Immunoscore immune checkpoint using spatial quantitative analysis of CD8 and PD-L1 markers is predictive of the efficacy of anti- PD1/PD-L1 immunotherapy in non-small cell lung cancer

Differential predictive value of resident memory CD8+T cell subpopulations in patients with non-small-cell lung cancer treated by immunotherapy

BACKGROUND

A high density of resident memory T cells (TRM) in tumors correlates with improved clinical outcomes in immunotherapy-treated patients. In most clinical studies, TRM are defined by the CD103 marker. However, it is clearly established that not all TRM express CD103, but can be defined by other markers (CD49a, CD69, etc). The frequency of these subpopulations of TRM expressing or not CD103 varies according to the location of the cancer. Little is known about their functionality and their predictive impact on response to immunotherapy. In preclinical models, only some subpopulations of TRM are associated with cancer vaccine efficacy.

METHODS

Multiparametric cytometry analyses were used to demonstrate the presence of TRM subpopulations in the lung in mice after vaccination and in fresh ex vivo human non-small cell lung cancer (NSCLC). An analysis of the T-cell repertoire of these TRM was conducted to search for their relationships. Multiplex immunofluorescence techniques were used to quantify intratumor infiltration of TRM subpopulations in two cohorts of patients with NSCLC. The impact on the clinical outcome of the TRM tumor infiltration was also investigated.

RESULTS

We identified two main TRM subpopulations in tumor-infiltrating lymphocytes derived from patients with NSCLC: one co-expressing CD103 and CD49a (double positive (DP)), and the other expressing only CD49a (simple positive (SP)); both exhibiting additional TRM surface markers like CD69. Despite higher expression of inhibitory receptors, DP TRM exhibited greater functionality compared with SP TRM. Analysis of T-cell receptor (TCR) repertoire and expression of the stemness marker TCF1 revealed shared TCRs between populations, with the SP subset appearing more progenitor-like phenotype. In the training cohort, PD-L1 (Programmed Death-Ligand 1) and TCF1+CD8+T cells predict response to anti-PD-1. In patient with NSCLC validation cohorts, only DP TRM predicted PD-1 blockade response. Multivariate analysis, including various biomarkers associated with responses to anti-PD-(L)1, such as total CD8, TCF1+CD8+T cells, and PD-L1, showed that only intratumoral infiltration by DP TRM remained significant.

CONCLUSIONS

This study highlights the non-equivalence of TRM subpopulations. The population of TRM co-expressing CD103 and CD49a appears to be the most functional and has the most significant capacity for predicting response to immunotherapy in multivariate analysis in patients with NSCLC.

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms

AIMS

To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.

METHODS

The study included samples from 50 early-stage NSCLCs. PD-L1 immunohistochemistry (IHC) stained slides (clone SP263) were scored manually and with two different AI tools (PathAI and Navify Digital Pathology) by three pathologists. TILs were digitally assessed on H&E and CD8 IHC stained sections with two different algorithms (PathAI and Navify Digital Pathology, respectively). The agreement between observers and methods for each biomarker was analysed. For PD-L1, the turn-around time (TAT) for manual versus AI-assisted scoring was recorded.

RESULTS

Agreement was higher in tumours with low PD-L1 expression regardless of the approach. Both AI-powered tools identified a significantly higher number of cases equal or above 1% PD-L1 tumour proportion score as compared with manual scoring (p=0.00015), a finding with potential therapeutic implications. Regarding TAT, there were significant differences between manual scoring and AI use (p value <0.0001 for all comparisons). The total TILs density with the PathAI algorithm and the total density of CD8+ cells with the Navify Digital Pathology software were significantly correlated (τ=0.49 (95% CI 0.37, 0.61), p value<0.0001).

CONCLUSIONS

This preliminary study supports the use of AI algorithms for the scoring of PD-L1 and TILs in patients with NSCLC.

Nanomaterial-based cancer immunotherapy: enhancing treatment strategies

Cancer immunotherapy has emerged as a pivotal approach for treating various types of cancer, incorporating strategies such as chimeric antigen receptor T-cell (CAR-T) therapy, immune checkpoint blockade therapy, neoantigen peptides, mRNA vaccines, and small molecule modulators. However, the clinical efficacy of these therapies is frequently constrained by significant adverse effects and limited therapeutic outcomes. In recent years, the integration of nanotechnology into cancer immunotherapy has gained considerable attention, showcasing notable advantages in drug delivery, targeted accumulation, controlled release, and localized administration. This review focuses on nanomaterial-based immunotherapeutic strategies, particularly the development and application of nanocarriers such as liposomes, lipid nanoparticles, polymeric nanoparticles, and self-assembling scaffolds. We examine how these strategies can enhance the efficacy of cancer immunotherapy while minimizing adverse effects and analyze their potential for clinical translation.

