bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial intelligence (AI) –powered spatial analysis of macrophages in tumor microenvironment and its association with interferon-gamma (IFNG) signature and immune phenotype (IP) in pan-cancer dataset.
Authors: Oum Chiyoon , Park Gahee , Song Heon , Lee Jinhee , Pereira Sergio , Song Sanghoon , Kim Sukjun , Jung Wonkyung , Ock Chan-Young 
Published: June 2023

2621 Background: Macrophages are known to play a crucial role in cancer progression, but few studies have reported macrophage spatial distribution and its association with genomic signatures and tumor-infiltrating lymphocytes (TIL). Here, we applied an AI-powered Whole Slide Image (WSI) analyzer in The Cancer Genome Atlas (TCGA) pan-cancer dataset to show correlation between specific localization of macrophage and anti-tumor activity, which is reflected by IFNG signature and IP based on spatial TIL density. Methods: For spatial macrophage analysis in TCGA pan-cancer dataset, we used Lunit SCOPE IO, an AI-powered H&E WSI analyzer, which identifies and quantifies various classes of cells such as tumor cell, macrophage, and TIL within cancer or stroma area. IPs were defined as follows: inflamed IP (IIP) as high intratumoral TIL (iTIL) and stromal TIL (sTIL); immune-excluded IP (IEP) as low iTIL and high sTIL; immune-desert IP (IDP) as low TIL overall. The immune cell profiling and quantification of the IFNG signature score were analyzed by using CIBERSORT and RSEM. Results: In TCGA dataset (N = 7,402), median macrophage density in total TME area (tMP) was 19.9 /mm2 (interquartile range [IQR] 9.0-44.9), and it was highly enriched in lung adenocarcinoma (median 65.1, IQR 34.3-110.0), lung squamous cell carcinoma (61, 32.4-101.3), and head and neck squamous cell carcinoma (39.4, 19.6-77.3). Based on IP classification, tMP was highly enriched in IIP (37.8, 18.7-73.8) compared to IEP (25.5, 13.8-48.6) and IDP (8.9, 4.4-17.1). Interestingly, spatial analysis showed that intratumoral macrophage density (iMP) in IIP was highly enriched compared to the others (mean fold change 2.57) and the difference of stromal macrophage density (sMP) among IPs was relatively smaller (mean fold change x1.85). Classified into four groups based on median values of iMP and sMP, high-iMP/high-sMP group had the highest proportion of IIP (47.3% vs 18.7%, p < 0.001), whereas low-iMP/high-sMP group had the highest one of IEP (54.9% vs 32.3%, p < 0.001) compared to the others, respectively. Moreover, the high-iMP/high-sMP group had the highest level of IFNG signature score among 4 groups (x2.13 times higher, p < 0.001). Notably, both iMP and sMP were significantly correlated with CD68 expression (rho 0.292 and 0.287) but CSF1R expression was more strongly correlated with iMP (rho 0.259) compared to sMP (rho 0.078). Conclusions: IIP, IFNG signature, and CSF1R expression are more strongly correlated with intratumoral macrophage density versus stromal macrophage density, suggesting a relationship between macrophage location and function in the TME warranting further investigation.
Role of HLA class-I antigen peptide loading complex components in immune evasion and treatment sensitivity in human lung cancer.
Authors: Costantini Adrien , Ranjan Kishu , Ferrone Soldano , Schalper Kurt A. 
