Clinicopathological Subgroups Research Articles

TROP2 in colorectal carcinoma: associations with histopathology, molecular phenotype, and patient prognosis.

Antibody-drug conjugates (ADCs) directed to trophoblast cell surface antigen 2 (TROP2) have gained approval as a therapeutic option for advanced triple-negative breast cancer, and TROP2 expression has been linked to unfavourable outcomes in various malignancies. In colorectal carcinoma (CRC), there is still a lack of comprehensive studies on its expression frequency and its prognostic implications in relation to the main clinicopathological parameters. We examined the expression of TROP2 in a large cohort of 1,052 CRC cases and correlated our findings with histopathological and molecular parameters, tumour stage, and patient outcomes. TROP2 was heterogeneously expressed in 214/1,052 CRCs (20.3%), with only a fraction of strongly positive tumours. TROP2 expression significantly correlated with an invasive histological phenotype (e.g. increased tumour budding/aggressive histopathological subtypes), advanced tumour stage, microsatellite stable tumours, and p53 alterations. While TROP2 expression was prognostic in univariable analyses of the overall cohort (e.g. for disease-free survival, p < 0.001), it exhibited distinct variations among important clinicopathological subgroups (e.g. right- versus left-sided CRC, microsatellite stable versus unstable CRC, Union for International Cancer Control [UICC] stages) and lost its significance in multivariable analyses that included stage and CRC histopathology. In summary, TROP2 is quite frequently expressed in CRC and associated with an aggressive histopathological phenotype and microsatellite stable tumours. Future clinical trials investigating anti-TROP2 ADCs should acknowledge the observed intratumoural heterogeneity, given that only a subset of TROP2-expressing CRC show strong positivity. The prognostic implications of TROP2 are complex and show substantial variations across crucial clinicopathological subgroups, thus indicating that TROP2 is a suboptimal parameter to predict patient prognosis.

Excellent concordance of the molecular classification between preoperative biopsy and final hysterectomy in endometrial carcinoma

ObjectiveThe 2023 International Federation of Gynecology and Obstetrics classification with molecular classification shows superior discriminatory ability compared to staging systems lacking molecular data. However, the accuracy of endometrial biopsy data in molecular classification remains uncertain. This study aimed to assess the concordance of molecular classifications between preoperative biopsy and hysterectomy to predict prognosis before surgical staging. MethodsEndometrial biopsies and corresponding hysterectomy specimens were collected at the National Cancer Center Hospital between 2012 and 2023. Immunohistochemistry for p53 and mismatch repair (MMR) proteins and next-generation sequencing of all exons of polymerase epsilon (POLE) were performed. Given the limited number of POLE mut cases in prior studies, we prepared a POLE mut-enriched cohort. Cohen's kappa estimates were used to determine concordance for molecular and clinicopathological subgroup assignments. ResultsAmong 70 patients classified into four molecular subtype groups, 33 exhibited POLE mutations, 15 showed loss of MMR protein expression, 13 had p53-abnormality, and 9 had no specific molecular profile. Concordance between biopsy and hysterectomy specimens was 100% (κ = 1.000). In contrast, histological types and grades between biopsy and surgical specimens showed moderate and substantial agreement (κ = 0.420 and κ = 0.780, respectively). ConclusionsMolecular subtypes were completely consistent with those derived from surgical specimens, demonstrating high concordance between preoperative and postoperative molecular classifications. This suggests that endometrial biopsies could reliably predict prognosis. Future studies should investigate how biopsy-based molecular profiling influences treatment planning and patient outcomes.

Comparative Immunohistochemical Analysis of Clinicopathological Subgroups in Hepatocellular Carcinomas from Japan and Indonesia

BackgroundStandardized pathological evaluation based on immunohistochemical (IHC) analysis could improve HCC diagnoses worldwide. We evaluated differences in clinicopathological subgroups in HCCs from two academic institutions in Tokyo-Japan, and Jakarta-Indonesia. MethodsClinicopathological parameters and molecular expression patterns were evaluated in 35 HCCs from Indonesia and 41 HCCs from Japan. IHC analysis of biliary/stem-cell (B/S) markers (cytokeratin 19, sal-like protein 4, epithelial cell adhesion molecule) and Wnt/β-catenin (W/B) signaling-related molecules (β-catenin, glutamine synthetase) could determine the IHC-based subgroups. For immunosubtypes categorization, CD3/CD79α double immunohistochemistry was done to evaluate the infiltration of T and B cells. CD34 staining allowed identification of vessels that encapsulated tumor clusters (VETC). ResultsIndonesian HCC patients were mostly <60 years old (66%) with a hepatitis B virus (HBV) background (82%), in contrast to Japanese HCC patients (8% and 19%, respectively, both P<0.001). In comparison with Japanese, Indonesian cases more frequently had >5 cm tumor size (74% vs 23%, P=0.001), poor differentiation (40% vs 24%), portal vein invasion (80% vs 61%), and AFP levels >500 ng/ml (45% vs 13%, P=0.005). No significant differences were found in the proportions of B/S, W/B, and -/- subgroups from both countries. No Immune-high tumors were observed among Indonesian cases, and Immune-low tumors (66%) were more common than in Japanese cases (54%). VETC-positive tumors in Indonesia were significantly more common (29%), and most were in the HBV (90%) and -/- subgroups (90%), whereas Japanese VETC cases (10%, P=0.030) were nonviral (100%) and W/B subgroups (75%). ConclusionIHC-based analysis more precisely reflected the clinicopathological differences of HCCs in Japan and Indonesia. These findings provide new insights into standardization attempts and HCC heterogeneity among countries.

