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Cancers
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Integrating Tumor Evolution Dynamics into the Treatment of Cancer
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Integrating Tumor Evolution Dynamics into the Treatment of Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Clinical Research of Cancer".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 7467

Special Issue Editor

Dr. Jingsong Zhang

E-Mail Website
Guest Editor
Moffitt Cancer Center, Tampa, FL, USA
Interests: tumor evolution; adaptive therapy; extinction therapy; mathematical modeling; DNA damage repair; clinical trial

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your research to this Special Issue which invites studies integrating tumor evolution dynamics into the treatment of cancer. Despite recent approvals of targeted therapies and immunotherapies, stage IV cancer remains incurable. While continued development of new drugs is needed, we believe that improved survival of metastatic cancer can be obtained through better utilization of existing agents using treatment strategies guided by intra-tumor evolutionary dynamics. Over the past 10 years, oncologists, evolutionary biologists, mathematicians, and cancer biologists have teamed up to challenge the current threat to a progression paradigm. The promises of the adaptive therapy approach have been seen in early-phase prostate cancer trials.  

I would like to invite you to submit your original research papers, reviews, communications, and clinical trial papers. Our goal is to facilitate communications and collaborations in this emerging field. Clinical trials with meaningful but negative results, or ongoing clinical trials with a novel design, will be accepted as well.

I look forward to receiving your contributions.

Dr. Jingsong Zhang
Guest Editor

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

  • tumor evolution
  • adaptive therapy
  • extinction therapy
  • mathematical modeling
  • clinical trial

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

28 pages, 1074 KiB  
Article
Evolutionary Analysis of TCGA Data Using Over- and Under- Mutated Genes Identify Key Molecular Pathways and Cellular Functions in Lung Cancer Subtypes
by Audrey R. Freischel, Jamie K. Teer, Kimberly Luddy, Jessica Cunningham, Yael Artzy-Randrup, Tamir Epstein, Kenneth Y. Tsai, Anders Berglund, John L. Cleveland, Robert J. Gillies, Joel S. Brown and Robert A. Gatenby
Cancers 2023, 15(1), 18; https://doi.org/10.3390/cancers15010018 - 20 Dec 2022
Cited by 3 | Viewed by 2173
Abstract
We identify critical conserved and mutated genes through a theoretical model linking a gene’s fitness contribution to its observed mutational frequency in a clinical cohort. “Passenger” gene mutations do not alter fitness and have mutational frequencies determined by gene size and the mutation [...] Read more.
We identify critical conserved and mutated genes through a theoretical model linking a gene’s fitness contribution to its observed mutational frequency in a clinical cohort. “Passenger” gene mutations do not alter fitness and have mutational frequencies determined by gene size and the mutation rate. Driver mutations, which increase fitness (and proliferation), are observed more frequently than expected. Non-synonymous mutations in essential genes reduce fitness and are eliminated by natural selection resulting in lower prevalence than expected. We apply this “evolutionary triage” principle to TCGA data from EGFR-mutant, KRAS-mutant, and NEK (non-EGFR/KRAS) lung adenocarcinomas. We find frequent overlap of evolutionarily selected non-synonymous gene mutations among the subtypes suggesting enrichment for adaptations to common local tissue selection forces. Overlap of conserved genes in the LUAD subtypes is rare suggesting negative evolutionary selection is strongly dependent on initiating mutational events during carcinogenesis. Highly expressed genes are more likely to be conserved and significant changes in expression (>20% increased/decreased) are common in genes with evolutionarily selected mutations but not in conserved genes. EGFR-mut cancers have fewer average mutations (89) than KRAS-mut (228) and NEK (313). Subtype-specific variation in conserved and mutated genes identify critical molecular components in cell signaling, extracellular matrix remodeling, and membrane transporters. These findings demonstrate subtype-specific patterns of co-adaptations between the defining driver mutation and somatically conserved genes as well as novel insights into epigenetic versus genetic contributions to cancer evolution. Full article
(This article belongs to the Special Issue Integrating Tumor Evolution Dynamics into the Treatment of Cancer)
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10 pages, 1225 KiB  
Article
A Phase 1b Adaptive Androgen Deprivation Therapy Trial in Metastatic Castration Sensitive Prostate Cancer
by Jingsong Zhang, Jill Gallaher, Jessica J. Cunningham, Jung W. Choi, Filip Ionescu, Monica S. Chatwal, Rohit Jain, Youngchul Kim, Liang Wang, Joel S. Brown, Alexander R. Anderson and Robert A. Gatenby
Cancers 2022, 14(21), 5225; https://doi.org/10.3390/cancers14215225 - 25 Oct 2022
Cited by 7 | Viewed by 2543
Abstract
Background: We hypothesize that cancer survival can be improved through adapting treatment strategies to cancer evolutionary dynamics and conducted a phase 1b study in metastatic castration sensitive prostate cancer (mCSPC). Methods: Men with asymptomatic mCSPC were enrolled and proceeded with a [...] Read more.
Background: We hypothesize that cancer survival can be improved through adapting treatment strategies to cancer evolutionary dynamics and conducted a phase 1b study in metastatic castration sensitive prostate cancer (mCSPC). Methods: Men with asymptomatic mCSPC were enrolled and proceeded with a treatment break after achieving > 75% PSA decline with LHRH analog plus an NHA. ADT was restarted at the time of PSA or radiographic progression and held again after achieving >50% PSA decline. This on-off cycling of ADT continued until on treatment imaging progression. Results: At data cut off in August 2022, only 2 of the 16 evaluable patients were off study due to imaging progression at 28 months from first dose of LHRH analog for mCSPC. Two additional patients showed PSA progression at 12.4 and 20.5 months and remain on trial. Since none of the 16 patients developed imaging progression at 12 months, the study succeeded in its primary objective of feasibility. The secondary endpoints of median time to PSA progression and median time to radiographic progression have not been reached at a median follow up of 26 months. Conclusions: It is feasible to use an individual’s PSA response and testosterone levels to guide intermittent ADT in mCSPC. Full article
(This article belongs to the Special Issue Integrating Tumor Evolution Dynamics into the Treatment of Cancer)
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13 pages, 1860 KiB  
Article
High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study
by William Meade, Allison Weber, Tin Phan, Emily Hampston, Laura Figueroa Resa, John Nagy and Yang Kuang
Cancers 2022, 14(16), 4033; https://doi.org/10.3390/cancers14164033 - 20 Aug 2022
Cited by 3 | Viewed by 2126
Abstract
Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult [...] Read more.
Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer. Full article
(This article belongs to the Special Issue Integrating Tumor Evolution Dynamics into the Treatment of Cancer)
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