Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer

Detecting HLA loss of heterozygosity within a standard diagnostic sequencing workflow for prognostic and therapeutic opportunities

To enable interrogation of tumor HLA LOH as a clinical diagnostic for precision oncology, we developed and validated an assay that detects HLA LOH within the context of an FDA-approved clinical diagnostic test, Tempus xT CDx. Validation was conducted via: (1) analytical evaluation of 17 archival patient samples and 42 cell line admixtures and (2) independent clinical evaluation of LOH prevalence in the HLA-A gene (HLA-A LOH) across 10,982 patients. To evaluate the prognostic relevance of HLA-A LOH we assessed 256 immunotherapy-treated non-small cell lung cancer (NSCLC) patients. To determine the feasibility of prospectively identifying and enrolling HLA-A LOH patients into a clinical trial, we established BASECAMP-1 (NCT04981119). We observed a positive predictive agreement of 97% and a negative predictive agreement of 100% in samples with ≥ 40% tumor purity. We observed HLA-A LOH in 16.1% of patients (1771/10,982), comparable to previous reports. HLA-A LOH was associated with longer survival among NSCLC adenocarcinoma patients (HR = 0.60, 95% CI [0.37, 0.96], p = 0.032) with a trend towards shorter survival among squamous cell patients (HR = 1.64, 95% CI [0.80, 3.41], p = 0.183). In 20 months, we prospectively screened 1720 subjects using the Tempus AWARE program, identifying 26 HLA-A*02 LOH patients at 8 sites, with 14 (54%) enrolled into BASECAMP-1. In conclusion, we developed and validated an investigational assay that detects tumor HLA LOH within an FDA-approved clinical diagnostic test, enabling HLA LOH utilization in diagnostic, prognostic, and therapeutic applications.

miR-16-5p specifically inhibits IFN-γ-regulated memory T helper cell differentiation in malignant pleural effusion of non-small cell lung cancer

Background

The mechanism for memory T helper (Th) cell differentiation in malignant pleural effusion (MPE) of non-small cell lung cancer (NSCLC) is poorly understood. MicroRNAs (miRNAs), as small non-coding RNA that regulate gene expression, play a crucial role in the regulation of memory Th cell differentiation. However, whether miRNAs can inhibit the differentiation of memory Th cells in MPE of NSCLC has not been reported. This study aimed to explore miR-16-5p specifically inhibits interferon-gamma (IFN-γ)-regulated memory Th cell differentiation in MPE of NSCLC.

Methods

A total of 30 patients with NSCLC and 30 age- and sex-matched patients, who were clinically diagnosed as benign pleural effusion (BPE) of lung disease and had not received any intervention, were collected. The expression of nucleic acids, miRNAs, and cytokines was detected by polymerase chain reaction (PCR), miRNA microarray, enzyme-linked immunosorbent assay (ELISA), flow cytometry, and western blotting.

Results

The expression of CD4+CD69+ T cells in NSCLC with MPE was lower than that in lung disease BPE. CD4+CD69+ T cells highly express CD45RO+ and mainly secrete anti-tumor cytokines IFN-γ, interleukin-2 (IL-2), and tumor necrosis factor-α (TNF-α). The expression of miR-16-5p in CD4+CD69+ CD45RO+ T cells in MPE was higher than that in BPE. Moreover, miR-16-5p can bind to both IFN-γ promoter and its 5'untranslated region (5'-UTR), suggesting that IFN-γ may be the target gene directly affected by miR-16-5p. IFN-γ also affects the differentiation of memory CD4+ T cells by regulating T-bet.

Conclusions

We believe that miR-16-5p may regulate the decrease of differentiation of naïve CD4+ T cells into memory CD4+CD69+ T cells through its target gene IFN-γ in MPE, thus reducing the number of cytokines that produce anti-tumor effects. It may be the main reason for the low response rate of lung cancer with MPE immunotherapy.

Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes

Background

Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC), characterized by the presence of epithelial and sarcoma-like components. The molecular and immune landscape of PSC has not been well defined.

Methods

Multiomics profiling of 21 pairs of PSCs with matched normal lung tissues was performed through targeted high-depth DNA panel, whole-exome, and RNA sequencing. We describe molecular and immune features that define subgroups of PSC with disparate genomic and immunogenic features as well as distinct clinical outcomes.

