Spatial multi-omics revealed the impact of tumor ecosystem heterogeneity on immunotherapy efficacy in patients with advanced non-small cell lung cancer treated with bispecific antibody

Current status and progress of PD-L1 detection: guiding immunotherapy for non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths and represents a substantial disease burden worldwide. Immune checkpoint inhibitors combined with chemotherapy are the standard first-line therapy for advanced NSCLC without driver mutations. Programmed death-ligand 1 (PD-L1) is currently the only approved immunotherapy marker. PD-L1 detection methods are diverse and have developed rapidly in recent years, such as improved immunohistochemical detection methods, the application of liquid biopsy in PD-L1 detection, genetic testing, radionuclide imaging, and the use of machine learning methods to construct PD-L1 prediction models. This review focuses on the detection methods and challenges of PD-L1 from different sources, and discusses the influencing factors of PD-L1 detection and the value of combined biomarkers. Provide support for clinical screening of immunotherapy-advantage groups and formulation of personalized treatment decisions.

Distinctive Phenotypic and Microenvironmental Characteristics of Neuroendocrine Carcinoma and Adenocarcinoma Components in Gastric Mixed Adenoneuroendocrine Carcinoma

This study aimed to conduct an in-depth examination of gene expression and microenvironmental profiles of gastric neuroendocrine carcinoma (NEC) and mixed adeno-NEC (MANEC). Tissue microarrays from 55 patients with gastric MANEC (N = 32) or NEC (N = 23) were analyzed using digital spatial profiling (GeoMx DSP, NanoString Technologies). Representative regions of interest were selected from the adenocarcinoma (ADC) portion (ADC-MANEC) and the NEC portion (NEC-MANEC) of the MANEC cores, and pure NEC (pNEC) cores. All regions of interest were separated into epithelial components and stromal components using the masking procedure in the GeoMx platform, followed by transcriptome analysis. Comparison of gene expression between ADC-MANEC and NEC-MANEC/pNEC identified several differentially expressed genes in the epithelial (including PEG10, MAP1B, STMN3, and AKT3) and stromal (FN1, COL1A1, SPARC, and BGN) components. Gene set enrichment analysis revealed that pathways related to the E2F target and G2M checkpoint were more enriched in NEC-MANEC and pNEC than in ADC-MANEC. Deconvolution analysis showed that the microenvironmental profile varied according to histologic differentiation. In ADC-MANEC, intraepithelial infiltrating immune cells were relatively more numerous, whereas fibroblasts in the stroma were more abundant in NEC-MANEC and pNEC. This study confirmed the distinct expression profile of each histologic component of MANEC according to its tumor vs stromal compartment using the DSP platform. Although each component of MANEC shares the same genetic origin, distinctive phenotypes should not be overlooked when managing patients with MANEC. This study provides a useful validation data set for future studies.

Advances in spatial transcriptomics and its applications in cancer research

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer

Objectives

To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.

Methods

This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).

Results

In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.

Conclusion

The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Functional impact of multi-omic interactions in lung cancer

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

BACKGROUND

Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy.

METHODS

We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP).

RESULTS

Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region.

CONCLUSIONS

These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

Long‑term survival of a patient with advanced lung cancer treated with targeted therapy and anti‑PD‑1 immunotherapy as multi‑line therapy: A case report

Lung cancer is the most common type of cancer worldwide. Lung adenocarcinoma, a type of non-small cell lung cancer (NSCLC), is a common type of lung cancer. In recent years, immunotherapy has become the primary method of treatment for several solid cancers, including NSCLC. In the present study, the case of a patient with NSCLC following left superior lobectomy is reported. Different systemic therapies failed, such as a pemetrexed + carboplatin regimen, paclitaxel liposome + cisplatin and pembrolizumab, and albumin-bound paclitaxel + toripalimab, but long-term survival was achieved following targeted therapy and anti-programmed cell death protein-1 (PD-1) immunotherapy. The patient survived for >4 years following lung cancer progression, which is notably longer than expected for patients with advanced lung cancer. In conclusion, the present case demonstrated the efficacy of targeted therapy and anti-PD-1 immunotherapy for the treatment of advanced lung cancer following the occurrence of drug resistance and progressive disease.

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

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

Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies

Non-small cell lung cancer (NSCLC) remains a cause of significant morbidity and mortality, despite significant advances made in its treatment using immune checkpoint inhibitors (ICIs) over the last decade; while a minority experience prolonged responses with ICIs, benefit is limited for most patients. The development of multiplexed antibody-based (MAB) spatial tissue imaging technologies has revolutionised analysis of the tumour microenvironment (TME), enabling identification of a wide range of cell types and subtypes, and analysis of the spatial relationships and interactions between them. Such study has the potential to translate into a greater understanding of treatment susceptibility and resistance, factors influencing prognosis and recurrence risk, and identification of novel therapeutic approaches and rational treatment combinations to improve patient outcomes in the clinic. Herein we review studies that have leveraged MAB technologies to deliver novel insights into the TME of NSCLC.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Multi-dimensional characterization of immunological profiles in small cell lung cancer uncovers clinically relevant immune subtypes with distinct prognoses and therapeutic vulnerabilities

