancer entities from The Cancer Genome Atlas, subgroups of tumors displayed high expression of the c-Score (c-Scorehi) with increased expression of immune checkpoint (IC) genes and transcriptional hallmarks of the cancer-immunity cycle. There was an incomplete association of the c-Score with high tumor mutation burden (TMB), with only 15% of c-Scorehi tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Scorehi tumors had a longer median time to progression (103 versus 72 days, P = 0.012) and overall survival (382 versus 196 days, P = 0.038) following ICI therapy initiation, compared to patients with low c-Score expression. We also found c-Score stratification to outperform TMB assignment for overall survival prediction (HR = 0.42 [0.22-0.79], P = 0.008 versus HR = 0.60 [0.29-1.27], P = 0.18, respectively). Assessment of the c-Score using the TIDE and PredictIO databases, which include ICI treatment outcomes from 10 tumor types, provided further support for the c-Score as a predictive ICI therapeutic biomarker. In summary, the c-Score identifies patients with hallmarks of T cell-inflammation and potential response to ICI treatment across cancer types, which is missed by TMB assignment.

Comprehensive Immune Profiling Unveils a Subset of Leiomyosarcoma with "Hot" Tumor Immune Microenvironment

Purpose: To investigate the immune biomarker in Leiomyosarcoma (LMS), which is rare and recognized as an immune cold cancer showing a poor response rate (<10%) to immune checkpoint inhibitors (ICIs). However, durable response and clinical benefit to ICIs has been observed in a few cases of LMS, including, but not only, LMS with tertiary lymphoid structure (TLS) structures. Patients and methods: We used comprehensive transcriptomic profiling and a deconvolution method extracted from RNA-sequencing gene expression data in two independent LMS cohorts, the International Cancer Genome Consortium (ICGC, N = 146) and The Cancer Genome Atlas (TCGA, N = 75), to explore tumor immune microenvironment (TIME) in LMS. Results: Unsupervised clustering analysis using the previously validated two methods, 90-gene signature and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), identified immune hot (I-H) and immune high (I-Hi) LMS, respectively, in the ICGC cohort. Similarly, immune active groups (T-H, T-Hi) were identified in the TCGA cohort using these two methods. These immune active ("hot") clusters were significantly associated, but not completely overlapping, with several validated immune signatures such as sarcoma immune class (SIC) classification and TLS score, T cell inflamed signature (TIS) score, immune infiltration score (IIS), and macrophage score (M1/M2), with more patients identified by our clustering as potentially immune hot. Conclusions: Comprehensive immune profiling revealed a subset of LMS with a distinct active ("hot") TIME, consistently associated with several validated immune signatures in other cancers. This suggests that the methodologies that we used in this study warrant further validation and development, which can potentially help refine our current immune biomarkers to select the right LMS patients for ICIs in clinical trials.

Cancer Immunology: Immune Escape of Tumors-Expression and Regulation of HLA Class I Molecules and Its Role in Immunotherapies

The addition of "avoiding immune destruction" to the hallmarks of cancer demonstrated the importance of cancer immunology and in particular the role of immune surveillance and escape from malignancies. However, the underlying mechanisms contributing to immune impairment and immune responses are diverse. Loss or reduced expression of the HLA class I molecules are major characteristics of human cancers resulting in an impaired recognition of tumor cells by CD8 + cytotoxic T lymphocytes. This is of clinical relevance and associated with worse patients outcome and limited efficacy of T-cell-based immunotherapies. Here, we summarize the role of HLA class I antigens in cancers by focusing on the underlying molecular mechanisms responsible for HLA class I defects, which are caused by either structural alterations or deregulation at the transcriptional, posttranscriptional, and posttranslational levels. In addition, the influence of HLA class I abnormalities to adaptive and acquired immunotherapy resistances will be described. The in-depth knowledge of the different strategies of malignancies leading to HLA class I defects can be applied to design more effective cancer immunotherapies.

Immune-checkpoint inhibitor resistance in cancer treatment: Current progress and future directions

Cancer treatment has been advanced with the advent of immune checkpoint inhibitors (ICIs) exemplified by anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), anti-programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) drugs. Patients have reaped substantial benefit from ICIs in many cancer types. However, few patients benefit from ICIs whereas the vast majority undergoing these treatments do not obtain survival benefit. Even for patients with initial responses, they may encounter drug resistance in their subsequent treatments, which limits the efficacy of ICIs. Therefore, a deepening understanding of drug resistance is critically important for the explorations of approaches to reverse drug resistance and to boost ICI efficacy. In the present review, different mechanisms of ICI resistance have been summarized according to the tumor intrinsic, tumor microenvironment (TME) and host classifications. We further elaborated corresponding strategies to battle against such resistance accordingly, which include targeting defects in antigen presentation, dysregulated interferon-γ (IFN-γ) signaling, neoantigen depletion, upregulation of other T cell checkpoints as well as immunosuppression and exclusion mediated by TME. Moreover, regarding the host, several additional approaches that interfere with diet and gut microbiome have also been described in reversing ICI resistance. Additionally, we provide an overall glimpse into the ongoing clinical trials that utilize these mechanisms to overcome ICI resistance. Finally, we summarize the challenges and opportunities that needs to be addressed in the investigation of ICI resistance mechanisms, with the aim to benefit more patients with cancer.

Immune Activation following Irbesartan Treatment in a Colorectal Cancer Patient: A Case Study

Colorectal cancers are one of the most prevalent tumour types worldwide and, despite the emergence of targeted and biologic therapies, have among the highest mortality rates. The Personalized OncoGenomics (POG) program at BC Cancer performs whole genome and transcriptome analysis (WGTA) to identify specific alterations in an individual's cancer that may be most effectively targeted. Informed using WGTA, a patient with advanced mismatch repair-deficient colorectal cancer was treated with the antihypertensive drug irbesartan and experienced a profound and durable response. We describe the subsequent relapse of this patient and potential mechanisms of response using WGTA and multiplex immunohistochemistry (m-IHC) profiling of biopsies before and after treatment from the same metastatic site of the L3 spine. We did not observe marked differences in the genomic landscape before and after treatment. Analyses revealed an increase in immune signalling and infiltrating immune cells, particularly CD8+ T cells, in the relapsed tumour. These results indicate that the observed anti-tumour response to irbesartan may have been due to an activated immune response. Determining whether there may be other cancer contexts in which irbesartan may be similarly valuable will require additional studies.

