Medical Imaging Biomarker Discovery and Integration Towards AI-Based Personalized Radiotherapy

Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment.

Whole Blood Holding Time Prior to Plasma Processing Alters microRNA Expression Profile

MicroRNAs (miRNAs) can exhibit aberrant expression under different physiological and pathological conditions. Therefore, differentially expressed circulating miRNAs have been a focus of biomarker discovery research. However, the use of circulating miRNAs comes with challenges which may hinder the reliability for their clinical application. These include varied sample collection protocols, storage times/conditions, sample processing and analysis methods. This study focused on examining the effect of whole blood holding time on the stability of plasma miRNA expression profiles. Whole blood samples were collected from healthy pregnant women and were held at 4°C for 30 min, 2 h, 6 h or 24 h prior to processing for plasma isolation. Plasma RNA was extracted and the expression of 179 miRNAs were analyzed. Unsupervised principal component analysis demonstrated that whole blood holding time was a major source of variation in miRNA expression profiles with 53 of 179 miRNAs showing significant changes in expression. Levels of specific miRNAs previously reported to be associated with pregnancy-associated complications such as hsa-miR-150-5p, hsa-miR-191-5p, and hsa-miR-29a-3p, as well as commonly used endogenous miRNA controls, hsa-miR-16-5p, hsa-miR-25-3p, and hsa-miR-223-3p were significantly altered with increase in blood holding time. Current protocols for plasma-based miRNA profiling for diagnostics describe major differences in whole blood holding periods ranging from immediately after collection to 26 h after. Our results demonstrate holding time can have dramatic effects on analytical reliability and reproducibility. This highlights the importance of standardization of blood holding time prior to processing for plasma in order to minimize introduction of non-biological variance in miRNA profiles.

Multivariate competing endogenous RNA network characterization for cancer MicroRNA biomarker discovery: a novel bioinformatics model with application to prostate cancer metastasis

Background MicroRNAs (miRNAs) are post-transcriptional regulators with the potential as biomarkers for cancer management. Data-driven competing endogenous RNA (ceRNA) network modeling is an effective way to decipher the complex interplay between miRNAs and spongers. However, no general rules are discovered for ceRNA network-based biomarker prioritization. Methods and Results In this study, a novel bioinformatics model was developed by integrating gene expression with multivariate miRNA-target data for ceRNA network-based biomarker discovery. Compared with traditional methods, the structural vulnerability in human lncRNA-miRNA-mRNA network was comprehensively analyzed, and the single-line regulatory or competing mode among miRNAs, lncRNAs and mRNAs was characterized and quantified as statistical evidence for miRNA biomarker identification. The application of this model to prostate cancer (PCa) metastasis identified a total of 12 miRNAs as putative biomarkers from metastatic PCa-specific lncRNA-miRNA-mRNA network and nine of them have been previously reported as biomarkers for PCa metastasis. The receiver operating characteristic curve and cell line qRT-PCR experiments demonstrated the power of miR-26b-5p, miR-130a-3p, and miR-363-3p as novel candidates for predicting PCa metastasis. Moreover, PCa-associated pathways such as prostate cancer signaling, ERK/MAPK signaling, and TGF-β signaling were significantly enriched by targets of identified miRNAs, indicating the underlying mechanisms of miRNAs in PCa carcinogenesis. Conclusions A novel ceRNA-based bioinformatics model was proposed and applied to screen candidate miRNA biomarkers for PCa metastasis. Functional validations using human samples and clinical data will be performed for future translational studies on the identified miRNAs.

Lipidomic profiling of human serum enables detection of pancreatic cancer

Pancreatic cancer has the worst prognosis among all cancers. Cancer screening of body fluids may improve the survival time prognosis of patients, who are often diagnosed too late at an incurable stage. Several studies report the dysregulation of lipid metabolism in tumor cells, suggesting that changes in the blood lipidome may accompany tumor growth. Here we show that the comprehensive mass spectrometric determination of a wide range of serum lipids reveals statistically significant differences between pancreatic cancer patients and healthy controls, as visualized by multivariate data analysis. Three phases of biomarker discovery research (discovery, qualification, and verification) are applied for 830 samples in total, which shows the dysregulation of some very long chain sphingomyelins, ceramides, and (lyso)phosphatidylcholines. The sensitivity and specificity to diagnose pancreatic cancer are over 90%, which outperforms CA 19-9, especially at an early stage, and is comparable to established diagnostic imaging methods. Furthermore, selected lipid species indicate a potential as prognostic biomarkers.

