Integrative (epi) Genomic Analysis to Predict Response to Androgen-Deprivation Therapy in Prostate Cancer

Building Minimized Epigenetic Clock by iPlex MassARRAY Platform

Epigenetic clocks are valuable tools for estimating both chronological and biological age by assessing DNA methylation levels at specific CpG dinucleotides. While conventional epigenetic clocks rely on genome-wide methylation data, targeted approaches offer a more efficient alternative. In this study, we explored the feasibility of constructing a minimized epigenetic clock utilizing data acquired through the iPlex MassARRAY technology. The study enrolled a cohort of relatively healthy individuals, and their methylation levels of eight specific CpG dinucleotides in genes SLC12A5, LDB2, FIGN, ACSS3, FHL2, and EPHX3 were evaluated using the iPlex MassARRAY system and the Illumina EPIC array. The methylation level of five studied CpG sites demonstrated significant correlations with chronological age and an acceptable convergence of data obtained by the iPlex MassARRAY and Illumina EPIC array. At the same time, the methylation level of three CpG sites showed a weak relationship with age and exhibited a low concordance between the data obtained from the two technologies. The construction of the epigenetic clock involved the utilization of different machine-learning models, including linear models, deep neural networks (DNN), and gradient-boosted decision trees (GBDT). The results obtained from these models were compared with each other and with the outcomes generated by other well-established epigenetic clocks. In our study, the TabNet architecture (deep tabular data learning architecture) exhibited the best performance (best MAE = 5.99). Although our minimized epigenetic clock yielded slightly higher age prediction errors compared to other epigenetic clocks, it still represents a viable alternative to the genome-wide epigenotyping array.

Combining the radiomics signature and HPV status for the risk stratification of patients with OPC

OBJECTIVE

The objective was to perform risk stratification of oropharyngeal cancer (OPC) for treatment de-escalation based on the radiomics analysis and human papillomavirus (HPV) status.

METHODS

A total of 265 patients with OPC who underwent baseline contrast-enhanced computed tomography were analyzed, and the patients were grouped into the training (n = 133) and test (n = 132) cohorts at a ratio of 1:1. Intratumoral and peritumoral radiomics features were extracted, and the radiomics signature (Rscore) was calculated using least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses.

RESULTS

Twelve features were selected to establish the radiomics signature (Rscore) of intratumoral regions +10-mm peritumoral regions, which yielded maximum AUCs of 0.835, 0.798, and 0.784 in the training, test, and validation cohorts, respectively. Patients with OPC were divided into the high-risk group, intermediate-risk group, and low-risk group based on the Rscore and HPV status and had different prognoses. Patients in the low-risk group benefit from radiotherapy alone, and patients in the intermediate-risk group only benefitted from chemoradiotherapy.

CONCLUSION

The radiomics signature appears to improve the predictive performance of clinical characteristics for oropharyngeal cancer. The combined stratification of the radiomics signature and HPV status might be preferred to select patients for de-escalated treatment.

Mechanism-centric regulatory network identifies NME2 and MYC programs as markers of Enzalutamide resistance in CRPC

Heterogeneous response to Enzalutamide, a second-generation androgen receptor signaling inhibitor, is a central problem in castration-resistant prostate cancer (CRPC) management. Genome-wide systems investigation of mechanisms that govern Enzalutamide resistance promise to elucidate markers of heterogeneous treatment response and salvage therapies for CRPC patients. Focusing on the de novo role of MYC as a marker of Enzalutamide resistance, here we reconstruct a CRPC-specific mechanism-centric regulatory network, connecting molecular pathways with their upstream transcriptional regulatory programs. Mining this network with signatures of Enzalutamide response identifies NME2 as an upstream regulatory partner of MYC in CRPC and demonstrates that NME2-MYC increased activities can predict patients at risk of resistance to Enzalutamide, independent of co-variates. Furthermore, our experimental investigations demonstrate that targeting MYC and its partner NME2 is beneficial in Enzalutamide-resistant conditions and could provide an effective strategy for patients at risk of Enzalutamide resistance and/or for patients who failed Enzalutamide treatment.

