Ultrasensitive PD-L1-Expressing Exosome Immunosensors Based on a Chemiluminescent Nickel-Cobalt Hydroxide Nanoflower for Diagnosis and Classification of Lung Adenocarcinoma.

Programmed death ligand-1 (PD-L1)-expressing exosomes are considered a potential marker for diagnosis and classification of lung adenocarcinoma (LUAD). There is an urgent need to develop highly sensitive and accurate chemiluminescence (CL) immunosensors for the detection of PD-L1-expressing exosomes. Herein, N-(4-aminobutyl)-N-ethylisopropanol-functionalized nickel-cobalt hydroxide (NiCo-DH-AA) with a hollow nanoflower structure as a highly efficient CL nanoprobe was synthesized using gold nanoparticles as a "bridge". The resulting NiCo-DH-AA exhibited a strong and stable CL emission, which was ascribed to the exceptional catalytic capability and large specific surface area of NiCo-DH, along with the capacity of AuNPs to facilitate free radical generation. On this basis, an ultrasensitive sandwich CL immunosensor for the detection of PD-L1-expressing exosomes was constructed by using PD-L1 antibody-modified NiCo-DH-AA as an effective signal probe and rabbit anti-CD63 protein polyclonal antibody-modified carboxylated magnetic bead as a capture platform. The immunosensor demonstrated outstanding analytical performance with a wide detection range of 4.75 × 103-4.75 × 108 particles/mL and a low detection limit of 7.76 × 102 particles/mL, which was over 2 orders of magnitude lower than the reported CL method for detecting PD-L1-expressing exosomes. Importantly, it was able to differentiate well not only between healthy persons and LUAD patients (100% specificity and 87.5% sensitivity) but also between patients with minimally invasive adenocarcinoma and invasive adenocarcinoma (92.3% specificity and 52.6% sensitivity). Therefore, this study not only presents an ultrasensitive and accurate diagnostic method for LUAD but also offers a novel, simple, and noninvasive approach for the classification of LUAD.

Analysis of PD-1/PD-L1 variations in lung cancer and association with immunotherapeutic efficacy and prognosis: A nonrandomized controlled trial.

INTRODUCTION: This study aims to explore Programmed Death Receptor-1 (PD-1) and Programmed Death Ligand-1 (PD-L1) variations in Lung Cancer (LC) tissues and Peripheral Blood (PPB) and their association with immunotherapy efficacy and prognosis. METHOD: 72 patients with LC were included in the LC group and 39 patients with concurrent benign lung disease were included in the benign group. PD-1/PDL-1 was compared in PPB and lung tissue. All LC patients were treated with immunotherapy. The relationship between PD-1/PDL-1 in LC tissue and PPB and immunotherapy efficacy was analyzed. Patients were divided into death and survival groups, and PD-1/PDL-1 in tumor tissues and PPB were compared. RESULTS: The authors found that PD-1 and PDL-1 positive expression in lung tissue and PPB in LC patients was elevated. Combined detection of PD-1 and PDL-1 was effective in diagnosing LC and evaluating the prognosis of LC patients. PD-1 and PDL-1 positive expression was reduced after disease remission while elevated in dead patients. The 3-year survival rate of patients with PD-1 positive expression was 45.45 % (25/55), which was lower (82.35 %, 14/17) than those with PD-1 negative expression. The 3-year survival rate of patients with positive and negative expression of PDL-1 was 48.78 % (20/41) and 61.29 % (19/31), respectively. DISCUSSION: The present results demonstrated that PD-1 and PDL-1 are abnormal in cancer tissue and PPB of LC patients. The combined detection of PD-1 and PDL-1 has diagnostic value for LC and evaluation value for the efficacy and prognosis of immunotherapy.

PD-L1 Immunohistochemistry in Gastric Cancer: Comparison of Combined Positive Score and Tumor Area Positivity Across 28-8, 22C3, and SP263 Assays.

