Classification of the tumor immune microenvironment and associations with outcomes in patients with metastatic melanoma treated with immunotherapies

Intratumoral and peritumoral radiomics of MRIs predicts pathologic complete response to neoadjuvant chemoimmunotherapy in patients with head and neck squamous cell carcinoma

BACKGROUND

For patients with locally advanced head and neck squamous cell carcinoma (HNSCC), combined programmed death receptor-1 inhibitor and chemotherapy improved response rate to neoadjuvant therapy. However, treatment response varies among patients. There is no tool to predict pathologic complete response (pCR) with high accuracy for now. To develop a tool based on radiomics features of MRI to predict pCR to neoadjuvant chemoimmunotherapy (NACI) may provide valuable assistance in treatment regimen determination for HNSCC.

METHODS

From January 2021 to April 2024, a total of 172 patients with HNSCC from three medical center, who received NACI followed by surgery, were included and allocated into a training set (n=84), an internal validation set (n=37) and an external validation set (n=51). Radiomics features were extracted from intratumoral and different peritumoral areas, and radiomics signature (Rad-score) for each area was constructed. A radiomics-clinical nomogram was developed based on Rad-scores and clinicopathological characteristics, tested in the validation sets, and compared with clinical nomogram and combined positive score (CPS) in predicting pCR.

RESULTS

The radiomics-clinical nomogram, incorporating peritumoral Rad-score, intratumoral Rad-score and CPS, achieved the highest accuracy with areas under the receiver operating characteristic curve of 0.904 (95% CI, 0.835 to 0.972) in the training cohort, 0.860 (95% CI, 0.722 to 0.998) in the internal validation cohort, and 0.849 (95% CI, 0.739 to 0.959) in the external validation cohort, respectively, which outperformed the clinical nomogram and CPS in predict pCR to NACI for HNSCC.

CONCLUSION

A nomogram developed based on intratumoral and peritumoral MRI radiomics features outperformed CPS, a widely employed biomarker, in predict pCR to NACI for HNSCC, which would provide incremental value in treatment regimen determination.

Redefining the tumor microenvironment with emerging therapeutic strategies

The environment surrounding a tumor, known as the tumor microenvironment (TME), plays a role in how cancer progresses and responds to treatment. It poses both challenges and opportunities for improving cancer therapy. Recent progress in understanding the TME complexity and diversity has led to approaches for treating cancer. This perspective discusses the strategies for targeting the TME, such as adjusting networks using extracellular vesicles to deliver drugs and enhancing immune checkpoint inhibitors (ICIS) through combined treatments. Furthermore, it highlights adoptive cell transfer (ACT) therapies as an option for tumors. By studying how components of the TME interact and utilizing technologies like single-cell RNA sequencing and spatial transcriptomics, we can develop more precise and efficient treatments for cancer. This article emphasizes the need to reshape the TME to boost antitumor immunity and overcome resistance to therapy, providing guidance for research and clinical practices in precision oncology.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

Study on the predictive value of pretreatment peripheral blood inflammatory markers regarding immunotherapy in patients with inoperable advanced or locally advanced oesophageal squamous cell carcinoma

OBJECTIVE

To explore the effects of pretreatment peripheral blood panimmune-inflammation value (PIV), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) on the efficacy and prognostic value of immunotherapy in patients with inoperable advanced or locally advanced oesophageal squamous cell carcinoma (ESCC).

METHODS

Clinical data of 107 inoperable advanced or locally advanced ESCC patients were retrospectively analysed between May 2019 and August 2023, the receiver operating characteristic curves (ROCs) of PIV, SII, NLR, and PLR in patients prior to immunotherapy were plotted, and their optimal cutoff values were determined. The risk factors were determined by univariate and multivariate analyses in groups based on the optimal cut-off values.

RESULTS

Peripheral blood PIV, SII and PLR before immunotherapy had predictive value for the optimal efficacy of immunotherapy in patients with inoperable advanced or locally advanced ESCC; patients with PIV ≥415.885, SII ≥834.295 and NLR ≥3.740 had a low objective response rate (ORR), disease control rate (DCR), a short progression-free survival (PFS) and overall survival (OS) after immunotherapy (p < 0.05). Patient tumour stage, distant lymph node metastasis, lung metastasis, liver metastasis, PIV, SII, and NLR were risk factors affecting PFS and OS (p < 0.05). Tumour stage and SII were independent risk factors affecting PFS and OS (p < 0.05).

CONCLUSION

In patients with inoperable advanced or locally advanced ESCC, peripheral blood PIV, SII, and NLR have predictive value for immunotherapy outcome, SII is an independent risk factor affecting the survival prognosis, and SII ≥834.295 suggests a poor prognosis from immunotherapy.

