Tumor Programmed Death Ligand 1 Research Articles

Results for patients with muscle-invasive bladder cancer (MIBC) in the CheckMate 274 trial.

4585 Background: In the CheckMate 274 trial, disease-free survival (DFS) was significantly improved with nivolumab (NIVO) vs placebo (PBO) both in intent-to-treat (ITT) patients (pts) (hazard ratio [HR], 0.70; 98.22% confidence interval [CI], 0.55–0.90; P < 0.001) and in pts with tumor programmed death ligand 1 (PD-L1) expression ≥ 1% (HR, 0.55; 98.72% CI, 0.35–0.85; P < 0.001). We report results for the subgroup of pts with bladder cancer, the most predominant type of urothelial carcinoma. Methods: CheckMate 274 is a phase 3, randomized, double-blind trial of adjuvant NIVO vs PBO in high-risk muscle-invasive urothelial carcinoma (bladder, ureter, renal pelvis) after radical resection. Pts were randomized 1:1 to NIVO 240 mg intravenously every 2 weeks or PBO for ≤ 1 year of adjuvant treatment and stratified by nodal status, prior neoadjuvant cisplatin, and tumor PD-L1 expression. Pts had radical resection ± neoadjuvant chemotherapy and were at high risk of recurrence on final pathologic staging. Primary endpoints were DFS in ITT pts and in pts with PD-L1 ≥ 1%. Non–urothelial tract recurrence-free survival (NUTRFS) was a secondary endpoint, and distant metastasis-free survival (DMFS) was an exploratory endpoint. This exploratory analysis focused on the subgroup of pts with muscle-invasive bladder cancer (MIBC) after radical resection. Results: Of 709 randomized pts in the trial, 560 had MIBC (NIVO, n = 279; PBO, n = 281). With a minimum follow-up of 11.0 months, a DFS benefit was observed with NIVO vs PBO in these pts, regardless of tumor PD-L1 expression (Table). DFS probability at 12 months in all MIBC pts was 66% with NIVO and 45% with PBO. DFS was improved with NIVO vs PBO across subgroups according to age, sex, ECOG performance status, nodal status, and PD-L1 expression status. Improvement in NUTRFS and DMFS with NIVO vs PBO was also observed (Table). Grade 3–4 treatment-related adverse events occurred in 17% and 6% of pts in the NIVO and PBO arms, respectively. Conclusions: Improvement in DFS was observed with NIVO over PBO in pts with MIBC after radical resection regardless of tumor PD-L1 expression. The DFS benefit was observed in all prespecified subgroups. These results further support adjuvant NIVO as a standard-of-care treatment for pts with high-risk MIBC after radical resection ± neoadjuvant cisplatin-based chemotherapy. Clinical trial information: NCT02632409. [Table: see text]

Real-world outcomes of immunotherapy regimens in first line non small cell lung cancer in lebanese patients.

e18741 Background: Immunotherapy provides improved treatment efficacy and survival in NSCLC patients. This is well established from clinical trials. However, the effectiveness of immunotherapy in real world practice is not well assessed. We aimed to characterize real-world outcomes for patients with NSCLC treated with 1L I-O in Lebanon. Methods: Patients aged ≥18 years with confirmed metastatic NSCLC who received either 1L I-O monotherapy or single-agent I-O combined with chemotherapy on or after January 1, 2014. Primary objectives were to examine overall survival (OS) and real-world progression-free survival. Multivariable analysis were made on correlation of different factors and survival. Results: among 135 patients, Median age at diagnosis was 68 years Most patients received chemotherapy alone as first line(54%), 18.3 % received immunotherapy and 27.8% received the combination treatment of chemotherapy and immunotherapy. Median PFS 1 was 7.7, 14, 11.1 months for chemotherapy, immunotherapy and chemo-immunotherapy respectively (p = 0.062). Median OS was 22.9 months (CI 95%, 17.7-28.1) with no significant correlation with treatment type (p = 0.85) multivariate analysis showed that having PDL levels between 1 and 49 (HRa = 0.15) and ≥50 (HRa = 0.12) compared to < 1 were significantly associated with longer PFS period, whereas having squamous (HRa = 3.45) and other (HRa = 27.36) type of cancer compared to adenocarcinoma was significantly associated with shorter PFS period. (HRa = 3.28) was significantly associated with lower overall survival period (p = 0.10).Concerning adverse effects, rash was the most frequent (14%) followed by pneumonitis (12.2%). Conclusions: In real world practice, IO was well tolerated in lebanese patients. Patients with high (≥50) and intermediate (between 1 and 49) tumor programmed death ligand 1 (PD-L1) expression demonstrated longer median OS vs those with low expression. We hypothesize that bone metastasis impairs immunotherapy efficacy.

Analysis of the contribution of macrophages to the overall tumor PD-L1 microenvironment using a screening multi-tumor tissue microarray and rapid multiplex fluorescence digital phenotyping approach.