PURPOSE: The clinical application of PD-L1 immunohistochemistry (IHC) testing is complicated by the availability of multiple IHC assays, scoring algorithms, and cutoffs. This study assessed the analytical comparability of three commercially available PD-L1 assays and two scoring algorithms used to assess PD-L1 status in gastric cancer (GC) samples. METHODS: Serial sections of 100 resected GC samples, with PD-L1 expression levels across the dynamic range, were stained with three in vitro diagnostic-grade PD-L1 assays (28-8, 22C3, and SP263). Three trained pathologists blindly and independently scored slides using combined positive score (CPS) and tumor area positivity (TAP) algorithms. Comprehensive statistical analyses were performed to evaluate analytical concordance. Digital image analysis (DIA) was used to objectively compare the technical performance of each assay by simulating CPS and TAP. RESULTS: Comparable staining patterns were observed with these three PD-L1 assays. Despite discernible variation in staining intensity, reproducible evaluations of PD-L1 positivity were observed. Inter- and intra-assay assessments of all three assays, using either CPS or TAP and the same PD-L1 cutoffs, demonstrated moderate to almost-perfect (interassay Cohen's kappa [κ] range, 0.47-0.83) and substantial to almost-perfect (intra-assay κ range, 0.77-1.00) agreement. Interpathologist assessment exhibited a significant level of concordance (intraclass correlation coefficient ≥0.92). No difference in technical performance was observed using DIA. CONCLUSION: This study highlights analytical concordance in PD-L1 testing between three major PD-L1 assays when TAP and CPS are applied. Comparability of the technical assay performance was further supported by independent DIA. These observations support cross-application flexibility of the different PD-L1 assays and scoring algorithms to characterize PD-L1 expression in GC.

Prognostic value of programmed cell death ligand 1 (PD-L1) expression in patients with stage III non-small cell lung cancer under different treatment types: a retrospective study.

OBJECTIVE: Currently programmed cell death protein 1 (PD-1) inhibitors in combination with other therapies are being evaluated to determine their efficacy in cancer treatment. However, the effect of PD-ligand (L) 1 expression on disease outcomes in stage III (EC III) non-small cell lung cancer is not completely understood. Therefore, this study aimed to assess the influence of PD-L1 expression on the outcomes of EC III non-small cell lung cancer. METHODS: This study was conducted on patients diagnosed with EC III non-small cell lung cancer who underwent treatment at a tertiary care hospital. PD-L1 expression was determined using immunohistochemical staining, all patients expressed PD-L1. Survival was estimated using the Kaplan-Meier method. Relationships between variables were assessed using Cox proportional regression models. RESULTS: A total of 49 patients (median age=69 years) with EC III non-small cell lung cancer and PD-L1 expression were evaluated. More than half of the patients were men, and most were regular smokers. The patients were treated with neoadjuvant chemotherapy, surgery, or sequential or combined chemotherapy and radiotherapy. The median progression-free survival of the entire cohort was 14.2 months, and the median overall survival was 20 months. There was no significant association between PD-L1 expression and disease progression, clinical characteristics, or overall survival. CONCLUSIONS: PD-L1 expression was not correlated with EC III non-small cell lung cancer outcomes. Whether these findings differ from the association with immune checkpoint inhibitors remains to be addressed in future studies.

PD-L1 expression in ovarian clear cell carcinoma using the 22C3 pharmDx assay.

