A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples.

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.

Role of autophagy-related genes in liver cancer prognosis.

Autophagy, a highly conserved process of protein and organelle degradation, has emerged as a critical regulator in various diseases, including cancer progression. In the context of liver cancer, the predictive value of autophagy-related genes remains ambiguous. Leveraging chip datasets from the TCGA and GTEx databases, we identified 23 differentially expressed autophagy-related genes in liver cancer. Notably, five key autophagy genes, PRKAA2, BIRC5, MAPT, IGF1, and SPNS1, were highlighted as potential prognostic markers, with MAPT showing significant overexpression in clinical samples. In vitro cellular assays further demonstrated that MAPT promotes liver cancer cell proliferation, migration, and invasion by inhibiting autophagy and suppressing apoptosis. Subsequent in vivo studies further corroborated the pro-tumorigenic role of MAPT by suppressing autophagy. Collectively, our model based on the five key genes provides a promising tool for predicting liver cancer prognosis, with MAPT emerging as a pivotal factor in tumor progression through autophagy modulation.

NCAPG2 promotes prostate cancer malignancy and stemness via STAT3/c-MYC signaling.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of cancer-related mortality among men worldwide, and its incidence has risen substantially in recent years. Therefore, there is an urgent need to identify novel biomarkers and precise therapeutic targets for managing PCa progression and recurrence. METHODS: We investigated the clinical significance of NCAPG2 in PCa by exploring public datasets and our tissue microarray. Receiver operating characteristic (ROC) curve and survival analyses were performed to evaluate the correlation between NCAPG2 and PCa progression. Cell proliferation, wound healing, transwell, flow cytometry, cell cycle, tumor sphere formation, immunofluorescence (IF), co-immunoprecipitation (co-IP), and chromatin immunoprecipitation (ChIP) assays were conducted to further elucidate the molecular mechanism of NCAPG2 in PCa. Subcutaneous and orthotopic xenograft models were applied to investigate the effects of NCAPG2 on PCa proliferation in vivo. Tandem mass tag (TMT) quantitative proteomics was utilized to detect proteomic changes under NCAPG2 overexpression. RESULTS: NCAPG2 was significantly upregulated in PCa, and its overexpression was associated with PCa progression and unfavorable prognosis. Knockdown of NCAPG2 inhibited the malignant behavior of PCa cells, whereas its overexpression promoted PCa aggressiveness. NCAPG2 depletion attenuated the development and growth of PCa in vivo. TMT quantitative proteomics analyses indicated that c-MYC activity was strongly correlated with NCAPG2 expression. The malignancy-promoting effect of NCAPG2 in PCa was mediated via c-MYC. NCAPG2 could directly bind to STAT3 and induce STAT3 occupancy on the MYC promoter, thus to transcriptionally activate c-MYC expression. Finally, we identified that NCAPG2 was positively correlated with cancer stem cell (CSC) markers and enhanced self-renewal capacity of PCa cells. CONCLUSIONS: NCAPG2 is highly expressed in PCa, and its level is significantly associated with PCa prognosis. NCAPG2 promotes PCa malignancy and drives cancer stemness via the STAT3/c-MYC signaling axis, highlighting its potential as a therapeutic target for PCa.

Clinicopathological and radiological stratification within FIGO 2018 stages improves risk-prediction in cervical cancer.