Published: June 2023

2623 Background: Determinants of sensitivity and resistance to immune checkpoint inhibitors (ICIs) are poorly understood. An intact HLA class-I antigen presentation machinery (APM) is necessary for tumor-antigen recognition by effector T-cells and disruptions to the APM can cause ICI resistance. The peptide loading complex (PLC) is a multi-chaperone complex located in the endoplasmic reticulum in which tapasin (Tap), calreticulin (CalR) and ERp57 play major roles. The PLC allows for optimal peptide selection and loading onto HLA class-I heavy chain and is critical for adequate cell surface presentation of peptide-HLA complexes. We studied the frequency and clinical significance of cancer-cell selective PLC protein defects in human NSCLC. Methods: Multiplex quantitative immunofluorescence (mQIF) was performed to simultaneously and spatially measure cytokeratin (CK), Tap, CalR, ERp57 and CD8 protein levels in 1,031 primary NSCLCs from 5 independent cohorts represented in tissue microarrays. The protein levels were measured in CK-positive cancer-cells and in CK-negative stromal cells. The presence of lower marker levels in malignant cells relative to stromal cells within the same sample was considered as cancer-cell selective downregulation. Cohorts #1-3 included baseline samples from patients treated without ICIs and Cohort #4 included samples from patients treated with ICIs. Cohort #5 contained lung adenocarcinomas clinically tested for EGFR and KRAS mutations. The association between the levels of Tap, CalR and ERp57, their association with clinicopathologic variables, CD8+ T-cell levels and outcomes were studied. Results: mQIF analysis identified cancer-cell selective downregulation of Tap, CalR and/or ERp57 in 73%, 81%, 68% and 75% of cases from Cohorts #1-4, respectively. The most common PLC alterations were cancer-cell downregulation of Tap alone in Cohorts #1 (32%), #3 (34%) and #4 (36%); and Tap-CalR in Cohort #2 (29%). Tap and ERp57 levels were significantly higher in stage IV than in earlier stage tumors. The PLC marker levels and the frequency of cancer-cell selective downregulation were comparable across EGFR and KRAS mutated samples. NSCLCs with cancer-cell Tap downregulation had lower CD8+ T-cell infiltration and shorter OS across all cohorts that reached statistical significance in Cohort #4 (median OS 12.08 vs 21.00, HR=1.72, 95% CI 1.02-2.89, p=0.034). The cancer-cell selective downregulation of CalR was associated with shorter OS in cohorts #1 and #3. Conclusions: HLA class-I PLC components show variable levels in treatment naïve NSCLCs. Downregulation of Tap in cancer-cells, alone or in combination with other PLC members occurs in up to 60% of cases and is associated with reduced CD8+ TILs and shorter OS preferentially in patients treated with ICIs. Cancer-cell Tap downregulation could mediate primary ICI resistance and has biomarker potential.
Using machine learning to characterize lung cancer microenvironment and the development of a model to predict the presence of similar microenvironment in other cancers.
Authors: Charifa Ahmad , Zhang Hong , Ip Andrew , Estella Jeffrey , Ma Wanlong , Mohtashamian Arash , Gutierrez Martin , Pecora Andrew L , Goy Andre , Albitar Maher 
Published: June 2023

2634 Background: Poor response to immune checkpoint inhibitors (ICI) in colorectal cancer (CRC) is believed to be due to lack of immune suppressive tumor microenvironment (TME). In contrast, lung cancer TME is believed to be significantly more immunologically active and responsible for the relative success of ICI in lung cancer. We evaluated the TME in lung cancer and CRC using 43 immune biomarkers quantified using RNA sequencing and developed a model to classify TME into immunologically active (similar to lung cancer) vs inactive (similar to colorectal). These 43 immune biomarkers included B- and T-cell markers, cytokines and chemokines. Methods: RNA was extracted from FFPE samples from 707 patients with lung cancer, 227 patients with CRC, 131 patients with breast cancer, 111 patients with ovarian cancer, and 72 patients with pancreatic cancer. The expression levels of the 42 immunological markers were quantified using next generation sequencing (NGS) as a part of larger targeted RNA sequencing panel of 1408 genes. Using a machine learning algorithm, we first selected the relative genes that distinguish between two classes using two criteria: performance of each gene with K-fold cross-validation (K=12) and second based on stability measure using statistical significance tests. The selected genes were then used to predict one class from the other using random forest classifier. Samples were divided into a training set (67%) and testing set (33%). Results: A Bayesian-based algorithm selected the expression of 20 genes that were significantly relevant in differentiating between immunologically active and inactive TME. Using these 20 genes in Random Forest model, we can distinguish between lung and CRC with AUC of 0.997 (95% CI: 0.992-1.00) in the training set and AUC of 0.923 (95% CI: 0.880-0.966) in the testing set. Testing 131 breast cancer samples showed 23 (18%) with TME that can be classified as immunologically active. Of 111 ovarian samples 13 (12%) showed immunologically active TME and of 72 pancreatic samples, 17 (24%) showed microenvironment classified as active. The 20 genes that were adequate to distinguish between TME active vs inactive are: CD74, FCGBP, IL1R1, CD44, CD274, FCGR2B, IL21R, IL1RAP, IL7R, CD79A, CCL2, CYFIP2, CD19, IL2RA, CD8A, CD79B, ID1, CD22, FZD10, and IL1B, listed in order of their importance. Conclusions: This data shows that lung cancer TME is significantly different from that of CRC. Only 20 immune biomarkers adequate to distinguish between the two TME. The relevant biomarkers included CD274 (PD-L1) as well as one marker for T-cells (CD8A) but three markers for B-cells (CD19, CD22 and CD79B). This suggests that B-cells play a significant role in immunologically active TME and should be explored further as biomarkers for predicting response to ICI therapy.