BACKGROUND: Ovarian clear cell carcinoma (OCCC), well known for its chemoresistance to platinum-based chemotherapy, exhibited a good response in clinical trials of anti-PD-1/PD-L1 inhibitors. By assessing PD-L1 expression, we sought to determine the potential therapeutic benefit of PD-1/PD-L1 inhibitors in OCCC. METHODS AND RESULTS: The retrospective study included 152 individuals with OCCC between 2019 and 2022 at Peking Union Medical College Hospital. Paired tumors of primary versus recurrent lesions (17 pairs from 15 patients) or primary versus metastatic lesions (11 pairs from 9 patients) were also included. The 22C3 pharmDx assay and whole sections were used for PD-L1 immunohistochemical staining. Pathologists with experience in premarket clinical trials evaluated PD-L1 expression based on various diagnostic criteria (TPS 1%, CPS 1, or CPS 10). The number and percentage of positive PD-L1 cases were 34 (22.4%, TPS ≥ 1%) and 59 (38.8%, CPS ≥ 1), respectively. Thirty-three (21.7%) of the cases had high PD-L1 expression (CPS ≥ 10). Half of the platinum-resistant patients (11/22) were PD-L1 positive (CPS ≥ 1). In addition, positive PD-L1 expression (CPS ≥ 1) was related to clinicopathological characteristics that represented a worse prognosis, such as advanced stages, lymph node metastasis, and distant metastasis (p = 0.032, p < 0.001 and p = 0.003, separately). PD-L1 was expressed equally or more in the recurrent lesion compared with its matched primary lesion. CONCLUSIONS: In conclusion, anti-PD-1/PD-L1 inhibitors are a promising therapeutic choice for OCCC. For evaluation of PD-L1 expression, CPS is more recommended than TPS. Evaluation of recurrent lesion was still suitable and predictive when the primary tumor tissue was not available. Distant metastatic lesions can serve as alternative samples for PD-L1 evaluation, while usage of lymphatic metastatic lesions is not recommended.

Prognostic value of PD-L1 expression in patients with anal cancer: a meta-analysis.

Background: The present meta-analysis was performed to evaluate the prognostic and clinicopathological significance of PD-L1 in anal cancer (AC). Methods: Hazard ratios (HRs) and 95% CIs regarding overall survival (OS) and progression-free survival (PFS) were calculated based on PD-L1 levels. Results: According to the combined data, PD-L1 showed no significant relationship with OS (HR = 0.76; 95% CI = 0.35-1.67; p = 0.502) or PFS (HR = 0.88; 95% CI = 0.35-2.33; p = 0.789) in patients with AC. Based on subgroup analysis, PD-L1 overexpression significantly predicted prolonged OS (HR = 0.38; 95% CI = 0.17-0.84; p = 0.017) in tumor node metastasis stages I-III and inferior PFS (HR = 2.73; 95% CI = 1.32-5.65; p = 0.007) in patients with stage I-IV AC. Conclusion: PD-L1 level assessed by immunohistochemistry did not significantly predict survival outcomes in AC cases.

[Box: see text].

A study on expression of programmed death ligand-1 in small cell lung carcinoma and correlation with clinicopathological parameters.

Small cell lung carcinoma (SCLC) is characterized by rapid growth and an aggressive clinical course. Standard therapy regimes have limited effects on disease course; therefore the prognosis of SCLC is poor. In the current study, the frequency of programmed death ligand 1 (PD-L1) expression in SCLC and its correlation with clinico-pathological features were evaluated. The study included 100 cases of SCLC wherein testing for PD-L1 was done with the SP263 clone on the Ventana benchmark XT system. Cases with > 1% PD-L1 expression in tumour cells or immune cells were categorized as positive. PD-L1 expression was identified in 14% of cases using the cut-off of ≥ 1%. The tumour proportion score was 10% and the immune proportion score was 9.78% using a cut-off of ≥ 1%. PD-L1 positive expression was more frequent in the male population with age > 40 years. All the patients with positive PD-L1 expression were smokers. In the PD-L1 positive group, presence of necrosis was identified in 71.4% of cases and when compared with the PD-L1 negative subgroup this finding was statistically significant (p = 0.010). Personalized targeted therapy for cases of SCLC is still under evaluation. The use of immunotherapeutic targets, such as PD-L1, may help to define a new treatment strategy for SCLC. Development of new treatment strategies may improve prognosis and survival.

