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

A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer

BACKGROUND

The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance.

OBJECTIVE

The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers.

METHODS

Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC).

RESULTS

The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance.

CONCLUSIONS

The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

Nanoparticle-based immunoengineering strategies for enhancing cancer immunotherapy

Cancer immunotherapy is a groundbreaking strategy that has revolutionized the field of oncology compared to other therapeutic strategies, such as surgery, chemotherapy, or radiotherapy. However, cancer complexity, tumor heterogeneity, and immune escape have become the main hurdles to the clinical application of immunotherapy. Moreover, conventional immunotherapies cause many harmful side effects owing to hyperreactivity in patients, long treatment durations and expensive cost. Nanotechnology is considered a transformative approach that enhances the potency of immunotherapy by capitalizing on the superior physicochemical properties of nanocarriers, creating highly targeted tissue delivery systems. These advantageous features include a substantial specific surface area, which enhances the interaction with the immune system. In addition, the capability to finely modify surface chemistry enables the achievement of controlled and sustained release properties. These advances have significantly increased the potential of immunotherapy, making it more powerful than ever before. In this review, we introduce recent nanocarriers for application in cancer immunotherapy based on strategies that target different main immune cells, including T cells, dendritic cells, natural killer cells, and tumor-associated macrophages. We also provide an overview of the role and significance of nanotechnology in cancer immunotherapy.

Cell cycle protein BORA is associated with colorectal cancer progression by AURORA-PLK1 cascades: a bioinformatics analysis

Colorectal cancer (CRC) is the third most diagnosed cancer in the world. A better understanding of the molecular mechanism of CRC is essential for making novel strategies for the CRC management and its prevention. The present study aims to explore the molecular mechanism through integrated bioinformatics analysis by analyzing genes and their co-expression pattern in normal and CRC states. GSE110223, GSE110224 and GSE113513 gene expression profiles were analyzed in this study. The co-expression networks for normal and tumor samples were constructed separately and analyzed to identify the modules, sub-networks and key genes. Gene regulatory network analysis was done to understand the regulatory mechanism of selected genes. Survival analysis was performed for the identified sub-networks and key genes to understand their role in CRC progression. A total of seven modules were detected and the KEGG pathway analysis revealed these modules were mainly enriched with cell cycle, metabolism and signaling-related pathways. E2F6 and ETV4 transcription factors regulating the activity of multiple genes of identified modules were found to be up-regulated in CRC. Six Sub-networks and seven key genes, BORA, CCT7, DTL, RUVBL1, RUVBL2, THEM6 and TMEM97 associated with the CRC progression were identified. Disease-gene association analysis identified a novel association of the BORA gene with CRC that activates and regulates the AURORA-PLK1 cascades in the cell cycle. Survival analysis indicates that the overexpressed BORA is associated with unfavourable overall survival in CRC. The mechanistic role of BORA in the regulation of cell cycle progression suggests that BORA might act as a potential therapeutic target for CRC.

New insights into molecular signaling pathways and current advancements in prostate cancer diagnostics & therapeutics

Prostate adenocarcinoma accounts for more than 20% of deaths among males due to cancer. It is the fifth-leading cancer diagnosed in males across the globe. The mortality rate is quite high due to prostate cancer. Despite the fact that advancements in diagnostics and therapeutics have been made, there is a lack of effective drugs. Metabolic pathways are altered due to the triggering of androgen receptor (AR) signaling pathways, and elevated levels of dihydrotestosterone are produced due to defects in AR signaling that accelerate the growth of prostate cancer cells. Further, PI3K/AKT/mTOR pathways interact with AR signaling pathway and act as precursors to promote prostate cancer. Prostate cancer therapy has been classified into luminal A, luminal B, and basal subtypes. Therapeutic drugs inhibiting dihydrotestosterone and PI3K have shown to give promising results to combat prostate cancer. Many second-generation Androgen receptor signaling antagonists are given either as single agent or with the combination of other drugs. In order to develop a cure for metastasized prostate cancer cells, Androgen deprivation therapy (ADT) is applied by using surgical or chemical methods. In many cases, Prostatectomy or local radiotherapy are used to control metastasized prostate cancer. However, it has been observed that after 1.5 years to 2 years of Prostatectomy or castration, there is reoccurrence of prostate cancer and high incidence of castration resistant prostate cancer is seen in population undergone ADT. It has been observed that Androgen derivation therapy combined with drugs like abiraterone acetate or docetaxel improve overall survival rate in metastatic hormone sensitive prostate cancer (mHSPC) patients. Scientific investigations have revealed that drugs inhibiting poly ADP Ribose polymerase (PARP) are showing promising results in clinical trials in the prostate cancer population with mCRPC and DNA repair abnormalities. Recently, RISUG adv (reversible inhibition of sperm under guidance) has shown significant results against prostate cancer cell lines and MTT assay has validated substantial effects of this drug against PC3 cell lines. Current review paper highlights the advancements in prostate cancer therapeutics and new drug molecules against prostate cancer. It will provide detailed insights on the signaling pathways which need to be targeted to combat metastasized prostate cancer and castration resistant prostate cancer.

