Subcutaneous Infliximab (CT-P13 SC) as Maintenance Therapy for Inflammatory Bowel Disease: Two Randomized Phase 3 Trials (LIBERTY).

BACKGROUND & AIMS: CT-P13 subcutaneous (SC), an SC formulation of the intravenous (IV) infliximab biosimilar CT-P13 IV, creates a unique exposure profile. The LIBERTY studies aimed to demonstrate superiority of CT-P13 SC vs placebo as maintenance therapy in patients with Crohn's disease (CD) and ulcerative colitis (UC). METHODS: Two randomized, placebo-controlled, double-blind studies were conducted in patients with moderately to severely active CD or UC and inadequate response or intolerance to corticosteroids and immunomodulators. All patients received open-label CT-P13 IV 5 mg/kg at weeks 0, 2, and 6. At week 10, clinical responders were randomized (2:1) to CT-P13 SC 120 mg or placebo every 2 weeks until week 54 (maintenance phase) using prefilled syringes. (Co-) primary end points were clinical remission and endoscopic response (CD) and clinical remission (UC) at week 54 (all-randomized population). RESULTS: Overall, 396 patients with CD and 548 patients with UC received induction treatment. At week 54 in the CD study, statistically significant higher proportions of CT-P13 SC-treated patients vs placebo-treated patients achieved clinical remission (62.3% vs 32.1%; P < .0001) and endoscopic response (51.1% vs 17.9%; P < .0001). In the UC study, clinical remission rates at week 54 were statistically significantly higher with CT-P13 SC vs placebo (43.2% vs 20.8%; P < .0001). Achievement of key secondary end points was significantly higher with CT-P13 SC vs placebo across both studies. CT-P13 SC was well tolerated, with no new safety signals identified. CONCLUSIONS: CT-P13 SC was more effective than placebo as maintenance therapy and was well tolerated in patients with moderately to severely active CD or UC who responded to CT-P13 IV induction. CLINICALTRIALS: gov, Numbers: NCT03945019 (CD) and NCT04205643 (UC).

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.

BACKGROUND: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS: AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS: Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS: This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS: An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION: This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.

Artificial Intelligence-Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non-Small-Cell Lung Cancer.

The tumor microenvironment can be classified into three immune phenotypes: inflamed, immune excluded, and immune-desert. Immunotherapy efficacy has been shown to vary by phenotype; yet, the mechanisms are poorly understood and demand further investigation. This study unveils the mechanisms using an artificial intelligence-powered software called Lunit SCOPE. Artificial intelligence was used to classify 965 samples of non-small-cell lung carcinoma from The Cancer Genome Atlas into the three immune phenotypes. The immune and mutational profiles that shape each phenotype using xCell, gene set enrichment analysis with RNA-sequencing data, and cBioportal were described. In the inflamed subtype, which showed higher cytolytic score, the enriched pathways were generally associated with immune response and immune-related cell types were highly expressed. In the immune excluded subtype, enriched glycolysis, fatty acid, and cholesterol metabolism pathways were observed. The KRAS mutation, BRAF mutation, and MET splicing variant were mostly observed in the inflamed subtype. The two prominent mutations found in the immune excluded subtype were EGFR and PIK3CA mutations. This study is the first to report the distinct immunologic and mutational landscapes of immune phenotypes, and demonstrates the biological relevance of the classification. In light of these findings, the study offers insights into potential treatment options tailored to each immune phenotype.

Efficacy of subcutaneous vs intravenous infliximab in rheumatoid arthritis: a post-hoc analysis of a randomized phase III trial.