Clinicopathological characteristics and tumor infiltrating immune cells associations of PD-L1 tumor expression in non-small cell lung cancer patients.

AIM: Our study aimed to perform on Moroccan patients' non-small cell lung carcinoma (NSCLC) concerning the relationship between PD-L1 tumor expression, clinicopathological features and tumor infiltrating immune cells (ICs). METHODS: This is a retrospective study (2019 to 2021) conducted on samples from Moroccan patients with NSCLC at the Pathological Anatomy Laboratory of Ibn Rochd University Hospital in Casablanca. Eligible participants for our study had to meet the following predefined criteria: age ≥18 years, histologically confirmed NSCLC, no prior therapeutic interventions, availability of clinical and pathological data, and a usable tumor sample for determining PD-L1 status. Exclusion criteria applied to patients with other types of lung cancer and unusable tumor samples. The evaluation of tumor and immune expression of PD-L1 was performed using immunohistochemistry (IHC), with the 22C3 clone on the Dako Autostainer Link 48 platform. Tumor PD-L1 expression was categorized into 3 levels: TPS <1% (negative expression), TPS 1-49% (low expression), and TPS ≥50% (high expression). ICs infiltrating the tumor expressing PD-L1 were considered positive when more than 1% of positive ICs were present. RESULTS: Among the 316 analyzed samples, 56.6% showed a negative expression of PD-L1, 16.8% displayed a low expression of PD-L1, and 26.6% exhibited a strong expression. Regarding the histological type, among patients with TPS ≥ 50%, 25.8% had adenocarcinoma. Among patients with TPS ≥ 50%, 24.81% were smokers. PD-L1 was also strongly expressed in the lung (28.2%) and bronchi (26.5%). PD-L1 expression (TPS ≥ 50%) was observed in 35.29% of early-stage patients. Concerning tumor cells (TCs), 27.5% of tumors infiltrated by ICs had TPS ≥ 50%. Furthermore, coexpression of PD-L1 on both TCs and ICs infiltrating the tumor was found in 27.8% of tumors. Statistical analysis demonstrated a significant association between tumor PD-L1 expression and smoking status (P=0.019). However, no significant difference was observed between PD-L1 expression and the presence of ICs infiltrating the tumor (P=0.652), as well as the IHC expression of PD-L1 on ICs (P=0.259). CONCLUSION: Our results demonstrate a significant association between PD-L1 expression and smoking status. However, no significant association was observed between PD-L1 expression and the presence of infiltrating ICs, nor with the IHC expression of PD-L1 on ICs. Our data underscore the importance of participating in the study of specific factors influencing PD-L1 expression in patients with NSCLC.

Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.

PURPOSE: Evaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: The AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS. RESULTS: In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS <1% group better than the pathologist's reading, with the TPS ≥50% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS <1% group). CONCLUSION: PD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.

Effects of Apatinib combined with Temozolomide on levels of sPD-1 and sPD-L1 in patients with drug-resistant recurrent glioblastoma.

OBJECTIVE: This study aimed to explore the effects of Apatinib combined with Temozolomide (TMZ) on the levels of Soluble PD-1 (sPD-1) and Soluble Programmed Death-1 Ligand (sPD-L1) in patients with drug-resistant recurrent Glioblastoma (GB). STUDY DESIGN: A total of 69 patients with recurrent GB from September 2020 to March 2022 were recruited and assigned to the control group (n = 34) and observation group (n = 35) according to different treatment options after tumor recurrence. The control group was treated with TMZ, and the observation group was treated with Apatinib combined with TMZ. Levels of sPD-1 and spd-l1, clinical efficacy, survival time and adverse reactions were observed and compared between the two groups. RESULTS: General data including gender, age, body mass index, and combined diseases indicated no statistical significance between groups (p > 0.05). Before the intervention, sPD-1 and sPD-L1 levels were not significantly different in the two groups (p > 0.05). After interventions, levels of PD-1 and sPD-L1 levels decreased significantly (p < 0.05). The objective remission rate and clinical benefit rate of the observation group were higher and overall survival and progression-free survival were longer than those of the control group (p < 0.05). No significant difference was observed in major adverse reactions among patients (p > 0.05). CONCLUSIONS: Apatinib combined with TMZ is safe and effective in the treatment of recurrent GB. The combined application of the two can reduce the levels of sPD-1 and sPD-L1, which has important clinical application value.

