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Systems oncology: toward the clinical application of cancer systems biology
Manuel Valladares-Ayerbes1, Mar Haz-Conde1 & Moisés Blanco-Calvo*,1 How many times do oncologists have to modify treatments due to their inefficacy? And how many times do cancer patients report intense side effects and toxicity of treatment? The answer is obvious: many, many times. Usually, medical decisions are supported by good evidence of effectiveness and benefit [1] , but are also based on heuristic approaches. In these situations, physicians try to hit the target using the available therapeutic options taking into account the results of properly designed randomized controlled trials and to manage adverse events. However, the lack of a holistic scientific approach for the definition of the right treatment for the right patient (i.e., personalized/precision medicine) based on biological and clinical data is an urgent problem to solve in several areas within medicine, such as psychiatry or oncology. In fact, in oncology, the problem is, if it should be, more complicated because patients and doctors have to take into account the prognostic dimension for each case in particular. However, until a few years ago, the biological sciences, whose knowledge supports medicine (among other applied
disciplines), were unable to provide the solution for this problem. With the advent of the new century, the development of high-throughput technologies for biological analyses, in particular for bio chemistry and molecular biology, made possible the generation of a huge amount of data from multiple sources (i.e., isolated cells, body fluids, tissues and so on) at multiple molecular levels (i.e., genomic, transcriptomic, proteomic and so on). These technical improvements could enable the implementation of a true personalized medicine by the simultaneous analysis of a large number of molecular parameters in each patient and disease. However, the integration of molecular and clinical data for achieving useful and understandable information for the decision-making process is necessary [2] . Systems biology is an emerging discipline that pursues the integration of multiple levels of biological information in order to attain an integral view and deep understanding of physiological processes both in healthy and pathological conditions. Systems biology employs methodological approaches based on mathematics and
KEYWORDS
• cancer systems biology • high-throughput technologies • precision oncology • systems
biology
“With the advent of the new
century, the development of high-throughput technologies for biological analyses ... made possible the generation of a huge amount of data from multiple sources ... at multiple molecular levels...”
Translational Cancer Research Lab, La Coruña Biomedical Research Institute (INIBIC) & Clinical Oncology Department, La Coruña University Hospital, As Xubias 84, 15006 La Coruña, Spain *Author for correspondence: Tel.: +34 981 178 000; Fax: +34 981 178 273; [email protected] 1
10.2217/FON.14.255 © 2015 Future Medicine Ltd
Future Oncol. (2015) 11(4), 553–555
part of
ISSN 1479-6694
553
Editorial Valladares-Ayerbes, Haz-Conde & Blanco-Calvo
“...the implementation of
systems oncology will trigger an authentic revolution in the consulting rooms of oncologists worldwide.”
554
bioinformatics to construct complex systems (or models) incorporating multiple data inputs [3] . Once systems are generated, their behavior can be studied in the presence of different external or internal stimuli or perturbations and, more importantly, the outcome for each of these cues can be anticipated. This allows the association of different alterations with different pathological conditions and, in addition, the selection of the most effective drug for restoring the initial homeostatic state. The application of systems biology to the study of cancer has been termed cancer systems biology [4] . Although cancer systems biology has made pivotal contributions to the integral study of this disease [5] , the discipline is still in its infancy and it must deal with a number of important challenges in order to provide definitive solutions for a true personalized oncology. Perhaps the most relevant challenge comes from the still limited capacity of the different ‘omics’ and their highthroughput technologies to generate data with the resolution that requires the tumor/patient analysis. Nowadays, we know that for achieving the full understanding of malignancies, the first step toward tailored disease assessment and administration of the suitable therapy, we should be able to analyze the tumor up to cell level. This is because in the last years our idea of tumors has undergone an evolution from their conception as a homogeneous whole to their conception as complex entities with high internal heterogeneity [6] . Cells in tumors are organized in many clones subjected to selection, which drives a spatiotemporal dynamic of expansion or regression in function of external and internal cues. So, the ratio of each clone