COBLL1, LPL and ZAP70 expression defines prognostic subgroups of chronic lymphocytic leukemia patients with high accuracy and correlates with IGHV mutational status

ARPC5 is transcriptionally activated by KLF4, and promotes cell migration and invasion in prostate cancer via up-regulating ADAM17 : ARPC5 serves as an oncogene in prostate cancer

BACKGROUND

Prostate cancer (PCa) is one of the most common cancers in men worldwide. Actin-related protein 2/3 complex subunit 5 (ARPC5) has been validated as a critical regulator in several kinds of human tumors. However, whether ARPC5 is implicated in PCa progression remains largely unknown.

METHODS

PCa specimens and PCa cell lines were obtained for detecting gene expressions using western blot and quantitative reverse transcriptase PCR (qRT-PCR). PCa cells transfected with ARPC5 shRNA or a disintegrin and metalloprotease 17 (ADAM17) overexpressed plasmids were harvested for assessing cell proliferation, migration and invasion by using cell counting kit-8 (CCK-8), colony formation and transwell assays, respectively. The interaction relationship between molecules was testified with chromatin immunoprecipitation and luciferase reporter assay. Xenograft mice model was conducted for confirming the role of ARPC5/ADAM17 axis in vivo.

RESULTS

Upregulated ARPC5 was observed in PCa tissues and cells, as well as forecasted poor prognosis of PCa patients. Depletion of ARPC5 inhibited PCa cell proliferation, migration and invasion. Krüppel-like factor 4 (KLF4) was identified to be a transcriptional activator of ARPC5 via binding with its promoter region. Furthermore, ADAM17 served as a downstream effector of ARPC5. ADAM17 overexpression overturned ARPC5 knockdown-induced repressive impacts on PCa progression in vitro and in vivo.

CONCLUSION

Collectively, ARPC5 was activated by KLF4 and upregulated ADAM17 to promote PCa progression, which might act as a promising therapeutic target and prognostic biomarker for PCa.

Radiation-response in primary fibroblasts of long-term survivors of childhood cancer with and without second primary neoplasms: the KiKme study

Background

The etiology and most risk factors for a sporadic first primary neoplasm in childhood or subsequent second primary neoplasms are still unknown. One established causal factor for therapy-associated second primary neoplasms is the exposure to ionizing radiation during radiation therapy as a mainstay of cancer treatment. Second primary neoplasms occur in 8% of all cancer survivors within 30 years after the first diagnosis in Germany, but the underlying factors for intrinsic susceptibilities have not yet been clarified. Thus, the purpose of this nested case–control study was the investigation and comparison of gene expression and affected pathways in primary fibroblasts of childhood cancer survivors with a first primary neoplasm only or with at least one subsequent second primary neoplasm, and controls without neoplasms after exposure to a low and a high dose of ionizing radiation.

Methods

Primary fibroblasts were obtained from skin biopsies from 52 adult donors with a first primary neoplasm in childhood (N1), 52 with at least one additional primary neoplasm (N2+), as well as 52 without cancer (N0) from the KiKme study. Cultured fibroblasts were exposed to a high [2 Gray (Gy)] and a low dose (0.05 Gy) of X-rays. Messenger ribonucleic acid was extracted 4 h after exposure and Illumina-sequenced. Differentially expressed genes (DEGs) were computed using limma for R, selected at a false discovery rate level of 0.05, and further analyzed for pathway enrichment (right-tailed Fisher’s Exact Test) and (in-) activation (z ≥|2|) using Ingenuity Pathway Analysis.

Results

After 0.05 Gy, least DEGs were found in N0 (n = 236), compared to N1 (n = 653) and N2+ (n = 694). The top DEGs with regard to the adjusted p-value were upregulated in fibroblasts across all donor groups (SESN1, MDM2, CDKN1A, TIGAR, BTG2, BLOC1S2, PPM1D, PHLDB3, FBXO22, AEN, TRIAP1, and POLH). Here, we observed activation of p53 Signaling in N0 and to a lesser extent in N1, but not in N2+. Only in N0, DNA (excision-) repair (involved genes: CDKN1A, PPM1D, and DDB2) was predicted to be a downstream function, while molecular networks in N2+ were associated with cancer, as well as injury and abnormalities (among others, downregulation of MSH6, CCNE2, and CHUK). After 2 Gy, the number of DEGs was similar in fibroblasts of all donor groups and genes with the highest absolute log₂ fold-change were upregulated throughout (CDKN1A, TIGAR, HSPA4L, MDM2, BLOC1SD2, PPM1D, SESN1, BTG2, FBXO22, PCNA, and TRIAP1). Here, the p53 Signaling-Pathway was activated in fibroblasts of all donor groups. The Mitotic Roles of Polo Like Kinase-Pathway was inactivated in N1 and N2+. Molecular Mechanisms of Cancer were affected in fibroblasts of all donor groups. P53 was predicted to be an upstream regulator in fibroblasts of all donor groups and E2F1 in N1 and N2+. Results of the downstream analysis were senescence in N0 and N2+, transformation of cells in N0, and no significant effects in N1. Seven genes were differentially expressed in reaction to 2 Gy dependent on the donor group (LINC00601, COBLL1, SESN2, BIN3, TNFRSF10A, EEF1AKNMT, and BTG2).