A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score?

Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-assisted systems could help pathologists determine the tumor proportion score (TPS). In this study, we developed an AI model to calculate the TPS of the PD-L1 22C3 assay and evaluated whether and how this AI-assisted system could help pathologists determine the TPS and analyze how AI-assisted systems could affect pathologists' assessment accuracy. We assessed the 4 methods of the AI-assisted system: (1 and 2) pathologists first assessed and then referred to automated AI scoring results (1, positive tumor cell percentage; 2, positive tumor cell percentage and visualized overlay image) for final confirmation, and (3 and 4) pathologists referred to the automated AI scoring results (3, positive tumor cell percentage; 4, positive tumor cell percentage and visualized overlay image) while determining TPS. Mixed-model analysis was used to calculate the odds ratios (ORs) with 95% CI for AI-assisted TPS methods 1 to 4 compared with pathologists' scoring. For all 584 samples of the tissue microarray, the OR for AI-assisted TPS methods 1 to 4 was 0.94 to 1.07 and not statistically significant. Of them, we found 332 discordant cases, on which the pathologists' judgments were inconsistent; the ORs for AI-assisted TPS methods 1, 2, 3, and 4 were 1.28 (1.06-1.54; P = .012), 1.29 (1.06-1.55; P = .010), 1.28 (1.06-1.54; P = .012), and 1.29 (1.06-1.55; P = .010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS method compared with the others was 0.99 to 1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases in which pathologists had difficulty determining the PD-L1 TPS.

A Pipeline for Evaluation of Machine Learning/Artificial Intelligence Models to Quantify Programmed Death Ligand 1 Immunohistochemistry.

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, programmed death ligand 1 (PD-L1) IHC is used as a companion diagnostic. However, the scoring of PD-L1 is complicated by its expression in cancer and immune cells. Separation of cancer and noncancer regions is needed to calculate tumor proportion scores (TPS) of PD-L1, which is based on the percentage of PD-L1-positive cancer cells. Evaluation of PD-L1 expression requires highly experienced pathologists and is often challenging and time-consuming. Here, we used a multi-institutional cohort of 77 lung cancer cases stained centrally with the PD-L1 22C3 clone. We developed a 4-step pipeline for measuring TPS that includes the coregistration of hematoxylin and eosin, PD-L1, and negative control (NC) digital slides for exclusion of necrosis, segmentation of cancer regions, and quantification of PD-L1+ cells. As cancer segmentation is a challenging step for TPS generation, we trained DeepLab V3 in the Visiopharm software package to outline cancer regions in PD-L1 and NC images and evaluated the model performance by mean intersection over union (mIoU) against manual outlines. Only 14 cases were required to accomplish a mIoU of 0.82 for cancer segmentation in hematoxylin-stained NC cases. For PD-L1-stained slides, a model trained on PD-L1 tiles augmented by registered NC tiles achieved a mIoU of 0.79. In segmented cancer regions from whole slide images, the digital TPS achieved an accuracy of 75% against the manual TPS scores from the pathology report. Major reasons for algorithmic inaccuracies include the inclusion of immune cells in cancer outlines and poor nuclear segmentation of cancer cells. Our transparent and stepwise approach and performance metrics can be applied to any IHC assay to provide pathologists with important insights on when to apply and how to evaluate commercial automated IHC scoring systems.