Biomarkers of systemic inflammation provide additional prognostic stratification in cancers of unknown primary.

Biomarkers of systemic inflammation have been shown to predict outcomes in patients with cancer of unknown primary (CUP). We sought to validate these findings in patients with confirmed CUP (cCUP) and explore their role alongside existing clinicopathological prognostic categories. CUP oncologist from across the United Kingdom were invited to include patients with cCUP referred to their local CUP multidisciplinary team. Patient demographics, clinical, pathological and outcome data were recorded and analysed. Data were available for 548 patients from four CUP services. 23% (n = 124) of patients met clinicopathological criteria for favourable-risk cCUP. On multivariate analysis c-reactive protein (CRP) (p < 0.001) and the Scottish Inflammatory Prognostic Score (SIPS: combining albumin and neutrophil count) (p < 0.001) were independently predictive of survival. CRP and SIPS effectively stratified survival in patients with both favourable-risk and poor-risk cCUP based on clinicopathological features. Biomarkers of systemic inflammation are reliable prognostic factors in patients with cCUP, regardless of clinicopathological subgroup. We recommend that CRP or SIPS are incorporated into routine clinical assessments of patients with cCUP as a tool to aid investigation and/or treatment decision-making across all groups. Established clinicopathological factors can then be used to inform management pathways and specific systemic anticancer therapy selection.

TSPO is a potential independent prognostic factor associated with cellular respiration and p16 in head and neck squamous cell carcinoma.

Treatment resistance and relapse are common problems in head and neck squamous cell carcinoma (HNSCC). Except for p16, no clinically accepted prognostic biomarkers are available for HNSCC. New biomarkers predictive of recurrence and survival are crucial for optimal treatment planning and patient outcome. High translocator protein (TSPO) levels have been associated with poor survival in cancer, but the role of TSPO has not been extensively evaluated in HNSCC. TSPO expression was determined in a large population-based tissue microarray cohort including 611 patients with HNSCC and evaluated for survival in several clinicopathological subgroups. A TCGA HNSCC cohort was used to further analyze the role of TSPO in HNSCC. TSPO expression was downregulated in more aggressive tumors. Low TSPO expression associated with worse 5-year survival and was an independent prognostic factor for disease-specific survival. Subgroup analyses showed that low TSPO expression associated with worse survival particularly in p16-positive oropharyngeal cancer. In silico analyses supported the prognostic role of TSPO. Cellular respiration had the highest significance in pathway analyses for genes expressed positively with TSPO. Decreased TSPO expression associates with poor prognosis in HNSCC. TSPO is a prognostic biomarker in HNSCC to potentially guide treatment stratification especially in p16-positive oropharyngeal cancer.

Influence of Co-Mutational Patterns in Disease Phenotype and Clinical Outcomes of Chronic Myelomonocytic Leukemia

