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Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 18724

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Dr. Wei Wu

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Guest Editor

Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94143, USA
Interests: cancer genomics; computational biology; Deep Learning in cancer research; long non-coding RNA; drug resistance

Prof. Dr. Trever G. Bivona

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Guest Editor

Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94143, USA
Interests: oncogenes; targeted therapy; drug resistance
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Dear Colleagues,

Cancer is currently viewed as a disease of evolving genomic instability and abnormal epigenomic modifications. Pioneering work had discovered and chromosomally mapped the genomic locations of oncogenes and tumor suppressor genes that are responsible for cancer initiation, progression, and metastasis. Through the efforts of cancer genome projects in the United States (e.g., The Cancer Genome Atlas research network, TCGA) and worldwide (International Cancer Genome Consortium, ICGC) and many other research groups, abundant genomic, transcriptomic, and proteomic data have been generated using state-of-the-art high throughput sequencing technologies. As such, we are witnessing mutation signatures, gene copy number alterations and aberrant gene expression profiles in specific types of cancer. Advances in sequencing technology and dramatic decreases in its cost offer the potential to accurately inspect the cancer genome at the level of single cells and with spatial resolution to understand cancer heterogeneity, the tumor microenvironment, spatial relationships, and the mechanisms of evolving drug resistance. Cutting-edge computational approaches and bioinformatics algorithms provide powerful toolkits to systematically identify clinically relevant biomarkers for early cancer diagnosis, prognosis, and stratification for precision cancer therapy.

This Special Issue will highlight recent developments in computational biology and cancer biology that allow the field to decode the cancer genome and tumor–tumor microenvironment ecosystem. A major focus is on the integration of multi-omics data such as WGS, WES, scRNAseq, and spatial transcriptomics and proteomics to better understand the basis of the initiation of cancer, its evolution, and drug tolerant persister cancer state and full therapy resistance. Unbiased, systematic analyses using multi-omics data could advance our knowledge to further improve cancer treatment. This Special Issue accepts all types of papers, including reviews, perspectives, or original research articles, from a wide range of types of cancers.

Dr. Wei Wu
Prof. Dr. Trever G. Bivona
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Research

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13 pages, 4147 KiB

Open AccessArticle

Preoperative Prediction of Perineural Invasion and Prognosis in Gastric Cancer Based on Machine Learning through a Radiomics–Clinicopathological Nomogram

by Heng Jia, Ruzhi Li, Yawei Liu, Tian Zhan, Yuan Li and Jianping Zhang

Cancers 2024, 16(3), 614; https://doi.org/10.3390/cancers16030614 - 31 Jan 2024

Viewed by 675

Abstract

Purpose: The aim of this study was to construct and validate a nomogram for preoperatively predicting perineural invasion (PNI) in gastric cancer based on machine learning, and to investigate the impact of PNI on the overall survival (OS) of gastric cancer patients. Methods: Data were collected from 162 gastric patients and analyzed retrospectively, and radiomics features were extracted from contrast-enhanced computed tomography (CECT) scans. A group of 42 patients from the Cancer Imaging Archive (TCIA) were selected as the validation set. Univariable and multivariable analyses were used to analyze the risk factors for PNI. The t-test, Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Radscores were calculated and logistic regression was applied to construct predictive models. A nomogram was developed by combining clinicopathological risk factors and the radscore. The area under the curve (AUC) values of receiver operating characteristic (ROC) curves, calibration curves and clinical decision curves were employed to evaluate the performance of the models. Kaplan–Meier analysis was used to study the impact of PNI on OS. Results: The univariable and multivariable analyses showed that the T stage, N stage and radscore were independent risk factors for PNI (p < 0.05). A nomogram based on the T stage, N stage and radscore was developed. The AUC of the combined model yielded 0.851 in the training set, 0.842 in the testing set and 0.813 in the validation set. The Kaplan–Meier analysis showed a statistically significant difference in OS between the PNI group and the non-PNI group (p < 0.05). Conclusions: A machine learning-based radiomics–clinicopathological model could effectively predict PNI in gastric cancer preoperatively through a non-invasive approach, and gastric cancer patients with PNI had relatively poor prognoses. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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14 pages, 6801 KiB