A ferroptosis-related prognostic model with excellent clinical performance based on the exploration of the mechanism of oral squamous cell carcinoma progression

As a hot topic today, ferroptosis is closely involved in the progression and treatment of cancer. Accordingly, we built a prognostic model around ferroptosis to predict the overall survival of OSCC patients. We used up to 6 datasets from 3 different databases to ensure the credibility of the model. Then, through differentially expressed, Univariate Cox, and Lasso regression analyses, a model composed of nine prognostic-related differently expressed ferroptosis-related genes (CISD2, DDIT4, CA9, ALOX15, ATG5, BECN1, BNIP3, PRDX5 and MAP1LC3A) were constructed. Moreover, Kaplan-Meier curves, Receiver Operating Characteristic curves and principal component analysis used to verify the model's predictive ability showed the model's superiority. To deeply understand the mechanism of ferroptosis affecting the occurrence, development and prognosis of OSCC, we performed enrichment analysis in different risk groups identified by the model. The results showed that numerous TP53-related, immune-related and ferroptosis-related functions and pathways were enriched. Further immune microenvironment analysis and mutation analysis have once again revealed the correlation between risk score and immunity and TP53 mutation. Finally, the correlation between risk score and OSCC clinical treatment, as well as Nomogram show the brilliant clinical application prospects of the prognostic model.

CSMD1 Mutation Related to Immunity Can Be Used as a Marker to Evaluate the Clinical Therapeutic Effect and Prognosis of Patients with Esophageal Cancer

INTRODUCTION

As a highly aggressive tumor with a poor prognosis, esophageal cancer (ESCA)'s relationship with gene mutations is unclear. Therefore, we tried to explore the role of gene mutation in ESCA progression and its relationship with immune response, clinical treatment, and prognosis.

METHODS

In addition to copy number variation (CNV) situations of common genes obtained from 2 public databases, the relationship between mutations and prognosis/tumor mutational burden (TMB) was also analyzed. Kaplan-Meier survival and Cox regression analysis were used to identify the CSMD1 mutation status as an independent predictor of prognosis. We also enriched related functions and pathways. Next, the relationship between 22 immune cells and CSMD1 mutation status was analyzed. In addition to the differences in the expression levels of immune checkpoint inhibitors (ICIs)-related genes between the high TMB and low TMB groups, the differences in the expression levels of ICIs/m6a/multi-drug resistance-related genes and the sensitivity of three chemotherapeutic drugs between CSMD1 mutant and the wild group were also compared. In addition to differences and prognostic analysis of CSMD1 expression, the correlation analysis between the expression of these genes/immune cells and the expression of CSMD1 was also performed. Finally, a nomogram that could efficiently and conveniently predict the survival probability of ESCA patients was constructed and verified.

RESULTS

We obtained 17 frequently mutated genes distribution. Mutation and loss of CSMD1 are frequent in ESCA. Only CSMD1 mutation can be used as an independent predictor of poor prognosis. Patients in the high TMB group have a lower survival probability. Wild CSMD1 may be involved in immune-related pathways. More helper T cells and fewer resting state dendritic cells were found in the CSMD1 mutant group. The PD-1 expression in the high TMB group showed higher. Paclitaxel sensitivity and ABCC1 expression were higher in the wild CSMD1 group. Most cancers show differential expression of CSMD1. Except for the prognosis of ESCA, the expression of CSMD1 is related to immune cell content and the expression of ICIs/m6a/multi-drug resistance related genes.