Introduction: Chronic myelomonocytic leukemia (CMML) is characterized by recurrent somatic mutations in a conserved number of genes and functional pathways. Although specific genomic events are associated with disease phenotype and prognosis, identification of genomically-defined disease subgroups based on common recurrent cooperative genomic patterns is needed to refine disease classification and identify disease subsets with unique clinical behavior. Methods: To evaluate if CMML is defined by recurrent co-mutational profiles defining unique clinicopathologic subgroups we evaluated a cohort of 296 patients (pts) with newly diagnosed CMML. Whole bone marrow DNA was subject to 81 gene targeted next-generation sequencing analysis. Akaike information criterion (AIC) method was applied to evaluate mixture model components defining clusters based on mutational profile. Expectation-maximization (EM) algorithm was used for mixture model fitting to multivariate binary data. Unsupervised cluster membership was chosen as a function of the component with the highest weight for any individual, and we examined means of cluster centers. Cox proportional hazards models, regressing overall survival (OS) and leukemia-free survival (LFS), were fit on cluster in some cases and normalized cluster weights in other cases, with and without adjustment for age and sex. Two degree of freedom likelihood ratio tests was performed on cluster and cluster weight in all models regardless of covariate adjustment. Results: Median age of the population was 70 years [range 30-71]; 160 (54%) pts had myeloproliferative CMML (MP-CMML) and 152 (51%) had CMML-2 by WHO classification. Most frequent mutations included TET2 (51%), ASXL1 (48%), SRSF2 (40%), RUNX1 (22%), NRAS (18%), CBL (14%) and KRAS (14%) all present in &amp;gt;10% of pts. Based on frequency and co-occurrence of mutations, AIC optimization identified 3 unique clusters. Clusters based on mutation landscape are shown in Figure 1A. Unsupervised clustering allowed attribution of individual patients to specific clusters. Cluster 1 (C1, n=107), was characterized by near universal presence of ASXL1 mutations (86%), with lower frequency of TET2 mutations, and enrichment of RUNX1 (35%), NRAS (28%) and U2AF1 (18%) mutations. SETBP1 and EZH2 mutations were nearly exclusively observed in C1 (16% and 20% of C1 pts, respectively). All pts with ETV6 mutations belonged to this cluster. Cluster 2 (C2, n=135) was defined by universal presence of TET2 mutations (93%) with frequent SRSF2 co-mutation (56%) and lower frequency of ASXL1 (34%), RUNX1 (15%) or NRAS (12%) mutations. Despite their overall low frequency, BRAF mutations were restricted to C2, while CBL mutations were observed at similar frequencies in C1 and C2. This suggests that co-mutation profiles and genomic context differs among genes involved in RAS pathway signaling. Cluster 3 (C3, n=54) included a minor and more genomically heterogenous group enriched for SF3B1 (25%), KRAS (24%), DNMT3A (22%) and TP53 (15%) mutations, with absence of ASXL1 mutations, and low frequency of TET2 (12%) mutations. All BCORL1 and WT1 mutations and most ASXL2 and CEBPA mutations were in C3. RUNX1 mutations not only were more frequent among C1 but appeared at higher median VAF compared to other clusters (C1: 40%, C2: 11%, C3: 25%, p=0.003). Mutations in U2AF1 had higher median VAF in C3 compared to cluster 1 (49% vs 43%, p=0.017). C1 was enriched for MP-CMML (C1: 64%; C2: 48%; C3: 52%, p&amp;lt;0.001) and had higher frequency of del(7q)/-7 (C1: 11%; C2 0%, C3: 6%, p&amp;lt;0.001) and trisomy 8 (C1: 15%, C2: 7%, C3: 6%, p=0.05). Complex karyotype (C1: 1%, C2: 1%, C3: 22%, p&amp;lt;0.001) was nearly exclusive of C3, while C2 was more likely to be associated with a normal karyotype (C1: 59%; C1: 73%; C3: 41%, p&amp;lt;0.001) and higher median hemoglobin (C1: 9.8g/dL; C2: 12.1g/dL, C3: 9.7g/dL, p&amp;lt;0.001). In total 66% of pts received therapy with hypomethylating agents with no differences in response, median time to or duration of response between clusters. Use of Cox proportional hazards model identified that, when corrected by age and sex, patients on C1 and C3, based on attributed cluster weights, had significantly worse LFS (p=0.02, Figurer 1B) and OS (p=0.05) than those in C2. Conclusions: These data suggest that somatic mutations in CMML have unique patterns of clustering that define disease phenotype and influence disease outcomes.

Paraneoplastic dermatomyositis associated with urothelial cancer: report of a case and systematic review of the literature.

The idiopathic inflammatory myopathies (IIM) are a collection of autoimmune diseases that have a substantial impact on the entire body and include conditions such as dermatomyositis (DM), polymyositis (PM), sporadic inclusion body myositis, and immune-mediated necrotizing myopathy. These disorders are characterized by symptoms such as muscular weakness, pain, and dermal rash. This systematic review is intended to explore the potential link between bladder cancer and DM/PM. We performed a comprehensive systematic search on PubMed and Scopus until August 2022 to identify relevant research studies. The studies that met our inclusion criteria focused on patients with urinary bladder cancer and dermatomyositis, and/or polymyositis. The patients' median age was 65.5 years (47-79), with the majority being male (15, 39.47%). Bladder cancer manifested before PM/DM in 5 (13.15%) patients, while in the majority of cases occurred after the cancer diagnosis. The stage of cancer at the time of the initial PM/DM diagnosis were mostly locally (11/20, 50%).During the first presentation, the patients had a median creatine kinase level of 2227 U/L, ranging between 44 and 10471. In one case, anti-TIF-1γ antibodies were found to be present. Among the cases with reported medical history (20/38), treatment immediately improved DM symptoms in 16 patients(53.8%) and in 3 patients(15%), symptoms of DM resurfaced during the period after the operation. Death was reported in 14 (36.8%) patients. In conclusion, our study provides knowledge and understanding for identifying specific risk factors in patients with the coexistence of bladder cancer and DM/PM and their management. During the initial and follow-up screening, age, gender, and the clinicopathological subgroup of myositis should be considered to ensure proper management of the condition.