2620 Background: Programmed death ligand-1 (PD-L1) contributes to immune suppression in the tumor microenvironment (TME) by interacting with programmed cell death-1 (PD-1) on infiltrating T lymphocytes leading to tumor immune escape. Application of omics technologies has shed light on the relevance of the TME for response to immunotherapies and development of novel treatment options. Here we have applied a multi-plex immunofluorescence/multi-tumor tissue microarray (TMA) approach to examine the immunobiology of different TMEs with respect to the contribution by tumor cells and macrophages to overall PD-L1 expression. Methods: A TMA comprising 11 tumor types and a total of 144 different donors, each represented by two cores [1mm; 1 from invasive margin (IM) and 1 from tumor center (TC)], was stained using Ultivue’s Immuno8 FixVUE panel (CD3, CD4, CD8, FOXP3, CD68, PD-1, PD-L1, pan-CK/SOX10). Whole slide images from two rounds of imaging (x20 magnification; four markers in each round) were aligned using Ultivue's UltiStacker software based on the nuclear counterstains from the two imaging rounds, to provide precise marker colocalization data. Cell phenotype data for each core was generated using Visiopharm software. Results: Overall PD-L1 positivity was greatest for NSCLC, SCLC, TNBC and gastric cancer ranging from approximately 600-1000 PD-L1+ cells/mm2/core, compared with CRC, breast, pancreatic, liver, and gastric esophageal junction (GOJ) cancers (approx. 50-300 PD-L1+ cells/mm2/core). Contribution by macrophages to overall PD-L1 expression (dual CD68+/PD-L1+) varied by tumor type representing 25-35% for NSCLC (SCC and ADC) and gastric cancer, whereas a converse pattern was apparent for SCLC, TNBC, breast (ER+ and Her2+) and pancreatic cancers where PD-L1+ macrophages accounted for a large proportion (approx. 60-85%) of overall tumor PD-L1 expression. Interestingly, as a proportion of total macrophage infiltration, approximately 65% of CD68+ macrophages were PD-L1+ for SCLC and TNBC, compared with less than 10% for pancreatic and liver cancer. Spatial analysis revealed PD-L1+ macrophage infiltration to be generally higher for IM versus TC, and the distribution between tumor (CK+) and stroma (CK-) remained similar despite exclusion of CD3+/CD8+ cytotoxic T cells from CK+ tumor regions for pancreatic and liver cancer. Conclusions: Taken together, these data illustrate the benefits of combining multiplexed immunofluorescence staining, with digital analysis of cell phenotypes within well characterized tumor samples to better understand the relative immunobiology of different TMEs. Here we demonstrate that the relative contribution of macrophages to the overall PD-L1 microenvironment varies between tumor types, which may help guide options for successful immunotherapy strategies.

A bioinformatics-based immune-related prognostic index for lung adenocarcinoma that predicts patient response to immunotherapy and common treatments

BackgroundThere is increasing evidence of the effectiveness of immune checkpoint blockade (ICB) therapy for the treatment of lung adenocarcinoma (LUAD). However, the benefits of ICB therapy vary among LUAD patients. Due to the research dimension, existing biomarkers, such as programmed death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB), could not reflect the complex tumor environment, and had low prediction accuracy of ICB. Therefore, we aimed to uncover a prognostic biomarker that could also predict whether a patient would benefit from ICB therapy and other common treatments from multiple dimensions, so as to improve the prediction accuracy of pre-treatment patients.MethodsBased on the LUAD dataset retrieved from The Cancer Genome Atlas (TCGA) database, 50 immune-related hub genes were identified using weighted gene co-expression network analysis and univariate Cox regression analyses. An immune-related gene prognostic index (IRGPI) was constructed using a Cox proportional-hazards model based on 15 genes and validated using GSE72094 dataset. We tested its prognostic accuracy by Kaplan-Meier (K-M) survival curves of the two datasets and assessed its predictive power by comparing area under curve (AUC) of IRGPI with existing biomarkers. Subsequently, we analyzed the molecular and immune characteristics, and evaluated the benefits of ICB by PD-L1 expression and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, predicted the inhibitory concentration 50 of common treatments drugs for two IRGPI score-related subgroups.ResultsPatients in the IRGPI-high subgroup had lower overall survival (OS) than patients in the IRGPI-low subgroup in K-M survival curve in two cohorts. And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively. A higher tumor mutation burden and PD-L1 expression and the tumor microenvironment (TME) landscape demonstrated that IRGPI-high subgroup patients may respond better to ICB therapy. Genomics of Drug Sensitivity in Cancer (GDSC) analysis indicated that the IRGPI-high subgroup showed greater sensitivity to chemotherapy.ConclusionsIRGPI is a prospective biomarker for evaluating whether a patient will benefit from ICB therapy and other treatments, and distinguishing patients with different molecular and immune characteristics.

Observer performance study to examine the feasibility of the AI-powered PD-L1 analyzer to assist pathologists’ assessment of PD-L1 expression using tumor proportion score in non–small cell lung cancer.