DISCUSSION

CSMD1 mutation could be used as an immune-related biomarker to predict prognosis and treatment effect of paclitaxel. Mutation and loss of CSMD1 may promote the progression of ESCA.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Chromatin and Epigenetic Dysregulation of Prostate Cancer Development, Progression, and Therapeutic Response

The dysregulation of chromatin and epigenetics has been defined as the overarching cancer hallmark. By disrupting transcriptional regulation in normal cells and mediating tumor progression by promoting cancer cell plasticity, this process has the ability to mediate all defined hallmarks of cancer. In this review, we collect and assess evidence on the contribution of chromatin and epigenetic dysregulation in prostate cancer. We highlight important mechanisms leading to prostate carcinogenesis, the emergence of castration-resistance upon treatment with androgen deprivation therapy, and resistance to antiandrogens. We examine in particular the contribution of chromatin structure and epigenetics to cell lineage commitment, which is dysregulated during tumorigenesis, and cell plasticity, which is altered during tumor progression.

Challenges and Opportunities of Genomic Approaches in Therapeutics Development

The magnitude of all therapeutic responses is significantly determined by genome structure, variation, and functional interactions. This determination occurs at many levels which are discussed in the current review. Well-established examples of structural variation between individuals are known to dictate an individual's response to numerous drugs, as clearly illustrated by warfarin. The exponential rate of genomic-based interrogation is coupled with an expanding repertoire of genomic technologies and applications. This is leading to an ever more sophisticated appreciation of how structural variation, regulation of transcription and genomic structure, both individually and collectively, define cell therapeutic responses.

Gene signatures based on therapy responsiveness provide guidance for combined radiotherapy and chemotherapy for lower grade glioma

For a long time, the guidance for adjuvant chemoradiotherapy for lower grade glioma (LGG) lacks instructions on the application timing and order of radiotherapy (RT) and chemotherapy. We, therefore, aimed to develop indicators to distinguish between the different beneficiaries of RT and chemotherapy, which would provide more accurate guidance for combined chemoradiotherapy. By analysing 942 primary LGG samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases, we trained and validated two gene signatures (Rscore and Cscore) that independently predicted the responsiveness to RT and chemotherapy (Rscore AUC = 0.84, Cscore AUC = 0.79) and performed better than a previous signature. When the two scores were combined, we divided patients into four groups with different prognosis after adjuvant chemoradiotherapy: RSCS (RT-sensitive and chemotherapy-sensitive), RSCR (RT-sensitive and chemotherapy-resistant), RRCS (RT-resistant and chemotherapy-sensitive) and RRCR (RT-resistant and chemotherapy-resistant). The order and dose of RT and chemotherapy can be adjusted more precisely based on this patient stratification. We further found that the RRCR group exhibited a microenvironment with significantly increased T cell inflammation. In silico analyses predicted that patients in the RRCR group would show a stronger response to checkpoint blockade immunotherapy than other patients.

pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma

Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable chemotherapy response prior to treatment administration remains a major challenge in clinical oncology. To tackle this challenge, we present a generalizable genome-wide computational framework pathCHEMO that uncovers interplay between transcriptomic and epigenomic mechanisms altered in biological pathways that govern chemotherapy response in cancer patients. Our approach is tested on patients with lung adenocarcinoma who received adjuvant standard-of-care doublet chemotherapy (i.e., carboplatin-paclitaxel), identifying seven molecular pathway markers of primary treatment response and demonstrating their ability to predict patients at risk of carboplatin-paclitaxel resistance in an independent patient cohort (log-rank p-value = 0.008, HR = 10). Furthermore, we extend our method to additional chemotherapy-regimens and cancer types to demonstrate its accuracy and generalizability. We propose that our model can be utilized to prioritize patients for specific chemotherapy-regimens as a part of treatment planning.

Nusrat Epsi et al. present pathCHEMO, a computational framework for uncovering transcriptomic and epigenomic pathways of chemoresistance in cancer that has the potential to improve clinical decision-making. They apply pathCHEMO to lung adenocarcinoma data from public databases, and identify seven molecular pathways implicated in carboplatin-paclitaxel resistance.