can change over time and, most important, this ratio can suffer changes depending on therapy due to clonal selection of resistant clones [7] . Therefore, detailed cell level analysis of tumors is necessary at multiple time points to monitor the course of disease and for the selection of the most favorable therapy in each moment. In addition, for the application of the right treatment for a particular patient, we also need to increase the knowledge about the molecular characteristics that are unique to that patient, in other words, the knowledge of interindividual heterogeneity. And finally, we need to develop strategies to generate true personalized drugs tailored for each patient and tumor. Currently, data provided by different ‘omics’ are already giving rise to novel molecular stratifications in different tumors. In the clinical
Future Oncol. (2015) 11(4)
setting, there are some modest examples of this kind of classification based on different transcriptomic signatures used for prognostic purposes [8–11] . But, these molecular classifications should be refined and improved in the next years with the incorporation of more and more detailed data. In this sense, several international consortia are performing important efforts analyzing different tumor types from the point of view of multiple ‘omics’. Perhaps, the most relevant contribution is being made by The Cancer Genome Atlas program, publishing comprehensive multilevel high-throughput analyses of different tumors, including, among others, breast, colon, gastric and lung cancer [12–16] . In the near future, with the improvements in high-throughput technologies, in the generation and management of molecular data, and in their integration by cancer systems biology, we could go beyond the traditional clinical and pathological classification of cancers, based only in an observable and assumable limited number of parameters. The new paradigm of molecular pathology involves the assumption of the principle that each individual and each tumor in each individual are unique, that is, ‘one person, one tumor’. So, the traditional pathological classification will be abandoned in favor of an individualized molecular characterization of each tumor in each patient. Moreover, the current research is moving the focus from the primary tumor to the analysis of other sample sources, including among others, stool, urine, saliva and blood derivatives. The main advantages of these sample sources are their easy accessibility and their consecution by low-invasive methods. As a consequence, the use of these kinds of samples has an additional benefit: they can be analyzed at multiple time points in order to monitor disease evolution and treatment response. In this sense, plasma samples have been previously used as ‘liquid biopsies’ to determine the mutational profile present in circulating cell-free DNA associated with the monitoring of drug response and therapeutic outcome [17,18] . Since plasma and serum also contain relevant information in noncoding RNA [19] and proteins [20] , the analysis of these samples with high-throughput technologies can represent an important step for future implementation of precision oncology. In fact, the usage of these kinds of samples is essential for the development of clinical trials based on the application of cancer systems biology to the management
future science group
Systems oncology: toward the clinical application of cancer systems biology and treatment of patients, in other words, the development of systems oncology. While cancer systems biology models are currently combined and tested with clinical data available from previous studies, systems oncology aspires to benefit from the integration of prospective clinical data. In this last step toward precision oncology, the knowledge derived from cancer systems biology should be moved to patients, both in clinical trials and tailored clinical decision-making. Thus, systems oncology will allow the future development and implementation of novel and improved tools for early diagnosis, disease assessment and administration of patient-specific and precise treatments in clinical oncology. But to achieve this goal, a key aspect to overcome is the technological barrier currently existing, so that all the power of cancer systems biology can be delivered to the clinical setting, using for example easy-to-use point-of-care tools. This last point is critical in the implementation of the new era of precision/personalized medicine, since the information generated by complex devices References 1
Eddy DM. Evidence-based medicine: a unified approach. Health Aff. 24(1), 9–17 (2005).
2
Dancey JE, Bedard PL, Onetto N, Hudson TJ. The genetic basis for cancer treatment decisions. Cell 148(3), 409–420 (2012).
3
Kristensen VN, Lingjærde OC, Russnes HG, Vollan HKM, Frigessi A, Børresen-Dale A-L. Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer. 14(5), 299–313 (2014).
4
Werner HMJ, Mills GB, Ram PT. Cancer Systems Biology: a peek into the future of patient care? Nat. Rev. Clin. Oncol. 11(3), 167–176 (2014).
