Chemotherapy Resistance in Advanced Ovarian Cancer Patients

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Abstract

Ovarian cancer is the seventh most common gynaecologic malignancy seen in women. Majority of the patients with ovarian cancer are diagnosed at the advanced stage making prognosis poor. The standard management of advanced ovarian cancer includes tumour debulking surgery followed by chemotherapy. Various types of chemotherapeutic regimens have been used to treat advanced ovarian cancer, but the most promising and the currently used standard first-line treatment is carboplatin and paclitaxel. Despite improved clinical response and survival to this combination of chemotherapy, numerous patients either undergo relapse or succumb to the disease as a result of chemotherapy resistance. To understand this phenomenon at a cellular level, various macromolecules such as DNA, messenger RNA and proteins have been developed as biomarkers for chemotherapy response. This review comprehensively summarizes the problem that pertains to chemotherapy resistance in advanced ovarian cancer and provides a good overview of the various biomarkers that have been developed in this field.

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Correlation between protein and mRNA abundance in yeast.

S P Gygi, Y Rochon, B R Franza … (1999)

We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeast Saccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, Jr., P. Hieter, B. Vogelstein, and K. W. Kinzler, Cell 88:243-251, 1997). We found that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data. Indeed, for some genes, while the mRNA levels were of the same value the protein levels varied by more than 20-fold. Conversely, invariant steady-state levels of certain proteins were observed with respective mRNA transcript levels that varied by as much as 30-fold. Another interesting observation is that codon bias is not a predictor of either protein or mRNA levels. Our results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient.

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Proteomic analysis of post-translational modifications.

Matthias Mann, Ole N. Jensen (2003)

Post-translational modifications modulate the activity of most eukaryote proteins. Analysis of these modifications presents formidable challenges but their determination generates indispensable insight into biological function. Strategies developed to characterize individual proteins are now systematically applied to protein populations. The combination of function- or structure-based purification of modified 'subproteomes', such as phosphorylated proteins or modified membrane proteins, with mass spectrometry is proving particularly successful. To map modification sites in molecular detail, novel mass spectrometric peptide sequencing and analysis technologies hold tremendous potential. Finally, stable isotope labeling strategies in combination with mass spectrometry have been applied successfully to study the dynamics of modifications.

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Adaptive therapy.

Robert A. Gatenby, Ariosto S. Silva, Robert J. Gillies … (2009)

A number of successful systemic therapies are available for treatment of disseminated cancers. However, tumor response is often transient, and therapy frequently fails due to emergence of resistant populations. The latter reflects the temporal and spatial heterogeneity of the tumor microenvironment as well as the evolutionary capacity of cancer phenotypes to adapt to therapeutic perturbations. Although cancers are highly dynamic systems, cancer therapy is typically administered according to a fixed, linear protocol. Here we examine an adaptive therapeutic approach that evolves in response to the temporal and spatial variability of tumor microenvironment and cellular phenotype as well as therapy-induced perturbations. Initial mathematical models find that when resistant phenotypes arise in the untreated tumor, they are typically present in small numbers because they are less fit than the sensitive population. This reflects the "cost" of phenotypic resistance such as additional substrate and energy used to up-regulate xenobiotic metabolism, and therefore not available for proliferation, or the growth inhibitory nature of environments (i.e., ischemia or hypoxia) that confer resistance on phenotypically sensitive cells. Thus, in the Darwinian environment of a cancer, the fitter chemosensitive cells will ordinarily proliferate at the expense of the less fit chemoresistant cells. The models show that, if resistant populations are present before administration of therapy, treatments designed to kill maximum numbers of cancer cells remove this inhibitory effect and actually promote more rapid growth of the resistant populations. We present an alternative approach in which treatment is continuously modulated to achieve a fixed tumor population. The goal of adaptive therapy is to enforce a stable tumor burden by permitting a significant population of chemosensitive cells to survive so that they, in turn, suppress proliferation of the less fit but chemoresistant subpopulations. Computer simulations show that this strategy can result in prolonged survival that is substantially greater than that of high dose density or metronomic therapies. The feasibility of adaptive therapy is supported by in vivo experiments. [Cancer Res 2009;69(11):4894-903] Major FindingsWe present mathematical analysis of the evolutionary dynamics of tumor populations with and without therapy. Analytic solutions and numerical simulations show that, with pretreatment, therapy-resistant cancer subpopulations are present due to phenotypic or microenvironmental factors; maximum dose density chemotherapy hastens rapid expansion of resistant populations. The models predict that host survival can be maximized if "treatment-for-cure strategy" is replaced by "treatment-for-stability." Specifically, the models predict that an optimal treatment strategy will modulate therapy to maintain a stable population of chemosensitive cells that can, in turn, suppress the growth of resistant populations under normal tumor conditions (i.e., when therapy-induced toxicity is absent). In vivo experiments using OVCAR xenografts treated with carboplatin show that adaptive therapy is feasible and, in this system, can produce long-term survival.

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Author and article information

Journal

Journal ID (nlm-ta): Biomark Cancer

Journal ID (iso-abbrev): Biomark Cancer

Journal ID (publisher-id): BIC

Journal ID (hwp): spbic

Title: Biomarkers in Cancer

Publisher: SAGE Publications (Sage UK: London, England )

ISSN (Electronic): 1179-299X

Publication date (Electronic): 05 July 2019

Publication date Collection: 2019

Volume: 11

Electronic Location Identifier: 1179299X19860815

Affiliations

[1 ]Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India

[2 ]Division of Clinical Oncology, National Institute of Cancer Prevention and Research, Noida, India

[3 ]Department of Medical Oncology, All India Institute of Medical Sciences, New Delhi, India

Author notes

[*]Gururao Hariprasad, Department of Biophysics, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India. Email: g.hariprasad@ 123456rediffmail.com

Article

Publisher ID: 10.1177_1179299X19860815

DOI: 10.1177/1179299X19860815

PMC ID: 6613062

PubMed ID: 31308780

SO-VID: f8145508-db4a-4058-9f66-74c7b255e5c1

License:

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

History

Date received : 13 April 2019

Date accepted : 8 May 2019

Custom metadata

cover-date January-December 2018

Keywords: chemo-therapy,advanced ovarian cancer,biomarkers,proteomics

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Keywords: chemo-therapy, advanced ovarian cancer, biomarkers, proteomics

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Chemotherapy Resistance in Advanced Ovarian Cancer Patients

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Journal of Circulating Biomarkers

Most cited references 195

Correlation between protein and mRNA abundance in yeast.

Proteomic analysis of post-translational modifications.

Adaptive therapy.

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