OBJECTIVES: The primary endpoint of the pivotal phase III study of infliximab (IFX) s.c. demonstrated non-inferiority of s.c. to i.v. IFX, based on 28-joint DAS-CRP (DAS28-CRP) improvement at week (W) 22 (NCT03147248). This post-hoc analysis investigated whether numerical differences in efficacy outcomes at W30/54 were statistically significant, using conservative imputation methods. METHODS: Patients with active RA and inadequate response to MTX received IFX i.v. 3 mg/kg at W0 and W2 (induction) and were randomized (1:1) to IFX s.c. 120 mg every 2 weeks or i.v. 3 mg/kg every 8 weeks thereafter (maintenance). Patients randomized to IFX i.v. switched to IFX s.c. from W30-54. This post-hoc analysis compared efficacy outcomes for s.c. and i.v. groups pre-switch (W30) and post-switch (W54) using last observation carried forward (LOCF) and non-responder imputation (NRI) methods. RESULTS: Of 343 randomized patients, 165 (IFX s.c.) and 174 (IFX i.v.) were analysed. At W30, significantly improved outcomes were identified with s.c. vs i.v. IFX for DAS28-CRP/DAS28-ESR/Clinical Disease Activity Index (CDAI)/Simplified Disease Activity Index (SDAI) scores (LOCF); ACR/good EULAR responses, DAS28-CRP/Boolean remission, and DAS28-CRP/DAS28-ESR/CDAI/SDAI low disease activity and remission (LOCF and/or NRI); and minimal clinically important difference in HAQ score (LOCF and NRI). After switching to IFX s.c. from IFX i.v., fewer significant between-group differences were identified at W54. CONCLUSION: IFX s.c. showed improved efficacy at W30 compared with IFX i.v., and the reduced between-group difference in efficacy outcomes at W54 after switching supports the results suggesting benefits of IFX s.c. compared with IFX i.v. at W30. TRIAL REGISTRATION: ClincialTrials.gov, http://clinicaltrials.gov, NCT03147248, https://clinicaltrials.gov/ct2/show/NCT03147248.

Transcranial direct current stimulation combined with amantadine in repetitive mild traumatic brain injury in rats.

BACKGROUND: Balance and memory deficits are common in patients with repetitive mild traumatic brain injury (mTBI). OBJECTIVE: To investigate the combined effects of amantadine and transcranial direct current stimulation (tDCS) on balance and memory in repetitive mTBI rat models. METHODS: In this prospective animal study, 40 repetitive mTBI rats were randomly assigned to four groups: tDCS, amantadine, combination of amantadine and anodal tDCS, and control. The tDCS group received four sessions of anodal tDCS for four consecutive days. The amantadine group received four intraperitoneal injections of amantadine for four consecutive days. The combination group received four intraperitoneal injections of amantadine and anodal tDCS for four consecutive days. Motor-evoked potential (MEP), rotarod test, and novel object test results were evaluated before mTBI, before treatment, and after treatment. RESULTS: All groups showed significant improvements in the rotarod and novel object tests, particularly the combination group. The combination group showed a significant improvements in duration (p < 0.01) and maximal speed in the rotarod test (p < 0.01), as well as an improvement in novel object ratio (p = 0.05) and MEP amplitude (p = 0.05) after treatment. The combination group exhibited a significant increase in novel object ratio compared to the tDCS group (p = 0.04). The GFAP integral intensity of the left motor cortex and hippocampus was the lowest in the combination group. CONCLUSION: Combination treatment with amantadine and tDCS had positive effects on balance and memory recovery after repetitive mTBI in rats. Therefore, we expect that the combination of amantadine and tDCS may be a treatment option for patients with repetitive mTBIs.

Transcranial direct current stimulation for balance and gait in repetitive mild traumatic brain injury in rats.