8529 Background: Programmed death ligand 1 (PD-L1) expression is the standard biomarker for PD-L1 inhibitors in advanced non-small cell lung cancer (NSCLC). However, evaluation of PD-L1 tumor proportion score (TPS) by pathologists causes inter-observer variation and demands time to interpret. This study aimed to evaluate the benefit of the artificial intelligence (AI) algorithm in assisting pathologists to determine TPS on PD-L1 immunohistochemistry (IHC) whole-slide images (WSIs) in NSCLC. Methods: Lunit SCOPE PD-L1, an AI-powered PD-L1 TPS analyzer, was developed from 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 WSIs stained by 22C3 pharmDx IHC. The AI model was developed based on a region-based convolutional neural network, and the model can detect and count PD-L1 positive or negative tumor cells from WSIs to calculate TPS. Seven independent board-certified pathologists scored ground truth (GT) of PD-L1 TPS from 199 WSI of NSCLC stained by 22C3 pharmDx IHC. TPS from each GT reader was grouped as negative (< 1%), low (1% to 49%), or high (≥ 50%). The GT of each slide was determined by the consensus of GT readers. Another twelve independent board-certified pathologists scored PD-L1 TPS from the same WSIs as observer performance testers (OPT). They scored TPS twice with a washout interval of 4 weeks, with or without AI assistance. TPS accuracy change and reading time of OPT reader according to the presence or absence of AI assistance were analyzed. Results: The standalone accuracy of the AI model was 0.809 (95% CI: 0.690–0.941). With AI assistance, the overall accuracy of TPS had been changed from 0.799 (95% confidence interval [CI]: 0.764–0.836) to 0.832 (95% CI: 0.796–0.869) (P = 0.004). AI assistance increased the accuracy rate in 11 out of 12 OPT readers. The result of the generalized linear mixed model revealed that AI assistance and specimen type affected the probability of correct answer, while the order of reading did not (Table). The mean time to read with AI was 195.4±506.5 (mean±standard deviation) seconds, which was significantly shorter than the mean time to read without AI (285.1±1578.4, P <0.001). Conclusions: This study demonstrates that an AI-powered PD-L1 TPS analyzer can assist board-certified pathologists in evaluating TPS of NSCLC by improving the accuracy of TPS group evaluation and reducing the time to read slides.[Table: see text]

Immuno-PET imaging of PD-L1 expression in patient-derived lung cancer xenografts with [68Ga]Ga-NOTA-Nb109.

Accurate evaluation of programmed death-ligand 1 (PD-L1) expression levels in cancer patients may be useful in the identification of potential candidates for anti-programmed death-1/PD-L1 (anti-PD-1/PD-L1) immune checkpoint therapy to improve the response rate of immune checkpoint blockade therapy. This study evaluated the feasibility of the nanobody-based positron emission tomography (PET) tracer [68Ga]Ga-NOTA-Nb109 for immuno-PET imaging of PD-L1 in lung cancer patient-derived xenograft (PDX). We constructed 2 PDXs of lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SCC) and used them for immuno-PET imaging. A 2-hour dynamic PET scanning was performed on the samples and the in vivo biodistribution and metabolism of [68Ga]Ga-NOTA-Nb109 were investigated using region of interest (ROI) analysis. The ex vivo biodistribution of [68Ga]Ga-NOTA-Nb109 in the 2 PDXs was investigated by static PET scanning. In addition, tumor PD-L1 expression in the 2 PDXs was evaluated by autoradiography, western blot, and immunohistochemical (IHC) analysis. Noninvasive PET imaging showed that [68Ga]Ga-NOTA-Nb109 can accurately and sensitively assess the PD-L1 expression in non-small cell lung cancer (NSCLC) PDX models. The maximum [68Ga]Ga-NOTA-Nb109 uptake by the ADC PDX LU6424 and the SCC PDX LU6437 were 3.13%±0.35% and 2.60%±0.32% injected dose per milliliter of tissue volume (ID/mL), respectively, at 20 min post injection. In vivo and ex vivo biodistribution analysis showed that [68Ga]Ga-NOTA-Nb109 was rapidly cleared through renal excretion and an enhanced signal-to-noise ratio (SNR) was achieved. Ex vivo PD-L1 expression analysis showed good agreement with in vivo PET imaging results. This study demonstrated that [68Ga]Ga-NOTA-Nb109 could be applied with PET imaging to noninvasively and accurately monitor PD-L1 expression in vivo for screening patients who may be responsive to immunotherapy and to guide the development of appropriate treatment strategies for such patients.

High exosomal PD-L1 expression in relation to lymph node progression in metastatic castration-resistant prostate cancer (mCRPC) treated with abiraterone (abi) or enzalutamide (enza).