Homologous recombination deficiency in triple-negative breast cancer: Multi-scale transcriptomics reveals distinct tumor microenvironments and limitations in predicting immunotherapy response

BACKGROUND

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and has the highest proportion of homologous recombination deficiency (HRD). HRD has been considered a biomarker of response to immune checkpoint inhibitors (ICIs), but the reality is more complicated. A comprehensive comparison of the tumor microenvironment (TME) in HRD and non-HRD TNBC samples may be helpful.

METHODS

Datasets from single-cell, spatial, and bulk RNA-sequencing were collected to explore the role of HRD in the development of TME at multiple scales. Based on the findings in the TME, machine learning algorithms were used to construct a response prediction model in eleven ICI therapy cohorts.

RESULTS

A more exhausted phenotype of T cells and a more tolerogenic phenotype of dendritic cells were found in the non-HRD group. HRD reprograms the predominant phenotype of cancer-associated fibroblasts (CAFs) from myofibroblastic CAFs to inflammatory-like CAFs. As interactions between myofibroblastic CAFs and other cells, DPP4-chemokines associated with reduced immune cell recruitment were unique in the non-HRD group. The prediction model based on DPP4-related genes had acceptable performance in predicting response, prognosis, and immune cell content. Higher HRD scores in bulk RNA-sequencing samples indicated more activated immune cell function, but not higher immune cell content, which may be affected by factors such as antigen-presenting capacity.

CONCLUSIONS

Based on multi-scale transcriptomics, our findings comprehensively reveal differences in the TME between HRD and non-HRD samples. Combining HRD with the prediction model or other methods for assessing immune cell content, may better predict response to ICIs in TNBC.

CD200+ cytotoxic T lymphocytes in the tumor microenvironment are crucial for efficacious anti-PD-1/PD-L1 therapy

Anti-PD-1/PD-L1 therapy, either by anti-PD-1 antibody or anti-PD-L1 antibody, has efficacy by reinvigorating tumor-infiltrating CD8⁺ T cells in a subset of patients with cancer, but it has unequal effects on heterogeneous CD8⁺ T cell populations. Hence, the subset crucial to efficacious PD-1 blockade therapy remains elusive. Here, we found an increase in tumor-infiltrating CD200⁺ cytotoxic T lymphocytes (CTLs) upon PD-1/PD-L1 blockade, with higher proportions of CD200⁺ T cells positively related to a favorable clinical outcome to anti-PD-1/PD-L1 therapy in three independent cohorts of patients with cancer. Using multiple mouse tumor models, we demonstrated that CD200⁺ CTLs are essential for efficacious anti-PD-L1 therapy. Mechanistically, we observed a unique chromatin landscape in CD200⁺ CTLs and found that these cells are enriched for tumor antigen-specific CTLs and have antitumor effector functions. Coinoculation of CD200⁺ CTLs with tumor cells led to robust tumor regression in two transplanted mouse models. Clinically, we found that infiltration of CD200⁺ CTLs into tumors could predict immunotherapy efficacy in six patient cohorts. Together, our findings reveal that CD200⁺ CTLs in the tumor microenvironment are crucial for efficacious anti-PD-1/PD-L1 therapy and could serve as a predictor of successful immunotherapy in the clinic.

Five-gene signature for the prediction of response to immune checkpoint inhibitors in patients with gastric and urothelial carcinomas

BACKGROUND

Ample evidence supports the potential of programmed death-ligand 1 (PD-L1) expression, detected by immunohistochemistry, as a predictive biomarker for immunotherapy in patients with advanced cancers. To predict the response to immune checkpoint inhibitors in patients with gastric and urothelial carcinomas, we aimed to replace PD-L1 combined positive score (CPS) with CD274 mRNA in the original four-gene signature and PD-L1 CPS model developed by us.

METHOD

We used quantitative real-time polymerase chain reaction (qRT-PCR) to measure the expression levels of five target genes in a cohort of 49 patients (33 with gastric cancer and 16 with urothelial carcinoma) who had received immunotherapy and whose therapeutic responses were available. The predictive performance was evaluated using R package maxstat.

RESULTS

Cutoff values of mRNA expression level were measured using the log-rank statistics for progression-free survival (PFS). Based on these cutoffs, immunotherapy responses were predicted and sorted into responder (n = 12, 24.5%) and non-responder (n = 37, 75.5%) groups. The median PFS values of predicted responders and non-responders were 14.8 months (95% confidence interval [CI]: 0-34.7) and 4.7 months (95% CI: 1.0-8.4, p = 0.02), respectively. Among the 12 predicted responders, 10 had microsatellite-stable tumors with a low tumor mutational burden. The actual clinical responses (complete and partial) were higher in the responder group than those in the non-responder group: 83.3% and 16.2%, respectively.

CONCLUSION

We modified a predictive biomarker for CD274 mRNA expression to predict the response to immunotherapy in patients with gastric or urothelial carcinomas.

Immune-related adverse events: A bibliometric analysis

Background

Despite providing clinical benefit, immune checkpoint inhibitors (ICIs) can cause immune-related adverse events (irAEs) in a number of patients. This study explored the development pattern in irAEs research from a bibliometric perspective.

Methods

We obtained articles and reviews related to irAEs from the Web of Science Core Collection (WoSCC) (retrieved on September 13, 2022). Using the R package "Bibliometrix", the main bibliometric features were calculated, and a three-filed plot was generated to show the relationship between authors, institutions, and topics. VOSviewer was used for co-authorship and keyword co-occurrence analysis and visualization. CiteSpace was used to detect burst references and keywords.

Results

A total of 3995 publications on irAEs were included. The United States (US), Japan, and China had the highest publications. The Journal for ImmunoTherapy of Cancer had the highest number of publications. In addition to "immune-related adverse events", "immune checkpoint inhibitors", "immunotherapy", and "nivolumab" were the most frequently used keywords.

Conclusions

A bibliometric analysis of 17 years of irAEs research was conducted to map a basic knowledge structure including countries, institutions, authors, journals, and publications. The findings provided a comprehensive perspective on the broad future of this research area.