BACKGROUND: Balance impairment and lack of postural orientation are serious problems in patients with repetitive mild traumatic brain injury (mTBI). OBJECTIVE: To investigate whether anodal transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) can improve balance control and gait in repetitive mTBI rat models. METHODS: In this prospective animal study, 65 repetitive mTBI rats were randomly assigned to two groups: the tDCS group and the control group. To create repetitive mTBI model rats, we induced mTBI in the rats for 3 consecutive days. The tDCS group received one session of anodal tDCS over the M1 area 24 h after the third induced mTBI, while the control group did not receive tDCS treatment. Motor-evoked potential (MEP), foot-fault test, and rotarod test were evaluated before mTBI, before tDCS and after tDCS. The Mann-Whitney U test and Wilcoxon signed rank test were used to assess the effects of variables between the two groups. RESULTS: Anodal tDCS over the M1 area significantly improved the amplitude of MEP in the tDCS group (p = 0.041). In addition, rotarod duration was significantly increased in the tDCS group (p = 0.001). The foot-fault ratio was slightly lower in the tDCS group, however, this was not statistically significant. CONCLUSION: Anodal tDCS at the M1 area could significantly improve the amplitude of MEP and balance function in a repetitive mTBI rat model. We expect that anodal tDCS would have the potential to improve balance in patients with repetitive mTBI.

Analyzing Somatic Genome Rearrangements in Human Cancers by Using Whole-Exome Sequencing.

Although exome sequencing data are generated primarily to detect single-nucleotide variants and indels, they can also be used to identify a subset of genomic rearrangements whose breakpoints are located in or near exons. Using >4,600 tumor and normal pairs across 15 cancer types, we identified over 9,000 high confidence somatic rearrangements, including a large number of gene fusions. We find that the 5' fusion partners of functional fusions are often housekeeping genes, whereas the 3' fusion partners are enriched in tyrosine kinases. We establish the oncogenic potential of ROR1-DNAJC6 and CEP85L-ROS1 fusions by showing that they can promote cell proliferation in vitro and tumor formation in vivo. Furthermore, we found that ∼4% of the samples have massively rearranged chromosomes, many of which are associated with upregulation of oncogenes such as ERBB2 and TERT. Although the sensitivity of detecting structural alterations from exomes is considerably lower than that from whole genomes, this approach will be fruitful for the multitude of exomes that have been and will be generated, both in cancer and in other diseases.

Comparison of Tumor Microenvironments between Primary Tumors and Lymph Node Metastases in Head and Neck Squamous Cell Carcinoma and Their Predictive Role in Immune Checkpoint Inhibitor Treatment.

The relationship between tumor microenvironments (TMEs) of regional lymph node metastases (LNMs) and primary tumors in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study compared tumor-infiltrating lymphocytes (TILs) and the immune phenotype (IP), characterized by spatial TIL distribution, between primary tumors and LNMs. Twenty-one HNSCC patients with regional LNM who received immune checkpoint inhibitors (ICIs) were included. A paired comparative analysis of TIL densities and IP between primary tumors and LNMs revealed no significant difference or correlation between TIL densities in primary tumors and LNMs. Their IPs were discordant in 12 patients (57.1%). Patients with high intratumoral TIL exhibited longer progression-free survival (PFS) than those with low intratumoral TIL in both primary tumors (median, 5.2 vs. 1.3 months, p = 0.003) and LNMs (median, 30.2 vs. 1.3 months, p = 0.012). Patients with inflamed IP exhibited longer PFS than those with non-inflamed IP in both primary tumors (median, 4.5 vs. 1.3 months, p = 0.043) and LNMs (median, 4.1 vs. 1.3 months, p = 0.037). Given the lack of correlation in TIL densities, the discrepancies in IP, and the predictive value of both TMEs, evaluating the TMEs of both primary tumors and LNMs may be beneficial for the precise use of ICIs in HNSCC. There was a significant discordance between the TME of primary tumors and LNMs, with implications in survival outcomes. Therefore, evaluating the TME of both the primary tumor and LNM could be beneficial for the precise use of ICIs in HNSCC.

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer.