e17038 Background: Programmed death-ligand 1 (PD-L1) has been recently found in tumor-derived exosomes and associated with progressive disease (PD) ( Poggio, Cell 2019). In localized prostate cancer, PD-L1 tumor expression has been shown to be a biomarker for node-positive patients (pts) ( Petitprez, Eur Urol Focus 2019) but its role in mCRPC is largely unexplored. We aimed to determine the potential of PD-L1 exosomal expression to predict lymph nodal PD in mCRPC receiving abi or enza. Methods: In a biomarker prospective study (REC 2192/2013), exosomes were isolated from plasma samples collected before first-line therapy with abi/enza between December 2014 and October 2018. RNA was extracted for analysis of PD-L1 by digital droplet PCR ( Del Re, Prostate Cancer Prostatic Dis 2021). Results: In our cohort of 55 node-positive mCRPC pts (median age: 76 years, range 47-89), the amount of exosomal PD-L1 expression (pts dichotomized in “high” and “low” as below or above median PD-L1/ACTB ratio = 0.0052) was associated with radiographic response after the first 3 months abi/enza therapy [high PD-L1 vs low PD-L1: 8 (25.8%) vs 0 (0%) PD, 23 (74.2%) vs 18 (75%) stable disease, and 0 vs 6 (25%) partial/complete response] (p = 0.0009). We observed an overall median progression-free survival (PFS) of 14.3 months [95% confidence interval (CI) 8.6-not reached] shorter in high PD-L1 compared to low PD-L1 10.6 (6.5-23.4) vs not reached [hazard ratio (HR) 3.1 (95% CI 1.1-8.5), p = 0.023]. In multivariable analysis including exosomal PD-L1 and androgen receptor (AR) variant 7, AR copy number gain detected in plasma, neutrophil lymphocyte ratio (NLR), lactate dehydrogenase, exosomal PD-L1; AR-V7 and AR gain were independent predictors of nodal PFS (HR 5.8, 95% CI 1.4-24.2, p = 0.01, HR 3.2, 95% CI 1.1-9.2, p = 0.03 and HR 12.9, 95% CI 2.3-73.2, p = 0.004), whereas AR-V7, AR gain, NLR > 3 were independent predictors of OS (HR 4.9, 95% CI 1.8-13.1, p = 0.001, HR 4.4, 95% CI 1.1-17, p = 0.02, and HR 3.2 95% CI 1.1-9.4, p = 0.03, respectively). Moreover, a trend of a worse OS was reported in high PD-L1 compared to low PD-L1 pts (HR 2.6, 95% CI 0.9-7.5, p = 0.07). Conclusions: In mCRPC pts treated with ARSI, exosomal PD-L1 expression may be considered as a predictive biomarker of lymph nodal response, playing a potential role in individualized disease monitoring and treatment combining between ARSI and immunotherapy. Larger evaluation of treatment decisions based on exosomal PD-L1 expression is now required.

FN3 linked nanobubbles as a targeted contrast agent for US imaging of cancer-associated human PD-L1.

PD-L1 (programmed death-ligand 1) targeted therapies may be useful for several cancers. The use of non-invasive diagnostic and prognostic molecular imaging platforms could improve clinical assessment of PD-L1 tumor status during these therapies. Contrast enhanced ultrasound molecular imaging (CE-USMI) techniques may offer versatile and cost-effective ways to detect and quantify the expression levels of cellular targets in vivo. However, conventional use of microbubbles as a blood pool contrast agent for CE-USMI is limited to accessing intravascular biomarkers rather than reflecting the tumor molecular status. Using a microfluidic based reconstruction process we therefore developed ultra-stable nanobubbles (NBs) as a contrast agent for molecular imaging of vascular and extravascular cell surface markers. We then functionalized these NBs by covalently linking to nanobody (FN3hPD-L1) targeting human (h)PD-L1 to measure the expression of human PD-L1 in the tumor microenvironment (TME) in vivo. We showed the specific binding of hPD-L1 targeted NBs in cell culture, and in xenografted mouse models of hPD-L1 expressing CT26 tumors. CE-USMI of hPD-L1 in the TME in vivo showed ~3-fold increase in contrast signal compared to non-targeted NBs. Overall, in vivo use of CE-USMI with hPD-L1 targeted NBs has the potential for clinical translation and imaging of human cancers during immunotherapy, and for prognostic evaluation of patient response to PD-L1 targeted immunotherapy.

Retrospective Review of Outcomes After Radiation Therapy for Oligoprogressive Disease on Immune Checkpoint Blockade

We retrospectively evaluated outcomes after radiation therapy for patients with oligoprogression on immune checkpoint inhibitors (ICI). We identified patients irradiated to ≤5 progressive lesions while receiving ICI between 2010 and 2020. We excluded patients whose systemic therapy was switched after radiation but before progression. We evaluated predictors of local control (LC), progression-free survival (PFS) and overall survival (OS). We screened 1423 patients and identified 120 who were eligible; the most common histologies were lung cancer (n=59) and melanoma (n=36). The median number of oligoprogressive lesions was 1. For the median LC of irradiated oligoprogressive lesions, PFS and OS were not reached at 6.41 (4.67-7.66) and 29.80 (22.54-43.33) months, respectively. Tumor histology, radiated site, or radiation modality were not associated with LC, PFS, or OS. Local response to radiation (P < .0001) and radiation of newly developed lesions (P=.02) were associated with LC. Predictors of PFS on univariate and multivariate analyses were best response to radiation (P=.006) and high programmed death ligand 1 tumor proportion score (P=.02). On multivariate analyses, OS was associated with cumulative oligoprogressive lesion volumes (P=.02) and duration of ICI before oligoprogression (P=.03). Promising outcomes were observed among patients irradiated for oligoprogression on ICI, especially those with a favorable local response, high tumor programmed death ligand 1 expression, and those receiving ICI for longer periods before oligoprogression. These data can help identify patients well suited for radiation therapy versus those who should switch systemic treatment.

CMTM6 and CMTM7: New leads for PD-L1 regulation in breast cancer cells undergoing EMT.