The Expression of PDL-1 and PD1 in the Microenvironment of Oral Squamous Cell Carcinoma

OBJECTIVE

Immune checkpoint proteins, especially PD-1/PD-L1, play a vital role in controlling the intensity and duration of the immune response. However, cancer cells often over-expression PDL-1 on their surfaces, which leads to the permanent activation of the PD-1/PDL-1 pathway and exhaustion of T cells and creates a resistant tumor microenvironment. This study aimed to analyze PD-1, PD-L1 expression in tumor cells (PDL-1/TC) and in Tumor-Infiltrating Lymphocytes (PDL-1/TILS) of OSCC patients and associated with and to correlate it with histologic grade of malignancy and clinicopathologic parameters.

METHODS

The sample consisted of 43 archived specimens of 43 patients of OSCC with Clinical features (gender, age, smoking, clinical stage) collected from medical records between 2014-2021. The intensity of PD-1, PDL-1/TC, PDL-1/TILS, positive cells were assessed by immunohistochemistry.

RESULT

PDL-1/TILS and PDL-1/TC were observed in all specimens except two cases in well-differentiated were negative. PDL-1/TILS was significant between histological grades(P=0.004<0.05). There was no significant between PDL-1/TC and PDL-1/TILS and between poorly differentiated and moderately differentiated groups' ROC P values (P=0.133, 0.340>0.05) respectively. There is a difference between PDL-1/TC and PDL-1/TILS between poorly differentiated and well-differentiated groups ROC P value (0.005, 0.028), Sensitivity (0.857, 0.857), specificity (0.765, 0.824) respectively and there is a difference between and PDL-1/TILS moderate differentiated and well-differentiated groups ROC P value (0.133,) Sensitivity (0.737), specificity (0.765). No differences between PDL-1/TC and moderate and well-differentiated groups P value (0.173). There is a significant correlation between PDL-1/TC and PDL-1/TILS with an age P>65 value (0.032) in the well-differentiated group. PDL-1/TC and PDL-1/TILS with the depth of invasion (DOI) in the well-differentiated group. No significant correlation was obtained between PDL-1/TC and PDL-1/TILS and smoking, clinical stage, and gender. No significant correlation was obtained between PD1 and the histological grades or clinicopathologic characteristics.

CONCLUSION

PDL-1/TILS and PDL-1/TC are independent prognostic factors in OSCC and PDL1-/TILS has an important role.

Comparative Analysis of Inhibitory and Activating Immune Checkpoints PD-1, PD-L1, CD28, and CD86 in Non-Melanoma Skin Cancer

The establishment of immunotherapy applying immune checkpoint inhibitors (ICI) has provided an important new option for the treatment of solid malignant diseases. However, different tumor entities show dramatically different responses to this therapy. BCC responds worse to anti-PD-1 ICIs as compared to cSCC. Differential immune checkpoint expression could explain this discrepancy and, therefore, the aim of this study was to analyze activating and inhibitory immune checkpoints in cSCC and BCC tissues. Tissue microarrays of the invasive front as well as the tumor core of BCC and cSCC samples were used to evaluate PD-1, PD-L1, CD28, and CD86 expression and their topographic distribution profiles by chromogenic immunohistochemistry. QuPath was used to determine the labeling index. The expression of PD-1, PD-L1, and CD28 was significantly higher in both the tumor core and the invasive front of cSCC samples as compared to BCC (p < 0.001). In addition, the ratios of PD-L1/CD86 (p < 0.001) and CD28/CD86 (p < 0.001) were significantly higher in cSCC. The invasive front of both tumor entities showed higher expression levels of all immune markers compared to the tumor core in both tumor entities. The significantly higher expression of PD-1, PD-L1, and CD28 in cSCC, along with the predominance of the inhibitory ligand PD-L1 as compared to the activating CD86 in cSCC, provide a potential explanation for the better objective response rates to anti-PD-1 immunotherapy as compared to BCC. Furthermore, the predominant site of interaction between the immune system and the tumor was within the invasive front in both tumor types.

The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition

Enabling the examination of cell-cell relationships in tissue, spatially resolved omics technologies have revolutionised our perspectives on cancer biology. Clinically, the development of immune checkpoint inhibitors (ICI) has advanced cancer therapeutics. However, a major challenge of effective implementation is the identification of predictive biomarkers of response. In this review we examine the potential added predictive value of spatial biomarkers of response to ICI beyond current clinical benchmarks.