PURPOSE: Recently, anti-programmed cell death-1/anti-programmed cell death ligand-1 (anti-PD1/L1) immunotherapy has been demonstrated for its efficacy when combined with cytotoxic chemotherapy in randomized phase 3 trials for advanced biliary tract cancer (BTC). However, no biomarker predictive of benefit has been established for anti-PD1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TIL) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD1. EXPERIMENTAL DESIGN: Pretreatment hematoxylin and eosin (H&E)-stained whole-slide images from 339 patients with advanced BTC who received anti-PD1 as second-line treatment or beyond, were employed for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD1. Next, data and images of the BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IP in BTC. RESULTS: Overall, AI-IP were classified as inflamed [high intratumoral TIL (iTIL)] in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 patients (49.3%), and immune-desert (low TIL overall) in 132 patients (38.9%). The inflamed IP group showed a substantially higher overall response rate compared with the noninflamed IP groups (27.5% vs. 7.7%, P < 0.001). Median overall survival and progression-free survival were significantly longer in the inflamed IP group than in the noninflamed IP group (OS, 12.6 vs. 5.1 months; P = 0.002; PFS, 4.5 vs. 1.9 months; P < 0.001). In the TCGA cohort analysis, the inflamed IP showed increased cytolytic activity scores and IFNγ signature compared with the noninflamed IP. CONCLUSIONS: AI-IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD1 therapy. Further validation is necessary in the context of anti-PD1/L1 plus gemcitabine-cisplatin.

Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types.

BACKGROUND: The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. METHODS: Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. RESULTS: We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. CONCLUSION: The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer.

Tumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693-0.805) in comparison to the pathologists' scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01-1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.

Papillary thyroid cancer immune phenotypes via tumor-infiltrating lymphocyte spatial analysis.

Standard-of-care treatment options provide an excellent prognosis for papillary thyroid cancers (PTCs); however, approximately 10% of cases are advanced PTCs, resulting in less than 50% 5-year survival rates. Understanding the tumor microenvironment is essential for understanding cancer progression and investigating potential biomarkers for treatment, such as immunotherapy. Our study focused on tumor-infiltrating lymphocytes (TILs), which are the main effectors of antitumor immunity and related to the mechanism of immunotherapy. Using an artificial intelligence model, we analyzed the density of intratumoral and peritumoral TILs in the pathologic slides of The Cancer Genome Atlas PTC cohort. Tumors were classified into three immune phenotypes (IPs) based on the spatial distribution of TILs: immune-desert (48%), immune-excluded (34%), and inflamed (18%). Immune-desert IP was mostly characterized by RAS mutations, high thyroid differentiation score, and low antitumor immune response. Immune-excluded IP predominantly consisted of BRAF V600E-mutated tumors and had a higher rate of lymph node metastasis. Inflamed IP was characterized by a high antitumor immune response, as demonstrated by a high cytolytic score, immune-related cell infiltrations, expression of immunomodulatory molecules (including immunotherapy target molecules), and enrichment of immune-related pathways. This study is the first to investigate IP classification using TILs in PTC through a tissue-based approach. Each IP had unique immune and genomic profiles. Further studies are warranted to assess the predictive value of IP classification in advanced PTC patients treated with immunotherapy.

Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as a predictive biomarker for axitinib in adenoid cystic carcinoma.

BACKGROUND: This study analyzed the predictive value of artificial intelligence (AI)-powered tumor-infiltrating lymphocyte (TIL) analysis in recurrent or metastatic (R/M) adenoid cystic carcinoma (ACC) treated with axitinib. METHODS: Patients from a multicenter, prospective phase II trial evaluating axitinib efficacy in R/M ACC were included in this study. H&E whole-side images of archival tumor tissues were analyzed by Lunit SCOPE IO, an AI-powered spatial TIL analyzer. RESULTS: Twenty-seven patients were included in the analysis. The best response was stable disease, and the median progression-free survival (PFS) was 11.1 months (95% CI, 9.2-13.7 months). Median TIL densities in the cancer and surrounding stroma were 25.8/mm2 (IQR, 8.3-73.0) and 180.4/mm2 (IQR, 69.6-342.8), respectively. Patients with stromal TIL density >342.5/mm2 exhibited longer PFS (p = 0.012). CONCLUSIONS: Cancer and stromal area TIL infiltration were generally low in R/M ACC. Higher stromal TIL infiltration was associated with a longer PFS with axitinib treatment.