OBJECTIVE: Assess the added prognostic value of the updated International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system, and to identify clinicopathological and radiological biomarkers for improved FIGO 2018 prognostication. METHODS: Patient data were retrieved from a prospectively collected patient cohort including all consenting patients with cervical cancer diagnosed and treated at Haukeland University Hospital during 2001-2022 (n = 948). All patients were staged according to the FIGO 2009 and FIGO 2018 guidelines based on available data for individual patients. MRI-assessed maximum tumor diameter and stromal tumor invasion, as well as histopathologically assessed lymphovascular space invasion were applied to categorize patients according to the Sedlis criteria. RESULTS: FIGO 2018 stage yielded the highest area under the receiver operating characteristic (ROC) curve (AUC) (0.86 versus 0.81 for FIGO 2009) for predicting disease-specific survival. The most common stage migration in FIGO 2018 versus FIGO 2009 was upstaging from stages IB/II to stage IIIC due to suspicious lymph nodes identified by PET/CT and/or MRI. In FIGO 2018 stage III patients, extent and size of primary tumor (p = 0.04), as well as its histological type (p = 0.003) were highly prognostic. Sedlis criteria were prognostic within FIGO 2018 IB patients (p = 0.04). CONCLUSIONS: Incorporation of cross-sectional imaging increases prognostic precision, as suggested by the FIGO 2018 guidelines. The 2018 FIGO IIIC stage could be refined by including the size and extent of primary tumor and histological type. The FIGO IB risk prediction could be improved by applying MRI-assessed tumor size and stromal invasion.

Prognostic Markers in the Era of Targeted Therapies.

BACKGROUND: Small molecules targeting Bruton's tyrosine kinase (BTK) and B-cell lymphoma-2 have become the standard of care for the treatment of chronic lymphocytic leukemia (CLL), replacing chemoimmunotherapy (CIT) in most clinical settings. Ongoing trials explore targeted combinations and minimal residual disease-driven treatment cessation. These dramatic shifts in the current and upcoming treatment landscape of CLL raise the need to reevaluate existing prognostic markers and develop novel ones. SUMMARY: This review examines prognostic markers in CLL patients treated with standard and investigational targeted therapies. Specifically, initial treatment of TP53 aberrant patients with a BTK inhibitor can achieve 70% progression-free survival (PFS) at 5 years, outperforming the 15% 5-year PFS with a CIT regimen containing fludarabine, cyclophosphamide, and rituximab (FCR). The prognostic implications of the immunoglobulin heavy chain variable gene (IGHV) mutation status have also changed. Unmutated IGHV is associated with inferior PFS and overall survival after FCR and inferior PFS with fixed-duration therapy with venetoclax and anti-CD20 monoclonal antibody but not with continuous BTK inhibitor treatment. KEY MESSAGES: (1) Genetic variables (e.g., TP53 aberration, IGHV mutation, complex karyotype) have a prognostic significance in CLL patients treated with targeted therapy. (2) Understanding the prognostic and predictive values of these markers is critical for the development of a risk-adapted treatment strategy in CLL.

Serum neurofilament light chain as a prognostic marker of all-cause mortality in a national sample of US adults.

Neurofilament light chain (NfL) is a neuron-specific structural protein released into the extracellular space, including body fluids, upon neuroaxonal damage. Despite evidence of a link in neurological disorders, few studies have examined the association of serum NfL with mortality in population-based studies. Data from the National Health and Nutrition Survey were utilized including 2,071 Non-Hispanic White, Non-Hispanic Black and Hispanic adult participants and adult participants of other ethnic groups (20-85 years) with serum NfL measurements who were followed for ≤ 6 years till 2019. We tested the association of serum NfL with mortality in the overall population and stratified by sex with the addition of potential interactive and mediating effects of cardio-metabolic risk factors and nutritional biomarkers. Elevated serum NfL levels (above median group) were associated with mortality risk compared to the below median NfL group in the overall sample (P = 0.010), with trends observed within each sex group (P < 0.10). When examining Loge NfL as a continuum, one standard deviation of Loge NfL was associated with an increased mortality risk (HR = 1.88, 95% CI 1.60-2.20, P < 0.001) in the reduced model adjusted for age, sex, race, and poverty income ratio; a finding only slightly attenuated with the adjustment of lifestyle and health-related factors. Four-way decomposition indicated that there was, among others, mediated interaction between NfL and HbA1c and a pure inconsistent mediation with 25(OH)D3 in predicting all-cause mortality, in models adjusted for all other covariates. Furthermore, urinary albumin-to-creatinine ratio interacted synergistically with NfL in relation to mortality risk both on the additive and multiplicative scales. These data indicate that elevated serum NfL levels were associated with all-cause mortality in a nationally representative sample of US adults.