Upfront Fluorouracil, Leucovorin, Oxaliplatin, and Irinotecan Plus Bevacizumab With or Without Atezolizumab for Patients With Metastatic Colorectal Cancer: Updated and Overall Survival Results of the ATEZOTRIBE Study

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.We report 4-year results of the phase II randomized AtezoTRIBE study. Eligible patients with metastatic colorectal cancer (mCRC) received first-line fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFOXIRI)/bevacizumab (control group, n = 73) or FOLFOXIRI/bevacizumab plus atezolizumab (experimental group, n = 145). We present overall survival (OS) and updated outcomes according to tumor immune-related biomarkers, both in the intention-to-treat (ITT) population and the cohort of patients with proficient mismatch repair (pMMR) tumors. Median follow-up was 45.2 months (IQR, 42.6-49.2). In the ITT population, median OS was 33.0 and 27.2 months for experimental and control groups, respectively (hazard ratio [HR], 0.78 [80% CI, 0.61 to 0.98]; P = .084). An interaction effect between Immunoscore Immune-Checkpoint (IC) and treatment arm was observed (Pint, .089), with higher benefit from atezolizumab in the Immunoscore IC-high group. In the pMMR cohort (N = 202), median OS was 30.8 and 29.2 months for experimental and control groups, respectively (HR, 0.80 [80% CI, 0.63 to 1.02]; P = .117). Interactions between treatment group and tumor mutational burden (TMB) and Immunoscore IC were reported (Pint, .043 and .092, respectively), with patients bearing TMB-high and Immunoscore IC-high tumors deriving higher benefit from the addition of atezolizumab. First-line FOLFOXIRI/bevacizumab plus atezolizumab improves OS in patients with mCRC. In the pMMR group, patients with Immunoscore IC-high and/or TMB-high tumors are identified as a subgroup of interest to further develop this treatment.

Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response.

Methods

By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts.

Results

RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups.

Conclusions

In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

A Mesoporous Gold Sensor Unveils Phospho PD-L1 in Extracellular Vesicles as a Proxy for PD-L1 Expression in Lung Cancer Tissue

Immune checkpoint inhibitors (ICIs) targeting programmed cell death ligand 1 (PD-L1), or its receptor, PD-1 have improved survival in patients with non-small-cell lung cancer (NSCLC). Assessment of PD-L1 expression requires tissue biopsy or fine needle aspiration that are currently used to identify patients most likely to respond to single agent anti-PD-1/PD-L1 therapy. However, obtaining sufficient tissue to generate a PD-L1 tissue proportion score (TPS) ≥ 50% using immunohistochemistry remains a challenge that potentially may be overcome by liquid biopsies. This study utilized a mesoporous gold sensor (MGS) assay to examine the phosphorylation status of PD-L1 in plasma extracellular vesicles (EV pPD-L1) and PD-L1 levels in plasma from NSCLC patient samples and their association with tumor PD-L1 TPS. The 3-dimensional mesoporous network of the electrodes provides a large surface area, high signal-to-noise ratio, and a superior electro-conductive framework, thereby significantly improving the detection sensitivity of PD-L1 nanosensing. Test (n = 20) (Pearson's r = 0.99) and validation (n = 45) (Pearson's r = 0.99) cohorts show that EV pPD-L1 status correlates linearly with the tumor PD-L1 TPS assessed by immunohistochemistry irrespective of the tumor stage, with 64% of patients overall showing detectable EV pPD-L1 levels in plasma. In contrast to the EV pPD-L1 results, plasma PD-L1 levels did not correlate with the tumor PD-L1 TPS score or EV pPD-L1 levels. These data demonstrate that EV pPD-L1 levels may be used to select patients for appropriate PD-1 and PD-L1 ICI therapy regimens in early, locally advanced, and advanced NSCLC and should be tested further in randomized controlled trials. Most importantly, the assay used has a less than 24h turnaround time, facilitating adoption of the test into the routine diagnostic evaluation of patients prior to therapy.

New insights into immune cells in cancer immunotherapy: from epigenetic modification, metabolic modulation to cell communication

Cancer is one of the leading causes of death worldwide, and more effective ways of attacking cancer are being sought. Cancer immunotherapy is a new and effective therapeutic method after surgery, radiotherapy, chemotherapy, and targeted therapy. Cancer immunotherapy aims to kill tumor cells by stimulating or rebuilding the body's immune system, with specific efficiency and high safety. However, only few tumor patients respond to immunotherapy and due to the complex and variable characters of cancer immune escape, the behavior and regulatory mechanisms of immune cells need to be deeply explored from more dimensions. Epigenetic modifications, metabolic modulation, and cell-to-cell communication are key factors in immune cell adaptation and response to the complex tumor microenvironment. They collectively determine the state and function of immune cells through modulating gene expression, changing in energy and nutrient demands. In addition, immune cells engage in complex communication networks with other immune components, which are mediated by exosomes, cytokines, and chemokines, and are pivotal in shaping the tumor progression and therapeutic response. Understanding the interactions and combined effects of such multidimensions mechanisms in immune cell modulation is important for revealing the mechanisms of immunotherapy failure and developing new therapeutic targets and strategies.