Open AccessArticle

Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer

by Jonghyun Lee, Seunghyun Cha, Jiwon Kim, Jung Joo Kim, Namkug Kim, Seong Gyu Jae Gal, Ju Han Kim, Jeong Hoon Lee, Yoo-Duk Choi, Sae-Ryung Kang, Ga-Young Song, Deok-Hwan Yang, Jae-Hyuk Lee, Kyung-Hwa Lee, Sangjeong Ahn, Kyoung Min Moon and Myung-Giun Noh

Cancers 2024, 16(2), 430; https://doi.org/10.3390/cancers16020430 - 19 Jan 2024

Cited by 2 | Viewed by 1024

Abstract

Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: −0.0094; AUPRC: −0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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19 pages, 3390 KiB

Open AccessArticle

CDC20 Is Regulated by the Histone Methyltransferase, KMT5A, in Castration-Resistant Prostate Cancer

by Zainab A. H. Alebady, Mahsa Azizyan, Sirintra Nakjang, Emma Lishman-Walker, Dhuha Al-Kharaif, Scott Walker, Hui Xian Choo, Rebecca Garnham, Emma Scott, Katya L. Johnson, Craig N. Robson and Kelly Coffey

Cancers 2023, 15(14), 3597; https://doi.org/10.3390/cancers15143597 - 13 Jul 2023

Viewed by 1916

Abstract

The methyltransferase KMT5A has been proposed as an oncogene in prostate cancer and therefore represents a putative therapeutic target. To confirm this hypothesis, we have performed a microarray study on a prostate cancer cell line model of androgen independence following KMT5A knockdown in the presence of the transcriptionally active androgen receptor (AR) to understand which genes and cellular processes are regulated by KMT5A in the presence of an active AR. We observed that 301 genes were down-regulated whilst 408 were up-regulated when KMT5A expression was reduced. KEGG pathway and gene ontology analysis revealed that apoptosis and DNA damage signalling were up-regulated in response to KMT5A knockdown whilst protein folding and RNA splicing were down-regulated. Under these conditions, the top non-AR regulated gene was found to be CDC20, a key regulator of the spindle assembly checkpoint with an oncogenic role in several cancer types. Further investigation revealed that KMT5A regulates CDC20 in a methyltransferase-dependent manner to modulate histone H4K20 methylation within its promoter region and indirectly via the p53 signalling pathway. A positive correlation between KMT5A and CDC20 expression was also observed in clinical prostate cancer samples, further supporting this association. Therefore, we conclude that KMT5A is a valid therapeutic target for the treatment of prostate cancer and CDC20 could potentially be utilised as a biomarker for effective therapeutic targeting. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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15 pages, 10500 KiB

Open AccessArticle

Biomarkers of Tumor Heterogeneity in Glioblastoma Multiforme Cohort of TCGA

by Garrett Winkelmaier, Brandon Koch, Skylar Bogardus, Alexander D. Borowsky and Bahram Parvin

Cancers 2023, 15(8), 2387; https://doi.org/10.3390/cancers15082387 - 20 Apr 2023

Viewed by 1890

Abstract

Tumor Whole Slide Images (WSI) are often heterogeneous, which hinders the discovery of biomarkers in the presence of confounding clinical factors. In this study, we present a pipeline for identifying biomarkers from the Glioblastoma Multiforme (GBM) cohort of WSIs from TCGA archive. The GBM cohort endures many technical artifacts while the discovery of GBM biomarkers is challenged because “age” is the single most confounding factor for predicting outcomes. The proposed approach relies on interpretable features (e.g., nuclear morphometric indices), effective similarity metrics for heterogeneity analysis, and robust statistics for identifying biomarkers. The pipeline first removes artifacts (e.g., pen marks) and partitions each WSI into patches for nuclear segmentation via an extended U-Net for subsequent quantitative representation. Given the variations in fixation and staining that can artificially modulate hematoxylin optical density (HOD), we extended Navab’s Lab method to normalize images and reduce the impact of batch effects. The heterogeneity of each WSI is then represented either as probability density functions (PDF) per patient or as the composition of a dictionary predicted from the entire cohort of WSIs. For PDF- or dictionary-based methods, morphometric subtypes are constructed based on distances computed from optimal transport and linkage analysis or consensus clustering with Euclidean distances, respectively. For each inferred subtype, Kaplan–Meier and/or the Cox regression model are used to regress the survival time. Since age is the single most important confounder for predicting survival in GBM and there is an observed violation of the proportionality assumption in the Cox model, we use both age and age-squared coupled with the Likelihood ratio test and forest plots for evaluating competing statistics. Next, the PDF- and dictionary-based methods are combined to identify biomarkers that are predictive of survival. The combined model has the advantage of integrating global (e.g., cohort scale) and local (e.g., patient scale) attributes of morphometric heterogeneity, coupled with robust statistics, to reveal stable biomarkers. The results indicate that, after normalization of the GBM cohort, mean HOD, eccentricity, and cellularity are predictive of survival. Finally, we also stratified the GBM cohort as a function of EGFR expression and published genomic subtypes to reveal genomic-dependent morphometric biomarkers. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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16 pages, 4302 KiB