Kidney sequelae in 281 Shiga toxin-producing Escherichia coli-hemolytic uremic syndrome patients after a median follow-up of 12 years.

BACKGROUND: A substantial proportion of patients with Escherichia coli-hemolytic uremic syndrome (STEC-HUS) evolve to chronic kidney disease (CKD). The objectives of this study were to evaluate long-term kidney outcomes and to identify CKD predictors. METHODS: In this single-center retrospective study, long-term outcomes of patients were analyzed according to the presence of complete recovery (CR) or CKD at last visit. Then, they were grouped into favorable (CR + CKD1) or poor (CKD2-5) outcome to compare predictors at diagnosis (sex, age, leukocytes, creatinine, hemoglobin, HUS severity score), dialysis duration, and follow-up time between them. RESULTS: Of 281 patients followed up for a median of 12 years, 139 (49%) had CR, 104 (37%) CKD1, 27 (10%) CKD2-4, and 11 (4%) CKD5. Thirty-eight patients progressed to CKD2-5 after a median of 4.8 years, 7% in the first 5 years, increasing to 8%, 10%, and 14% after 5-10 years, 10-15 years, and > 15 years, respectively. They were younger, had higher baseline hemoglobin and leukocytes, and required longer dialysis and follow-up than those with favorable outcome. By multivariate analysis, days of dialysis and follow-up time remained as independent predictors of poor outcome. The best cutoff for days of dialysis was 10 days. After 5 years, 20% of those dialyzed ≥ 10 days evolved to CKD2-5 versus 1% of those non-dialyzed or dialyzed < 10 days. CONCLUSIONS: Fifty-one percent of patients evolved to CKD after 12 years of follow-up and 14% to CKD2-5. Ten days of dialysis was the best cutoff to recognize outcomes. In some cases, kidney damage was evident after 15 years of surveillance, highlighting the need for follow-up until adulthood in all STEC-HUS patients.

Predicting treatment response using machine learning: A registered report.

OBJECTIVE: Previous research on psychotherapy treatment response has mainly focused on outpatients or clinical trial data which may have low ecological validity regarding naturalistic inpatient samples. To reduce treatment failures by proactively screening for patients at risk of low treatment response, gain more knowledge about risk factors and to evaluate treatments, accurate insights about predictors of treatment response in naturalistic inpatient samples are needed. METHODS: We compared the performance of different machine learning algorithms in predicting treatment response, operationalized as a substantial reduction in symptom severity as expressed in the Patient Health Questionnaire Anxiety and Depression Scale. To achieve this goal, we used different sets of variables-(a) demographics, (b) physical indicators, (c) psychological indicators and (d) treatment-related variables-in a naturalistic inpatient sample (N = 723) to specify their joint and unique contribution to treatment success. RESULTS: There was a strong link between symptom severity at baseline and post-treatment (R2 = .32). When using all available variables, both machine learning algorithms outperformed the linear regressions and led to an increment in predictive performance of R2 = .12. Treatment-related variables were the most predictive, followed psychological indicators. Physical indicators and demographics were negligible. CONCLUSIONS: Treatment response in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators. Regularization via machine learning algorithms leads to higher predictive performances as opposed to including nonlinear and interaction effects. Heterogenous aspects of mental health have incremental predictive value and should be considered as prognostic markers when modelling treatment processes.

Comment on, "Creactive protein levels, the prognostic nutritional index, and the lactate dehydrogenasetolymphocyte ratio are important prognostic factors in primary central nervous system lymphoma: a singlecenter study of 223 patients".