Timing of immunotherapy-induced pneumonitis in patients with primary lung malignancy.
Authors: Winter Megan , Gilani Madiha Atif , Anipindi Manasa , Venigalla Tejaswi , Doreswamy Shriya 
Published: June 2023

2647 Background: Pneumonitis is a widely recognized side effect of immunotherapy with increased incidence in non-small cell lung cancers as compared to other solid organ malignancies. A review of the literature demonstrates an incidence of 5% in patients receiving PD-1/PD-L1 inhibitors. While biopsy provides definitive diagnosis of immunotherapy-induced pneumonitis, clinical symptoms and radiologic features are most utilized with treatment guided by the grade of toxicity. The ability to predict the timing, which patients, and to what extent pneumonitis will present has been a significant challenge. Our study aims to look at the timing of onset of immunotherapy-induced pneumonitis in patients treated with checkpoint inhibitors for primary lung cancer to elicit patterns which may assist in diagnosis and appropriate treatment. Methods: This is a single-institution, retrospective study reviewing patients with primary lung cancer who received checkpoint inhibitors between January 2018 and January 2023. The focus was on the date of initiation of therapy and the date of onset of pneumonitis. Immunotherapy-induced pneumonitis was defined as development of abnormal imaging findings with or without respiratory symptoms following initiation of immunotherapy and graded by the Common Terminology Criteria for Adverse Events (CTCAE). Additional patient demographics were collected to assess secondary patterns. Results: A total of 117 patients met the specified search criteria. Of these, 10 patients (8.5%) met the criteria for immunotherapy-induced pneumonitis. The average time between receiving the first dose of immunotherapy and the development of immunotherapy-induced pneumonitis was 66.5 days (range of 1 to 163 days). The average number of cycles received before diagnosis was 3.7, with most cases occurring after less than 5 cycles provided (7 out of 10). The grade of pneumonitis ranged from 1 to 5. All patients had non-small cell lung cancer, with the most prevalent histology being squamous cell carcinoma, ranging from stage III to stage IV. Durvalumab was the most prevalent immunotherapy (6 of the 10). Conclusions: The timing of immunotherapy-induced pneumonitis, while variable, occurred after receiving less than 5 cycles in majority of our cases, with most occurring after just 1 cycle. Interestingly, durvalumab was the most common immunotherapy in these cases. While our study is limited by the number of patients, the incidence of pneumonitis in our study was close to that of the national average and may, also, be reflective of the timing of immunotherapy-induced pneumonitis. Additional studies with larger numbers of patients are needed.
Risk of immune-related adverse events (irAE) with immune checkpoint inhibitors (ICI) in patients (pts) with family history (FH) of autoimmune disease (AID).
Authors: Grover Piyush , Li Isabel , Menzies Alexander M. , Long Georgina V. 
Published: June 2023

2648 Background: Pts with personal history (PH) of AID are at increased risk of irAE following ICI. Risk of irAE in pts with FH of AID is unknown. Methods: In this retrospective cohort study, consecutive pts were recruited from 2 Australian melanoma cancer centers. Pts who received anti-PD-1 (PD1) based ICI with FH of AID were eligible. Pts with PH of AID were excluded. Toxicity data (CTCAE v5.0) was obtained and stratified by FH of AID (first/second/third degree relative). Descriptive statistics and Fisher’s Exact test were used for analysis. Results: 396 melanoma cases were screened, of which 44 (11.1%) were eligible for inclusion. Baseline characteristics are summarized. 57 irAE occurred in 44 pts. The most common irAE were dermatological (n=11; 19.3%), colitis (n=8; 14.0%), endocrinopathies (n=8; 14.0%), hepatitis (n=7; 12.3%) and arthritis (n=7; 12.3%). Among 57 irAE, frequency of Grade 1,2,3 and 4 irAE was 16 (28.1%), 26 (45.6%), 12 (21.0%) and 3 (5.3%) respectively. Corticosteroids were used for 31 (54.4%) irAE. Hospital admission for management of irAE occurred in 11 pts (25.0%). ICI was discontinued in 20 pts (45.5%) due to irAE. In pts with irAE, FH of that irAE/AID was present in 6 pts (13.6%) (n= 2 x arthritis, n= 2 x psoriasis, n= 1 x Crohn’s disease, n= 1 x Grave’s disease), and 4 of these patients had a first-degree relative with an AID. 3/6 pts had ICI ceased due to an irAE (peak toxicity Grade was 3) and 4/6 pts had RECIST response. There was no association in development of irAE between patients with FH of AID in a first-degree relative and those with FH of AID in second or third degree relative (p=0.17). Rate of irAE in pts with FH of AID was not statistically higher to that reported in registration trials of similar regimens (p=0.40). Conclusions: FH of AID is common in melanoma pts and was not associated with higher rate or morbidity of irAE. FH AID should be routinely collected, and larger scale studies need to be performed. [Table: see text]
Mirzotamab clezutoclax as monotherapy and in combination with taxane therapy in relapsed/refractory solid tumors: Dose expansion results.