Highly Efficient Nitrogen-Fixing Microbial Hydrogel Device for Sustainable Solar Hydrogen Production.

Conversion of sunlight and organic carbon substrates to sustainable energy sources through microbial metabolism has great potential for the renewable energy industry. Despite recent progress in microbial photosynthesis, the development of microbial platforms that warrant efficient and scalable fuel production remains in its infancy. Efficient transfer and retrieval of gaseous reactants and products to and from microbes are particular hurdles. Here, inspired by water lily leaves floating on water, a microbial device designed to operate at the air-water interface and facilitate concomitant supply of gaseous reactants, smooth capture of gaseous products, and efficient sunlight delivery is presented. The floatable device carrying Rhodopseudomonas parapalustris, of which nitrogen fixation activity is first determined through this study, exhibits a hydrogen production rate of 104 mmol h-1  m-2 , which is 53 times higher than that of a conventional device placed at a depth of 2 cm in the medium. Furthermore, a scaled-up device with an area of 144 cm2 generates hydrogen at a high rate of 1.52 L h-1  m-2 . Efficient nitrogen fixation and hydrogen generation, low fabrication cost, and mechanical durability corroborate the potential of the floatable microbial device toward practical and sustainable solar energy conversion.

Artificial Intelligence-Powered Whole-Slide Image Analyzer Reveals a Distinctive Distribution of Tumor-Infiltrating Lymphocytes in Neuroendocrine Neoplasms.

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework.

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

A Randomized Clinical Trial of Regdanvimab in High-Risk Patients With Mild-to-Moderate Coronavirus Disease 2019.

Background: We evaluated clinical effectiveness of regdanvimab (CT-P59), a severe acute respiratory syndrome coronavirus 2 neutralizing monoclonal antibody, in reducing disease progression and clinical recovery time in patients with mild-to-moderate coronavirus disease 2019 (COVID-19), primarily Alpha variant. Methods: This was phase 3 of a phase 2/3 parallel-group, double-blind, randomized clinical trial. Outpatients with mild-to-moderate COVID-19 were randomized to single-dose regdanvimab 40 mg/kg (n = 656) or placebo (n = 659), alongside standard of care. The primary endpoint was COVID-19 disease progression up to day 28 among "high-risk" patients. Key secondary endpoints were disease progression (all randomized patients) and time to recovery (high-risk and all randomized patients). Results: Of 1315 randomized patients, 880 were high risk; the majority were infected with Alpha variant. The proportion with disease progression was lower (14/446, 3.1% [95% confidence interval {CI}, 1.9%-5.2%] vs 48/434, 11.1% [95% CI, 8.4%-14.4%]; P < .001) and time to recovery was shorter (median, 9.27 days [95% CI, 8.27-11.05 days] vs not reached [95% CI, 12.35-not calculable]; P < .001) with regdanvimab than placebo. Consistent improvements were seen in all randomized and non-high-risk patients who received regdanvimab. Viral load reductions were more rapid with regdanvimab. Infusion-related reactions occurred in 11 patients (4/652 [0.6%] regdanvimab, 7/650 [1.1%] placebo). Treatment-emergent serious adverse events were reported in 5 of (4/652 [0.6%] regdanvimab and 1/650 [0.2%] placebo). Conclusions: Regdanvimab was an effective treatment for patients with mild-to-moderate COVID-19, significantly reducing disease progression and clinical recovery time without notable safety concerns prior to the emergence of the Omicron variant. Clinical Trials Registration: NCT04602000; 2020-003369-20 (EudraCT).

Comparative efficacy of subcutaneous (CT-P13) and intravenous infliximab in adult patients with rheumatoid arthritis: a network meta-regression of individual patient data from two randomised trials.