This study by Zuo et al. (2024) investigates the prognostic significance of C-reactive protein (CRP) levels, the prognostic nutritional index (PNI), and the lactate dehydrogenase-to-lymphocyte ratio (LLR) in primary central nervous system lymphoma (PCNSL) using data from 223 patients. The research demonstrates that these markers are critical in predicting patient outcomes, offering novel insights beyond traditional prognostic models like the MSKCC and IELSG scores. Despite its strengths, the study's retrospective design and lack of validation cohort limit its generalizability. Future research should focus on validating these findings in diverse, multicenter settings and integrating these markers with existing prognostic models to improve clinical decision-making. Longitudinal studies and advanced statistical methods are recommended to further explore the interactions between these factors and their impact on patient outcomes, potentially leading to the development of targeted therapies for PCNSL.

Research on the toxicological prognostic significance of age-related genes in endometrial cancer unveiling key factors in patient prognosis.

This study investigates the influence of aging-related genes on endometrial cancer, a prominent gynecological malignancy with rising incidence and mortality. By analyzing gene expression differences between cancerous and normal endometrial tissues, 42 aging-related genes were identified as differentially expressed. Utilizing the TCGA-UCEC sample, consensus clustering divided the samples into two molecular subgroups, Aging low and Aging high, based on their expression profiles. These subgroups showed distinct prognoses and survival rates, with the Aging high group associated with DNA repair and cell cycle pathways, and the Aging low group showing suppressed metabolic pathways and increased immune cell infiltration, suggesting a potential for better immunotherapy outcomes. Mutation analysis did not find significant differences in mutation frequencies between the groups, but a high Tumor Mutation Burden (TMB) correlated with better prognosis. A risk score model was also developed, showcasing significant prognostic power. Further analysis of the SIX1 gene revealed its overexpression in cancer cells. Drug sensitivity tests indicated that the low-risk group might respond better to chemotherapy. This research underscores the significance of aging-related genes in endometrial cancer, offering insights into their prognostic value and therapeutic potential, which could lead to personalized treatment approaches and enhanced patient management.

Exploring the Role of Clustered Mutations in Carcinogenesis and Their Potential Clinical Implications in Cancer.

Abnormal cell proliferation and growth leading to cancer primarily result from cumulative genome mutations. Single gene mutations alone do not fully explain cancer onset and progression; instead, clustered mutations-simultaneous occurrences of multiple mutations-are considered to be pivotal in cancer development and advancement. These mutations can affect different genes and pathways, resulting in cells undergoing malignant transformation with multiple functional abnormalities. Clustered mutations influence cancer growth rates, metastatic potential, and drug treatment sensitivity. This summary highlights the various types and characteristics of clustered mutations to understand their associations with carcinogenesis and discusses their potential clinical significance in cancer. As a unique mutation type, clustered mutations may involve genomic instability, DNA repair mechanism defects, and environmental exposures, potentially correlating with responsiveness to immunotherapy. Understanding the characteristics and underlying processes of clustered mutations enhances our comprehension of carcinogenesis and cancer progression, providing new diagnostic and therapeutic approaches for cancer.

Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach.

INTRODUCTION: Optimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome. METHODS: We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data. RESULTS: Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes. CONCLUSIONS: Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.

T-Cell Dynamics Predicts Prognosis of Patients with Hepatocellular Carcinoma Receiving Atezolizumab Plus Bevacizumab.