Pan-cancer landscape of AID-related mutations, composite mutations, and their potential role in the ICI response

Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome, or targeted sequencing. AID mutations are present at pan-cancer level with higher frequency in hematological cancers and higher presence at transcriptionally active TAD domains. AID synergizes initial hotspot mutations by a second composite mutation. AID mutational load was found to be independently associated with a favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2000 samples. Finally, we found that AID-related neoepitopes, resulting from mutations at more frequent hotspots if compared to other mutational signatures, enhance CXCL13/CCR5 expression, immunogenicity, and T-cell exhaustion, which may increase ICI sensitivity.

A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma

Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.

Unraveling tumor microenvironment heterogeneity in malignant pleural mesothelioma identifies biologically distinct immune subtypes enabling prognosis determination

Background

Malignant pleural mesothelioma (MPM) is a rare and intractable disease exhibiting a remarkable intratumoral heterogeneity and dismal prognosis. Although immunotherapy has reshaped the therapeutic strategies for MPM, patients react with discrepant responsiveness.

Methods

Herein, we recruited 333 MPM patients from 5 various cohorts and developed an in-silico classification system using unsupervised Non-negative Matrix Factorization and Nearest Template Prediction algorithms. The genomic alterations, immune signatures, and patient outcomes were systemically analyzed across the external TCGA-MESO samples. Machine learning-based integrated methodology was applied to identify a gene classifier for clinical application.

Results

The gene expression profiling-based classification algorithm identified immune-related subtypes for MPMs. In comparison with the non-immune subtype, we validated the existence of abundant immunocytes in the immune subtype. Immune-suppressed MPMs were enriched with stroma fraction, myeloid components, and immunosuppressive tumor-associated macrophages (TAMs) as well exhibited increased TGF-β signature that informs worse clinical outcomes and reduced efficacy of anti-PD-1 treatment. The immune-activated MPMs harbored the highest lymphocyte infiltration, growing TCR and BCR diversity, and presented the pan-cancer immune phenotype of IFN-γ dominant, which confers these tumors with better drug response when undergoing immune checkpoint inhibitor (ICI) treatment. Genetically, BAP1 mutation was most commonly found in patients of immune-activated MPMs and was associated with a favorable outcome in a subtype-specific pattern. Finally, a robust 12-gene classifier was generated to classify MPMs with high accuracy, holding promise value in predicting patient survival.

Conclusions

We demonstrate that the novel classification system can be exploited to guide the identification of diverse immune subtypes, providing critical biological insights into the mechanisms driving tumor heterogeneity and responsible for cancer-related patient prognoses.

Combining autophagy and immune characterizations to predict prognosis and therapeutic response in lung adenocarcinoma

Background

Autophagy, a key regulator of programmed cell death, is critical for maintaining the stability of the intracellular environment. Increasing evidence has revealed the clinical importance of interactions between autophagy and immune status in lung adenocarcinoma. The present study evaluated the potential of autophagy-immune-derived biomarkers to predict prognosis and therapeutic response in patients with lung adenocarcinoma.

Methods

Patients from the GSE72094 dataset were randomized 7:3 to a training set and an internal validation set. Three independent cohorts, TCGA, GSE31210, and GSE37745, were used for external verification. Unsupervised hierarchical clustering based on autophagy- and immune-associated genes was used to identify autophagy- and immune-associated molecular patterns, respectively. Significantly prognostic autophagy-immune genes were identified by LASSO analysis and by univariate and multivariate Cox regression analyses. Differences in tumor immune microenvironments, functional pathways, and potential therapeutic responses were investigated to differentiate high-risk and low-risk groups.

Results

High autophagy status and high immune status were associated with improved overall survival. Autophagy and immune subtypes were merged into a two-dimensional index to characterize the combined prognostic classifier, with 535 genes defined as autophagy-immune-related differentially expressed genes (DEGs). Four genes (C4BPA, CD300LG, CD96, and S100P) were identified to construct an autophagy-immune-related prognostic risk model. Survival and receiver operating characteristic (ROC) curve analyses showed that this model was significantly prognostic of survival. Patterns of autophagy and immune genes differed in low- and high-risk patients. Enrichment of most immune infiltrating cells was greater, and the expression of crucial immune checkpoint molecules was higher, in the low-risk group. TIDE and immunotherapy clinical cohort analysis predicted that the low-risk group had more potential responders to immunotherapy. GO, KEGG, and GSEA function analysis identified immune- and autophagy-related pathways. Autophagy inducers were observed in patients in the low-risk group, whereas the high-risk group was sensitive to autophagy inhibitors. The expression of the four genes was assessed in clinical specimens and cell lines.

Conclusions

The autophagy-immune-based gene signature represents a promising tool for risk stratification in patients with lung adenocarcinoma, guiding individualized targeted therapy or immunotherapy.

TMBur: a distributable tumor mutation burden approach for whole genome sequencing

Background

Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance.

Methods

We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan–Meier and cox-proportional hazards analyses.

Results

TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012).

Conclusions

TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01348-z.

The CCTG PA.7 phase II trial of gemcitabine and nab-paclitaxel with or without durvalumab and tremelimumab as initial therapy in metastatic pancreatic ductal adenocarcinoma

Immunotherapy-based monotherapy treatment in metastatic pancreatic ductal adenocarcinoma (mPDAC) has shown limited benefit outside of the mismatch repair deficiency setting, while safety and efficacy of combining dual-checkpoint inhibitor immunotherapy with chemotherapy remains uncertain. Here, we present results from the CCTG PA.7 study (NCT02879318), a randomized phase II trial comparing gemcitabine and nab-paclitaxel with and without immune checkpoint inhibitors durvalumab and tremelimumab in 180 patients with mPDAC. The primary endpoint was overall survival. Secondary endpoints included progression-free survival and objective response rate. Results of the trial were negative as combination immunotherapy did not improve survival among the unselected patient population (p = 0.72) and toxicity was limited to elevation of lymphocytes in the combination immunotherapy group (p = 0.02). Exploratory baseline circulating tumor DNA (ctDNA) sequencing revealed increased survival for patients with KRAS wildtype tumors in both the combination immunotherapy (p = 0.001) and chemotherapy (p = 0.004) groups. These data support the utility of ctDNA analysis in PDAC and the prognostic value of ctDNA-based KRAS mutation status.