5
Jain M, Nilsson R, Sharma S et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336(6084), 1040–1044 (2012).
6
7
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011). Aparicio S, Caldas C. The implications of clonal genome evolution for cancer medicine. N. Engl. J. Med. 368(9), 842–851 (2013).
future science group
Editorial
analyzed by complex mathematical and bioinformatics approaches should be easily accessible and understandable for practitioner. This issue should be addressed through the development of user friendly software and cheaper technologies allowing the analysis of all tumor/patient samples with a reasonable cost [2] . Undoubtedly, all these challenges will be solved in the near future, and the implementation of systems oncology will trigger an authentic revolution in the consulting rooms of oncologists worldwide. Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript. Van de Vijver MJ, He YD, van’t Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347(25), 1999–2009 (2002).
14 Cancer Genome Atlas Research Network.
Paik S, Shak S, Tang G et al. A multigene assay to predict recurrence of tamoxifentreated, node-negative breast cancer. N. Engl. J. Med. 351(27), 2817–2826 (2004).
15 Cancer Genome Atlas Research Network.
10 Salazar R, Roepman P, Capella G et al. Gene
16 Cancer Genome Atlas Research Network.
8
9
expression signature to improve prognosis prediction of stage II and III colorectal cancer. J. Clin. Oncol. 29(1), 17–24 (2011). 11 Gray RG, Quirke P, Handley K et al.
Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J. Clin. Oncol. 29(35), 4611–4619 (2011). 12 The Cancer Genome Atlas Network.
Comprehensive molecular portraits of human breast tumours. Nature 490(7418), 61–70 (2012). 13 The Cancer Genome Atlas Network.
Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407), 330–337 (2012).
Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513 (7517), 202–209 (2014). Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511), 543–550 (2014). Comprehensive genomic characterization of squamous cell lung cancers. Nature 489(7417), 519–525 (2012). 17 Dawson S-J, Tsui DWY, Murtaza M et al.
Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013). 18 Bettegowda C, Sausen M, Leary RJ et al.
Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl Med. 6(224), 224ra24 (2014). 19 Schwarzenbach H, Nishida N, Calin GA,
Pantel K. Clinical relevance of circulating cell-free microRNAs in cancer. Nat. Rev. Clin. Oncol. 11(3), 145–156 (2014). 20 Hanash SM, Pitteri SJ, Faca VM. Mining the
plasma proteome for cancer biomarkers. Nature 452(7187), 571–579 (2008).
www.futuremedicine.com
555
Systems oncology: toward the clinical application of cancer systems biology
Manuel Valladares-Ayerbes1, Mar Haz-Conde1 & Moisés Blanco-Calvo*,1 How many times do oncologists have to modify treatments due to their inefficacy? And how many times do cancer patients report intense side effects and toxicity of treatment? The answer is obvious: many, many times. Usually, medical decisions are supported by good evidence of effectiveness and benefit [1] , but are also based on heuristic approaches. In these situations, physicians try to hit the target using the available therapeutic options taking into account the results of properly designed randomized controlled trials and to manage adverse events. However, the lack of a holistic scientific approach for the definition of the right treatment for the right patient (i.e., personalized/precision medicine) based on biological and clinical data is an urgent problem to solve in several areas within medicine, such as psychiatry or oncology. In fact, in oncology, the problem is, if it should be, more complicated because patients and doctors have to take into account the prognostic dimension for each case in particular. However, until a few years ago, the biological sciences, whose knowledge supports medicine (among other applied
disciplines), were unable to provide the solution for this problem. With the advent of the new century, the development of high-throughput technologies for biological analyses, in particular for bio chemistry and molecular biology, made possible the generation of a huge amount of data from multiple sources (i.e., isolated cells, body fluids, tissues and so on) at multiple molecular levels (i.e., genomic, transcriptomic, proteomic and so on). These technical improvements could enable the implementation of a true personalized medicine by the simultaneous analysis of a large number of molecular parameters in each patient and disease. However, the integration of molecular and clinical data for achieving useful and understandable information for the decision-making process is necessary [2] . Systems biology is an emerging discipline that pursues the integration of multiple levels of biological information in order to attain an integral view and deep understanding of physiological processes both in healthy and pathological conditions. Systems biology employs methodological approaches based on mathematics and
KEYWORDS
• cancer systems biology • high-throughput technologies • precision oncology • systems
biology
“With the advent of the new
century, the development of high-throughput technologies for biological analyses ... made possible the generation of a huge amount of data from multiple sources ... at multiple molecular levels...”