Programmed death‐ligand 1 (PD‐L1) expression has long been used as a biomarker to stratify patients with cancer who will benefit from anti‐PD‐1/PD‐L1 immunotherapy. However, the use of PD‐L1 as a biomarker to guide treatment decisions has recently been called into question due to its dynamic and heterogeneous expression within each tumor and among different tumors as well as during tumor cell plasticity. Therefore, understanding the molecular basis of PD‐L1 expression would enable delineating its value as a reliable biomarker in the clinic. Here, we provide our perspective on the involvement of CMTM6 and CMTM7 as new lead candidates for the regulation of PD‐L1 in breast tumors undergoing an epithelial to mesenchymal transition.

Efficacy of Immune Checkpoint Inhibitors in SMARCA4-Deficient Thoracic Tumor

BackgroundSMARCA4-deficient thoracic tumor is a novel disease entity characterized by mutations in SMARCA4 resulting in loss of its expression. They could be divided according to their phenotypes; carcinoma or sarcomatoid. It remains unclear how many patients with these SMARCA4-deficient tumors could benefit from inhibitors of programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1). MethodsSMARCA4-deficient thoracic tumor cases were retrospectively identified from pathology and gene expression databases at the National Cancer Center Hospital in Japan. Clinical outcomes of patients treated with PD-1/PD-L1 inhibitors were reviewed. ResultsEighteen patients with SMARCA4-deficient thoracic tumor [carcinoma (n = 10), sarcomatoid (n = 7), and ambiguous type (n = 1)] were identified. Of the twelve [carcinoma (n = 7), sarcomatoid (n = 5)] who had received immune checkpoint inhibitors (ICIs), 5 [carcinoma (n = 3), sarcomatoid (n = 2)] showed a partial response, all of whom had received an ICI as the first-line therapy. The overall response rate was The PD-L1 tumor proportion scores of the 5 responding patients were 100%, 80%, 5% (n = 2), and less than 1%. The median progression-free survival (PFS) of all the patients was 2.4 months [95% confidence interval (CI), 1.1 months–not achieved (NA)], while the median PFS of the 3 patients who received first-line ICIs was not reached (95% CI, 1.1 months–NA). ConclusionPD-1/PD-L1 inhibitors showed promising results in the treatment of SMARCA4-deficient tumor. Further studies, especially on patient selection and combination therapy, are needed.

Prognostic and Predictive Role of PD-L1 Expression in Stage III Non-small Cell Lung Cancer Treated With Definitive Chemoradiation and Adjuvant Durvalumab

It is unclear whether programmed death ligand 1 (PD-L1) expression is prognostic or predictive of immunotherapy benefit among patients with stage III non-small cell lung cancer (NSCLC) treated with definitive chemoradiation and adjuvant durvalumab. We determined pretreatment tumor PD-L1 expression for 312 patients with stage III NSCLC treated with definitive chemoradiation and at least 1 dose of adjuvant durvalumab between November 2017 and April 2021 across the national Veterans Health Administration. Progression-free survival (PFS) and overall survival (OS) in PD-L1 expression subgroups (<1%, 1%-49%, and 50%-100%) were compared with 994 patients with stage III NSCLC treated without adjuvant durvalumab from 2015 to 2016. PD-L1 expression was <1%, 1% to 49%, and 50% to 100% in 109 (34.9%), 96 (30.7%), and 107 (34.3%) patients, respectively. Increasing PD-L1 expression was associated with longer PFS (adjusted hazard ratio [aHR], 0.84 per 25% absolute increase in expression; 95% confidence interval [CI], 0.75-0.94; P=.003) and OS (aHR, 0.86 per 25% absolute increase in expression; 95% CI, 0.74-0.99; P=.036). Compared with the no-durvalumab group, PFS was longer for PD-L1 50% to 100% (aHR, 0.44; 95% CI, 0.32-0.60; P < .001) and PD-L1 1% to 49% (aHR, 0.64; 95% CI, 0.47-0.86; P=.003) but not PD-L1 <1% (aHR, 0.84; 95% CI, 0.64-1.10; P=.19). Similar results were found for OS, with no significant difference between the no-durvalumab group and PD-L1 <1% (aHR, 0.81; 95% CI, 0.58-1.13; P=.22). Increasing tumor PD-L1 expression is prognostic for PFS and OS among patients with stage III NSCLC treated with adjuvant durvalumab, and patients with PD-L1 expression <1% may have limited benefit from adjuvant durvalumab.

Conditional survival and long-term efficacy with nivolumab plus ipilimumab versus sunitinib in patients with advanced renal cell carcinoma.