Conclusion

Our results show dose-dependent differences in the radiation response between N1/N2+ and N0. While mechanisms against genotoxic stress were activated to the same extent after a high dose in all groups, the radiation response was impaired after a low dose in N1/N2+, suggesting an increased risk for adverse effects including carcinogenesis, particularly in N2+.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00520-6.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Reciprocal interactions among Cobll1, PACSIN2, and SH3BP1 regulate drug resistance in chronic myeloid leukemia

Cobll1 affects blast crisis (BC) progression and tyrosine kinase inhibitor (TKI) resistance in chronic myeloid leukemia (CML). PACSIN2, a novel Cobll1 binding protein, activates TKI‐induced apoptosis in K562 cells, and this activation is suppressed by Cobll1 through the interaction between PACSIN2 and Cobll1. PACSIN2 also binds and inhibits SH3BP1 which activates the downstream Rac1 pathway and induces TKI resistance. PACSIN2 competitively interacts with Cobll1 or SH3BP1 with a higher affinity for Cobll1. Cobll1 preferentially binds to PACSIN2, releasing SH3BP1 to promote the SH3BP1/Rac1 pathway and suppress TKI‐mediated apoptosis and eventually leading to TKI resistance. Similar interactions among Cobll1, PACSIN2, and SH3BP1 control hematopoiesis during vertebrate embryogenesis. Clinical analysis showed that most patients with CML have Cobll1 and SH3BP1 expression at the BC phase and BC patients with Cobll1 and SH3BP1 expression showed severe progression with a higher blast percentage than those without any Cobll1, PACSIN2, or SH3BP1 expression. Our study details the molecular mechanism of the Cobll1/PACSIN2/SH3BP1 pathway in regulating drug resistance and BC progression in CML.

Genetic Profiling in Children With Acute Lymphoblastic Leukemia Referred for Allogeneic Hematopoietic Stem Cell Transplantation

Introduction

Hematopoietic stem cell transplantation (HSCT) is the essential and often the only curative therapeutic option in high risk and relapsed pediatric acute lymphoblastic leukemia (ALL).

Methods

The objective of the study was to investigate whole-genome expression in children with high risk or relapsed ALL referred for HSCT. Gene expression was assessed in 18 children with ALL referred for HSCT (10 high risk, 8 relapsed; median age of 9.4 years) and in a control group of 38 obese children (median age of 14.1 years). Whole-genome expression was assessed in leukocytes using GeneChip® HumanGene 1.0 ST microarray.

Results

The analysis of genomic profiles revealed a significantly lower expression of 21 genes with a defined function, involved in immunoglobulin production, lymphocyte function, or regulation of DNA processing in ALL patients referred for HSCT compared with the control group.

Conclusion

Genome expression of patients with ALL in remission referred to HSCT revealed deep immunosuppression of both B-cell and T-cell lineages, which may increase the probability of donor cell engraftment.

Recent progress of prognostic biomarkers and risk scoring systems in chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is the most prevalent adult leukemia with high heterogeneity in the western world. Thus, investigators identified a number of prognostic biomarkers and scoring systems to guide treatment decisions and validated them in the context of immunochemotherapy. A better understanding of prognostic biomarkers, including serum markers, flow cytometry outcomes, IGHV mutation status, microRNAs, chromosome aberrations and gene mutations, have contributed to prognosis in CLL. Del17p/ TP53 mutation, NOTCH1 mutation, CD49d, IGHV mutation status, complex karyotypes and microRNAs were reported to be of predictive values to guide clinical decisions. Based on the biomarkers above, classic prognostic models, such as the Rai and Binet staging systems, MDACC nomogram, GCLLSG model and CLL-IPI, were developed to improve risk stratification and tailor treatment intensity. Considering the presence of novel agents, many investigators validated the conventional prognostic biomarkers in the setting of novel agents and only TP53 mutation status/del 17p and CD49d expression were reported to be of prognostic value. Whether other prognostic indicators and models can be used in the context of novel agents, further studies are required.

Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis

BACKGROUND

Chronic lymphocytic leukaemia (CLL) is the most common cancer of the lymphatic system in Western countries. Several clinical and biological factors for CLL have been identified. However, it remains unclear which of the available prognostic models combining those factors can be used in clinical practice to predict long-term outcome in people newly-diagnosed with CLL.

OBJECTIVES

To identify, describe and appraise all prognostic models developed to predict overall survival (OS), progression-free survival (PFS) or treatment-free survival (TFS) in newly-diagnosed (previously untreated) adults with CLL, and meta-analyse their predictive performances.

SEARCH METHODS

We searched MEDLINE (from January 1950 to June 2019 via Ovid), Embase (from 1974 to June 2019) and registries of ongoing trials (to 5 March 2020) for development and validation studies of prognostic models for untreated adults with CLL. In addition, we screened the reference lists and citation indices of included studies.

SELECTION CRITERIA

We included all prognostic models developed for CLL which predict OS, PFS, or TFS, provided they combined prognostic factors known before treatment initiation, and any studies that tested the performance of these models in individuals other than the ones included in model development (i.e. 'external model validation studies'). We included studies of adults with confirmed B-cell CLL who had not received treatment prior to the start of the study. We did not restrict the search based on study design.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form to collect information based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Independent pairs of review authors screened references, extracted data and assessed risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). For models that were externally validated at least three times, we aimed to perform a quantitative meta-analysis of their predictive performance, notably their calibration (proportion of people predicted to experience the outcome who do so) and discrimination (ability to differentiate between people with and without the event) using a random-effects model. When a model categorised individuals into risk categories, we pooled outcome frequencies per risk group (low, intermediate, high and very high). We did not apply GRADE as guidance is not yet available for reviews of prognostic models.

MAIN RESULTS

From 52 eligible studies, we identified 12 externally validated models: six were developed for OS, one for PFS and five for TFS. In general, reporting of the studies was poor, especially predictive performance measures for calibration and discrimination; but also basic information, such as eligibility criteria and the recruitment period of participants was often missing. We rated almost all studies at high or unclear risk of bias according to PROBAST. Overall, the applicability of the models and their validation studies was low or unclear; the most common reasons were inappropriate handling of missing data and serious reporting deficiencies concerning eligibility criteria, recruitment period, observation time and prediction performance measures. We report the results for three models predicting OS, which had available data from more than three external validation studies: CLL International Prognostic Index (CLL-IPI) This score includes five prognostic factors: age, clinical stage, IgHV mutational status, B2-microglobulin and TP53 status. Calibration: for the low-, intermediate- and high-risk groups, the pooled five-year survival per risk group from validation studies corresponded to the frequencies observed in the model development study. In the very high-risk group, predicted survival from CLL-IPI was lower than observed from external validation studies. Discrimination: the pooled c-statistic of seven external validation studies (3307 participants, 917 events) was 0.72 (95% confidence interval (CI) 0.67 to 0.77). The 95% prediction interval (PI) of this model for the c-statistic, which describes the expected interval for the model's discriminative ability in a new external validation study, ranged from 0.59 to 0.83. Barcelona-Brno score Aimed at simplifying the CLL-IPI, this score includes three prognostic factors: IgHV mutational status, del(17p) and del(11q). Calibration: for the low- and intermediate-risk group, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of four external validation studies (1755 participants, 416 events) was 0.64 (95% CI 0.60 to 0.67); 95% PI 0.59 to 0.68. MDACC 2007 index score The authors presented two versions of this model including six prognostic factors to predict OS: age, B2-microglobulin, absolute lymphocyte count, gender, clinical stage and number of nodal groups. Only one validation study was available for the more comprehensive version of the model, a formula with a nomogram, while seven studies (5127 participants, 994 events) validated the simplified version of the model, the index score. Calibration: for the low- and intermediate-risk groups, the pooled survival per risk group corresponded to the frequencies observed in the model development study, although the score seems to overestimate survival for the high-risk group. Discrimination: the pooled c-statistic of the seven external validation studies for the index score was 0.65 (95% CI 0.60 to 0.70); 95% PI 0.51 to 0.77.