Translational Cancer Research Lab, La Coruña Biomedical Research Institute (INIBIC) & Clinical Oncology Department, La Coruña University Hospital, As Xubias 84, 15006 La Coruña, Spain *Author for correspondence: Tel.: +34 981 178 000; Fax: +34 981 178 273; [email protected] 1
10.2217/FON.14.255 © 2015 Future Medicine Ltd
Future Oncol. (2015) 11(4), 553–555
part of
ISSN 1479-6694
553
Editorial Valladares-Ayerbes, Haz-Conde & Blanco-Calvo
“...the implementation of
systems oncology will trigger an authentic revolution in the consulting rooms of oncologists worldwide.”
554
bioinformatics to construct complex systems (or models) incorporating multiple data inputs [3] . Once systems are generated, their behavior can be studied in the presence of different external or internal stimuli or perturbations and, more importantly, the outcome for each of these cues can be anticipated. This allows the association of different alterations with different pathological conditions and, in addition, the selection of the most effective drug for restoring the initial homeostatic state. The application of systems biology to the study of cancer has been termed cancer systems biology [4] . Although cancer systems biology has made pivotal contributions to the integral study of this disease [5] , the discipline is still in its infancy and it must deal with a number of important challenges in order to provide definitive solutions for a true personalized oncology. Perhaps the most relevant challenge comes from the still limited capacity of the different ‘omics’ and their highthroughput technologies to generate data with the resolution that requires the tumor/patient analysis. Nowadays, we know that for achieving the full understanding of malignancies, the first step toward tailored disease assessment and administration of the suitable therapy, we should be able to analyze the tumor up to cell level. This is because in the last years our idea of tumors has undergone an evolution from their conception as a homogeneous whole to their conception as complex entities with high internal heterogeneity [6] . Cells in tumors are organized in many clones subjected to selection, which drives a spatiotemporal dynamic of expansion or regression in function of external and internal cues. So, the ratio of each clone can change over time and, most important, this ratio can suffer changes depending on therapy due to clonal selection of resistant clones [7] . Therefore, detailed cell level analysis of tumors is necessary at multiple time points to monitor the course of disease and for the selection of the most favorable therapy in each moment. In addition, for the application of the right treatment for a particular patient, we also need to increase the knowledge about the molecular characteristics that are unique to that patient, in other words, the knowledge of interindividual heterogeneity. And finally, we need to develop strategies to generate true personalized drugs tailored for each patient and tumor. Currently, data provided by different ‘omics’ are already giving rise to novel molecular stratifications in different tumors. In the clinical
Future Oncol. (2015) 11(4)
setting, there are some modest examples of this kind of classification based on different transcriptomic signatures used for prognostic purposes [8–11] . But, these molecular classifications should be refined and improved in the next years with the incorporation of more and more detailed data. In this sense, several international consortia are performing important efforts analyzing different tumor types from the point of view of multiple ‘omics’. Perhaps, the most relevant contribution is being made by The Cancer Genome Atlas program, publishing comprehensive multilevel high-throughput analyses of different tumors, including, among others, breast, colon, gastric and lung cancer [12–16] . In the near future, with the improvements in high-throughput technologies, in the generation and management of molecular data, and in their integration by cancer systems biology, we could go beyond the traditional clinical and pathological classification of cancers, based only in an observable and assumable limited number of parameters. The new paradigm of molecular pathology involves the assumption of the principle that each individual and each tumor in each individual are unique, that is, ‘one person, one tumor’. So, the traditional pathological classification will be abandoned in favor of an