A Window of Opportunity: Perilymph Sampling from the Round Window Membrane Can Advance Inner Ear Diagnostics and Therapeutics

In the clinical setting, the pathophysiology of sensorineural hearing loss is poorly defined and there are currently no diagnostic tests available to differentiate between subtypes. This often leaves patients with generalized treatment options such as steroids, hearing aids, or cochlear implantation. The gold standard for localizing disease is direct biopsy or imaging of the affected tissue; however, the inaccessibility and fragility of the cochlea make these techniques difficult. Thus, the establishment of an indirect biopsy, a sampling of inner fluids, is needed to advance inner ear diagnostics and allow for the development of novel therapeutics for inner ear disease. A promising source is perilymph, an inner ear liquid that bathes multiple structures critical to sound transduction. Intraoperative perilymph sampling via the round window membrane of the cochlea has been successfully used to profile the proteome, metabolome, and transcriptome of the inner ear and is a potential source of biomarker discovery. Despite its potential to provide insight into inner ear pathologies, human perilymph sampling continues to be controversial and is currently performed only in conjunction with a planned procedure where the inner ear is opened. Here, we review the safety of procedures in which the inner ear is opened, highlight studies where perilymph analysis has advanced our knowledge of inner ear diseases, and finally propose that perilymph sampling could be done as a stand-alone procedure, thereby advancing our ability to accurately classify sensorineural hearing loss.

Delayed processing of blood samples impairs the accuracy of mRNA-based biomarkers

Background: The transcriptome of peripheral white blood cells (PWBCs) contains valuable physiological information, thus making them a prime biological sample for investigating mRNA-based biomarkers. However, prolonged storage of whole blood samples can alter gene transcript abundance in PWBCs, compromising the results of biomarker discovery. Here, we designed an experiment to interrogate the impacts of delayed processing of whole blood samples on gene transcript abundance in PWBCs. We hypothesized that storing blood samples for 24 hours at 4°C would cause RNA degradation resulting in altered transcriptome profiles. Results: We produced RNA-sequencing data for 30 samples collected from five estrus synchronized heifers (Bos taurus). We quantified transcript abundance for 12,414 protein-coding genes in PWBCs. Analysis of parameters of RNA quality revealed no statistically significant differences (P>0.05) between samples collected from the jugular vein and coccygeal vein, as well as among samples processed after one, three, six, or eight hours. However, samples processed after 24 hours of storage had a lower RNA integrity number value (P=0.03) in comparison to those processed after one hour of storage. Next, we analyzed RNA-sequencing data between samples using those processed after one hour of storage as the baseline for comparison. Interestingly, evaluation of 3/5 prime bias revealed no differences between genes with lower transcript abundance in samples stored for 24 hours relative to one hour. In addition, sequencing coverage of transcripts was similar between samples from the 24-hour and one-hour groups. We identified four and 515 genes with differential transcript abundance in samples processed after storage for eight and 24 hours, respectively, relative to samples processed after one hour. Conclusions: The PWBCs respond to prolonged cold storage by increasing genes related to active chromatin compaction which in turn reduces gene transcription. This alteration in transcriptome profiles can impair the accuracy of mRNA-based biomarkers. Therefore, blood samples collected for mRNA-based biomarker discovery should be refrigerated immediately and processed within six hours post sampling.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

Innate immune responses in patients treated with SBRT irradiation enhances prostate cancer remissions

Stereotactic body radiation therapy (SBRT) is a curative therapeutic modality employing large fractional doses of highly conformal radiation therapy for cancer treatment. To understand the mechanisms underlying clinical responses to radiation therapy, SBRT offers a unique window for high-throughput analysis of post-radiation molecular events to inform predictive biomarker discovery and strategies for multi-disciplinary therapeutics. We performed a longitudinal analysis of plasma proteins and metabolites from patients treated with prostate SBRT, comparing cohorts of patients in clinical remission to cohorts experiencing PSA-determined cancer progression. We observed the onset of post-SBRT DNA Damage Response (DDR), cell cycle arrest, and immune response signaling in patients within one hour of treatment and innate immune response signaling that persisted for up to three months following treatment. Furthermore, patients in remission experienced more robust immune responses and metabolite elevations consistent with a pro-inflammatory, M1-mediated innate immune activation in the short-term following SBRT, whereas patients with disease progression had less robust immune responses and M2-mediated metabolite elevations. We interpret these data to support a critical role for innate immune activation in the clinical outcomes of patients receiving radiation therapy for prostate cancer potentially improving future multidisciplinary therapeutic strategies.