Results

In total, 27 canonical cancer gene mutations were identified, with TP53 the most frequently mutated gene, followed by KRAS. Interestingly, most TP53 and KRAS mutations were earlier genomic events mapped to the trunks of the tumors, suggesting branching evolution in most PSC tumors. We identified two distinct molecular subtypes of PSC, driven primarily by immune infiltration and signaling. The Immune High (IM-H) subtype was associated with superior survival, highlighting the impact of immune infiltration on the biological and clinical features of localized PSCs.

Conclusions

We provided detailed insight into the mutational landscape of PSC and identified two molecular subtypes associated with prognosis. IM-H tumors were associated with favorable recurrence-free survival and overall survival, highlighting the importance of tumor immune infiltration in the biological and clinical features of PSCs.

Beta-defensin index: A functional biomarker for oral cancer detection

There is an unmet clinical need for a non-invasive and cost-effective test for oral squamous cell carcinoma (OSCC) that informs clinicians when a biopsy is warranted. Human beta-defensin 3 (hBD-3), an epithelial cell-derived anti-microbial peptide, is pro-tumorigenic and overexpressed in early-stage OSCC compared to hBD-2. We validate this expression dichotomy in carcinoma in situ and OSCC lesions using immunofluorescence microscopy and flow cytometry. The proportion of hBD-3/hBD-2 levels in non-invasively collected lesional cells compared to contralateral normal cells, obtained by ELISA, generates the beta-defensin index (BDI). Proof-of-principle and blinded discovery studies demonstrate that BDI discriminates OSCC from benign lesions. A multi-center validation study shows sensitivity and specificity values of 98.2% (95% confidence interval [CI] 90.3-99.9) and 82.6% (95% CI 68.6-92.2), respectively. A proof-of-principle study shows that BDI is adaptable to a point-of-care assay using microfluidics. We propose that BDI may fulfill a major unmet need in low-socioeconomic countries where pathology services are lacking.

ADGRE5-centered Tsurv model in T cells recognizes responders to neoadjuvant cancer immunotherapy

Background

Neoadjuvant immunotherapy with anti-programmed death-1 (neo-antiPD1) has revolutionized perioperative methods for improvement of overall survival (OS), while approaches for major pathologic response patients' (MPR) recognition along with methods for overcoming non-MPR resistance are still in urgent need.

Methods

We utilized and integrated publicly-available immune checkpoint inhibitors regimens (ICIs) single-cell (sc) data as the discovery datasets, and innovatively developed a cell-communication analysis pipeline, along with a VIPER-based-SCENIC process, to thoroughly dissect MPR-responding subsets. Besides, we further employed our own non-small cell lung cancer (NSCLC) ICIs cohort's sc data for validation in-silico. Afterward, we resorted to ICIs-resistant murine models developed by us with multimodal investigation, including bulk-RNA-sequencing, Chip-sequencing and high-dimensional cytometry by time of flight (CYTOF) to consolidate our findings in-vivo. To comprehensively explore mechanisms, we adopted 3D ex-vivo hydrogel models for analysis. Furthermore, we constructed an ADGRE5-centered Tsurv model from our discovery dataset by machine learning (ML) algorithms for a wide range of tumor types (NSCLC, melanoma, urothelial cancer, etc.) and verified it in peripheral blood mononuclear cells (PBMCs) sc datasets.

Results

Through a meta-analysis of multimodal sequential sc sequencing data from pre-ICIs and post-ICIs, we identified an MPR-expanding T cells meta-cluster (MPR-E) in the tumor microenvironment (TME), characterized by a stem-like CD8+ T cluster (survT) with STAT5-ADGRE5 axis enhancement compared to non-MPR or pre-ICIs TME. Through multi-omics analysis of murine TME, we further confirmed the existence of survT with silenced function and immune checkpoints (ICs) in MPR-E. After verification of the STAT5-ADGRE5 axis of survT in independent ICIs cohorts, an ADGRE5-centered Tsurv model was then developed through ML for identification of MPR patients pre-ICIs and post-ICIs, both in TME and PBMCs, which was further verified in pan-cancer immunotherapy cohorts. Mechanistically, we unveiled ICIs stimulated ADGRE5 upregulation in a STAT5-IL32 dependent manner in a 3D ex-vivo system (3D-HYGTIC) developed by us previously, which marked Tsurv with better survival flexibility, enhanced stemness and potential cytotoxicity within TME.