Authors: Carneiro Benedito A. , Perets Ruth , Dowlati Afshin , LoRusso Patricia , Yonemori Kan , He Lei , Munasinghe Wijith , Noorani Behnam , Johnson Eric Feral , Zugazagoitia Jon 
Published: June 2023

3027 Background: Mirzotamab clezutoclax (ABBV-155; Mirzo-C) is a first-in-class antibody-drug conjugate with three components: an anti–B7-H3 monoclonal antibody, a solubilizing linker, and a BCL-XL inhibitor payload. By specific targeting of BCL-XL, synergy with other anticancer agents was observed in preclinical models. When combined with a taxane, clinical antitumor activity was reported in relapsed and/or refractory (R/R) solid tumors during the dose escalation phase of an ongoing Phase 1, open-label study (NCT03595059). Methods: In the dose expansion phase of the Phase 1 study, patients were treated as follows: (i) R/R small cell lung cancer (SCLC) with Mirzo-C monotherapy, (ii) non-small cell lung cancer (NSCLC) with Mirzo-C + docetaxel, and (iii) hormone positive, Her2 negative, post–CDK4/6 inhibitor breast cancer (BrCa) with Mirzo-C + paclitaxel. Mirzo-C was administered at the recommended Phase 2 dose determined at dose escalation. Primary objectives were safety and overall response rate (ORR). Results: As of November 18, 2022, 78 patients were enrolled (SCLC n=14; NSCLC n=36; BrCa n=28). The median (range) follow-up was 2.0 (0.6–9.1) months for the SCLC cohort, 4.9 (0.6–8.5) for NSCLC, and 10.4 (0.8–13.3) for BrCa. The median number of prior lines of systemic therapy was 2 in the SCLC cohort, 2 in NSCLC, and 6 in BrCa. Prior taxane therapy was received by 7% of patients in the SCLC cohort, 36% in NSCLC, and 64% in BrCa. The most common adverse events (AEs) in SCLC were fatigue, increased aspartate aminotransferase, diarrhea, and headache (21%, n=3 for each AE); fatigue was the most common AE in NSCLC (58%, n=21; Grade 3/4: 3%, n=1) and in BrCa (61%, n=17). Thrombocytopenia, a result of the systemic effects of BCL-XL inhibitors, was not observed in the Mirzo-C monotherapy cohort. Neutropenia/decreased neutrophil count was not observed in the SCLC monotherapy cohort but was common with BrCa paclitaxel combo (64%, n=18; Grade 3/4: 29%, n=8) and NSCLC docetaxel combo (53%, n=19; Grade 3/4: 44%, n=16); 5 patients in the NSCLC cohort (14%) and 1 in BrCa (4%) had febrile neutropenia. Confirmed ORR was 0% in the SCLC cohort, 11% in NSCLC, and 18% in BrCa. Conclusions: Mirzo-C demonstrated a tolerable safety profile as monotherapy and with taxanes. No thrombocytopenia or neutropenia was observed with monotherapy. No single-agent activity of Mirzo-C was observed in SCLC. Combination treatments were active with high best overall response (BOR) and disease control rates in heavily pretreated NSCLC and BrCa patients, where Grade 3/4 neutropenia was observed. It is unknown if Mirzo-C exacerbated the neutropenia risk associated with taxanes. Clinical trial information: NCT03595059 . [Table: see text]
Construction of DNA methylation-based biomarkers for diagnosis of hepatocellular carcinoma.
