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Cancers
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Genomic Medicine in Cancer
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Genomic Medicine in Cancer

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 October 2023) | Viewed by 34662

Special Issue Editors

Dr. Maria Rossing

E-Mail
Guest Editor
Department of Endocrinology and Metabolism, Copenhagen University Hospital, 2100 Copenhagen, Denmark
Interests: genomics; cancer; mutational signatures; transcriptomic signatures; whole-genome-sequencing; epigenome; precision cancer medicine
Dr. Valtteri Wirta

E-Mail Website
Guest Editor
Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17165 Stockholm, Sweden
Interests: bioinformatics; genomics; biochemistry; data analysis; cancer

Special Issue Information

Dear colleagues,

Genomic medicine is a growing medical discipline that involves using genomic information about a patient as part of their clinical care. Genomic medicine is the fundament for pursuing personalized medicine, and it is rapidly changing the future of medicine. As the costs of sequencing are vastly dropping, the way is paved for a broader implementation of genomic medicine in clinical care. 

Cancer is a disease of the genome, and enormous efforts are directed toward a clearer understanding of this heterogeneous collection of diseases. The expansion of our insight into cancer genomes is mostly driven by the rapid development in sequencing technologies all the way from the early identification of oncogenes and tumor suppressors to the full annotation of the most common cancers resulting in the so-called genomic landscapes of cancer. The major advances in sequencing technologies followed by the development of computational tools have enabled analyses such as whole‐exome, whole-genome, and RNA sequencing to be implemented in routine clinical settings, thus supporting the emerging clinical relevance of genomics in cancer medicine. The cancer genome is somewhat dynamic, and each cancer evolves with the accumulation of several types of somatic mutations, copy number alterations, epigenetic factors, and structural variants. These changes can occur in a predisposed genetic background, such as hereditary cancers which again cause diverse patterns for the individual tumor genome.

Thus, accepting the fact that cancer is a genomic disease and combining this with the growing insights into targeted therapies, the road to precision oncology has been paved. Precision oncology is based on the theory that the examination of both patients’ genome and the tumor genome will direct the clinician to the effective drug. Basic science in cancer genomics is crucial; however, translating the results into clinical care, where every cancer patient undergoes an upfront genomic profile, is where genomic medicine in cancer will demonstrate its worth over the coming years. This critical step is dependent on solid collaborations of different areas of expertise, including mathematicians, data scientists, molecular biologists, and clinicians.

Dr. Maria Rossing
Dr. Valtteri Wirta
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 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.

Keywords

  • genomics
  • cancer
  • mutational signatures
  • transcriptomic signatures
  • whole-genome sequencing
  • epigenome
  • precision cancer medicine

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Published Papers (5 papers)