Prognostic value and immune-infiltration pattern of FOXD3-AS1 in patients with glioma.

Gliomas are difficult-to-treat brain tumors due to their aggressive nature, rapid proliferation, and high invasiveness (Zhang et al., J Cell Biochem, 2019, 120 (9), 15106-15118; Ge et al., Int J Biochem Cell Biol, 2021, 139, 106054). FOXD3-AS1 has been identified as an emerging potential target for tumor prediction and treatment in many studies (Qin et al., Front Oncol, 2021, 11, 688027). However, the utility of FOXD3-AS1 has not been reported in glioma patients (Li et al., Cancer Manag Res, 2021, 13, 9037-9048). The differential profiles of FOXD3-AS1 in TCGA-GBMLGG database were analyzed across clinical subgroups. The analysis of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) revealed that a high level of FOXD3-AS1 was associated with a poor prognosis and survival outcome. Based on the Cox regression analysis, FOXD3-AS1 was found to be a high-risk factor for glioma that affects prognosis outcomes independently. More importantly, because oxidative stress is closely linked to glioma prognosis, we focused on the potential mechanisms of six oxidative stress co-expressed genes with FOXD3-AS1. In addition, the predictive value of FOXD3-AS1 was determined for each clinical subgroup status. The ROC curve results showed that FOXD3-AS1 had a good predictive performance. A stratified clinicopathological subgroup analysis revealed that high expression of FOXD3-AS1 is associated with a poor prognosis. This also indicates a link between FOXD3-AS1 and tumorigenesis and prognosis, which has potential application value. Furthermore, the immune cell infiltration of FOXD3-AS1 and the signal marker correlation suggested that immune cell infiltration differed significantly between immune cell subsets. To the best of our knowledge, this is the first report to investigate FOXD3-AS1 in glioma and how it may modulate GBM and LGG immune microenvironments. Furthermore, FOXD3-AS1 was detected in tumor and paraneoplastic tissues using RT-qPCR. Transwell analysis verified the migration and invasion of the FOXD3-AS1 knockout group in vitro to a certain extent. In conclusion, FOXD3-AS1 can be used as a prognostic indicator for GBM and LGG, and it is closely related to immune infiltration and response to oxidative stress, which may contribute to the advancement of glioma immunotherapy research.

Identification of an unfolded protein response-related signature for predicting the prognosis of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy. An effective prognosis prediction model is urgently needed for treatment optimization. The differentially expressed unfolded protein response (UPR)‒related genes between pancreatic tumor and normal tissue were analyzed using the TCGA-PDAC dataset, and these genes that overlapped with UPR‒related prognostic genes from the E-MTAB-6134 dataset were further analyzed. Univariate, LASSO and multivariate Cox regression analyses were applied to establish a prognostic gene signature, which was evaluated by Kaplan‒Meier curve and receiver operating characteristic (ROC) analyses. E‒MTAB‒6134 was set as the training dataset, while TCGA-PDAC, GSE21501 and ICGC-PACA-AU were used for external validation. Subsequently, a nomogram integrating risk scores and clinical parameters was established, and gene set enrichment analysis (GSEA), tumor immunity analysis and drug sensitivity analysis were conducted. A UPR-related signature comprising twelve genes was constructed and divided PDAC patients into high- and low-risk groups based on the median risk score. The UPR-related signature accurately predicted the prognosis and acted as an independent prognostic factor of PDAC patients, and the AUCs of the UPR-related signature in predicting PDAC prognosis at 1, 2 and 3 years were all more than 0.7 in the training and validation datasets. The UPR-related signature showed excellent performance in outcome prediction even in different clinicopathological subgroups, including the female (p<0.0001), male (p<0.0001), grade 1/2 (p<0.0001), grade 3 (p=0.028), N0 (p=0.043), N1 (p<0.001), and R0 (p<0.0001) groups. Furthermore, multiple immune-related pathways were enriched in the low-risk group, and risk scores in the low-risk group were also associated with significantly higher levels of tumor-infiltrating lymphocytes (TILs). In addition, DepMap drug sensitivity analysis and our validation experiment showed that PDAC cell lines with high UPR-related risk scores or UPR activation are more sensitive to floxuridine, which is used as an antineoplastic agent. Herein, we identified a novel UPR-related prognostic signature that showed high value in predicting survival in patients with PDAC. Targeting these UPR-related genes might be an alternative for PDAC therapy. Further experimental studies are required to reveal how these genes mediate ER stress and PDAC progression.

PERFORM: a non-interventional study assessing the patients' treatment starting with 1L palbociclib in HR+/HER2- ABC.