Considerable interlaboratory variation in PD-L1 positivity for head and neck squamous cell carcinoma in the Netherlands- A nationwide evaluation study.

AIMS: Patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC) are eligible for first-line immune checkpoint inhibition if their tumour is positive for programmed death ligand 1 (PD-L1) determined by the combined positive score (CPS). This nationwide study, using real-world data, investigated the developing PD-L1 testing landscape in the first 3 years after introduction of the test in HNSCC and examined interlaboratory variation in PD-L1 positivity rates. METHODS: Pathology reports of HNSCC patients mentioning PD-L1 were extracted from the Dutch Pathology Registry (Palga). Tumour and PD-L1 testing characteristics were analysed per year and interlaboratory variation in PD-L1 positivity rates was assessed using funnel plots with 95% confidence limits around the overall mean. RESULTS: A total of 817 PD-L1 tests were reported in 702 patients among 19 laboratories; 85.2% of the tests on histological material were stated to be positive. The national PD-L1 positivity rate differed significantly per year during the study period (79.7-89.9%). The use of the recommended 22C3 antibody increased from 59.9 to 74.3%. A total of 673 PD-L1 tests on histological material from 12 laboratories were analysed to investigate interlaboratory variation. Four (33%) deviated significantly from the national mean of PD-L1-positive cases using CPS ≥ 1 cut-off, while two (17%) deviated significantly for CPS ≥ 20 cut-off. CONCLUSION: In the first 3 years of PD-L1 assessment in HNSCC, the testing landscape became more uniform. However, interlaboratory variation in PD-L1 positivity rates between Dutch laboratories was substantial. This implies that there is a need for further test standardisation to reduce this variation.

Coaxial dual-path electrochemical biosensing and logic strategy-based detection of lung cancer-derived exosomal PD-L1.

Exosomal programmed death ligand-1 (ExoPD-L1) is a vital marker of immune activation in the early stages of tumor therapy and it can inhibit anti-tumor immune responses. However, due to the low expression of ExoPD-L1 in cancer cells, it is difficult to perform highly sensitive assays and accurately differentiate cancer sources. Therefore, we constructed a coaxial dual-path electrochemical biosensor for highly accurate identification and detection of ExoPD-L1 from lung cancer based on chemical-biological coaxial nanomaterials and nucleic acid molecular signal amplification strategies. The measurements showed that the detected ExoPD-L1 concentrations ranged from 6 × 102 particles per mL to 6 × 108 particles per mL, and the detection limit was 310 particles per mL. Compared to other sensors, the electrochemical biosensor designed in this study has a lower detection limit and a wider detection range. Furthermore, we also successfully identified lung cancer-derived ExoPD-L1 by analyzing multiple protein biomarkers expressed on exosomes through the "AND" logic strategy. This sensor platform is expected to realize highly sensitive detection and accurate analysis of multiple sources of ExoPD-L1 and provide ideas for the clinical detection of ExoPD-L1.

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

PD-L1 expression in tumor and inflammatory cells is associated with favorable tumor features and favorable prognosis in muscle-invasive urothelial carcinoma of the bladder not treated by immune checkpoint inhibitors.