Tumor-induced erythroid precursor-differentiated myeloid cells mediate immunosuppression and curtail anti-PD-1/PD-L1 treatment efficacy

Despite the unprecedented success of immune checkpoint inhibitors (ICIs) as anti-cancer therapy, it remains a prevailing clinical need to identify additional mechanisms underlying ICI therapeutic efficacy and potential drug resistance. Here, using lineage tracking in cancer patients and tumor-bearing mice, we demonstrate that erythroid progenitor cells lose their developmental potential and switch to the myeloid lineage. Single-cell transcriptome analyses reveal that, notwithstanding quantitative differences in erythroid gene expression, erythroid differentiated myeloid cells (EDMCs) are transcriptionally indistinguishable from their myeloid-originated counterparts. EDMCs possess multifaceted machinery to curtail T cell-mediated anti-tumor responses. Consequently, EDMC content within tumor tissues is negatively associated with T cell inflammation for the majority of solid cancers; moreover, EDMC enrichment, in accordance with anemia manifestation, is predictive of poor prognosis in various cohorts of patients undergoing ICI therapy. Together, our findings reveal a feedforward mechanism by which tumors exploit anemia-triggered erythropoiesis for myeloid transdifferentiation and immunosuppression.

Whole genome and transcriptome analysis enhances precision cancer treatment options

BACKGROUND

Recent advances are enabling delivery of precision genomic medicine to cancer clinics. While the majority of approaches profile panels of selected genes or hotspot regions, comprehensive data provided by whole genome and transcriptome sequencing and analysis (WGTA) presents an opportunity to align a much larger proportion of patients to therapies.

PATIENTS AND METHODS

Samples from 570 patients with advanced or metastatic cancer of diverse types enrolled in the Personalized OncoGenomics (POG) program underwent WGTA. DNA-based data, including mutations, copy number, and mutation signatures, were combined with RNA-based data, including gene expression and fusions, to generate comprehensive WGTA profiles. A multidisciplinary molecular tumour board used WGTA profiles to identify and prioritize clinically actionable alterations and inform therapy. Patient responses to WGTA-informed therapies were collected.

RESULTS

Clinically actionable targets were identified for 83% of patients, 37% of whom received WGTA-informed treatments. RNA expression data were particularly informative, contributing to 67% of WGTA-informed treatments; 25% of treatments were informed by RNA expression alone. Of a total 248 WGTA-informed treatments, 46% resulted in clinical benefit. RNA expression data were comparable to DNA-based mutation and copy number data in aligning to clinically beneficial treatments. Genome signatures also guided therapeutics including platinum, PARP inhibitors, and immunotherapies. Patients accessed WGTA-informed treatments through clinical trials (19%), off-label use (35%), and as standard therapies (46%) including those which would not otherwise have been the next choice of therapy, demonstrating the utility of genomic information to direct use of chemotherapies as well as targeted therapies.

CONCLUSIONS

Integrating RNA expression and genome data illuminated treatment options that resulted in 46% of treated patients experiencing positive clinical benefit, supporting the use of comprehensive WGTA profiling in clinical cancer care.

CLINICAL TRIAL NUMBER

NCT02155621.

The Neoantigen Landscape of the Coding and Noncoding Cancer Genome Space

Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy because with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden has not been adopted as a measure to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. A pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing was analyzed to report mutation burden and neoantigen counts within the coding and noncoding regions. The efficacy of tumor neoantigen burden, reported as tumor neoantigen count (TNC), including neoantigens derived from the expression of noncoding regions, compared with TMB as a predictor of response to immunotherapy for 80 patients who had received treatment, was evaluated. TMB was found to be the best predictor of response to immunotherapy, whereas expression-derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments.

mTOR pathway gene mutations predict response to immune checkpoint inhibitors in multiple cancers

BACKGROUND

mTOR pathway is known to promote cancer malignancy and influence cancer immunity but is unknown for its role in immune checkpoint inhibitors (ICI) therapy.

METHODS

Using Memorial Sloan-Kettering Cancer Center dataset (MSKCC), we extracted mTOR pathway gene mutations for stepwise Cox regression in 1661 cancer patients received ICI. We associated the mutation of the gene signature resulted from the stepwise Cox regression with the 1661 patients' survival. Other 553 ICI-treated patients were collected from 6 cohorts for validation. We also performed this survival association in patients without ICI treatment from MSKCC as discovery (n = 2244) and The Cancer Genome Atlas (TCGA) as validation (n = 763). Pathway enrichment analysis were performed using transcriptome profiles from TCGA and IMvigor210 trial to investigate the potential mechanism.

RESULTS

We identified 8 genes involved in mTOR pathway, including FGFR2, PIK3C3, FGFR4, FGFR1, FGF3, AKT1, mTOR, and RPTOR, resulted from stepwise Cox regression in discovery (n = 1661). In both discovery (n = 1661) and validation (n = 553), the mutation of the 8-gene signature was associated with better survival of the patients treated with ICI, which was independent of tumor mutation burden (TMB) and mainly attributed to the missense mutations. This survival association was not observed in patients without ICI therapy. Intriguingly, the mutation of the 8-gene signature was associated with increased TMB and PD1/PD-L1 expression. Immunologically, pathways involved in anti-tumor immune response were enriched in presence of this mutational signature in mTOR pathway, leading to increased infiltration of immune effector cells (e.g., CD8 + T cells, NK cells, and M1 macrophages), but decreased infiltration of immune inhibitory M2 macrophages.

CONCLUSIONS

These results suggested that mTOR pathway gene mutations were predictive of better survival upon ICI treatment in multiple cancers, likely by its association with enhanced anti-tumor immunity. Larger studies are warranted to validate our findings.

DNA Hypermethylation-Regulated CX3CL1 Reducing T Cell Infiltration Indicates Poor Prognosis in Wilms Tumour

Objective

To investigate the role of chemokines in Wilms tumours, especially their chemotaxis to immune cells and the role of DNA methylation in regulating the expression level of chemokines.

Methods

RNAseqV2 gene expression and clinical data were downloaded from the TARGET database. DNA methylation data were downloaded from the GEO and cBioPortal database. The difference analysis and Kaplan-Meier(KM) analysis of chemokines were performed by edgeR package. Then predictive model based on chemokines was constructed by lasso regression and multivariate COX regression. ROC curve, DCA curve, Calibration curve, and Nomogram were used to evaluate the prognostic model. MCPcounter and Cibersort algorithm was used to calculate the infiltration of immune cells in Wilms tumour and para-tumour samples. Then the difference analysis of the immune cells was performed. The relationship between chemokines and immune cells were calculated by Pearson correlation. In addition, DNA methylation differences between Wilms tumour and para-tumour samples was performed. The correlation between DNA methylation and mRNA expression was calculated by Pearson correlation. Western blot(WB)and immunofluorescence were used to confirm the differential expression of CX3CL1 and T cells, and the correlation between them.