Atezolizumab and bevacizumab show promise for treating hepatocellular carcinoma (HCC), but identifying responsive patients remains challenging, due to tumor heterogeneity. This study explores immune dynamics following this combination therapy. Between 2020 and 2023, 29 patients with advanced HCC who received atezolizumab plus bevacizumab at Severance Hospital, Seoul, were enrolled in this study. Peripheral blood mononuclear cells were analyzed using flow cytometry and statistical methods to assess immune alterations and identify biomarkers. Baseline characteristics showed a diverse HCC cohort with a mean age of 64 years and 82.8% male predominance. Absence of extrahepatic metastasis was associated with better overall survival. Immune responses revealed distinct CD4+ T-cell phenotypes between the 'partial response (PR) + stable disease (SD)' and 'progressive disease (PD)' groups, with an overall increase in CD8+ T-cell phenotypes. Patients with higher frequencies of CD8+PD-1+Ki-67+ T cells experienced significantly improved overall survival, while those with lower frequencies of CD4+Foxp3+PD-1+LAG3+ T cells also had notable survival benefits. These findings enhance the overall understanding of immune responses to this combination therapy, facilitating improved patient stratification and personalized therapeutic approaches for HCC.

Breast Cancer Plasticity after Chemotherapy Highlights the Need for Re-Evaluation of Subtyping in Residual Cancer and Metastatic Tissues.

This research paper presents a novel approach to identifying biomarkers that can be used to prognosticate patients with triple-negative breast cancer (TNBC) eligible for neoadjuvant therapy. The study utilized survival and RNA sequencing data from a cohort of TNBC patients and identified 276 genes whose expression was related to survival in such patients. The gene expression data were then used to classify patients into two major groups based on the presence or absence of Wingless/Integrated-pathway (Wnt-pathway) and mesenchymal (Mes) markers (Wnt/Mes). Patients with a low expression of Wnt/Mes-related genes had a favorable outcome, with no deaths observed during follow-up, while patients with a high expression of Wnt/Mes genes had a higher mortality rate of 50% within 19 months. The identified gene list could be validated and potentially used to shape treatment options for TNBC patients eligible for neoadjuvant therapy providing valuable insights into the development of more effective treatments for TNBC. Our data also showed significant variation in gene expression profiles before and after chemotherapy, with most tumors switching to a more mesenchymal/stem cell-like profile. To verify this observation, we performed an in silico analysis to classify breast cancer tumors in Prediction Analysis of Microarray 50 (PAM50) molecular classes before treatment and after treatment using gene expression data. Our findings demonstrate that following drug intervention and metastasis, certain tumors undergo a transition to alternative subtypes, resulting in diminished therapeutic efficacy. This underscores the necessity for reevaluation of patients who have experienced relapse or metastasis post-chemotherapy, with a focus on molecular subtyping. Tailoring treatment strategies based on these refined subtypes is imperative to optimize therapeutic outcomes for affected individuals.

Prognostic Markers in Tyrosine Kinases Specific to Basal-like 2 Subtype of Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC), a heterogeneous and therapeutically challenging subtype, comprises over 50% of patients categorized into basal-like 1 (BL1) and basal-like 2 (BL2) intrinsic molecular subtypes. Despite their shared basal-like classification, BL2 is associated with a poor response to neoadjuvant chemotherapy and reduced relapse-free survival compared to BL1. Here, the study focused on identifying subtype-specific markers for BL2 through transcriptomic analysis of TNBC patients using RNA-seq and clinical integration. Six receptor tyrosine kinase (TK) genes, including EGFR, EPHA4, EPHB2, PDGFRA, PDGFRB, and ROR1, were identified as potential differentiators for BL2. Correlations between TK mRNA expression and TNBC prognosis, particularly EGFR, PDGFRA, and PDGFRB, revealed potential synergistic interactions in pathways related to cell survival and proliferation. Our findings also suggest promising dual markers for predicting disease prognosis. Furthermore, RT-qPCR validation demonstrated that identified BL2-specific TKs were expressed at a higher level in BL2 than in BL1 cell lines, providing insights into unique characteristics. This study advances the understanding of TNBC heterogeneity within the basal-like subtypes, which could lead to novel clinical treatment approaches and the development of targeted therapies.

Protein-Based Predictive Biomarkers to Personalize Neoadjuvant Therapy for Bladder Cancer-A Systematic Review of the Current Status.