BACKGROUND: A high level of PD-L1 expression is the most relevant predictive parameter for response to immune checkpoint inhibitor (CPI) therapy in urinary bladder cancer. Existing data on the relationship between PD-L1 expression and the natural course of disease are controversial and sparse. METHODS: To expand our understanding of the relationship between PD-L1 expression and parameters of cancer aggressiveness, PD-L1 was analyzed on tissue microarrays containing 2710 urothelial bladder carcinomas including 512 patients with follow-up data who underwent radical cystectomy and follow-up therapies in the pre-immune checkpoint inhibitor therapy era. RESULTS: Tumor cell positivity in ≥10% of cells were seen in 513 (20%) and an immune cell positivity occurred in 872 (34%) of 2566 interpretable cancers. PD-L1 positivity in tumor cells increased from pTaG2 low grade (0.9% positive) to pTaG3 high grade (4.1%; p = 0.0255) and was even higher in muscle-invasive (pT2-4) carcinomas (29.3%; p < 0.0001). However, within pT2-4 carcinomas, PD-L1 positivity was linked to low pT stage (p = 0.0028), pN0 (p < 0.0001), L0 status (p = 0.0005), and a better prognosis within 512 patients with cystectomy who never received CPIs (p = 0.0073 for tumor cells and p = 0.0086 for inflammatory cells). PD-L1 staining in inflammatory cells was significantly linked to PD-L1 staining in tumor cells (p < 0.0001) and both were linked to a positive p53 immunostaining (p < 0.0001). CONCLUSION: It cannot be fully excluded that the strong statistical link between PD-L1 status and favorable histological tumor features as well as better prognosis could influence the outcome of studies evaluating CPIs in muscle-invasive urothelial carcinoma.

Comparison of programmed death-ligand 1 expression in adenocarcinoma and squamous cell carcinoma of the cervix in paraffin blocks of patients with cervical cancer.

AIMS: Cervical cancer (CC) is a common malignancy in women, predominantly caused by human papillomavirus. The most subtypes are adenocarcinomas (AC) and squamous cell carcinomas (SCC), which show various features and treatment responses. Programmed death-ligand 1 (PD-L1) and programmed cell death protein 1 (PD-1) as Immune checkpoint molecules, play a role in immune evasion. We investigated PD-L1 expression in AC and SCC of the cervix and explored its link to clinical characteristics. METHODS AND RESULTS: The present cross-sectional research was done between 2016 and 2022 on samples in Shahid Beheshti University of Medical Sciences-affiliated hospitals in Iran. Histological tissue samples of CCs (16 AC and 48 SCC) were assessed, and clinical information was obtained by reviewing their medical documents. PD-L1 expression was evaluated by immunohistochemistry and we used the combined positive score. SCC cases showed a higher (not significant) PD-L1 expression. The PD-L1 expression and clinical characteristics were not significantly correlated in both subgroups. CONCLUSION: Although SCC cases exhibited higher PD-L1 expression, this difference was non-significant. More investigations should highlight the role of PD-L1 in CC and the potential benefits of immunotherapy.

[Evaluation of PD-L1 expression and intratumoral lymphocytic infiltration in triple-negative invasive breast carcinoma]. / Evaluación de la expresión de PD-L1 e infiltración linfocitaria intratumoral en carcinoma de mama invasivo triple negativo.

INTRODUCTION: Triple negative breast cancer endophenotype (TNBC) is one of the least frequent and without therapeutic target; therefore we propose to study the correlation of PD-L1 immune checkpoint with the establishment of tumor microenvironment assessed by intratumoral stromal lymphocyte infiltration (TILS) and its importance in clinical practice. METHODS: A retrospective case-control study was performed, with 31 cases of triple-negative infiltrating breast carcinoma and 57 unmatched controls of Luminal A, Luminal B and HER-2 endophenotype seen in one year. The following variables were evaluated: histologic type and grade, PD-L1 expression with clone 22C3, TILS, lymphovascular invasion, tumor size, lymph node involvement and metastasis. Statistical analysis was performed with the chi-square test and Spearman correlation coefficient test. RESULTS: a statistically significant negative correlation was found between TILS and PD-L1 (rho - 0.106, p 0.025), indicating that the higher the expression of PD-L1, the lower the intratumoral lymphocytic infiltration. In the TILS B (10-40% TILS) and C (40-90% TILS) groups where there was a marked intratumoral inflammatory infiltrate, a greater number of patients were negative for PD-L1 (CPS <10) with 16 and 10 cases, respectively. For TNBC cases a negative association coefficient was identified (rho -0.378) with statistical significance (p 0.01). DISCUSSION: The association between TNBC, TILS and PDL1 expression was established, which is important for the establishment of target therapies and the development of precision medicine.