Results

A total of 16 chemokines were differentially expressed in tumour and para-tumour samples. A total of seven chemokines were associated with survival. CCL2 and CX3CL1 were positively correlated with prognosis, while high expression of CCL3, CCL8, CCL15, CCL18 and CXCL9 predicted poor prognosis. By lasso regression and multivariate COX regression, CCL3, CCL15, CXCL9 and CX3CL1 were finally included to construct a prediction model. The model shows good prediction ability. MCPcounter and Cibersort algorithm both showed that T cells were higher in para-tumour tissues than cancer tissues. Correlation analysis showed that CX3CL1 had a strong correlation with T cells. These were verified by Weston blot and immunofluorescence. DNA methylation analysis showed that various chemokines were different in para-tumours and tumours. CX3CL1 was hypermethylated in tumours, and the degree of methylation was negatively correlated with mRNA expression.

Conclusion

1. There is low T cell infiltration in nephroblastoma. 2. Chemokines such as CX3CL1 indicate a favourable prognosis and positively correlate with the number of T cells. 3. chemokines such as CX3CL1 are negatively regulated by DNA hypermethylation.

Immunotherapy for Management of Thymic Epithelial Tumors: A Double-Edged Sword

Thymic epithelial tumors (TETs) are rare thoracic cancers that are broadly classified as thymomas and thymic carcinomas. Surgery is the cornerstone of management for early-stage disease. There are a limited number of effective treatment options for patients with advanced or recurrent disease. The occurrence of paraneoplastic autoimmune disorders in patients with TETs, especially thymomas, creates significant challenges for the development of immunotherapy, including immune checkpoint inhibitors, as a feasible treatment option. In addition, patients with TETs are at increased risk for the development of immune-mediated toxicity with a predilection for musculoskeletal and neuromuscular adverse events upon treatment with immunotherapy. The identification of biomarkers of response and toxicity is expected to play a key role in harnessing the benefits of immunotherapy for patients with TETs. In this paper we review the biology of TETs and the potential effects on the tolerability of immunotherapy. The results of clinical trials of immune checkpoint inhibitors for the treatment of advanced TETs are described to understand the potential risks and benefits of immunotherapy. We also provide an overview of future avenues for treatment with novel immunotherapeutic modalities and opportunities to develop biomarkers to improve the safety and tolerability of immunomodulatory treatments in patients with TETs.

Assessment of Clinical Response Following Atezolizumab and Bevacizumab Treatment in Patients With Neuroendocrine Tumors: A Nonrandomized Clinical Trial

Importance

Therapies for patients with advanced well-differentiated neuroendocrine tumors (NETs) have expanded but remain inadequate, with patients dying of disease despite recent advances in NET therapy. While patients with other cancers have seen long-term disease control and tumor regression with the application of immunotherapies, initial prospective studies of single-agent programmed cell death 1 inhibitors in NET have been disappointing.

Objective

To evaluate the response rate following treatment with the combination of the vascular endothelial growth factor inhibitor bevacizumab with the programmed cell death 1 ligand 1 inhibitor atezolizumab in patients with advanced NETs.

Design, Setting, and Participants

This single-arm, open-label nonrandomized clinical study in patients with rare cancers included 40 patients with advanced, progressive grade 1 to 2 NETs (20 with pancreatic NETs [pNETs] and 20 with extrapancreatic NETs [epNETs]) treated at a tertiary care referral cancer center between March 31, 2017, and February 19, 2019. Data were analyzed from June to September 2021.

Interventions

Patients received intravenous bevacizumab and atezolizumab at standard doses every 3 weeks until progression, death, or withdrawal.

Main Outcomes and Measures

The primary end point was objective radiographic response using Response Evaluation Criteria in Solid Tumors, version 1.1, with progression-free survival (PFS) as a key secondary end point.

Results

Following treatment of the 40 study patients with bevacizumab and atezolizumab, objective response was observed in 4 patients with pNETs (20%; 95% CI, 5.7%-43.7%) and 3 patients with epNETs (15%; 95% CI, 3.2%-37.9%). The PFS was 14.9 (95% CI, 4.4-32.0) months and 14.2 (95% CI, 10.2-19.6) months in these cohorts, respectively.

Conclusions and Relevance

In this nonrandomized clinical trial, findings suggest that clinical responses in patients with NET may follow treatment with the combination of bevacizumab and atezolizumab, with a PFS consistent with effective therapies.

Trial Registration

ClinicalTrials.gov Identifier: NCT03074513.

CRISPR activation screen identifies BCL-2 proteins and B3GNT2 as drivers of cancer resistance to T cell-mediated cytotoxicity

The cellular processes that govern tumor resistance to immunotherapy remain poorly understood. To gain insight into these processes, here we perform a genome-scale CRISPR activation screen for genes that enable human melanoma cells to evade cytotoxic T cell killing. Overexpression of four top candidate genes (CD274 (PD-L1), MCL1, JUNB, and B3GNT2) conferred resistance in diverse cancer cell types and mouse xenografts. By investigating the resistance mechanisms, we find that MCL1 and JUNB modulate the mitochondrial apoptosis pathway. JUNB encodes a transcription factor that downregulates FasL and TRAIL receptors, upregulates the MCL1 relative BCL2A1, and activates the NF-κB pathway. B3GNT2 encodes a poly-N-acetyllactosamine synthase that targets >10 ligands and receptors to disrupt interactions between tumor and T cells and reduce T cell activation. Inhibition of candidate genes sensitized tumor models to T cell cytotoxicity. Our results demonstrate that systematic gain-of-function screening can elucidate resistance pathways and identify potential targets for cancer immunotherapy.

Loss-of-function CRISPR-based screens have identified several genes associated with cancer resistance to T cell-induced cytotoxicity. Here the authors perform a genome-scale, gain-of-function CRISPR screen and identify candidate genes, including the poly-N-acetyllactosamine synthase B3GNT2, whose overexpression confers tumor cell resistance to T cell cytotoxicity

The predictive efficacy of tumor mutation burden in immunotherapy across multiple cancer types: A meta-analysis and bioinformatics analysis

•

The FDA has approved pembrolizumab in all cancers with TMB > 10Mut/Mb based on the findings from the phase 2 KEYNOTE-158 study. However, predictive efficacy of TMB is greeted with many skepticisms.