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Research

20 pages, 3352 KiB  
Article
A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns
by Collin A. Melton, Peter Freese, Yifan Zhou, Archana Shenoy, Siddhartha Bagaria, Christopher Chang, Chih-Chung Kuo, Eric Scott, Subashini Srinivasan, Gordon Cann, Manami Roychowdhury-Saha, Pei-Yun Chang and Amoolya H. Singh
Cancers 2024, 16(1), 82; https://doi.org/10.3390/cancers16010082 - 23 Dec 2023
Cited by 4 | Viewed by 7388
Abstract
Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate [...] Read more.
Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman’s correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10−3 and 10−4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden. Full article
(This article belongs to the Special Issue Genomic Medicine in Cancer)
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16 pages, 3437 KiB  
Article
Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer
by Stephanie J. Yaung, Christine Ju, Sandeep Gattam, Alan Nicholas, Nicolas Sommer, Johanna C. Bendell, Herbert I. Hurwitz, John J. Lee, Fergal Casey, Richard Price and John F. Palma
Cancers 2022, 14(9), 2240; https://doi.org/10.3390/cancers14092240 - 29 Apr 2022
Cited by 1 | Viewed by 2381
Abstract
Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) [...] Read more.
Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) vs. FOLFOX-BEV for first-line treatment of metastatic colorectal cancer. We sequenced 146 pre-induction and 89 post-induction patient plasmas with a 198-kilobase capture-based assay, and applied Mutant-Allele Tumor Heterogeneity (MATH), a traditionally tissue-based calculation of allele frequency distribution, on somatic mutations detected in plasma. Higher levels of MATH, particularly in the post-induction sample, were associated with shorter progression-free survival (PFS). Patients with high MATH vs. low MATH in post-induction plasma had shorter PFS (7.2 vs. 11.7 months; hazard ratio, 3.23; 95% confidence interval, 1.85–5.63; log-rank p < 0.0001). These results suggest ctDNA-based tumor heterogeneity may have potential prognostic value in metastatic cancers. Full article
(This article belongs to the Special Issue Genomic Medicine in Cancer)
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21 pages, 3234 KiB  
Article
A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data
by Migle Gabrielaite, Mathias Husted Torp, Malthe Sebro Rasmussen, Sergio Andreu-Sánchez, Filipe Garrett Vieira, Christina Bligaard Pedersen, Savvas Kinalis, Majbritt Busk Madsen, Miyako Kodama, Gül Sude Demircan, Arman Simonyan, Christina Westmose Yde, Lars Rønn Olsen, Rasmus L. Marvig, Olga Østrup, Maria Rossing, Finn Cilius Nielsen, Ole Winther and Frederik Otzen Bagger
Cancers 2021, 13(24), 6283; https://doi.org/10.3390/cancers13246283 - 14 Dec 2021
Cited by 44 | Viewed by 13749
Abstract
Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed [...] Read more.
Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard—SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants. Full article
(This article belongs to the Special Issue Genomic Medicine in Cancer)
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21 pages, 15300 KiB  
Article
Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
by Fereshteh Izadi, Benjamin P. Sharpe, Stella P. Breininger, Maria Secrier, Jane Gibson, Robert C. Walker, Saqib Rahman, Ginny Devonshire, Megan A. Lloyd, Zoë S. Walters, Rebecca C. Fitzgerald, Matthew J. J. Rose-Zerilli, Tim J. Underwood and on behalf of OCCAMS
Cancers 2021, 13(14), 3394; https://doi.org/10.3390/cancers13143394 - 6 Jul 2021
Cited by 8 | Viewed by 4577
Abstract
Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (20–37%), as is the overall survival benefit at five years (9%). The EAC genome is complex and heterogeneous between [...] Read more.
Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (20–37%), as is the overall survival benefit at five years (9%). The EAC genome is complex and heterogeneous between patients, and it is not yet understood whether specific mutational patterns may result in chemotherapy sensitivity or resistance. To identify associations between genomic events and response to NAC in EAC, a comparative genomic analysis was performed in 65 patients with extensive clinical and pathological annotation using whole-genome sequencing (WGS). We defined response using Mandard Tumor Regression Grade (TRG), with responders classified as TRG1–2 (n = 27) and non-responders classified as TRG4–5 (n =38). We report a higher non-synonymous mutation burden in responders (median 2.08/Mb vs. 1.70/Mb, p = 0.036) and elevated copy number variation in non-responders (282 vs. 136/patient, p < 0.001). We identified copy number variants unique to each group in our cohort, with cell cycle (CDKN2A, CCND1), c-Myc (MYC), RTK/PIK3 (KRAS, EGFR) and gastrointestinal differentiation (GATA6) pathway genes being specifically altered in non-responders. Of note, NAV3 mutations were exclusively present in the non-responder group with a frequency of 22%. Thus, lower mutation burden, higher chromosomal instability and specific copy number alterations are associated with resistance to NAC. Full article
(This article belongs to the Special Issue Genomic Medicine in Cancer)
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14 pages, 35184 KiB  
Article
Pan-Cancer Analysis Reveals Distinct Metabolic Reprogramming in Different Epithelial–Mesenchymal Transition Activity States
by Ji-Yong Sung and Jae-Ho Cheong
Cancers 2021, 13(8), 1778; https://doi.org/10.3390/cancers13081778 - 8 Apr 2021
Cited by 13 | Viewed by 4808
Abstract
Epithelial–mesenchymal transition (EMT) is critical for cancer development, invasion, and metastasis. Its activity influences metabolic reprogramming, tumor aggressiveness, and patient survival. Abnormal tumor metabolism has been identified as a cancer hallmark and is considered a potential therapeutic target. We profiled distinct metabolic signatures [...] Read more.
Epithelial–mesenchymal transition (EMT) is critical for cancer development, invasion, and metastasis. Its activity influences metabolic reprogramming, tumor aggressiveness, and patient survival. Abnormal tumor metabolism has been identified as a cancer hallmark and is considered a potential therapeutic target. We profiled distinct metabolic signatures by EMT activity using data from 9452 transcriptomes across 31 different cancer types from The Cancer Genome Atlas. Our results demonstrated that ~80 to 90% of cancer types had high carbohydrate and energy metabolism, which were associated with the high EMT group. Notably, among the distinct EMT activities, metabolic reprogramming in different immune microenvironments was correlated with patient prognosis. Nine cancer types showed a significant difference in survival with the presence of high EMT activity. Stomach cancer showed elevated energy metabolism and was associated with an unfavorable prognosis (p < 0.0068) coupled with high expression of CHST14, indicating that it may serve as a potential drug target. Our analyses highlight the prevalence of cancer type-dependent EMT and metabolic reprogramming activities and identified metabolism-associated genes that may serve as potential therapeutic targets. Full article
(This article belongs to the Special Issue Genomic Medicine in Cancer)
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