The clinical outcome of patients with muscle-invasive bladder cancer (MIBC) is poor despite the approval of neoadjuvant chemotherapy or immunotherapy to improve overall survival after cystectomy. MIBC subtypes, immune, transcriptome, metabolomic signatures, and mutation burden have the potential to predict treatment response but none have been incorporated into clinical practice, as tumor heterogeneity and lineage plasticity influence their efficacy. Using the PRISMA statement, we conducted a systematic review of the literature, involving 135 studies published within the last five years, to identify studies reporting on the prognostic value of protein-based biomarkers for response to neoadjuvant therapy in patients with MIBC. The studies were grouped based on biomarkers related to molecular subtypes, cancer stem cell, actin-cytoskeleton, epithelial-mesenchymal transition, apoptosis, and tumor-infiltrating immune cells. These studies show the potential of protein-based biomarkers, especially in the spatial context, to reduce the influence of tumor heterogeneity on a biomarker's prognostic capability. Nevertheless, currently, there is little consensus on the methodology, reagents, and the scoring systems to allow reliable assessment of the biomarkers of interest. Furthermore, the small sample size of several studies necessitates the validation of potential prognostic biomarkers in larger multicenter cohorts before their use for individualizing neoadjuvant therapy regimens for patients with MIBC.

Diagnostic and Prognostic Markers for Pancreatitis and Pancreatic Ductal Adenocarcinoma.

Diagnostic markers are desperately needed for the early detection of pancreatic ductal adenocarcinoma (PDA). We describe sets of markers expressed in temporal order in mouse models during pancreatitis, PDA initiation and progression. Cell type specificity and the differential expression of PDA markers were identified by screening single cell (sc) RNAseq from tumor samples of a mouse model for PDA (KIC) at early and late stages of PDA progression compared to that of a normal pancreas. Candidate genes were identified from three sources: (1) an unsupervised screening of the genes preferentially expressed in mouse PDA tumors; (2) signaling pathways that drive PDA, including the Ras pathway, calcium signaling, and known cancer genes, or genes encoding proteins that were identified by differential mass spectrometry (MS) of mouse tumors and conditioned media from human cancer cell lines; and (3) genes whose expression is associated with poor or better prognoses (PAAD, oncolnc.org). The developmental progression of PDA was detected in the temporal order of gene expression in the cancer cells of the KIC mice. The earliest diagnostic markers were expressed in epithelial cancer cells in early-stage, but not late-stage, PDA tumors. Other early markers were expressed in the epithelium of both early- and late-state PDA tumors. Markers that were expressed somewhat later were first elevated in the epithelial cancer cells of the late-stage tumors, then in both epithelial and mesenchymal cells, or only in mesenchymal cells. Stromal markers were differentially expressed in early- and/or late-stage PDA neoplasia in fibroblast and hematopoietic cells (lymphocytes and/or macrophages) or broadly expressed in cancer and many stromal cell types. Pancreatitis is a risk factor for PDA in humans. Mouse models of pancreatitis, including caerulein treatment and the acinar-specific homozygous deletion of differentiation transcription factors (dTFs), were screened for the early expression of all PDA markers identified in the KIC neoplasia. Prognostic markers associated with a more rapid decline were identified and showed differential and cell-type-specific expression in PDA, predominately in late-stage epithelial and/or mesenchymal cancer cells. Select markers were validated by immunohistochemistry in mouse and human samples of a normal pancreas and those with early- and late-stage PDA. In total, we present 2165 individual diagnostic and prognostic markers for disease progression to be tested in humans from pancreatitis to late-stage PDA.

Chemokines and NSCLC: Emerging role in prognosis, heterogeneity, and therapeutics.