BackgroundConditional survival estimates provide critical prognostic information for patients with advanced renal cell carcinoma (aRCC). Efficacy, safety, and conditional survival outcomes were assessed in CheckMate 214 (ClinicalTrials.gov identifier NCT02231749) with a minimum follow‐up of 5 years.MethodsPatients with untreated aRCC were randomized to receive nivolumab (NIVO) (3 mg/kg) plus ipilimumab (IPI) (1 mg/kg) every 3 weeks for 4 cycles, then either NIVO monotherapy or sunitinib (SUN) (50 mg) daily (four 6‐week cycles). Efficacy was assessed in intent‐to‐treat, International Metastatic Renal Cell Carcinoma Database Consortium intermediate‐risk/poor‐risk, and favorable‐risk populations. Conditional survival outcomes (the probability of remaining alive, progression free, or in response 2 years beyond a specified landmark) were analyzed.ResultsThe median follow‐up was 67.7 months; overall survival (median, 55.7 vs 38.4 months; hazard ratio, 0.72), progression‐free survival (median, 12.3 vs 12.3 months; hazard ratio, 0.86), and objective response (39.3% vs 32.4%) benefits were maintained with NIVO+IPI versus SUN, respectively, in intent‐to‐treat patients (N = 550 vs 546). Point estimates for 2‐year conditional overall survival beyond the 3‐year landmark were higher with NIVO+IPI versus SUN (intent‐to‐treat patients, 81% vs 72%; intermediate‐risk/poor‐risk patients, 79% vs 72%; favorable‐risk patients, 85% vs 72%). Conditional progression‐free survival and response point estimates were also higher beyond 3 years with NIVO+IPI. Point estimates for conditional overall survival were higher or remained steady at each subsequent year of survival with NIVO+IPI in patients stratified by tumor programmed death ligand 1 expression, grade ≥3 immune‐mediated adverse event experience, body mass index, and age.ConclusionsDurable clinical benefits were observed with NIVO+IPI versus SUN at 5 years, the longest phase 3 follow‐up for a first‐line checkpoint inhibitor‐based combination in patients with aRCC. Conditional estimates indicate that most patients who remained alive or in response with NIVO+IPI at 3 years remained so at 5 years.

Camrelizumab Plus Apatinib in Treatment-Naive Patients With Advanced Nonsquamous NSCLC: A Multicenter, Open-Label, Single-Arm, Phase 2 Trial

IntroductionOur preclinical work suggests that low-dose angiogenesis inhibition could potentiate programmed cell death protein 1 and programmed death-ligand 1 (PD-L1) blockade. In a cohort of our multicenter phase 1b and 2 study (NCT03083041), promising antitumor activity was observed with camrelizumab plus low-dose apatinib in chemotherapy-pretreated patients with advanced nonsquamous NSCLC. We hereby reported the results in treatment-naive patients (cohort 4) from the same study. MethodsEligible patients had untreated advanced nonsquamous NSCLC with a high tumor mutational burden (TMB) (tissue TMB >10 mutations per megabase or blood TMB ≥1.54 mutations per megabase) and without sensitizing EGFR or ALK alterations. Patients received camrelizumab 200 mg intravenously every 2 weeks plus apatinib 250 mg orally once daily. The primary end point was the objective response rate (ORR) per investigator. ResultsA total of 25 patients were enrolled and treated. A total of 10 (40.0%) confirmed partial responses and 13 (52.0%) stable diseases were observed. The ORR was 40.0% (95% confidence interval [CI]: 21.1–61.3) and disease control rate was 92.0% (95% CI: 74.0–99.0). With a median follow-up of 19.5 months, the median progression-free survival was 9.6 months (95% CI: 5.5–not reached), whereas the overall survival was not reached; the median duration of response was 15.6 months (95% CI: 3.8–not reached). Similar ORR and progression-free survival were observed regardless of PD-L1 tumor proportion score (≥1% versus <1%). The most common treatment-related grade 3 or higher adverse events were increased gamma-glutamyltransferase (24.0%), increased alanine aminotransferase (16.0%), and abnormal hepatic function (16.0%). ConclusionsFrontline camrelizumab plus low-dose apatinib exhibited promising clinical activity with acceptable safety in patients with advanced nonsquamous NSCLC regardless of PD-L1 expression.

A computed tomography-based radiomics signature for predicting expression of programmed death ligand 1 in head and neck squamous cell carcinoma.

Accurate prediction of the expression of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) before immunotherapy is crucial. This study was performed to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to predict the expression of PD-L1 in HNSCC. In total, 157 patients with confirmed HNSCC who underwent CECT scans and immunohistochemical examination of tumor PD-L1 expression were enrolled in this study. The patients were divided into a training set (n = 104; 62 PD-L1-positive and 42 PD-L1-negative) and an external validation set (n = 53; 34 PD-L1-positive and 19 PD-L1-negative). A radiomics signature was constructed from radiomics features extracted from the CECT images, and a radiomics score was calculated. Performance of the radiomics signature was assessed using receiver operating characteristics analysis. Nine features were finally selected to construct the radiomics signature. The performance of the radiomics signature to distinguish between a PD-L1-positive and PD-L1-negative status in both the training and validation sets was good, with an area under the receiver operating characteristics curve of 0.852 and 0.802 for the training and validation sets, respectively. A CECT-based radiomics signature was constructed to predict the expression of PD-L1 in HNSCC. This model showed favorable predictive efficacy and might be useful for identifying patients with HNSCC who can benefit from anti-PD-L1 immunotherapy. • Accurate prediction of the expression of PD-L1 in HNSCC before immunotherapy is crucial. • A CECT-based radiomics signature showed favorable predictive efficacy in estimation of the PD-L1 expression status in patients with HNSCC.