Outcome of First-Line Treatment With Pembrolizumab According to KRAS/TP53 Mutational Status for Nonsquamous Programmed Death-Ligand 1-High (≥50%) NSCLC in the German National Network Genomic Medicine Lung Cancer

INTRODUCTION

Programmed death-ligand 1 expression currently represents the only validated predictive biomarker for immune checkpoint inhibition in metastatic NSCLC in the clinical routine, but it has limited value in distinguishing responses. Assessment of KRAS and TP53 mutations (mut) as surrogate for an immunosupportive tumor microenvironment (TME) might help to close this gap.

METHODS

A total of 696 consecutive patients with programmed death-ligand 1-high (≥50%), nonsquamous NSCLC, having received molecular testing within the German National Network Genomic Medicine Lung Cancer between 2017 and 2020, with Eastern Cooperative Oncology Group performance status less than or equal to 1 and pembrolizumab as first-line palliative treatment, were included into this retrospective cohort analysis. Treatment efficacy and outcome according to KRAS/TP53 status were correlated with TME composition and gene expression analysis of The Cancer Genome Atlas lung adenocarcinoma cohort.

RESULTS

Proportion of KRASmut and TP53mut was 53% (G12C 25%, non-G12C 28%) and 51%, respectively. In KRASmut patients, TP53 comutations increased response rates (G12C: 69.7% versus 46.5% [TP53mut versus wild-type (wt)], p = 0.004; non-G12C: 55.4% versus 39.5%, p = 0.03), progression-free survival (G12C: hazard ratio [HR] = 0.59, p = 0.009, non-G12C: HR = 0.7, p = 0.047), and overall survival (G12C: HR = 0.72, p = 0.16, non-G12C: HR = 0.56, p = 0.002), whereas no differences were observed in KRASwt patients. After a median follow-up of 41 months, G12C/TP53mut patients experienced the longest progression-free survival and overall survival (33.7 and 65.3 mo), which correlated with high tumor-infiltrating lymphocyte densities in the TME and up-regulation of interferon gamma target genes. Proinflammatory pathways according to TP53 status (mut versus wt) were less enhanced and not different in non-G12C and KRASwt, respectively.

CONCLUSIONS

G12C/TP53 comutations identify a subset of patients with a very favorable long-term survival with immune checkpoint inhibitor monotherapy, mediated by highly active interferon gamma signaling in a proinflammatory TME.

Prognostic models for immunotherapy in non-small cell lung cancer: A comprehensive review

The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of lung cancer. Given the limited clinical benefits of immunotherapy in patients with non-small cell lung cancer (NSCLC), various predictors have been shown to significantly influence prognosis. However, no single predictor is adequate to forecast patients' survival benefit. Therefore, it's imperative to develop a prognostic model that integrates multiple predictors. This model would be instrumental in identifying patients who might benefit from ICIs. Retrospective analysis and small case series have demonstrated the potential role of these models in prognostic prediction, though further prospective investigation is required to evaluate more rigorously their application in these contexts. This article presents and summarizes the latest research advancements on immunotherapy prognostic models for NSCLC from multiple omics perspectives and discuss emerging strategies being developed to enhance the domain.

Principles of risk assessment in colon cancer: immunity is key

In clinical practice, the administration of adjuvant chemotherapy (ACT) following tumor surgical resection raises a critical dilemma for stage II colon cancer (CC) patients. The prognostic features used to identify high-risk CC patients rely on the pathological assessment of tumor cells. Currently, these factors are considered for stratifying patients who may benefit from ACT at early CC stages. However, the extent to which these factors predict clinical outcomes (i.e. recurrence, survival) remains highly controversial, also uncertainty persists regarding patients' response to treatment, necessitating further investigation. Therefore, an imperious need is to explore novel biomarkers that can reliably stratify patients at risk, to optimize adjuvant treatment decisions. Recently, we evaluated the prognostic and predictive value of Immunoscore (IS), an immune digital-pathology assay, in stage II CC patients. IS emerged as the sole significant parameter for predicting disease-free survival (DFS) in high-risk patients. Moreover, IS effectively stratified patients who would benefit most from ACT based on their risk of recurrence, thus predicting their outcomes. Notably, our findings revealed that digital IS outperformed the visual quantitative assessment of the immune response conducted by expert pathologists. The latest edition of the WHO classification for digestive tumor has introduced the evaluation of the immune response, as assessed by IS, as desirable and essential diagnostic criterion. This supports the revision of current cancer guidelines and strongly recommends the implementation of IS into clinical practice as a patient stratification tool, to guide CC treatment decisions. This approach may provide appropriate personalized therapeutic decisions that could critically impact early-stage CC patient care.