BACKGROUND: A subcutaneous (SC) formulation of infliximab biosimilar CT-P13 is approved in Europe for the treatment of adult patients with rheumatoid arthritis (RA). It may offer improved efficacy versus intravenous (IV) infliximab formulations. METHODS: A network meta-regression was conducted using individual patient data from two randomised trials in patients with RA, which compared CT-P13 SC with CT-P13 IV, and CT-P13 IV with reference infliximab IV. In this analysis, CT-P13 SC was compared with CT-P13 IV, reference infliximab IV and pooled data for both reference infliximab IV and CT-P13 IV. Outcomes included changes from baseline in 28-joint Disease Activity Score based on C-reactive protein (DAS28-CRP), Simplified Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI), and rates of remission, low disease activity or clinically meaningful improvement in functional disability per Health Assessment Questionnaire-Disability Index (HAQ-DI). RESULTS: The two studies enrolled 949 patients with RA; pooled data for 840 and 751 patients were evaluable at weeks 30 and 54, respectively. For the CT-P13 SC versus pooled IV treatment arm comparison, differences in changes from baseline in DAS28-CRP (- 0.578; 95% confidence interval [CI] - 0.831, - 0.325; p < 0.0001), CDAI (- 3.502; 95% CI - 5.715, - 1.289; p = 0.002) and SDAI (- 4.031; 95% CI - 6.385, - 1.677; p = 0.0008) scores at 30 weeks were statistically significant in favour of CT-P13 SC. From weeks 30 to 54, the magnitude of the differences increased and remained statistically significant in favour of CT-P13 SC. Similar results were observed for the comparison of CT-P13 SC with CT-P13 IV and with reference infliximab IV. Statistically significant differences at week 30 favoured CT-P13 SC over the pooled IV treatment arms for the proportions of patients achieving EULAR-CRP good response, American College of Rheumatology (ACR) 50 and ACR70 responses, DAS28-CRP-defined remission, low disease activity (DAS28-CRP, CDAI and SDAI criteria) and clinically meaningful HAQ-DI improvement. CONCLUSIONS: CT-P13 SC was associated with greater improvements in DAS28-CRP, CDAI and SDAI scores and higher rates of clinical response, low disease activity and clinically meaningful improvement in functional disability, compared with CT-P13 IV and reference infliximab IV. TRIAL REGISTRATION: EudraCT, 2016-002125-11 , registered 1 July 2016; EudraCT 2010-018646-31 , registered 23 June 2010.

Efficacy and safety of subcutaneous infliximab versus adalimumab, etanercept and intravenous infliximab in patients with rheumatoid arthritis: a systematic literature review and meta-analysis.

OBJECTIVES: There are few comparative data for tumor necrosis factor inhibitors in patients with rheumatoid arthritis (RA). METHODS: Historical data for reference product/biosimilar intravenous infliximab, or adalimumab and etanercept, were pooled and compared with phase 3 study results for a subcutaneous (SC) formulation of the infliximab biosimilar CT-P13, in a systematic review and meta-analysis (PROSPERO: CRD42019149621). RESULTS: The authors identified 13 eligible controlled trials that randomized over 5400 participants to prespecified treatments of interest. Comparison with pooled historical data suggested a numerical advantage for CT-P13 SC over intravenous infliximab for almost every prespecified efficacy outcome evaluated, including Disease Activity Score in 28 joints (C-reactive protein/erythrocyte sedimentation rate), Clinical/Simplified Disease Activity Index scores, American College of Rheumatology responses, and multiple measures of disease remission and low disease activity; for the majority of outcomes, there was no overlap in 95% confidence intervals between groups. A numerical advantage for CT-P13 SC was also observed for safety outcomes (adverse events, infections, and discontinuations). Similar, but less marked, trends were observed for comparison with historical efficacy and safety data for adalimumab/etanercept. CONCLUSION: CT-P13 SC offers an improved or similar benefit-to-harm ratio compared with infliximab (intravenous) and adalimumab/etanercept, for the treatment of moderate-to-severe RA.