Conclusion

Our research provides insights into mechanisms underlying MPR in neo-antiPD1 and a well-performed model for the identification of non-MPR.

Opportunities and tradeoffs in single-cell transcriptomic technologies

Recent technological and algorithmic advances enable single-cell transcriptomic analysis with remarkable depth and breadth. Nonetheless, a persistent challenge is the compromise between the ability to profile high numbers of cells and the achievement of full-length transcript coverage. Currently, the field is progressing and developing new and creative solutions that improve cellular throughput, gene detection sensitivity and full-length transcript capture. Furthermore, long-read sequencing approaches for single-cell transcripts are breaking frontiers that have previously blocked full transcriptome characterization. We here present a comprehensive overview of available options for single-cell transcriptome profiling, highlighting the key advantages and disadvantages of each approach.

Cancer CD39 drives metabolic adaption and mal-differentiation of CD4+ T cells in patients with non-small-cell lung cancer

While ectonucleotidase CD39 is a cancer therapeutic target in clinical trials, its direct effect on T-cell differentiation in human non-small-cell lung cancer (NSCLC) remains unclear. Herein, we demonstrate that human NSCLC cells, including tumor cell lines and primary tumor cells from clinical patients, efficiently drive the metabolic adaption of human CD4+ T cells, instructing differentiation of regulatory T cells while inhibiting effector T cells. Of importance, NSCLC-induced T-cell mal-differentiation primarily depends on cancer CD39, as this can be fundamentally blocked by genetic depletion of CD39 in NSCLC. Mechanistically, NSCLC cells package CD39 into their exosomes and transfer such CD39-containing exosomes into interacting T cells, resulting in ATP insufficiency and AMPK hyperactivation. Such CD39-dependent NSCLC-T cell interaction holds well in patients-derived primary tumor cells and patient-derived organoids (PDOs). Accordingly, genetic depletion of CD39 alone or in combination with the anti-PD-1 immunotherapy efficiently rescues effector T cell differentiation, instigates anti-tumor T cell immunity, and inhibits tumor growth of PDOs. Together, targeting cancer CD39 can correct the mal-differentiation of CD4+ T cells in human NSCLC, providing in-depth insight into therapeutic CD39 inhibitors.

Dynamic monitoring of serum tumor markers as prognostic factors in patients with advanced non-small-cell lung cancer treated with first-line immunotherapy: a multicenter retrospective study

Background

To date, no specific studies have reported the use of dynamic serum tumor markers (STMs) as prognostic factors in patients with advanced non-small-cell lung cancer (NSCLC) who receive first-line immunotherapy. Therefore, it is unclear whether STMs can be used as a prognostic factor for first-line immunotherapy in advanced NSCLC.

Objectives

To elucidate the role of STMs in monitoring immunotherapy response in advanced NSCLC. Patients were treated with first-line programmed cell death-1/programmed cell death ligand-1 inhibitors at four Chinese centers.

Design

This was a multicenter retrospective study.

Methods

Blood samples were collected at baseline and after 6-8 weeks of treatment. Computed tomography scans were used to evaluate treatment efficacy according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. Post-treatment drops in STMs [Serum carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin fragment 19 (CYFRA21-1), carbohydrate antigen 19-9 (CA19-9), and carbohydrate antigen 125 (CA125)] were decreased ⩾20% (Group C) over baseline was used as cutoff level for defining a marker response. If STMs were increased by ⩾20% after treatment, the therapeutic effect was limited (Group A). Patients with STM changes between a 20% increase or decrease were enrolled in Group B. In univariate and multivariate stepwise Cox regression analyses, STMs and RECIST responses were analyzed for their impact on progression-free survival (PFS) and overall survival (OS).