Small-cell lung cancer (SCLC) is generally considered a 'homogenous' disease, with little documented inter-tumor heterogeneity in treatment guidance or prognosis evaluation. The precise identification of clinically relevant molecular subtypes remains incomplete and their translation into clinical practice is limited. In this retrospective cohort study, we comprehensively characterized the immune microenvironment in SCLC by integrating transcriptional and protein profiling of formalin-fixation-and-paraffin-embedded (FFPE) samples from 29 patients. We identified two distinct disease subtypes: immune-enriched (IE-subtype) and immune-deprived (ID-subtype), displaying heterogeneity in immunological, biological, and clinical features. The IE-subtype was characterized by abundant immune infiltrate and elevated levels of interferon-alpha/gamma (IFNα/IFNγ) and inflammatory response, while the ID-subtype featured a complete lack of immune infiltration and a more proliferative phenotype. These two immune subtypes are associated with clinical benefits in SCLC patients treated with adjuvant therapy, with the IE-subtype exhibiting a more favorable response leading to improved survival and reduced disease recurrence risk. Additionally, we identified and validated a personalized prognosticator of immunophenotyping, the CCL5/CXCL9 chemokine index (CCI), using machine learning. The CCI demonstrated superior predictive abilities for prognosis and clinical benefits in SCLC patients, validated in our institute immunohistochemistry cohort and multicenter bulk transcriptomic data cohorts. In conclusion, our study provides a comprehensive and multi-dimensional characterization of the immune architecture of SCLC using clinical FFPE samples and proposes a new immune subtyping conceptual framework enabling risk stratification and the appropriate selection of individualized therapy.

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

BACKGROUND

KN046 is a novel bispecific antibody targeting programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). This multicenter phase I trial investigated the safety, tolerability, pharmacokinetics (PK), and efficacy of KN046 in patients with advanced solid tumors.

METHODS

Patients who failed standard treatment were included. KN046 was administered at doses of 1, 3, and 5 mg/kg every 2 weeks (Q2W), 5 mg/kg every 3 weeks (Q3W), and 300 mg Q3W based on the modified toxicity probability interval method in the dose-escalation phase; the recommended dose was used in the expansion phase. Primary objectives were maximum tolerated dose (MTD) and recommended phase II dose (RP2D) in escalation and preliminary efficacy in expansion. Secondary objectives included PK, pharmacodynamics, safety, and tolerability of KN046. We also explored biomarkers based on PD-L1 expression, multiplex immunofluorescence (mIF) staining, and RNAseq-derived nCounter platform.

RESULTS

Totally, 100 eligible patients were enrolled, including 59 with nasopharyngeal carcinoma (NPC), 36 with epidermal growth factor receptor (EGFR)-mutated non-small-cell lung cancer (NSCLC), and those with other advanced solid tumors. The most common treatment-related adverse events (TRAEs) were rash (33.0%), pruritus (31.0%), and fatigue (20.0%). Grade ≥3 TRAEs were observed in 14.0% of participants. No dose-limiting toxicity occurred in the dose-escalation phase, and the MTD was not reached. The RP2D was determined as 5 mg/kg Q2W according to the pharmacokinetic-pharmacodynamic model, the preliminary exposure-response analysis, and the overall safety profile. Among 88 efficacy-evaluable participants, the objective response rate (ORR) was 12.5%, and the median duration of response was 16.6 months. In the NPC subgroup, the ORR was 15.4%, and the median overall survival (OS) was 24.7 (95% CI 16.3 to not estimable) months. In the EGFR-mutant NSCLC subgroup, the ORR was 6.3%. mIF analysis results showed patients with high CD8 expression showed longer median OS (27.1 vs 9.2 months, p=0.02); better prognosis was observed in patients with high CD8 and PD-L1 expression.

CONCLUSIONS

KN046 was well tolerated and showed promising antitumor efficacy in advanced solid tumors, especially in patients with NPC. The combination of both CD8 and PD-L1 expression improved the prediction of KN046 response.

TRIAL REGISTRATION NUMBERS

NCT03733951 .

Targeting Angiogenesis in the Era of Biliary Tract Cancer Immunotherapy: Biological Rationale, Clinical Implications, and Future Research Avenues

Although biliary tract cancers are traditionally considered rare in Western countries, their incidence and mortality rates are rising worldwide. A better knowledge of the genomic landscape of these tumor types has broadened the number of molecular targeted therapies, including angiogenesis inhibitors. The role of immune checkpoint inhibitors (ICIs) could potentially change the first-line therapeutic approach, but monotherapy with ICIs has shown disappointing results in CCA. Several clinical trials are evaluating combination strategies that include immunotherapy together with other anticancer agents with a synergistic activity. The tumor microenvironment (TME) composition plays a pivotal role in the prognosis of BTC patients. The accumulation of immunosuppressive cell types, such as tumor-associated macrophages (TAMs) and regulatory T-cells, together with the poor infiltration of cytotoxic CD8+ T-cells, is known to predispose to a poor prognosis owing to the establishment of resistance mechanisms. Likewise, angiogenesis is recognized as a major player in modulating the TME in an immunosuppressive manner. This is the mechanistic rationale for combination treatment schemes blocking both immunity and angiogenesis. In this scenario, this review aims to provide an overview of the most recent completed or ongoing clinical trials combining immunotherapy and angiogenesis inhibitors with/without a chemotherapy backbone.