Prognostic value of peripheral blood neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, pan-immune-inflammation value and systemic immune-inflammation index for the efficacy of immunotherapy in patients with advanced gastric cancer

Aim: The study aimed to assess the value of pretreatment peripheral blood neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), pan-immune-inflammation value (PIV) and systemic immune-inflammation index (SII) for predicting immunotherapy prognosis and efficacy in advanced gastric cancer (GC). Methods: A total of 84 advanced GC patients received immunotherapy were retrospectively collected. The optimal cut-off values were determined by receiver operating characteristic curves. The univariate and multivariate analysis investigated the effects of NLR, PLR, PIV and SII on patients prognosis. Results: NLR, PLR, PIV and SII had predictive value of efficacy. NLR ≥3.65 was an independent risk factor for worse outcomes. Conclusion: NLR, PLR, PIV and SII have predictive value of efficacy and NLR ≥3.65 suggests a poor prognosis following immunotherapy in advanced GC.

CXCL family-related classification predicts prognosis and response to immunotherapy in patients with head and neck squamous cell carcinoma based on TCGA and GEO databases

Background

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent malignant cancer worldwide. The cysteine X cysteine (CXC) chemokine family contains 17 members, which are reportedly crucial for the growth, invasion, metastasis, and microenvironment of tumor cells. Although the precise functions of CXC ligands (CXCLs) in HNSCC are unclear, these proteins may play important roles in controlling tumor growth and forming the tumor immune environment.

Methods

We downloaded the RNA sequencing and matched clinicopathological data of 379 patients with HNSCC as the training set from The Cancer Genome Atlas and two datasets from the Gene Expression Omnibus for use as validation sets.

Results

Through consensus clustering, we identified two subtypes of HNSCC associated with the CXCL family, named cluster1 and cluster2. Patients with the cluster1 subtype showed favourable clinical outcomes, significant immune cell infiltration, and improved immune response signalling pathway modulation. We also developed a nomogram of CXCL family scores for therapeutic use and for predicting the overall survival (OS) of patients with HNSCC. Patients with lower scores showed longer OS and higher immune cell infiltration in their tissues.

Conclusions

We developed a new classification method for HNSCC using the CXCL gene family, which can be used clinically to evaluate the prognosis and response to immunotherapy in patients with HNSCC.

Tumorous IRE1α facilitates CD8+T cells-dependent anti-tumor immunity and improves immunotherapy efficacy in melanoma

BACKGROUND

Tumor cells frequently suffer from endoplasmic reticulum (ER) stress. Previous studies have extensively elucidated the role of tumorous unfolded protein response in melanoma cells, whereas the effect on tumor immunology and the underlying mechanism remain elusive.

METHODS

Bioinformatics, biochemical assays and pre-clinical mice model were employed to demonstrate the role of tumorous inositol-requiring transmembrane kinase/endoribonuclease 1α (IRE1α) in anti-tumor immunity and the underlying mechanism.

RESULTS

We firstly found that IRE1α signaling activation was positively associated with the feature of tumor-infiltrating lymphocytes. Then, pharmacological ER stress induction by HA15 exerted prominent anti-tumor effect in immunocompetent mice and was highly dependent on CD8+T cells, paralleled with the reshape of immune cells in tumor microenvironment via tumorous IRE1α-XBP1 signal. Subsequently, tumorous IRE1α facilitated the expression and secretion of multiple chemokines and cytokines via XBP1-NF-κB axis, leading to increased infiltration and anti-tumor capacity of CD8+T cells. Ultimately, pharmacological induction of tumorous ER stress by HA15 brought potentiated therapeutic effect along with anti-PD-1 antibody on melanoma in vivo.

CONCLUSIONS

Tumorous IRE1α facilitates CD8+T cells-dependent anti-tumor immunity and improves immunotherapy efficacy by regulating chemokines and cytokines via XBP1-NF-κB axis. The combination of ER stress inducer and anti-PD-1 antibody could be promising for increasing the efficacy of melanoma immunotherapy.

Organ specific microenvironmental MR1 expression in cutaneous melanoma

The microenvironment is an important regulator of intertumoral trafficking and activity of immune cells. Understanding how the immune system can be tailored to maintain anti-tumor killing responses in metastatic disease remains an important goal. Thus, immune mediated eradication of metastasis requires the consideration of organ specific microenvironmental cues. Using a xenograft model of melanoma metastasis in adult zebrafish, we perturbed the dynamic balance between the infiltrating immune cells in the metastatic setting using a suite of different transgenic zebrafish. We employed intravital imaging coupled with metabolism imaging (FLIM) to visualize and map the organ specific metabolism with near simultaneity in multiple metastatic lesions. Of all the MHC complexes examined for brain and skeletal metastases, we determined that there is an organ specific expression of mhc1uba (human ortholog, MR1) for both the melanoma cells and the resident and infiltrating immune cells. Specifically, immune clusters did not express mhc1uba in brain metastatic lesions in immune competent fish. Finally, the differential immune response drove organ specific metabolism where tumor glycolysis was increased in brain metastases compared to skeletal and parental lines as measured using fluorescence lifetime imaging microscopy (FLIM). As MR1 belongs to the MHC class I molecules and is a target of immunotherapeutic drugs, we believe that our data presents an opportunity to understand the relationship between organ specific tumor metabolism and drug efficacy in the metastatic setting.