•

Cancer patients with high TMB have a better prognosis. Notably, there is no evidence shown whether the good prognosis is caused by the tumor itself or by immunotherapy.

•

We compared the meta subgroup analysis by tumor types with non-ICIs from TCGA database and found that immunotherapy can increase, not reduce, cancer patients’ OS with high TMB.

•

TMB is a promising therapeutic and prognostic biomarker for immunotherapy, which indicates a better ORR, DCB, OS and PFS.

Purpose

To explore the predictive efficacy of tumor mutation burden (TMB) as a potential biomarker for cancer patients treated with Immune checkpoint inhibitors (ICIs).

Methods

We systematically searched PubMed, Cochrane Library, Embase and Web of Science for clinical studies (published between Jan 1, 2014 and Aug 30, 2021) comparing immunotherapy patients with high TMB to patients with low TMB. Our main endpoints were objective response rate (ORR), durable clinical benefit (DCB), overall survival (OS) and progress-free Survival (PFS). Moreover, we downloaded simple nucleotide variation (SNV) data of 33 major cancer types from the TCGA database as non-ICIs group, and compared the high TMB patients’ OS between the non-ICIs group and meta-analysis results.

Results

Of 10,450 identified studies, 41 were eligible and were included in our analysis (7713 participants). Compared with low TMB patients receiving ICIs, high TMB yielded a better ORR (RR = 2.73; 95% CI: 2.31–3.22; P = 0.043) and DCB (RR = 1.93; 95% CI: 1.64–2.28; P = 0.356), and a significantly increased OS (HR =0.24; 95% CI: 0.21–0.28; P < 0.001) and PFS (HR = 0.38; 95% CI: 0.34–0.42; P < 0.001). Furthermore, compared with non-ICIs group from the TCGA database, immunotherapy can improve OS in some cancer types with high TMB and better prognosis, including colorectal cancer, gastric cancer, lung cancer, melanoma and pan-cancer.

Conclusion

TMB is a promising therapeutic and prognostic biomarker for immunotherapy, which indicates a better ORR, DCB, OS and PFS. If there is a standard for TMB assessment and cut-off, it could improve the management of different cancers.

Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy

Background

In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them.

Methods

In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses.

Results

We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined.

Conclusion

Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-021-01462-z.

Clinical response to nivolumab in an INI1-deficient pediatric chordoma correlates with immunogenic recognition of brachyury

Poorly differentiated chordoma (PDC) is a recently recognized subtype of chordoma characterized by expression of the embryonic transcription factor, brachyury, and loss of INI1. PDC primarily affects children and is associated with a poor prognosis and limited treatment options. Here we describe the molecular and immune tumour microenvironment profiles of two paediatric PDCs produced using whole-genome, transcriptome and whole-genome bisulfite sequencing (WGBS) and multiplex immunohistochemistry. Our analyses revealed the presence of tumour-associated immune cells, including CD8+ T cells, and expression of the immune checkpoint protein, PD-L1, in both patient samples. Molecular profiling provided the rationale for immune checkpoint inhibitor (ICI) therapy, which resulted in a clinical and radiographic response. A dominant T cell receptor (TCR) clone specific for a brachyury peptide-MHC complex was identified from bulk RNA sequencing, suggesting that targeting of the brachyury tumour antigen by tumour-associated T cells may underlie this clinical response to ICI. Correlative analysis with rhabdoid tumours, another INI1-deficient paediatric malignancy, suggests that a subset of tumours may share common immune phenotypes, indicating the potential for a therapeutically targetable subgroup of challenging paediatric cancers.

ENPEP as a potential predictor of immune checkpoint inhibitor efficacy

Background

The gene ENPEP encodes glutamyl aminopeptidase, which can cut N‐terminal aspartic acid from angiotensin II, and is related to tumorigenesis and immune microenvironment, however, the association between the expression of ENPEP and benefits of immune checkpoint inhibitors (ICIs) has had no investigation.

Methods

We assess the immunotherapeutic predictive performance of ENPEP expression and mutation in multiple cohorts, including one discovery cohort (Pender cohort), four validation cohorts (Hugo cohort; Liu cohort; Mariathasan cohort; Zhao cohort), and one mutation cohort (Miao cohort). Cohorts from The Cancer Genome Atlas (TCGA) were used to explore mechanism and analysis prognosis.

Results

In the discovery cohort, patients with lower ENPEP expression had superior response rates (47.2% vs. 36.1%) and over‐all survival (OS) (HR [95% CI] = 0.61 [0.39–0.96]; p = 0.032) compared with those with higher ENPEP expression. The association between ENPEP and immunotherapy efficacy was consistently observed in validation cohorts (Hugo: OS HR [95% CI] = 0.41 [0.11–1.45], p = 0.158; Liu: OS HR [95% CI] = 0.73 [0.44–1.20], p = 0.211; Mariathasan: OS HR [95% CI] = 0.84 [0.65–1.09], p = 0.181; Zhao: OS HR [95% CI] = 0.20 [0.04–1.01], p = 0.033; Pooled cohort: OS HR [95% CI] = 0.76 [0.61–0.95], p = 0.015), and in the mutation cohort (ENPEP mutation vs. wild type (WT), OS HR [95% CI] = 0.46 [0.26–0.93], p = 0.017). Reliably, ENPEP is associated with M2 macrophage infiltration and activation in TCGA.

Conclusions

Our results demonstrated ENPEP is a potential biomarker to classify patients’ response to ICIs treatment.