The prospective, non-interventional PERFORM study describes and analyzes the effectiveness of palbociclib in combination with endocrine therapy (aromatase inhibitor or fulvestrant) as first-line treatment for patients with locally advanced or metastatic HR+/HER2- breast cancer in the real-world setting in Germany and Austria. PERFORM will reflect current patient characteristics and routine treatment patterns including treatment sequences and time to subsequent (chemo)therapy. Besides, second-line treatment effectiveness and patient-relevant end points such as longitudinal patient-reported outcome measurements beyond disease progression will be analyzed. Accounting for the heterogenous real-world patient population, data on clinicopathologic subgroups underrepresented in clinical trials such as elderly or male will be analyzed. Taken together, PERFORM will close knowledge gaps from clinical trials in real world.

Development of a 4-miRNA prognostic signature for endometrial cancer.

To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan–Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.

Value of the prognostic nutritional index (PNI) in patients with newly diagnosed, CD5-positive diffuse large B-cell lymphoma: A multicenter retrospective study of the Huaihai Lymphoma Working Group.

CD5-positive diffuse large B-cell lymphoma (DLBCL) is a clinically rare subtype of DLBCL with aggressive clinical manifestations and a poor prognosis. It has been demonstrated that the prognostic nutritional index (PNI), an indicator of nutritional status and systemic inflammation, is a significant prognostic factor for several types of lymphoma. The objective of this multicenter retrospective study was to explore the prognostic value of the PNI in patients with CD5-positive DLBCL. In total, 207 patients with CD5-positive DLBCL were recruited from 11 centers of the Huaihai Lymphoma Working Group. Maximally selected rank statistics analysis was used to identify optimal cutoff points for the PNI. A Cox proportional hazards model was used for univariable and multivariable analyses. Kaplan-Meier curves were used to calculate survival rates and draw survival curves, and the log-rank test was used to compare differences between groups. The median age at diagnosis was 61 years, and the 5-year overall survival rate was 47.5%. According to the maximally selected rank statistics analysis, a score of 49.7 was the optimal cutoff point for the PNI. Subgroup analysis showed that the PNI could re-stratify patients in BCL-2-negative, MYC-negative, high-intermediate-risk and high-risk International Prognostic Index, BCL-6-positive and BCL-6-negative, high Ki-67 score (≥0.9), Ann Arbor stage III/IV, Eastern Cooperative Oncology Group performance status ≥2, and germinal center B subgroups. Multivariable analysis revealed that PNI, age, Eastern Cooperative Oncology Group performance status, albumin level, and red blood cell count were independent prognostic factors for CD5-positive DLBCL. The PNI was a significant prognostic indicator for CD5-positive DLBCL and was able to re-stratify the prognosis for clinicopathologic subgroups of patients with CD5-positive DLBCL.

Innovative signature establishment using lymphangiogenesis-related lncRNA pairs to predict prognosis of hepatocellular carcinoma

AimsHepatocellular carcinoma (HCC) remains a major tumoral burden globally, and its heterogeneity encumbers prognostic prediction. The lymphangiogenesis-related long non-coding RNAs (lrlncRNAs) reported to be implicated in immune response regulation show potential importance in predicting the prognostic and therapeutic outcome. Hence, this study aims to establish a lrlncRNA pairs-based signature not requiring specific expression levels of transcripts, which displays promising clinical practicality and satisfactory predictive capability. Main methodsTranscriptomic and clinical information of the Liver Hepatocellular Carcinoma (LIHC) project retrieved from the TCGA portal were used to find differently expressed lrlncRNA (DElrlncRNA) via analysis performed between lymphangiogenesis-related genes (lr-genes) and lncRNAs(lrlncRNA), and to ultimately construct the signature based on lrlncRNA pairs screened out via Lasso and Cox regression analyses. Akaike information criterion (AIC) values were computed to find the cut-off point optimum for high-risk and low-risk group allocation. The signature then underwent trials in terms of its predictive value for survival, clinicopathological features, immune cells infiltration in tumoral microenvironment, selected checkpoint biomarkers and chemosensitivity. Key findingsA novel lymphangiogenesis-related lncRNA pair signature was established using nine lrlncRNA pairs identified and significantly related to overall survival, clinicopathological features, immune cells infiltration and susceptibility to chemotherapy. Moreover, the signature efficacy was verified in acknowledged clinicopathological subgroups and partially validated by qRT-PCR assay in various human HCC cell lines. SignificanceThe novel lrlncRNA-pairs based signature was shown to effectively and independently estimate HCC prognosis and help screen patients suitable for anti-tumor immunotherapy and chemotherapy.

Deep learning signature from chest CT and association with immunotherapy outcomes in EGFR/ALK-negative NSCLC.