Results

The analysis included 716 patients. By multivariate analysis, CEA, NSE, CYFRA21-1, CA19-9, and CA125 (Group A versus Group B and Group A versus Group C) were associated with significant differences in PFS. Similar results were observed in the OS analysis. Similar results were observed in the adenocarcinoma subgroup analyses. In squamous cell carcinoma subgroup analyses, there was no statistical difference in PFS (p = 0.147) or OS (p = 0.068) between Group A and Group B for CA125.

Conclusion

The increase and decrease in serum levels of STMs might be reliable prognostic factors for immunotherapy efficacy in NSCLC patients.

Cytotoxic CD4+ T cells in chronic viral infections and cancer

CD4+ T cells play an important role in immune responses against pathogens and cancer cells. Although their main task is to provide help to other effector immune cells, a growing number of infections and cancer entities have been described in which CD4+ T cells exhibit direct effector functions against infected or transformed cells. The most important cell type in this context are cytotoxic CD4+ T cells (CD4+ CTL). In infectious diseases anti-viral CD4+ CTL are mainly found in chronic viral infections. Here, they often compensate for incomplete or exhausted CD8+ CTL responses. The induction of CD4+ CTL is counter-regulated by Tregs, most likely because they can be dangerous inducers of immunopathology. In viral infections, CD4+ CTL often kill via the Fas/FasL pathway, but they can also facilitate the exocytosis pathway of killing. Thus, they are very important effectors to keep persistent virus in check and guarantee host survival. In contrast to viral infections CD4+ CTL attracted attention as direct anti-tumor effectors in solid cancers only recently. Anti-tumor CD4+ CTL are defined by the expression of cytolytic markers and have been detected within the lymphocyte infiltrates of different human cancers. They kill tumor cells in an antigen-specific MHC class II-restricted manner not only by cytolysis but also by release of IFNγ. Thus, CD4+ CTL are interesting tools for cure approaches in chronic viral infections and cancer, but their potential to induce immunopathology has to be carefully taken into consideration.

Prognostic Factors and Markers in Non-Small Cell Lung Cancer: Recent Progress and Future Challenges

Lung cancer is a highly aggressive neoplasm and, despite the development of recent therapies, tumor progression and recurrence following the initial response remains unsolved. Several questions remain unanswered about non-small cell lung cancer (NSCLC): (1) Which patients will actually benefit from therapy? (2) What are the predictive factors of response to MAbs and TKIs? (3) What are the best combination strategies with conventional treatments or new antineoplastic drugs? To answer these questions, an integrative literature review was carried out, searching articles in PUBMED, NCBI-PMC, Google Academic, and others. Here, we will examine the molecular genetics of lung cancer, emphasizing NSCLC, and delineate the primary categories of inhibitors based on their molecular targets, alongside the main treatment alternatives depending on the type of acquired resistance. We highlighted new therapies based on epigenetic information and a single-cell approach as a potential source of new biomarkers. The current and future of NSCLC management hinges upon genotyping correct prognostic markers, as well as on the evolution of precision medicine, which guarantees a tailored drug combination with precise targeting.

Response Prediction Model of Atezolizumab plus Bevacizumab in Patients with Unresectable Hepatocellular Carcinoma: Multicenter Retrospective Study

INTRODUCTION

Atezolizumab plus bevacizumab (ATZ+BV) treatment has become the first-line regimen for unresectable hepatocellular carcinoma (u-HCC). Prediction of response to it might be clinically beneficial. Using peripheral blood parameters, we aimed to construct a prediction model for ATZ+BV treatment.

METHODS

Clinical records of 119 patients with u-HCC treated by ATZ+BV were retrospectively analyzed. The primary outcome measurement was defined as any-size reduction at the initial image evaluation. Using baseline values of peripheral blood parameters, a prediction model was constructed by univariate and multivariate logistic regression analysis. Validation was performed internally by bootstrap method.