Digital Pathology for Better Clinical Practice

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

NF-κB in the Radiation Response of A549 Non-Small Cell Lung Cancer Cells to X-rays and Carbon Ions under Hypoxia

Cellular hypoxia, detectable in up to 80% of non-small cell lung carcinoma (NSCLC) tumors, is a known cause of radioresistance. High linear energy transfer (LET) particle radiation might be effective in the treatment of hypoxic solid tumors, including NSCLC. Cellular hypoxia can activate nuclear factor κB (NF-κB), which can modulate radioresistance by influencing cancer cell survival. The effect of high-LET radiation on NF-κB activation in hypoxic NSCLC cells is unclear. Therefore, we compared the effect of low (X-rays)- and high (12C)-LET radiation on NF-κB responsive genes' upregulation, as well as its target cytokines' synthesis in normoxic and hypoxic A549 NSCLC cells. The cells were incubated under normoxia (20% O2) or hypoxia (1% O2) for 48 h, followed by irradiation with 8 Gy X-rays or 12C ions, maintaining the oxygen conditions until fixation or lysis. Regulation of NF-κB responsive genes was evaluated by mRNA sequencing. Secretion of NF-κB target cytokines, IL-6 and IL-8, was quantified by ELISA. A greater fold change increase in expression of NF-κB target genes in A549 cells following exposure to 12C ions compared to X-rays was observed, regardless of oxygenation status. These genes regulate cell migration, cell cycle, and cell survival. A greater number of NF-κB target genes was activated under hypoxia, regardless of irradiation status. These genes regulate cell migration, survival, proliferation, and inflammation. X-ray exposure under hypoxia additionally upregulated NF-κB target genes modulating immunosurveillance and epithelial-mesenchymal transition (EMT). Increased IL-6 and IL-8 secretion under hypoxia confirmed NF-κB-mediated expression of pro-inflammatory genes. Therefore, radiotherapy, particularly with X-rays, may increase tumor invasiveness in surviving hypoxic A549 cells.

Clinical and Therapeutic Characteristics of Hospitalized Patients with Advanced Lung Cancer in Najran, Saudi Arabia: A Retrospective Study

BACKGROUND

Lung cancer is one of the top causes of cancer deaths globally, including in Saudi Arabia. Although several prognostic markers have been established, the clinical features and outcomes of lung cancer in Saudi Arabia are not well understood. This study aimed to describe the clinical and therapeutic characteristics of advanced lung cancer in Najran, Saudi Arabia.

METHOD

A retrospective chart review of 44 patients diagnosed with advanced lung cancer between June 2018 and September 2021 and treated at the Oncology Center of King Khalid Hospital in Najran City, Saudi Arabia. The clinicopathological features, treatment used, response, and survival outcomes were collected and analyzed.

RESULT

The mean age was 69.3 ± 10.7 years, most of them (n = 35, 79.5%) were male and older than 70 years (n = 24, 54.5%). Adenocarcinoma was the most observed cancer (n = 35, 79.5%), followed by squamous cell carcinoma in six (13.6%). Most cases (n = 42, 95.5%) were in stage IV. Epidermal growth factor receptor (EGFR) mutations were positive in two (4.5%) cases and ALK mutation was positive in two (4.5%) cases. Metastasis to pleura with pleural effusion was the common presentation (n = 41, 93%). Chemotherapy was administered as the first line in 19 cases (43.2%) while 25 cases (56.8%) received chemoimmunotherapy. The commonest chemoimmunotherapy regimen used was carboplatin-pemetrexed-pembrolizumab in 16 (36.4%), followed by carboplatin-paclitaxel-pembrolizumab in 9 (20.5%) cases. The response to initial systemic therapy was as follows disease progression, stable disease, and complete remission in 10 (22.7%), 33 (75.0%), and 1 (2.3%), respectively. Median progression-free survival was 8.7 months (interquartile range (IQR): 5.7-11.4), and the median overall survival was 12.3 months (IQR: 11.1-13.4). Among the total documented 36 (81.8%) dead cases, disease progression was the main cause of death in 25 cases (56.8%). Using chemoimmunotherapy as the first-line therapy was associated with numerical survival improvement compared to using chemotherapy alone (HR: 0.75; 95% CI: 0.39-1.46) however, it was not statistically significant (p = 0.397).