Establishment of a tumor immune microenvironment-based molecular classification system of breast cancer for immunotherapy

Antitumor immunotherapy can enable promising and durable responses following their clinical application. However, heterogeneity in the tumor immune microenvironment leads to differences in the individual response rates. In this study, we identified novel immune-related molecular subclasses of breast cancer using a non-negative matrix factorization analysis. We enrolled 4184 patients with breast cancer, including 1104 patients from The Cancer Genome Atlas as a training cohort and 3080 patients from another four independent datasets as validation cohorts. In the training cohort, 36.9% of patients who exhibited significantly higher immunocyte infiltration and enrichment of immune response-associated signatures were categorized into an immune class, which was confirmed by probing the expression of immunocyte markers (CD3, CD19, and CD163). Within the immune class, 53.3% of patients belonged to an immune-suppressed subclass, characterized by the activation of stroma-related signatures and immune-suppressive cells. The remaining patients in the immune class were allocated to an immune-activated subclass. The interferon-γ and granzyme B levels were higher in the immune-activated subclass, whereas the transforming growth factor-β1 and programmed cell death-1 (PD-1) levels were higher in the immune-suppressed subclass. The established molecular classification system was recapitulated in validation cohorts. The immune-activated subclass was predicted to have a better response to anti-PD-1 immunotherapy. The immune-related subclasses were associated with differences in copy number alterations, tumor mutation burden, neoantigens, tumor-infiltrating lymphocyte enrichment, PD-1/programmed death-ligand 1 expression, mutation landscape, and various infiltration immunocytes. Overall, we established a novel immune-related molecular classification of breast cancer, which may be used to select candidate patients for immunotherapy.

The association between CD8+ tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: A systematic review and meta-analysis

BACKGROUND

The responses of cancer patients to immune checkpoint inhibitors (ICIs) vary in success. CD8+ tumor infiltrating lymphocytes (TILs) play a key role in killing tumor cells. This study aims to evaluate the prognostic role of CD8+ TILs in cancer patients treated with ICIs.

METHODS

We systematically searched all publications from PubMed, EMBASE, and Cochrane Library until 12 Jul 2021 without any restriction of language or article types. Studies assessing high versus low CD8+ TILs in predicting efficacy and survival of various cancer patients were included. The outcomes included overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). The study protocol is prospectively registered on PROSPERO (registration number CRD42021233654).

FINDINGS

Findings: A total of 33 studies consisting of 2559 cancer patients were included. The result showed that high CD8+ TILs were significantly associated with better OS (HR, 0.52; 95% confidence interval: 0.41-0.67; p < 0.001), PFS (HR, 0.52; 95% confidence interval: 0.40-0.67; p < 0.001) and ORR (OR, 4.08; 95% confidence interval: 2.73-6.10; p  < 0.001) in patients treated with ICIs. Subgroup analyses suggested that patients with high CD8+ TILs had a better clinical benefit, regardless of different treatments (ICI mono therapy, or combination therapy), cancer types (NSCLC, melanoma and others), and CD8+ T cells locations (intra-tumor, stroma, and invasive margin). The higher baseline circulating CD8+ T cells from peripheral blood did not contribute to the improved OS (HR, 0.93; 95% confidence interval: 0.67-1.29; p = 0.67) and PFS (HR, 0.89; 95% confidence interval: 0.60-1.32; p = 0.56) compared with the low baseline.

INTERPRETATION

Interpretation: Our results suggested that high intra-tumoral, stromal, or invasive marginal, but not circulating CD8+ T cells, can predict treatment outcomes in patients with ICIs therapy across different cancers, in either single-agent ICIs or combination with other therapies.

FUNDING

Funding: China National Science Foundation (Grant No. 82,022,048, 81,871,893), Key Project of Guangzhou Scientific Research Project (Grant No. 201,804,020,030), High-level university construction project of Guangzhou medical university (Grant No. 20,182,737, 201,721,007, 201,715,907, 2,017,160,107); National key R & D Program (Grant No. 2017YFC0907903 & 2017YFC0112704) and the Guangdong high level hospital construction "reaching peak" plan.

Combining immune checkpoint inhibitors with chemotherapy in advanced solid tumours: A review

The use of immune checkpoint inhibitors (ICIs), especially anti-programmed cell death 1 (PD1) and anti-programmed cell death ligand 1 (PD-L1), has changed practices in oncology, becoming a new standard of care in first or subsequent lines for several cancer subtypes. Recent data have highlighted the ability of standard chemotherapy to enhance immunogenicity and/or to break immunoresistance of the tumour and its microenvironment, leading to a rationale for the use of ICIs in combination with the standard chemotherapy regimen to improve efficacy of cancer treatment. Here, we propose to review randomised clinical trials evaluating concomitant administration of ICIs and chemotherapy, to assess clinical efficacy and safety profiles in advanced solid tumours. Association of these two modes of action on treatments has shown improved overall survival and better objective response rates than standard chemotherapy, especially in first-line treatment of non-small cell lung cancer (NSCLC) and for PD1/PD-L1 enriched tumours, highlighting a potential synergistic effect of this treatment combination in certain tumour types. However, improved survival results with the use of anti-PD-L1 avelumab as a maintenance schedule for bladder cancer raises the question of the most appropriate approach between sequential and concomitant administration of chemoimmunotherapy. To date, no trials have compared in a head-to-head protocol the administration of concomitant chemoimmunotherapy with chemotherapy, used for tumour debulking, followed by administration of ICIs. Regarding the tolerance profile, no new safety signals were found with the combination tested to date. Interestingly, recent results have shown an improved Progression Free survival 2 (PFS2) (defined as the progression after the next line of therapy) in head-and-neck cancers or NSCLC after a first-line pembrolizumab-chemotherapy combination, suggesting a potential long-lasting effect of ICIs when used in combination in the first-line setting.

Prognostic Role of Tumor Mutational Burden in Cancer Patients Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis

Purpose

Immunotherapy is regarded as the most promising treatment for cancer. However, immune checkpoint inhibitors (ICIs) are not effective for all patients. Herein, we conducted a systematic review and meta-analysis to explore whether tumor mutational burden (TMB) can be used as a potential prognostic biomarker for cancer patients treated with ICIs.

Methods

We systematically retrieved relevant literature published in the PubMed, Embase, Web of Science, and Cochrane databases up to December 28, 2020. All cohort studies and clinical trials that reported hazard ratios (HRs) for overall (OS) and progression-free survival (PFS), as well as the corresponding 95% confidence intervals (CIs) of high and low TMB patients, were included. All statistical analyses were performed using the R software.

Results

Pooled results from a total of 32 studies with 6,131 participants showed significantly increased OS (HR: 0.61, 95% CI: 0.53–0.71; P <0.01) and PFS (HR: 0.51, 95% CI: 0.44–0.60; P <0.01) for the high TMB group receiving ICIs as compared to the low TMB group. Particularly, results were found to be more significant in studies with larger sample sizes (≥30), Western patients, higher TMB cutoff values (≥20 mut/Mb), anti–PD-1 therapy, and when the sample source was tissue and tumor type was either melanoma, small cell lung cancer, or gastric cancer.