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.

Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients

BACKGROUND

Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment.

OBJECTIVE

This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models.

METHODS

We performed machine-learning (ML) analysis to screen pathogenic survival-related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L, MYH9, and DRD4 as candidate genes. These three genes and tumor stages were used to generate survival prediction models. Moreover, the genes were validated by experimental and clinical analyses, and the theranostic application of the survival prediction models was assessed.

RESULTS

RABGAP1L, MYH9, and DRD4 were identified as survival-related candidate genes by ML and in silico system analysis. The survival prediction model using the expression of the three genes showed higher predictive performance when applied to predict the prognosis of CC patients. A series of functional analyses revealed that each knockdown of three genes reduced the protumor activity of CC cells. In particular, validation with an independent cohort of CC patients confirmed that the coexpression of MYH9 and DRD4 gene expression reflected poorer clinical outcomes in terms of overall survival and disease-free survival.

CONCLUSIONS

Our survival prediction approach will contribute to providing information on patients and developing a therapeutic strategy for CC patients.

A pan-cancer analysis of copper homeostasis-related gene lipoyltransferase 1: Its potential biological functions and prognosis values

As a key copper homeostasis-related molecule, lipoyltransferase 1 (LIPT1) is an essential enzyme for the activation of mitochondrial 2-ketoacid dehydrogenase, participating in fatty acylation. However, the biological significances of LIPT1 in the pan-cancer are unclear. Here, we comprehensively analyzed the functional characteristics of LIPT1 in human cancers and its roles in immune response. We found that LIPT1 was down-regulated in some cancers. And LIPT1 overexpression is associated with favorable prognosis in these patients, such as breast cancer, clear cell renal cell carcinoma, ovarian cancer and gastric cancer. We also explored the mutational status and methylation levels of LIPT1 in human cancers. Gene enrichment analysis indicated that abnormally expressed LIPT1 was significantly associated with immune cells infiltration, such as B cells, CD8⁺ T cells and cancer-associated fibroblast cells. The result from single cell sequencing reflected the important roles of LIPT1 in the regulation of several biological behaviors of cancer cells, such as DNA damage response and cell apoptosis. Taken together, our research could provide a comprehensive overview about the significances of LIPT1 in human pan-cancer progression, prognosis and immune.

Predictive biomarkers for personalized medicine in breast cancer

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

The Determinants of B Cell Receptor Signaling as Prototype Molecular Biomarkers of Leukemia

Advanced genome-wide association studies (GWAS) identified several transforming mutations in susceptible loci which are recognized as valuable prognostic markers in chronic lymphocytic leukemia (CLL) and B cell lymphoma (BCL). Alongside, robust genetic manipulations facilitated the generation of preclinical mouse models to validate mutations associated with poor prognosis and refractory B cell malignancies. Taken together, these studies identified new prognostic markers that could achieve characteristics of precision biomarkers for molecular diagnosis. On the contrary, the idea of augmented B cell antigen receptor (BCR) signaling as a transforming cue has somewhat receded despite the efficacy of Btk and Syk inhibitors. Recent studies from several research groups pointed out that acquired mutations in BCR components serve as faithful biomarkers, which become important for precision diagnostics and therapy, due to their relevant role in augmented BCR signaling and CLL pathogenesis. For example, we showed that expression of a single point mutated immunoglobulin light chain (LC) recombined through the variable gene segment IGLV3-21, named IGLV3-21^R110, marks severe CLL cases. In this perspective, we summarize the molecular mechanisms fine-tuning B cell transformation, focusing on immunoglobulin point mutations and recurrent mutations in tumor suppressors. We present a stochastic model for gain-of-autonomous BCR signaling and subsequent neoplastic transformation. Of note, additional mutational analyses on immunoglobulin heavy chain (HC) derived from non-subset #2 CLL IGLV3-21^R110 cases endorses our perspective. Altogether, we propose a model of malignant transformation in which the augmented BCR signaling creates a conducive platform for the appearance of transforming mutations.