Open AccessArticle

Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy

by Yanjing Dong, Jiang Zhang, Saikt Lam, Xinyu Zhang, Anran Liu, Xinzhi Teng, Xinyang Han, Jin Cao, Hongxiang Li, Francis Karho Lee, Celia Waiyi Yip, Kwokhung Au, Yuanpeng Zhang and Jing Cai

Cancers 2023, 15(7), 2032; https://doi.org/10.3390/cancers15072032 - 29 Mar 2023

Cited by 2 | Viewed by 1584

Abstract

(1) Background: Acute oral mucositis is the most common side effect for nasopharyngeal carcinoma patients receiving radiotherapy. Improper or delayed intervention to severe AOM could degrade the quality of life or survival for NPC patients. An effective prediction method for severe AOM is needed for the individualized management of NPC patients in the era of personalized medicine. (2) Methods: A total of 242 biopsy-proven NPC patients were retrospectively recruited in this study. Radiomics features were extracted from contrast-enhanced CT (CECT), contrast-enhanced T1-weighted (cT1WI), and T2-weighted (T2WI) images in the primary tumor and tumor-related area. Dosiomics features were extracted from 2D or 3D dose-volume histograms (DVH). Multiple models were established with single and integrated data. The dataset was randomized into training and test sets at a ratio of 7:3 with 10-fold cross-validation. (3) Results: The best-performing model using Gaussian Naive Bayes (GNB) (mean validation AUC = 0.81 ± 0.10) was established with integrated radiomics and dosiomics data. The GNB radiomics and dosiomics models yielded mean validation AUC of 0.6 ± 0.20 and 0.69 ± 0.14, respectively. (4) Conclusions: Integrating radiomics and dosiomics data from the primary tumor area could generate the best-performing model for severe AOM prediction. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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18 pages, 1612 KiB

Open AccessArticle

Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

by Lok-Man Ho, Sai-Kit Lam, Jiang Zhang, Chi-Leung Chiang, Albert Chi-Yan Chan and Jing Cai

Cancers 2023, 15(4), 1105; https://doi.org/10.3390/cancers15041105 - 09 Feb 2023

Cited by 5 | Viewed by 2574

Abstract

This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann–Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038–0.063, AUC = 0.690–0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047–0.070, AUC = 0.699–0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028–0.074, AUC = 0.719–0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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14 pages, 4742 KiB

Open AccessArticle

Quantitative Spatial Characterization of Lymph Node Tumor for N Stage Improvement of Nasopharyngeal Carcinoma Patients

by Jiang Zhang, Xinzhi Teng, Saikit Lam, Jiachen Sun, Andy Lai-Yin Cheung, Sherry Chor-Yi Ng, Francis Kar-Ho Lee, Kwok-Hung Au, Celia Wai-Yi Yip, Victor Ho-Fun Lee, Zhongshi Lin, Yongyi Liang, Ruijie Yang, Ying Han, Yuanpeng Zhang, Feng-Ming (Spring) Kong and Jing Cai