Conclusion

TMB is a promising independent prognostic biomarker for cancer patients receiving ICIs, which could provide a new potential therapeutic strategy for high TMB patients who have failed traditional therapy. Furthermore, consistency in the key aspects of TMB assessment is expected in the future.

Systematic Review Registration

[https://www.crd.york.ac.uk/PROSPERO], Prospective Register of Systematic Reviews (PROSPERO), identifier: CRD42021229016.

Loss of IL-34 Expression Indicates Poor Prognosis in Patients With Lung Adenocarcinoma

Interleukin 34 (IL-34), an additional ligand of the colony-stimulating factor-1 receptor (CSF-1R), promotes the secretion of pro-inflammatory cytokines and stimulates NF-κB and JNK-related signaling pathways. However, the potential mechanism and prognostic value of IL-34 in lung adenocarcinoma (LUAD) remain obscure. In this study, IL-34 was found to be downregulated in LUAD tissues compared with para-carcinoma tissues, and loss of IL-34 expression was correlated with shorter overall survival (OS), which was validated by bioinformatics\ analysis in TCGA (The Cancer Genome Atlas) cohort and immunohistochemical analysis in the NTU (Nantong University) cohort, respectively. Subsequently, loss of IL-34 promotes negative regulation of the immune system and inhibits the infiltration of immune cells. Moreover, IL-34 deficiency was shown to be an independent adverse prognostic factor for patients with LUAD, and subgroup analysis indicated that IL-34 might contribute to the stratified management of patients with LUAD. IL-34-based nomogram model significantly improved the accuracy of prognostic predictions for OS of patients with LUAD, both in the TCGA cohort and the NTU cohort. Taken together, our data suggested that loss of IL-34 expression is associated with poor prognosis and negative regulation of the immune system of patients with LUAD, contributing to the stratified management of patients with LUAD.

Prognostic Impact of Memory CD8(+) T Cells on Immunotherapy in Human Cancers: A Systematic Review and Meta-Analysis

Objective

The objective of this systematic review and meta-analysis was to determine the prognostic value of memory CD8(+) T cells in cancer patients with immunotherapy.

Methods

EMBASE, MEDLINE (PubMed), and Web of Science databases were searched to identify suitabile articles published before March 2021. Risk of bias on the study level was assessed using the Cochrane Bias Risk Assessment Tool. The hazard ratios (HRs) and 95% confidence intervals (CIs) of pooled progression-free survival (PFS) and overall survival (OS) were calculated using RevMan 5.4 to evaluate the prognostic impact of memory CD8(+) T cells.

Results

In total, nine studies were included in the final analysis. High levels of memory CD8(+) T cells were significantly closely correlated with better progression-free survival (PFS) and overall survival (OS) of cancer patients with immunotherapy (PFS, HR 0.64, 95% CI 0.53-0.78; OS, HR 0.37, 95% CI 0.21-0.65). Memory CD8(+) T cells still have significant prognostic value in cancer patients given immunotherapy alone after excluding of other interfering factors such as chemotherapy, radiotherapy, and targeted therapy (PFS, HR 0.65, 95% CI 0.48-0.89; OS, HR 0.23, 95% CI 0.13-0.42). However, high memory CD8(+) T cells levels did not correspond to a longer PFS or OS in cancer patients with non-immunotherapy (PFS, HR 1.05, 95% CI 0.63-1.73; OS, HR 1.29, 95% CI 0.48-3.48). Thus, memory CD8(+) T cells might be a promising predictor in cancer patients with immunotherapy.

Conclusions

The host's overall immune status, and not only the tumor itself, should be considered to predict the efficacy of immunotherapy in cancer patients. This study is the first to show the significant prognostic value of memory CD8(+) T cells in immunotherapy of cancer patients through systematic review and meta-analysis. Thus, the detection of memory CD8(+) T cells has a considerable value in clinical practice in cancer patients with immunotherapy. Memory CD8(+) T cells may be promising immunotherapy targets.

MHC Class I Deficiency in Solid Tumors and Therapeutic Strategies to Overcome It

It is now well accepted that the immune system can control cancer growth. However, tumors escape immune-mediated control through multiple mechanisms and the downregulation or loss of major histocompatibility class (MHC)-I molecules is a common immune escape mechanism in many cancers. MHC-I molecules present antigenic peptides to cytotoxic T cells, and MHC-I loss can render tumor cells invisible to the immune system. In this review, we examine the dysregulation of MHC-I expression in cancer, explore the nature of MHC-I-bound antigenic peptides recognized by immune cells, and discuss therapeutic strategies that can be used to overcome MHC-I deficiency in solid tumors, with a focus on the role of natural killer (NK) cells and CD4 T cells.

Validation of the Combined Biomarker for Prediction of Response to Checkpoint Inhibitor in Patients with Advanced Cancer

Although immune checkpoint inhibitors can induce durable responses in patients with multiple types of advanced cancer, only a limited number of patients have a known reliable biomarker. This study aimed to validate the IMmunotherapy Against GastrIc Cancer (IMAGiC) model, which was developed based on a previous study of four-gene and PD-L1 level, to predict immunotherapy response. We developed a clinical assay for formalin-fixed paraffin-embedded samples using quantitative real-time polymerase chain reaction to measure the expression level of the previously published four-gene set. The predictive performance was validated in a cohort of 89 patients with several advanced tumor types. The IMAGiC score was derived from tumor samples of 89 patients consisting of eight cancer types, and 73 out of 89 patients available for clinical response were analyzed with clinicopathological factors. The IMAGiC group (responder vs. non-responder) was determined with a specific value of the IMAGiC score as a cutoff, which was set by log-rank statistics for progression-free survival (PFS) divided the patients into 56 (76.7%) non-responders and 17 (23.3%) responders. Clinical responders (complete response/partial response) were higher in the IMAGiC responder group than in the non-responder group (70.6 vs. 21.4%). The median PFS of the IMAGiC responder group and non-responder was 20.8 months (95% CI 9.1-not reached) and 6.7 months (95% CI 4.9-11.1, p = 0.007), respectively. Among the 17 IMAGiC responders, 11 patients had tumor mutation burden-low and microsatellite-stable tumors. This study validated a predictive model based on a four-gene expression signature. Along with conventional biomarkers, our model could be useful for predicting response to immunotherapy in patients with advanced cancer.