Lung cancer persists to contribute to one-quarter of cancer-associated deaths. Among the different histologies, non-small cell lung cancer (NSCLC) alone accounts for 85% of the cases. The development of therapies involving immune checkpoint inhibitors and angiogenesis inhibitors has increased patients' survival probability and reduced mortality rates. Developing targeted therapies against essential genetic alterations also translates to better treatment strategies. But the benefits still seem farfetched due to the development of drug resistance and refractory tumors. In this review, we have highlighted the interplay of different tumor microenvironment components, essentially discussing the chemokine families (CC, CXC, C, and CX3C) that regulate the tumor biology in NSCLC and promote tumor growth, metastasis, and associated heterogeneity. The development of therapeutics and prognostic markers is a complex and multipronged approach. However, some essential chemokines can act as critical players for being considered potential prognostic markers and therapeutic targets.

Multi-institutional Assessment of Pathologist Scoring HER2 Immunohistochemistry.

The HercepTest was approved 20+ years ago as the companion diagnostic test for trastuzumab in human epidermal growth factor 2 (HER2) or ERBB2 gene-amplified/overexpressing breast cancers. Subsequent HER2 immunohistochemistry (IHC) assays followed, including the now most common Ventana 4B5 assay. Although this IHC assay has become the clinical standard, its reliability, reproducibility, and accuracy have largely been approved and accepted on the basis of concordance among small numbers of pathologists without validation in a real-world setting. In this study, we evaluated the concordance and interrater reliability of scoring HER2 IHC in 170 breast cancer biopsies by 18 breast cancer-specialized pathologists from 15 institutions. We used the Observers Needed to Evaluate Subjective Tests method to determine the plateau of concordance and the minimum number of pathologists needed to estimate interrater agreement values for large numbers of raters, as seen in the real-world setting. We report substantial discordance within the intermediate categories (<1% agreement for 1+ and 3.6% agreement for 2+) in the 4-category HER2 IHC scoring system. The discordance within the IHC 0 cases is also substantial with an overall percent agreement (OPA) of only 25% and poor interrater reliability metrics (0.49 Fleiss' kappa, 0.55 intraclass correlation coefficient). This discordance can be partially reduced by using a 3-category system (28.8% vs 46.5% OPA for 4-category and 3-category scoring systems, respectively). Observers Needed to Evaluate Subjective Tests plots suggest that the OPA for the task of determining a HER2 IHC score 0 from not 0 plateaus statistically around 59.4% at 10 raters. Conversely, at the task of scoring HER2 IHC as 3+ or not 3+ pathologists' concordance was much higher with an OPA that plateaus at 87.1% with 6 raters. This suggests that legacy HER2 IHC remains valuable for finding the patients in whom the ERBB2 gene is amplified but unacceptably discordant in assigning HER2-low or HER2-negative status for the emerging HER2-low therapies.

Recent Developments in Diagnostic and Prognostic Biomarkers for Colorectal Cancer: A Narrative Review.

BACKGROUND: Colorectal cancer was reported as the second most common cause of cancer death worldwide, in the year 2020. This disease is an important public health problem considering its high incidence and mortality rates. SUMMARY: The molecular events that lead to colorectal cancer include genetic and epigenetic abnormalities. Some of the most important molecular mechanisms involved include the APC/ß-catenin pathway, the microsatellite pathway, and the CpG island hypermethylation. Evidence in the literature supports a role for the microbiota in the development of colon carcinogenesis, and specific microbes may contribute to or prevent carcinogenesis. Progress in prevention, screening, and management has improved the overall prognosis of the disease when diagnosed at an early stage; yet metastatic disease continues to have a poor long-term prognosis due to late-stage diagnosis and treatment failure. Biomarkers are a key tool for early detection and prognosis and aim to reduce morbidity and mortality associated with colorectal cancer. The main focus of this narrative review is to provide an update on the recent development of diagnostic and prognostic biomarkers in stool, blood, and tumor tissue samples. KEY MESSAGES: The review focuses on recent investigations in microRNAs, cadherins, Piwi-interacting RNAs, circulating cell-free DNA, and microbiome biomarkers which can be applied for the diagnosis and prognosis of colorectal cancer.