9061 Background: Many clinicopathological and molecular features are associate with clinical benefit from immune checkpoint inhibitors (ICIs) for patients with non-small-cell lung cancer (NSCLC), yet none was exclusive underscoring the heterogeneity of lung cancers. As images may provide a holistic view of cancer, we attempted deep learning to chest CT scans to derive a predictor of response to ICIs and test its benefit relative to known clinicopathological factors. Methods: 928 stage IV, EGFR/ALK-negative NSCLC patients treated with ICIs alone or in combination (MD Anderson GEMINI Database) were divided into training (CTtr = 572), validation (CTva = 78), and testing (CTte = 278) cohorts, balancing the distribution of clinicopathological and radiological factors. Progression-free (PFS) and overall survival (OS) were defined as outcomes. We analyzed whole lung, including tumor and normal parenchyma of chest CT images ≤ 3 months prior to ICI treatment. An ensemble learning model (CT-deep-learning) to clustering patients into high vs low risk groups of PFS or OS was developed by fusing risk scores from four independent deep learning networks (supervised, unsupervised, and hybrid). This CT-deep-learning model was further evaluated in different clinicopathological subgroups. Finally, a composite model (CT-Clinic-path) was built by combining image model with clinicopathological factors. Antolini's concordance index (C-index) was used to assess model performance. Results: Median PFS and OS were shorter in the high-risk vs low-risk group as defined by CT-deep-learning: PFS (CTtr: 4.2 vs 9.6 mons; HR 1.96; 95% CI 1.62-2.38; P &lt; 0.0001; CTva: 3.7 vs 10.2 mons; HR 2.32; 95% CI 1.32-4.07; P = 0.0025; CTte: 3.6 vs 9.1 mons; HR 1.89; 95% CI 1.39-2.56; P &lt; 0.0001) and OS (CTtr: 16.0 vs 31.4 mons; HR 2.19; 95% CI 1.72-2.79; P &lt; 0.0001; CTva: 12.7 vs 28.6 mons; HR 2.01; 95% CI 1.04-3.88; P = 0.035; CTte: 14.8 vs 32.0 mons; HR 1.84; 95% CI 1.31-2.60; P = 0.0004). CT-deep-learning outperformed clinicopathologic features known to associate with ICI benefit, such as histology, smoking status, PD-L1 expression, and remained to be an independent (P &lt; 0.001) prognostic factor on multivariate analysis. Furthermore, integrating CT-deep-learning to clinicopathological variables improved prediction performance with a net reclassification up to 7% (Clinic-path model, C-indices 0.60 – 0.62 vs CT-clinic-path model, 0.64 - 0.65 for PFS; Clinic-path model 0.64 – 0.67 vs CT-clinic-path model 0.69 – 0.71 for OS). Conclusions: We have developed and validated a deep learning signature associated with PFS and OS in ICI-treated NSCLC patients, which appears to be independent of and superior to known clinicopathological biomarkers. If validated, this signature may strengthen the predictive value of clinicopathological factors and facilitate selecting appropriate patients for ICI-based therapies.

Establishment and Validation of Coagulation Factor-Based Nomogram for Predicting the Recurrence-Free Survival of Prostate Cancer

Introduction: We aimed to establish and validate a coagulation feature-based nomogram to predict recurrence-free survival in prostate cancer patients. Methods: The study included 168 prostate cancer patients who had received radical prostatectomy between 2012 and 2018. Kaplan-Meier plot and log-rank analysis were used to screen recurrence-free survival-related features. The nomogram was established by combining the significant coagulation features with clinicopathological characteristics by using Cox regression analysis. The accuracy and clinical significance of the nomogram model were assessed by the receiver operating characteristic curve, Kaplan-Meier plot, and calibration plot. We explored the correlation between coagulation pathway activity and patient prognosis in public datasets by using gene set variation analysis (GSVA). Results: The results suggested that patients classified by the nomogram into the high-risk subgroup showed unfavorable prognoses compared with those in the low-risk subgroup in both the training (log-rank p < 0.0001) and validation (log-rank p = 0.0004) cohorts. The nomogram model exhibited high discriminative accuracy in the training cohort (1-year area under the curve [AUC] of 0.74 and 3-year AUC of 0.69), which was confirmed in the internal validation cohort (C-index = 0.651). The calibration plots confirmed good concordance for the prediction of recurrence-free survival at 1 and 3 years. Subgroup analyses confirmed the utility of this model in different clinicopathological subgroups. Finally, GSVA suggested that patients with higher coagulation pathway scores mostly had unfavorable prognoses compared to those with lower scores, a result consistent with the findings above. Conclusions: We developed a practical nomogram model for predicting recurrence-free survival in prostate cancer patients. This model may offer clinicians prognostic assessments and facilitate personalized treatment.

Impact of clinicopathological features on immune-based combinations for advanced urothelial carcinoma: a meta-analysis.