CONCLUSION

In this study, the majority of lung cancer patients were male and over 70 years old. Adenocarcinoma was the most common histological type. Metastasis to pleura with pleural effusion was the common presentation. The most common treatment used was chemoimmunotherapy with a regimen of carboplatin-pemetrexed-pembrolizumab. Addressing the possible causes of delayed diagnosis of lung cancer is crucial for improved survival outcomes.

Prognostic and predictive role of immune microenvironment in colorectal cancer

Colorectal cancer (CRC) represents a molecularly heterogeneous disease and one of the most frequent causes of cancer-related death worldwide. The traditional classification of CRC is based on pathomorphological and molecular characteristics of tumor cells (mucinous, ring-cell carcinomas, etc.), analysis of mechanisms of carcinogenesis involved (chromosomal instability, microsatellite instability, CpG island methylator phenotype) and mutational statuses of commonly altered genes (KRAS, NRAS, BRAF, APC, etc.), as well as expression signatures (CMS 1-4). It is also suggested that the tumor microenvironment is a key player in tumor progression and metastasis in CRC. According to the latest data, the immune microenvironment can also be predictive of the response to immune checkpoint inhibitors. In this review, we highlight how the immune environment influences CRC prognosis and sensitivity to systemic therapy.

Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging

The spatial localisation of immune cells within tumours are key to understand the intercellular communications that can dictate clinical outcomes. Here, we demonstrate an analysis pipeline for highly multiplexed CODEX data to phenotype and profile spatial features and interactions in NSCLC patients that subsequently received PD1 axis immunotherapy. We found that regulatory T cells (Tregs) are enriched in non-responding patients and this was consistent with their localization within stromal and peripheral tumour-margins. Proximity-based interactions between Tregs and both monocytes (p = 0.009) and CD8+ T cells (p = 0.009) were more frequently found in non-responding patients, while macrophages were more frequently located in proximity to HLADR+ tumour cells (p = 0.01) within responding patients. Cellular neighbourhoods analysis indicated that both macrophages (p = 0.003) and effector CD4+ T cells (p = 0.01) in mixed tumour neighbourhoods, as well as CD8+ T cells (p = 0.03) in HLADR+ tumour neighbourhoods were associated with favorable clinical response. Evaluation of the inferred regulatory functions between immune cells relative to the tumour suggested that macrophages exhibit an immunosuppressive phenotype against both CD4+ and CD8+ T cells, and that this association scores more highly in ICI refractory patients. These spatial patterns are associated with overall survival in addition to ICI response and may thus indicate features for the functional understanding of the tumour microenvironment.

Immunosurveillance in clinical cancer management

The progression of cancer involves a critical step in which malignant cells escape from control by the immune system. Antineoplastic agents are particularly efficient when they succeed in restoring such control (immunosurveillance) or at least establish an equilibrium state that slows down disease progression. This is true not only for immunotherapies, such as immune checkpoint inhibitors (ICIs), but also for conventional chemotherapy, targeted anticancer agents, and radiation therapy. Thus, therapeutics that stress and kill cancer cells while provoking a tumor-targeting immune response, referred to as immunogenic cell death, are particularly useful in combination with ICIs. Modern oncology regimens are increasingly using such combinations, which are referred to as chemoimmunotherapy, as well as combinations of multiple ICIs. However, the latter are generally associated with severe side effects compared with single-agent ICIs. Of note, the success of these combinatorial strategies against locally advanced or metastatic cancers is now spurring successful attempts to move them past the postoperative (adjuvant) setting to the preoperative (neoadjuvant) setting, even for patients with operable cancers. Here, the authors critically discuss the importance of immunosurveillance in modern clinical cancer management.

Predictive role of intracranial PD-L1 expression in a real-world cohort of NSCLC patients treated with immune checkpoint inhibition following brain metastasis resection

BACKGROUND

Emerging evidence suggests that treatment of NSCLC brain metastases with immune checkpoint inhibitors (ICIs) is associated with response rates similar to those of extracranial disease. Programmed death-ligand 1 (PD-L1) tumor proportion score (TPS) serves as a predictive biomarker for ICI response. However, the predictive value of brain metastasis-specific (intracranial) PD-L1 TPS is not established. We investigated the role of intra- and extracranial PD-L1 TPS in NSCLC patients treated with ICI following brain metastasis resection.