Introducción: El endofenotipo de cáncer de mama triple negativo (TNBC) es uno de los menos frecuentes y sin diana terapéutica, por tanto, se plantea estudiar la correlación del punto de control inmunológico PD-L1 con el establecimiento de microambiente tumoral evaluado por la infiltración linfocitaria intratumoral estromal (TILs) y su importancia en la práctica clínica. Métodos: Se realizó un estudio retrospectivo de casos y controles, con 31 casos de carcinoma infiltrante de la mama triple negativo y 57 controles no pareados de endofenotipo Luminal A, Luminal B y HER-2 atendidos en un año. Se evaluaron las variables: tipo y grado histológico, expresión PD-L1 con el clon 22C3, TILs, invasión linfovascular, tamaño tumoral, compromiso de ganglios linfáticos y metástasis. El análisis estadístico se ejecutó con la prueba de chi cuadrado y prueba de coeficiente de correlación de Spearman. Resultados: Se encontró una correlación negativa estadísticamente significativa entre TILs y PD-L1 (rho - 0.106, p 0.025), indicando que a mayor expresión de PD-L1, es menor la infiltración linfocitaria intratumoral. En los grupos de TILs B (10-40% TILs) y C (40-90% TILs) donde se presenta marcado infiltrado inflamatorio intratumoral se evidenció mayor número de pacientes negativos para PD-L1 (CPS <10) con 16 y 10 casos respectivamente. Para los casos TNBC se logró identificar un coeficiente de asociación negativa (rho -0.378) y con significancia estadística (p 0.01). Discusión: Se estableció la asociación de TNBC, TILs y expresión de PDL1, lo cual es importante para la instauración de terapias diana y el desarrollo de la medicina de precisión.

Endobronchial Ultrasound Guided Transbronchial Needle Aspiration and PD-L1 Yields.

PURPOSE: Immunotherapy is a leading approach for treating advanced non-small cell lung cancer (NSCLC) by targeting the PD-1/PD-L1 checkpoint signaling pathway, particularly in tumors expressing high levels of PD-L1 (Jug et al. in J Am Soc Cytopathol 9:485-493, 2020; Perrotta et al. in Chest 158: 1230-1239, 2020). Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive method to obtain tissue for molecular studies, including PD-L1 analysis, in unresectable tumors (Genova et al. in Front Immunol 12: 799455, 2021; Wang et al. in Ann Oncol 29: 1417-1422, 2018). This study aimed to assess the adequacy of PD-L1 assessment in EBUS-TBNA cytology specimens. METHODS: Data was collected retrospectively from patients who underwent EBUS-TBNA between 2017 and 2021 for suspected lung cancer biopsy. Samples positive for NSCLC were examined for PD-L1 expression. EBUS was performed by experienced practitioners, following institutional guidelines of a minimum of five aspirations from positively identified lesions. Sample adequacy for molecular testing was determined by the pathology department. RESULTS: The analysis involved 387 NSCLC cases (149 squamous cell, 191 adenocarcinoma, 47 unspecified). Of the 263 EBUS-TBNA specimens tested for PD-L1, 237 (90.1%) were deemed adequate. While 84% adhered to the protocol, adherence did not yield better results. Significantly higher PD-L1 adequacy was observed in squamous cell carcinomas (93.2%) compared to adenocarcinoma (87.6%). The number of aspirations and sedation type did not correlate with PD-L1 adequacy in either cancer type, but lesion size and location had a significant impact in adenocarcinomas. Adenocarcinoma exhibited higher PD-L1 expression (68%) compared to squamous cell carcinoma (48%). CONCLUSION: EBUS-TBNA offers high yields for assessing immunotherapy markers like PD-L1, with satisfactory adequacy regardless of NSCLC subtype, lesion size, or location.