Aims: Immune checkpoint inhibitors (ICIs) have recently revolutionized the treatment landscape of metastatic urothelial carcinoma. The authors performed a meta-analysis aiming to evaluate the predictive value of Eastern Cooperative Oncology Group performance status, age, sex, liver metastasis and histology in trials comparing first-line ICI-based combinations with chemotherapy in metastatic urothelial carcinoma patients. Methods: Hazard ratios were analyzed. Results: ICI-based combinations significantly decreased the risk of death in several clinicopathological subgroups, including patients with no liver metastases (hazard ratio: 0.84; 95% CI: 0.74-0.95) and those with an Eastern Cooperative Oncology Group performance status of 0 (hazard ratio: 0.84; 95% CI: 0.72-0.97). Conclusion: The benefit of ICI-based combinations over chemotherapy in metastatic urothelial carcinoma was consistent across several clinicopathological subgroups, although a proportion of patients responded to chemotherapy alone.

Establishment and validation of a cholesterol metabolism-related prognostic signature for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents the most important type of liver cancer, the 5-year survival rate for advanced HCC is 2%. The heterogeneity of HCC makes previous models fail to achieve satisfactory results. The role of Cholesterol-based metabolic reprogramming in cancer has attracted more and more attention. In this study, we screened cholesterol metabolism-related genes (CMRGs) based on a systematical analysis from TCGA and GEO database. Then, we constructed a prognostic signature based on the screened 5 CMRGs: FDPS, FABP5, ANXA2, ACADL and HMGCS2. The clinical value of the five CMRGs was validated by TCGA database and HPA database. HCC patients were assigned to the high-risk and low-risk groups on the basis of median risk score calculated by the five CMRGs. We evaluated the signature in TCGA database and validated in ICGC database. The results revealed that the prognostic signature had good prognostic performance, even among different clinicopathological subgroups. The function analysis linked CMRGs with KEGG pathway, such as cell adhesion molecules, drug metabolism-cytochrome P450 and other related pathways. In addition, patients in the high-risk group exhibited characteristics of high TP53 mutation, high immune checkpoints expression and high immune cell infiltration. Furthermore, based on the prognostic signature, we identified 25 most significant small molecule drugs as potential drugs for HCC patients. Finally, a nomogram combined risk score and TNM stage was constructed. These results indicated our prognostic signature has an excellent prediction performance. This study is expected to provide a potential diagnostic and therapeutic strategies for HCC.

Loss of SATB2 Occurs More Frequently Than CDX2 Loss in Colorectal Carcinoma and Identifies Particularly Aggressive Cancers in High-Risk Subgroups

Simple SummaryThe immunohistochemical analysis of Special AT-rich sequence-binding protein 2 (SATB2) is increasingly being used to detect colorectal differentiation. Our study aimed to investigate SATB2 expression levels and the prognostic relevance of SATB2 loss in colorectal carcinoma (CRC), especially in comparison with CDX2, the standard marker of colorectal differentiation. We tested SATB2 expression in 1039 CRCs and identified SATB2 as a strong prognosticator in the overall cohort as well as in specific subcohorts, including high-risk subgroups. Compared to CDX2, SATB2 showed a higher prognostic power but was lost at a much higher frequency, generally rendering SATB2 as the less sensitive marker for colorectal differentiation compared to CDX2.Background: Special AT-rich sequence-binding protein 2 (SATB2) has emerged as an alternative immunohistochemical marker to CDX2 for colorectal differentiation. However, the distribution and prognostic relevance of SATB2 expression in colorectal carcinoma (CRC) have to be further elucidated. Methods: SATB2 expression was analysed in 1039 CRCs and correlated with clinicopathological and morphological factors, CDX2 expression as well as survival parameters within the overall cohort and in clinicopathological subgroups. Results: SATB2 loss was a strong prognosticator in univariate analyses of the overall cohort (p < 0.001 for all survival comparisons) and in numerous subcohorts including high-risk scenarios (UICC stage III/high tumour budding). SATB2 retained its prognostic relevance in multivariate analyses of these high-risk scenarios (e.g., UICC stage III: DSS: p = 0.007, HR: 1.95), but not in the overall cohort (DSS: p = 0.1, HR: 1.25). SATB2 loss was more frequent than CDX2 loss (22.2% vs. 10.2%, p < 0.001) and of higher prognostic relevance with only moderate overlap between SATB2/CDX2 expression groups. Conclusions: SATB2 loss is able to identify especially aggressive CRCs in high-risk subgroups. While SATB2 is the prognostically superior immunohistochemical parameter compared to CDX2 in univariate analyses, it appears to be the less sensitive marker for colorectal differentiation as it is lost more frequently.

Overall survival in patients with hormone receptor-positive, HER2-negative, advanced or metastatic breast cancer treated with a cyclin-dependent kinase 4/6 inhibitor plus fulvestrant: a US Food and Drug Administration pooled analysis.