METHODS

Clinical data from NSCLC patients treated with ICI following brain metastasis resection (n = 64) were analyzed. PD-L1 TPS of brain metastases (n = 64) and available matched extracranial tumor tissue (n = 44) were assessed via immunohistochemistry. Statistical analyses included cut point estimation via maximally selected rank statistics, Kaplan-Meier estimates, and multivariable Cox regression analysis for intracranial progression-free survival (icPFS), extracranial progression-free survival (ecPFS), and overall survival (OS).

RESULTS

PD-L1 expression was found in 54.7% of brain metastases and 68.2% of extracranial tumor tissues, with a median intra- and extracranial PD-L1 TPS of 7.5% (0 - 50%, IQR) and 15.0% (0 - 80%, IQR), respectively. In matched tissue samples, extracranial PD-L1 TPS was significantly higher than intracranial PD-L1 TPS (p = 0.013). Optimal cut points for intracranial and extracranial PD-L1 TPS varied according to outcome parameter assessed. Notably, patients with a high intracranial PD-L1 TPS (> 40%) exhibited significantly longer icPFS as compared to patients with a low intracranial PD-L1 TPS (≤ 40%). The cut point of 40% for intracranial PD-L1 TPS was independently associated with OS, icPFS and ecPFS in multivariable analyses.

CONCLUSION

Our study highlights the potential role of intracranial PD-L1 TPS in NSCLC, which could be used to predict ICI response in cases where extracranial tissue is not available for PD-L1 assessment as well as to specifically predict intracranial response.

Clinicopathological characteristics correlated with programmed cell death-ligand 1 expression in advanced lung adenocarcinoma

Background

Recent studies have shown that immune checkpoint inhibitors (ICIs) targeting programmed cell death-ligand 1 (PD-L1) have potential benefits in patients with non-small cell lung cancer (NSCLC) subgroups, while the clinicopathological characteristics associated with PD-L1 expression have not been well established. The purpose of this study was to detect the expression level of PD-L1 in tumor tissues of patients with advanced lung adenocarcinoma (ADC) and analyze its possible relationship with clinicopathological characteristics, so as to identify the predictors of PD-L1 expression.

Methods

This retrospective study was conducted by analyzing the clinicopathological and imaging characteristics of hospitalized advanced lung ADC patients with PD-L1 available data and admitted to the respiratory department of our hospital. The expression level of PD-L1 in fresh-frozen tumor tissue samples of 136 advanced ADC patients was analyzed by immunohistochemistry. The patients were divided into positive and negative groups based on a cut-off of 1% PD-L1 expression level. Subsequently, the significant correlation between PD-L1 levels and clinicopathological features were evaluated. The predictive performance of clinicopathological characteristics on PD-L1 expression was evaluated and the optimal cut-off values were identified by plotting the receiver operating characteristic (ROC) curve.

Results

The expression level of PD-L1 was related to sex, clinical stage, serum carcinoembryonic antigen (CEA), neuron specific enolase (NSE), white blood cell (WBC), and tumor (T) and metastasis (M) stage. Multivariate logistic regression analysis showed the CEA, NSE, T stage, and WBC were independent predictors of PD-L1 positive expression in lung ADC patients. The ROC curve suggested the model combining CEA with NSE [area under the curve (AUC) =0.815] could better predict the expression levels of PD-L1. The optimal cut-off values for identifying advanced lung ADC patients with PD-L1 positive were CEA ≤13.38 ng/mL and NSE ≤42.35 ng/mL, with sensitivity and specificity of 85.4% and 55.6%, and 92.7% and 32.1%, respectively.

Conclusions

Some commonly used clinicopathological features are related to the histological expression of PD-L1. The serum CEA, NSE, T stage, and WBC values can be used as indicators to predict the expression level of PD-L1 in advanced lung ADC, and are used as predictors to evaluate the efficacy of ICIs before treatment.

Multi-Institutional Evaluation of Pathologists' Assessment Compared to Immunoscore

BACKGROUND

The Immunoscore (IS) is a quantitative digital pathology assay that evaluates the immune response in cancer patients. This study reports on the reproducibility of pathologists' visual assessment of CD3+- and CD8+-stained colon tumors, compared to IS quantification.

METHODS

An international group of expert pathologists evaluated 540 images from 270 randomly selected colon cancer (CC) cases. Concordance between pathologists' T-score, corresponding hematoxylin-eosin (H&E) slides, and the digital IS was evaluated for two- and three-category IS.