RESULTS

The primary outcome was achieved in 46 patients. Univariate analysis showed that C-reactive protein (CRP), alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin (DCP), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were possible predictors. CRP and DCP, and NLR and PLR had correlation (correlation coefficient >0.3), so we used CRP and NLR as representative factors, respectively. Multivariate analysis constructed the following prediction model: Logit = 1.62-0.61×[CRP] -0.38×[Log10AFP] -0.37×[NLR]. Bootstrapped median (95% confidence interval) of coefficients of CRP, Log10AFP, NLR were -0.64 (-1.46 ∼ -0.11), -0.40 (-0.82 ∼ -0.03), and -0.38 (-0.74 ∼ -0.05), respectively. The area under the receiver operating characteristic curve (95% confidence interval) was 0.73 (0.60-0.80). Median overall survival of the favorably and unfavorably predicted groups were 17.0 and 11.0 months (p = 0.03), respectively.

DISCUSSION

In patients with u-HCC treated by ATZ+BEV, a prediction model constructed using baseline values of CRP, AFP, and NLR had impact on any-size reduction at the initial image evaluation and on prognosis.

Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma

Immune checkpoint inhibitor (ICI) treatment has created the opportunity of improved outcome for patients with hepatocellular carcinoma (HCC). However, only a minority of HCC patients benefit from ICI treatment owing to poor treatment efficacy and safety concerns. There are few predictive factors that precisely stratify HCC responders to immunotherapy. In this study, we developed a tumour microenvironment risk (TMErisk) model to divide HCC patients into different immune subtypes and evaluated their prognosis. Our results indicated that virally mediated HCC patients who had more common tumour protein P53 (TP53) alterations with lower TMErisk scores were appropriate for ICI treatment. HCC patients with alcoholic hepatitis who more commonly harboured catenin beta 1 (CTNNB1) alterations with higher TMErisk scores could benefit from treatment with multi-tyrosine kinase inhibitors. The developed TMErisk model represents the first attempt to anticipate tumour tolerance of ICIs in the TME through the degree of immune infiltration in HCCs.

Novel EGFR-mutant mouse models of lung adenocarcinoma reveal adaptive immunity requirement for durable osimertinib response

Lung cancers bearing oncogenically-mutated EGFR represent a significant fraction of lung adenocarcinomas (LUADs) for which EGFR-targeting tyrosine kinase inhibitors (TKIs) provide a highly effective therapeutic approach. However, these lung cancers eventually acquire resistance and undergo progression within a characteristically broad treatment duration range. Our previous study of EGFR mutant lung cancer patient biopsies highlighted the positive association of a TKI-induced interferon γ transcriptional response with increased time to treatment progression. To test the hypothesis that host immunity contributes to the TKI response, we developed novel genetically-engineered mouse models of EGFR mutant lung cancer bearing exon 19 deletions (del19) or the L860R missense mutation. Both oncogenic EGFR mouse models developed multifocal LUADs from which transplantable cancer cell lines sensitive to the EGFR-specific TKIs, gefitinib and osimertinib, were derived. When propagated orthotopically in the left lungs of syngeneic C57BL/6 mice, deep and durable shrinkage of the cell line-derived tumors was observed in response to daily treatment with osimertinib. By contrast, orthotopic tumors propagated in immune deficient nu/nu or Rag1-/- mice exhibited modest tumor shrinkage followed by rapid progression on continuous osimertinib treatment. Importantly, osimertinib treatment significantly increased intratumoral T cell content and decreased neutrophil content relative to diluent treatment. The findings provide strong evidence supporting the requirement for adaptive immunity in the durable therapeutic control of EGFR mutant lung cancer.

Immune biology of NSCLC revealed by single-cell technologies: implications for the development of biomarkers in patients treated with immunotherapy

First-line immunotherapy in non-small-cell lung cancer largely improved patients' survival. PD-L1 testing is required before immune checkpoint inhibitor initiation. However, this biomarker fails to accurately predict patients' response. On the other hand, immunotherapy exposes patients to immune-related toxicity, the mechanisms of which are still unclear. Hence, there is an unmet need to develop clinically approved predictive biomarkers to better select patients who will benefit the most from immune checkpoint inhibitors and improve risk management. Single-cell technologies provide unprecedented insight into the tumor and its microenvironment, leading to the discovery of immune cells involved in immune checkpoint inhibitor response or toxicity. In this review, we will underscore the potential of the single-cell approach to identify candidate biomarkers improving non-small-cell lung cancer patients' care.