Cyclin-dependent kinase 4/6 inhibitors (CDKIs) are oral targeted agents approved for use in combination with endocrine therapy as first-line or second-line treatment of patients with hormone receptor-positive, HER2-negative, advanced or metastatic breast cancer. We previously reported the pooled analyses of progression-free survival in patients in specific clinicopathological subgroups, all of whom received consistent benefit from the addition of a CDKI to hormonal therapy. Here, we report the pooled overall survival results in patients treated with a CDKI and fulvestrant. In this exploratory analysis, we pooled individual patient data from three phase 3 randomised trials of CDKI or placebo in combination with fulvestrant in patients with breast cancer submitted to the US Food and Drug Administration and approved before Aug 1, 2020, in support of marketing applications. All analysed patients were aged at least 18 years, had an Eastern Cooperative Oncology Group performance status of 0-1, had hormone receptor-positive, HER2-negative advanced or metastatic breast cancer, and received at least one dose of CDKI or placebo in combination with fulvestrant. The median overall survival was estimated using Kaplan-Meier methods, and hazard ratios (HRs) with corresponding 95% CIs were estimated using Cox regression models. Patients were analysed collectively, by number of previous lines of systemic endocrine therapy in any disease setting (first-line or endocrine naive vs second-line and later), and in various clinicopathological subgroups of interest. The estimated median overall survival was not reported by group when the pooled population included patients treated across lines of therapy because of potential patient heterogeneity. All results presented are considered exploratory and hypothesis generating. Across the three pooled trials, 1960 patients were randomly assigned between Oct 7, 2013, and June 10, 2016 (12 patients were not treated and 1296 [66%] patients were randomly assigned to CDKI and 652 [33%] to placebo). In all treated patients (n=1948), the estimated HR for overall survival was 0·77 (95% CI 0·68-0·88), with a median follow-up of 43·7 months (IQR 37·8-47·7) and deaths in 935 (48%) of the 1948 patients. The difference in estimated median overall survival was 7·1 months, favouring CDKIs. In patients who received CDKIs or placebo in combination with fulvestrant as first-line systemic endocrine therapy (two trials; n=396), the estimated HR for overall survival was 0·74 (95% CI 0·52-1·07), with a median follow-up of 39·4 months (IQR 37·0-42·2). 123 (31%) of these patients died. The difference in estimated median overall survival could not be calculated because median overall survival was not estimable (95% CI 50·9-not estimable) in the CDKI group and was 45·7 months (95% CI 41·7-not estimable) in the placebo group. In patients who received CDKIs or placebo in combination with fulvestrant as second-line or later systemic endocrine therapy (three trials; n=1552), the estimated HR for overall survival was 0·77 (95% CI 0·67-0·89), with a median follow-up of 45·1 months (95% CI 39·2-48·5). 812 (52%) of these patients died. The difference in estimated median overall survival was 7·0 months, favouring CDKIs. The addition of CDKIs to fulvestrant resulted in a consistent overall survival benefit in all pooled patients and within most clinicopathological subgroups of interest. These findings support the existing standard of care of CDKIs plus fulvestrant for the treatment of patients with hormone receptor-positive, HER2-negative, advanced breast cancer. None.

Construction of a Novel Immune-Related lncRNA Pair Signature with Prognostic Significance for Kidney Clear Cell Renal Cell Carcinoma.

Background Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in the urinary system, among which the clear cell renal cell carcinoma (ccRCC) is the most common subtype. The immune-related long noncoding ribonucleic acids (irlncRNAs) which are abundant in immune cells and immune microenvironment (IME) have potential significance in evaluating the prognosis and effects of immunotherapy. The signature based on irlncRNA pairs and independent of the exact expression level seems to have a latent predictive significance for the prognosis of patients with malignant tumors but has not been applied in ccRCC yet. Method In this article, we retrieved The Cancer Genome Atlas (TCGA) database for the transcriptome profiling data of the ccRCC and performed coexpression analysis between known immune-related genes (ir-genes) and lncRNAs to find differently expressed irlncRNA (DEirlncRNA). Then, we adopted a single-factor test and a modified LASSO regression analysis to screen out ideal DEirlncRNAs and constructed a Cox proportional hazard model. We have sifted 28 DEirlncRNA pairs, 12 of which were included in this model. Next, we compared the area under the curve (AUC), found the cutoff point by using the Akaike information criterion (AIC) value, and distinguished the patients with ccRCC into a high-risk group and a low-risk group using this value. Finally, we tested this model by investigating the relationship between risk score and survival, clinical pathological characteristics, cells in tumor immune microenvironment, chemotherapy, and targeted checkpoint biomarkers. Results A novel immune-related lncRNA pair signature consisting of 12 DEirlncRNA pairs was successfully constructed and tightly associated with overall survival, clinical pathological characteristics, cells in tumor immune microenvironment, and reactiveness to immunotherapy and chemotherapy in patients with ccRCC. Besides, the efficacy of this signature was verified in some commonly used clinicopathological subgroups and could serve as an independent prognostic factor in patients with ccRCC. Conclusions This signature was proven to have a potential predictive significance for the prognosis of patients with ccRCC and the efficacy of immunotherapy.