RESULTS

Non-concordant T-scores were reported in more than 92% of cases. Disagreement between semi-quantitative visual assessment of T-score and the reference IS was observed in 91% and 96% of cases before and after training, respectively. Statistical analyses showed that the concordance index between pathologists and the digital IS was weak in two- and three-category IS, respectively. After training, 42% of cases had a change in T-score, but no improvement was observed with a Kappa of 0.465 and 0.374. For the 20% of patients around the cut points, no concordance was observed between pathologists and digital pathology analysis in both two- and three-category IS, before or after training (all Kappa < 0.12).

CONCLUSIONS

The standardized IS assay outperformed expert pathologists' T-score evaluation in the clinical setting. This study demonstrates that digital pathology, in particular digital IS, represents a novel generation of immune pathology tools for reproducible and quantitative assessment of tumor-infiltrated immune cell subtypes.

Light on life: immunoscore immune-checkpoint, a predictor of immunotherapy response

In the last decade, a plethora of immunotherapeutic strategies have been designed to modulate the tumor immune microenvironment. In particular, immune checkpoint (IC) blockade therapies present the most promising advances made in cancer treatment in recent years. In non-small cell lung cancer (NSCLC), biomarkers predicting response to IC treatments are currently lacking. We have recently identified Immunoscore-IC, a powerful biomarker that predicts the efficiency of immune-checkpoint inhibitors (ICIs) in NSCLC patients. Immunoscore-IC is an in vitro diagnostic assay that quantifies densities of PD-L1+, CD8+ cells, and distances between CD8+ and PD-L1+ cells in the tumor microenvironment. Immunoscore-IC can classify responder vs non-responder NSCLC patients for ICIs therapy and is revealed as a promising predictive marker of response to anti-PD-1/PD-L1 immunotherapy in these patients. Immunoscore-IC has also shown a significant predictive value, superior to the currently used PD-L1 marker. In colorectal cancer (CRC), the addition of atezolizumab to first-line FOLFOXIRI plus bevacizumab improved progression-free survival (PFS) in patients with previously untreated metastatic CRC. In the AtezoTRIBE trial, Immunoscore-IC emerged as the first biomarker with robust predictive value in stratifying pMMR metastatic CRC patients who critically benefit from checkpoint inhibitors. Thus, Immunoscore-IC could be a universal biomarker to predict response to PD-1/PD-L1 checkpoint inhibitor immunotherapy across multiple cancer indications. Therefore, cancer patient stratification (by Immunoscore-IC), based on the presence of T lymphocytes and PD-L1 potentially provides support for clinicians to guide them through combination cancer treatment decisions.

Overexpression of SERPINA3 suppresses tumor progression by modulating SPOP/NF‑κB in lung cancer

The pathogenesis mechanism of lung cancer is very complex, with high incidence and mortality. Serpin family A member 3 (SERPINA3) expression levels were reduced in the sera of patients with lung cancer and may be a candidate diagnostic and prognostic survival biomarker in lung cancer, as previously reported. However, the detailed biological functions of SERPINA3 in the pathogenesis of lung cancer remain unknown. In the present study, it was aimed to explore the effects of SERPINA3 on the occurrence of lung cancer. SERPINA3 expression was assessed using bioinformatics database analysis and experimental detection. Then, the biological effects of SERPINA3 were investigated in a cell culture system and a xenograft model of human lung cancer. The potential regulatory mechanism of SERPINA3 in lung cancer was explored by data‑independent acquisition mass spectrometry (DIA‑MS) detection and further validated by western blotting (WB). The results indicated that SERPINA3 expression levels were significantly downregulated in lung cancer tissues and cell lines. At the cellular level, it was revealed that overexpressed SERPINA3 inhibited cell growth, proliferation, migration and invasion and promoted the apoptosis of lung cancer cells. Moreover, overexpressed SERPINA3 enhanced the sensitivity of lung cancer cells to osimertinib. In vivo, a xenograft model of human lung cancer was established with BALB/c nude mice. After the injection of A549 cells, the tumor growth of the tumor‑bearing mice in the SERPINA3‑overexpressing group increased more slowly, and the tumor volume was smaller than that in the empty‑vector group. Mechanistically, a total of 65 differentially expressed proteins were identified. It was found that the speckle‑type POZ protein (SPOP) was significantly upregulated in SERPINA3‑overexpressing H157 cells using DIA‑MS detection and analysis. WB validation showed that SPOP expression increased, and NF‑kappaB (NF‑κB) p65 was inhibited in cell lines and tumor tissues of mice when SERPINA3 was overexpressed. The present findings suggest that SERPINA3 is involved in the development of lung cancer and has an antineoplastic role in lung cancer.