AUTHORS' CONCLUSIONS

Despite the large number of published studies of prognostic models for OS, PFS or TFS for newly-diagnosed, untreated adults with CLL, only a minority of these (N = 12) have been externally validated for their respective primary outcome. Three models have undergone sufficient external validation to enable meta-analysis of the model's ability to predict survival outcomes. Lack of reporting prevented us from summarising calibration as recommended. Of the three models, the CLL-IPI shows the best discrimination, despite overestimation. However, performance of the models may change for individuals with CLL who receive improved treatment options, as the models included in this review were tested mostly on retrospective cohorts receiving a traditional treatment regimen. In conclusion, this review shows a clear need to improve the conducting and reporting of both prognostic model development and external validation studies. For prognostic models to be used as tools in clinical practice, the development of the models (and their subsequent validation studies) should adapt to include the latest therapy options to accurately predict performance. Adaptations should be timely.

Actin regulators in cancer progression and metastases: From structure and function to cytoskeletal dynamics

The cytoskeleton is a central factor contributing to various hallmarks of cancer. In recent years, there has been increasing evidence demonstrating the involvement of actin regulatory proteins in malignancy, and their dysregulation was shown to predict poor clinical prognosis. Although enhanced cytoskeletal activity is often associated with cancer progression, the expression of several inducers of actin polymerization is remarkably reduced in certain malignancies, and it is not completely clear how these changes promote tumorigenesis and metastases. The complexities involved in cytoskeletal induction of cancer progression therefore pose considerable difficulties for therapeutic intervention; it is not always clear which cytoskeletal regulator should be targeted in order to impede cancer progression, and whether this targeting may inadvertently enhance alternative invasive pathways which can aggravate tumor growth. The entire constellation of cytoskeletal machineries in eukaryotic cells are numerous and complex; the system is comprised of and regulated by hundreds of proteins, which could not be covered in a single review. Therefore, we will focus here on the actin cytoskeleton, which encompasses the biological machinery behind most of the key cellular functions altered in cancer, with specific emphasis on actin nucleating factors and nucleation-promoting factors. Finally, we discuss current therapeutic strategies for cancer which aim to target the cytoskeleton.

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets

Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

COBLL1 modulates cell morphology and facilitates androgen receptor genomic binding in advanced prostate cancer

Androgen receptor (AR) signaling is essential for prostate cancer progression and acquiring resistance to hormone therapy. However, the molecular pathogenesis through AR activation has not been fully understood. We performed integrative transcriptomic analysis to compare the AR program in a castration-resistant prostate cancer (CRPC) model with that in their parental hormone-sensitive cells. We found that the gene cordon-bleu-like 1 (COBLL1) is highly induced by AR in CRPC model cells. The expression of COBLL1 that possesses an actin-binding domain is up-regulated in clinical prostate cancer tissues and is associated with a poor prognosis for prostate cancer patients. COBLL1 is involved in the cancer cell morphogenesis to a neuron-like cell shape observed in the CRPC model cells, promoting cell growth and migration. Moreover, nuclear COBLL1 interacts with AR to enhance complex formation with CDK1 and facilitates AR phosphorylation for genomic binding in CRPC model cells. Thus, our findings showed the mechanistic relevance of cordon-bleu proteins during the AR-mediated progression to CRPC.

Expression of COBLL1 encoding novel ROR1 binding partner is robust predictor of survival in chronic lymphocytic leukemia

Chronic lymphocytic leukemia is a disease with up-regulated expression of the transmembrane tyrosine-protein kinase ROR1, a member of the Wnt/planar cell polarity pathway. In this study, we identified COBLL1 as a novel interaction partner of ROR1. COBLL1 shows clear bimodal expression with high levels in chronic lymphocytic leukemia patients with mutated IGHV and approximately 30% of chronic lymphocytic leukemia patients with unmutated IGHV. In the remaining 70% of chronic lymphocytic leukemia patients with unmutated IGHV, COBLL1 expression is low. Importantly, chronic lymphocytic leukemia patients with unmutated IGHV and high COBLL1 have an unfavorable disease course with short overall survival and time to second treatment. COBLL1 serves as an independent molecular marker for overall survival in chronic lymphocytic leukemia patients with unmutated IGHV. In addition, chronic lymphocytic leukemia patients with unmutated IGHV and high COBLL1 show impaired motility and chemotaxis towards CCL19 and CXCL12 as well as enhanced B-cell receptor signaling pathway activation demonstrated by increased PLCγ2 and SYK phosphorylation after IgM stimulation. COBLL1 expression also changes during B-cell maturation in non-malignant secondary lymphoid tissue with a higher expression in germinal center B cells than naïve and memory B cells. Our data thus suggest COBLL1 involvement not only in chronic lymphocytic leukemia but also in B-cell development. In summary, we show that expression of COBLL1, encoding novel ROR1-binding partner, defines chronic lymphocytic leukemia subgroups with a distinct response to microenvironmental stimuli, and independently predicts survival of chronic lymphocytic leukemia with unmutated IGHV.