Authors: Xu Ximing , Chen Jiayu , Shen Yang 
Published: June 2023

3048 Background: Hepatocellular carcinoma is the most common malignant tumor in China. Most of the patients are found in the advanced stage, which causes its low survival rate. According to the latest epidemiological data, hepatocellular carcinoma is the second leading cause of cancer mortality. DNA methylation is closely related to tumor development. In this study, differentially expressed methylated genes related to tumorigenesis and development were screened from the database and the diagnostic model was constructed for early screening. Methods: Limma package was used to analyze the differences between TCGA _ LIHC liver cancer and GSE113409 normal blood methylation sites and differentially expressed methylation sites (logFC>1，p<0.05) were identified. The TCGA _ LIHC methylation database was used as the training set. Lasso method, and random forest method were used for screening the differentially expressed methylation sites for the construction of the diagnostic model. After that, the characteristic coefficient of methylation sites and ROC curve of the diagnostic model were obtained from the training set. According to the cd-score, HCC patients could be diagnosed in the early stage, thus improved their survival. Results: Limma package was used to analyze the differences between TCGA _ LIHC liver cancer and GSE113409 normal blood methylation sites and 7109 differentially expressed methylation sites (logFC>1，p<0.05) were identified. Then the top 1000 differentially expressed methylation sites were selected. The TCGA _ LIHC methylation database was used as the training set. 30 methylation sites were screened by lasso method, and 20 methylation sites were screened by random forest. Taking the intersection, a total of 6 methylation sites were obtained for diagnostic model: cg00346208, cg00084798, cg00458878, cg00006397, cg00325910, cg00399027. The characteristic coefficient of methylation sites were displayed in the table below and the AUC of the diagnostic model was 0.9991579. High cd-score tended to have hepatocellular carcinoma, and vice versa. Conclusions: We have identified several DNA methylation markers for the diagnosis of hepatocellular carcinoma from GEO and TCGA databases. This model can predict hepatocellular carcinoma and the AUC is higher than that of AFP, which means it may become a more sensitive indicator for the diagnosis of liver cancer. [Table: see text]
Tumor mutational burden as a predictive biomarker for molecularly matched therapy in two independent pan-cancer cohorts.
Authors: Rieke Damian Tobias , de Bortoli Till , Benary Manuela , Horak Peter , Lamping Mario , Stintzing Sebastian , Tinhofer Inge , Leyvraz Serge , Schäfer Reinhold , Klauschen Frederick , Keller Ulrich , Stenzinger Albrecht , Froehling Stefan , Kurzrock Razelle , Jelas Ivan , Keilholz Ulrich 
Published: June 2023

3066 Background: Personalized cancer therapy based on molecular tumor aberrations has shown efficacy in a subset of tumors. Novel biomarkers are warranted to help extend this benefit to a larger patient population. Tumor mutational burden (TMB) is an established predictive biomarker for immune checkpoint inhibition. Its role for molecularly matched therapy is unknown. Methods: Comprehensive molecular profiling including whole-exome and RNA-sequencing was performed in 104 patients with advanced cancer within the DKTK MASTER program. 55 patients received a systemic therapy excluding immunotherapy. TMB and survival were analyzed in patients receiving molecularly matched therapy (n=34) or non-molecularly matched therapy (n=21) as well as in an independent published cohort of patients receiving molecularly matched (n=68) or non-molecularly matched (n=40) therapy (excluding immunotherapy). TMB and co-occurring driver mutations were analyzed in the DKTK MASTER cohort. Results: The median TMB of 1.68 mutations per megabase (mut/Mb) in the 34 patients receiving molecularly matched therapy was used to stratify patients into TMB-high and -low groups. Median overall survival (OS) and progression-free survival (PFS) were significantly shorter in the TMB-high than in the TMB-low group (OS: 4 months [95% CI, 3.3 to 7.6] versus 19 months [95%CI, 10.0 to not reached] p < 0.001, Hazard Ratio (HR) 4.4 [95%CI, 1.9 to 10.2], p<0.001; PFS 1.8 months [95%CI, 1.1 to 3.6] versus 8.5 months [95% CI, 2.8 to 17.0] p = 0.004, HR 2.9 [95%CI, 1.4 to 6.2], p=0.005). No significant differences were observed in patients receiving non-molecularly informed systemic therapy (OS HR 0.7 [95%CI, 0.2 to 2.6], p=0.635 and PFS HR 1.2 [95%CI, 0.4 to 3.5] p=0.701). In the validation cohort, shorter progression-free survival was also identified in the TMB-high group (median TMB cut-off of 4 mut/Mb; HR 2.2 [95%CI, 1.3 to 3.7] p=0.005) treated with molecularly matched therapy but not with non-molecularly informed therapy (HR 1.1 [95%CI, 0.5 to 2.2] p=0.848). TMB was associated with co-occurring driver mutations (n=104, r = 0.78 [95%CI, 0.68 to 0.85], p<0.001). Conclusions: High TMB was previously established as a positive predictive biomarker associated with efficacy of immune checkpoint inhibitor treatments. Our study reveals high TMB as a negative predictive biomarker associated with low efficacy of molecularly informed systemic therapy, potentially due to co-occurring driver mutations. Thus, TMB is a candidate predictive pan-cancer biomarker for combined precision oncology and immunotherapy programs.