Cancers 2023, 15(1), 230; https://doi.org/10.3390/cancers15010230 - 30 Dec 2022

Cited by 2 | Viewed by 1215

Abstract

This study aims to investigate the feasibility of improving the prognosis stratification of the N staging system of Nasopharyngeal Carcinoma (NPC) from quantitative spatial characterizations of metastatic lymph node (LN) for NPC in a multi-institutional setting. A total of 194 and 284 NPC patients were included from two local hospitals as the discovery and validation cohort. Spatial relationships between LN and the surrounding organs were quantified by both distance and angle histograms, followed by principal component analysis. Independent prognostic factors were identified and combined with the N stage into a new prognostic index by univariate and multivariate Cox regressions on disease-free survival (DFS). The new three-class risk stratification based on the constructed prognostic index demonstrated superior cross-institutional performance in DFS. The hazard ratios of the high-risk to low-risk group were 9.07 (p < 0.001) and 4.02 (p < 0.001) on training and validation, respectively, compared with 5.19 (p < 0.001) and 1.82 (p = 0.171) of N3 to N1. Our spatial characterizations of lymph node tumor anatomy improved the existing N-stage in NPC prognosis. Our quantitative approach may facilitate the discovery of new anatomical characteristics to improve patient staging in other diseases. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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Review

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18 pages, 1165 KiB

Open AccessReview

Long Non-Coding RNAs as Emerging Targets in Lung Cancer

by Jovanka Gencel-Augusto, Wei Wu and Trever G. Bivona

Cancers 2023, 15(12), 3135; https://doi.org/10.3390/cancers15123135 - 10 Jun 2023

Cited by 5 | Viewed by 1391

Abstract

Long non-coding RNAs (LncRNAs) are mRNA-like molecules that do not encode for proteins and that are longer than 200 nucleotides. LncRNAs play important biological roles in normal cell physiology and organism development. Therefore, deregulation of their activities is involved in disease processes such as cancer. Lung cancer is the leading cause of cancer-related deaths due to late stage at diagnosis, distant metastasis, and high rates of therapeutic failure. LncRNAs are emerging as important molecules in lung cancer for their oncogenic or tumor-suppressive functions. LncRNAs are highly stable in circulation, presenting an opportunity for use as non-invasive and early-stage cancer diagnostic tools. Here, we summarize the latest works providing in vivo evidence available for lncRNAs role in cancer development, therapy-induced resistance, and their potential as biomarkers for diagnosis and prognosis, with a focus on lung cancer. Additionally, we discuss current therapeutic approaches to target lncRNAs. The evidence discussed here strongly suggests that investigation of lncRNAs in lung cancer in addition to protein-coding genes will provide a holistic view of molecular mechanisms of cancer initiation, development, and progression, and could open up a new avenue for cancer treatment. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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22 pages, 715 KiB

Open AccessReview

Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives

by Ihsan Ullah, Le Yang, Feng-Ting Yin, Ye Sun, Xing-Hua Li, Jing Li and Xi-Jun Wang

Cancers 2022, 14(22), 5545; https://doi.org/10.3390/cancers14225545 - 11 Nov 2022

Cited by 16 | Viewed by 3643

Abstract

Colorectal cancer (CRC) is common Cancer as well as the third leading cause of mortality around the world; its exact molecular mechanism remains elusive. Although CRC risk is significantly correlated with genetic factors, the pathophysiology of CRC is also influenced by external and internal exposures and their interactions with genetic factors. The field of CRC research has recently benefited from significant advances through Omics technologies for screening biomarkers, including genes, transcripts, proteins, metabolites, microbiome, and lipidome unbiasedly. A promising application of omics technologies could enable new biomarkers to be found for the screening and diagnosis of CRC. Single-omics technologies cannot fully understand the molecular mechanisms of CRC. Therefore, this review article aims to summarize the multi-omics studies of Colorectal cancer, including genomics, transcriptomics, proteomics, microbiomics, metabolomics, and lipidomics that may shed new light on the discovery of novel biomarkers. It can contribute to identifying and validating new CRC biomarkers and better understanding colorectal carcinogenesis. Discovering biomarkers through multi-omics technologies could be difficult but valuable for disease genotyping and phenotyping. That can provide a better knowledge of CRC prognosis, diagnosis, and treatments. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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18 pages, 1679 KiB

Open AccessReview

Quantitative Framework for Bench-to-Bedside Cancer Research

by Aubhishek Zaman and Trever G. Bivona

Cancers 2022, 14(21), 5254; https://doi.org/10.3390/cancers14215254 - 26 Oct 2022

Viewed by 1452

Abstract

Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical–protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets. Full article

(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)

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