Computer-assisted three-dimensional quantitation of programmed death-ligand 1 in non-small cell lung cancer using tissue clearing technology

Immune checkpoint blockade therapy has revolutionized non-small cell lung cancer treatment. However, not all patients respond to this therapy. Assessing the tumor expression of immune checkpoint molecules, including programmed death-ligand 1 (PD-L1), is the current standard in predicting treatment response. However, the correlation between PD-L1 expression and anti-PD-1/PD-L1 treatment response is not perfect. This is partly caused by tumor heterogeneity and the common practice of assessing PD-L1 expression based on limited biopsy material. To overcome this problem, we developed a novel method that can make formalin-fixed, paraffin-embedded tissue translucent, allowing three-dimensional (3D) imaging. Our protocol can process tissues up to 150 μm in thickness, allowing anti-PD-L1 staining of the entire tissue and producing high resolution 3D images. Compared to a traditional 4 μm section, our 3D image provides 30 times more coverage of the specimen, assessing PD-L1 expression of approximately 10 times more cells. We further developed a computer-assisted PD-L1 quantitation method to analyze these images, and we found marked variation of PD-L1 expression in 3D. In 5 of 33 needle-biopsy-sized specimens (15.2%), the PD-L1 tumor proportion score (TPS) varied by greater than 10% at different depth levels. In 14 cases (42.4%), the TPS at different depth levels fell into different categories (< 1%, 1–49%, or ≥ 50%), which can potentially influence treatment decisions. Importantly, our technology permits recovery of the processed tissue for subsequent analysis, including histology examination, immunohistochemistry, and mutation analysis. In conclusion, our novel method has the potential to increase the accuracy of tumor PD-L1 expression assessment and enable precise deployment of cancer immunotherapy.

PD-L1 Antibody Pharmacokinetics and Tumor Targeting in Mouse Models for Infectious Diseases.

BackgroundProgrammed death-ligand 1 (PD-L1) regulates immune homeostasis by promoting T-cell exhaustion. It is involved in chronic infections and tumor progression. Nuclear imaging using radiolabeled anti-PD-L1 antibodies can monitor PD-L1 tissue expression and antibody distribution. However, physiological PD-L1 can cause rapid antibody clearance from blood at imaging doses. Therefore, we hypothesized that inflammatory responses, which can induce PD-L1 expression, affect anti-PD-L1 antibody distribution. Here, we investigated the effects of three different infectious stimuli on the pharmacokinetics and tumor targeting of radiolabeled anti-PD-L1 antibodies in tumor-bearing mice.Materials/MethodsAnti-mouse-PD-L1 and isotype control antibodies were labelled with indium-111 ([111In]In-DTPA-anti-mPD-L1 and [111In]In-DTPA-IgG2a, respectively). We evaluated the effect of inflammatory responses on the pharmacokinetics of [111In]In-DTPA-anti-mPD-L1 in RenCa tumor-bearing BALB/c mice in three conditions: lipopolysaccharide (LPS), local Staphylococcus aureus, and heat-killed Candida albicans. After intravenous injection of 30 or 100 µg of [111In]In-DTPA-anti-mPD-L1 or [111In]In-DTPA-IgG2a, blood samples were collected 1, 4, and 24 h p.i. followed by microSPECT/CT and ex vivo biodistribution analyses. PD-L1 expression, neutrophil, and macrophage infiltration in relevant tissues were evaluated immunohistochemically.ResultsIn 30 µg of [111In]In-DTPA-anti-mPD-L1 injected tumor-bearing mice the LPS-challenge significantly increased lymphoid organ uptake compared with vehicle controls (spleen: 49.9 ± 4.4%ID/g versus 21.2 ± 6.9%ID/g, p < 0.001), resulting in lower blood levels (3.6 ± 1.6%ID/g versus 11.5 ± 7.2%ID/g; p < 0.01) and reduced tumor targeting (8.1 ± 4.5%ID/g versus 25.2 ± 5.2%ID/g, p < 0.001). Local S. aureus infections showed high PD-L1+ neutrophil influx resulting in significantly increased [111In]In-DTPA-anti-mPD-L1 uptake in affected muscles (8.6 ± 2.6%ID/g versus 1.7 ± 0.8%ID/g, p < 0.001). Heat-killed Candida albicans (Hk-C. albicans) challenge did not affect pharmacokinetics. Increasing [111In]In-DTPA-anti-mPD-L1 dose to 100 µg normalized blood clearance and tumor uptake in LPS-challenged mice, although lymphoid organ uptake remained higher. Infectious stimuli did not affect [111In]In-DTPA-IgG2a pharmacokinetics.ConclusionsThis study shows that anti-PD-L1 antibody pharmacokinetics and tumor targeting can be significantly altered by severe inflammatory responses, which can be compensated for by increasing the tracer dose. This has implications for developing clinical PD-L1 imaging protocols in onco-immunology. We further demonstrate that radiolabeled anti-PD-L1 antibodies can be used to evaluate PD-L1 expression changes in a range of infectious diseases. This supports the exploration of using these techniques to assess hosts’ responses to infectious stimuli.

Expanding the immunotherapy roadmap for hepatocellular carcinoma.

In a recent Lancet Oncology article, Yau etal. report the CheckMate 459 trial results. This is the first phase III trial comparing the single-agent anti-programmed death protein 1 (PD-1) therapy nivolumab to the tyrosine kinase inhibitor sorafenib for treatment-naive patients with advanced hepatocellular carcinoma.

Analysis of disease-free survival in CheckMate 274 by PD-L1 combined positive score and tumor proportion score.

491 Background: CheckMate 274 demonstrated a significant improvement in disease-free survival (DFS) with nivolumab (NIVO) versus placebo (PBO) both in the intent-to-treat population (hazard ratio [HR], 0.70; 98.22% confidence interval [CI], 0.55–0.90; P &lt; 0.001) and in patients (pts) with tumor programmed death ligand 1 (PD-L1) expression ≥ 1% assessed by the tumor proportion score (TPS) (HR, 0.55; 98.72% CI, 0.35–0.85; P &lt; 0.001). An exploratory subgroup analysis showed a trend toward a DFS benefit with NIVO in pts with TPS &lt; 1% (0.82; 95% CI, 0.63–1.06). To further characterize the relationship between PD-L1 expression and NIVO efficacy, we report an analysis of DFS based on PD-L1 expression in both tumor and immune cells using the combined positive score (CPS). Methods: CheckMate 274 is a phase 3, randomized, double-blind, multicenter trial of NIVO versus PBO in pts with high-risk muscle-invasive urothelial carcinoma after radical surgery. Pts were randomized 1:1 to NIVO 240 mg or PBO every 2 weeks intravenously for 1 year of adjuvant treatment. The primary endpoints of the study are DFS in the intent-to-treat population and in pts with TPS ≥ 1%. The Dako PD-L1 IHC 28-8 pharmDx assay was used to evaluate TPS. CPS was determined retrospectively from previously stained immunohistochemistry slides using the CPS algorithm. CPS was calculated as the number of both PD-L1 positive tumor and immune cells divided by the number of viable tumor cells in the evaluable tumor area, multiplied by 100; TPS was similarly calculated with the number of PD-L1 positive tumor cells as the numerator. This analysis only included pts with both quantifiable CPS and TPS. Results: Of the 629 pts with quantifiable TPS and CPS, 249 (40%) had TPS ≥ 1% (NIVO, n = 124; PBO, n = 125), 380 (60%) had TPS &lt; 1% (NIVO, n = 191; PBO, n = 189), 557 (89%) had CPS ≥ 1 (NIVO, n = 281; PBO, n = 276), and 72 (11%) had CPS &lt; 1 (NIVO, n = 34; PBO, n = 38). Within TPS &lt; 1% pts, 81% (n = 309) had CPS ≥ 1. The number of pts and the DFS outcomes in pts with TPS ≥ 1% and CPS ≥ 1 are shown in the Table. In pts with TPS &lt; 1% who also had CPS ≥ 1, median DFS (95% CI) was 19.2 (15.6–33.4) months with NIVO versus 10.1 (8.2–19.4) months with PBO. The HR for NIVO versus PBO in these pts was 0.73 (95% CI, 0.54–0.99). Conclusions: This exploratory analysis of PD-L1 expression by CPS showed a higher proportion of pts with CPS ≥ 1 than TPS ≥ 1%, and that most pts with TPS &lt; 1% had CPS ≥ 1. In the CPS ≥ 1 subgroup, median DFS with NIVO was more than double that with placebo. These results support the conclusion that pts with TPS &lt; 1% also benefit from adjuvant NIVO. Clinical trial information: NCT02632409. [Table: see text]

Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images.

BackgroundEpidermal growth factor receptor (EGFR) genotyping and programmed death ligand-1 (PD-L1) expressions are of paramount importance for treatment guidelines such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional identification of EGFR or PD-L1 status requires surgical or biopsied tumor specimens, which are obtained through invasive procedures associated with risk of morbidities and may be unavailable to access tissue samples. Here, we developed an artificial intelligence (AI) system that can predict EGFR and PD-L1 status in using non-invasive computed tomography (CT) images.MethodsA multitask AI system including deep learning (DL) module, radiomics (RA) module, and joint (JO) module combining the DL, RA, and clinical features was developed, trained, and optimized with CT images to predict the EGFR and PD-L1 status. We used feature selectors and feature fusion methods to find the best model among combinations of module types. The models were evaluated using the areas under the receiver operating characteristic curves (AUCs).ResultsOur multitask AI system yielded promising performance for gene expression status, subtype classification, and joint prediction. The AUCs of DL module achieved 0.842 (95% CI, 0.825–0.855) in the EGFR mutated status and 0.805 (95% CI, 0.779–0.829) in the mutated-EGFR subtypes discrimination (19Del, L858R, other mutations). DL module also demonstrated the AUCs of 0.799 (95% CI, 0.762–0.854) in the PD-L1 expression status and 0.837 (95% CI, 0.775–0.911) in the positive-PD-L1 subtypes (PD-L1 tumor proportion score, 1%–49% and ≥50%). Furthermore, the JO module of our AI system performed well in the EGFR and PD-L1 joint cohort, with an AUC of 0.928 (95% CI, 0.909–0.946) for distinguishing EGFR mutated status and 0.905 (95% CI, 0.886–0.930) for discriminating PD-L1 expression status.ConclusionOur AI system has demonstrated the encouraging results for identifying gene status and further assessing the genotypes. Both clinical indicators and radiomics features showed a complementary role in prediction and provided accurate estimates to predict EGFR and PD-L1 status. Furthermore, this non-invasive, high-throughput, and interpretable AI system can be used as an assistive tool in conjunction with or in lieu of ancillary tests and extensive diagnostic workups to facilitate early intervention.