individualized molecular characterization of each tumor in each patient. Moreover, the current research is moving the focus from the primary tumor to the analysis of other sample sources, including among others, stool, urine, saliva and blood derivatives. The main advantages of these sample sources are their easy accessibility and their consecution by low-invasive methods. As a consequence, the use of these kinds of samples has an additional benefit: they can be analyzed at multiple time points in order to monitor disease evolution and treatment response. In this sense, plasma samples have been previously used as ‘liquid biopsies’ to determine the mutational profile present in circulating cell-free DNA associated with the monitoring of drug response and therapeutic outcome [17,18] . Since plasma and serum also contain relevant information in noncoding RNA [19] and proteins [20] , the analysis of these samples with high-throughput technologies can represent an important step for future implementation of precision oncology. In fact, the usage of these kinds of samples is essential for the development of clinical trials based on the application of cancer systems biology to the management
future science group
Systems oncology: toward the clinical application of cancer systems biology and treatment of patients, in other words, the development of systems oncology. While cancer systems biology models are currently combined and tested with clinical data available from previous studies, systems oncology aspires to benefit from the integration of prospective clinical data. In this last step toward precision oncology, the knowledge derived from cancer systems biology should be moved to patients, both in clinical trials and tailored clinical decision-making. Thus, systems oncology will allow the future development and implementation of novel and improved tools for early diagnosis, disease assessment and administration of patient-specific and precise treatments in clinical oncology. But to achieve this goal, a key aspect to overcome is the technological barrier currently existing, so that all the power of cancer systems biology can be delivered to the clinical setting, using for example easy-to-use point-of-care tools. This last point is critical in the implementation of the new era of precision/personalized medicine, since the information generated by complex devices References 1
Eddy DM. Evidence-based medicine: a unified approach. Health Aff. 24(1), 9–17 (2005).
2
Dancey JE, Bedard PL, Onetto N, Hudson TJ. The genetic basis for cancer treatment decisions. Cell 148(3), 409–420 (2012).
3
Kristensen VN, Lingjærde OC, Russnes HG, Vollan HKM, Frigessi A, Børresen-Dale A-L. Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer. 14(5), 299–313 (2014).
4
Werner HMJ, Mills GB, Ram PT. Cancer Systems Biology: a peek into the future of patient care? Nat. Rev. Clin. Oncol. 11(3), 167–176 (2014).
5
Jain M, Nilsson R, Sharma S et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336(6084), 1040–1044 (2012).
6
7
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011). Aparicio S, Caldas C. The implications of clonal genome evolution for cancer medicine. N. Engl. J. Med. 368(9), 842–851 (2013).
future science group
Editorial
analyzed by complex mathematical and bioinformatics approaches should be easily accessible and understandable for practitioner. This issue should be addressed through the development of user friendly software and cheaper technologies allowing the analysis of all tumor/patient samples with a reasonable cost [2] . Undoubtedly, all these challenges will be solved in the near future, and the implementation of systems oncology will trigger an authentic revolution in the consulting rooms of oncologists worldwide. Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript. Van de Vijver MJ, He YD, van’t Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347(25), 1999–2009 (2002).
14 Cancer Genome Atlas Research Network.
Paik S, Shak S, Tang G et al. A multigene assay to predict recurrence of tamoxifentreated, node-negative breast cancer. N. Engl. J. Med. 351(27), 2817–2826 (2004).
15 Cancer Genome Atlas Research Network.
10 Salazar R, Roepman P, Capella G et al. Gene
16 Cancer Genome Atlas Research Network.
8
9
expression signature to improve prognosis prediction of stage II and III colorectal cancer. J. Clin. Oncol. 29(1), 17–24 (2011). 11 Gray RG, Quirke P, Handley K et al.
Validation study of a quantitative multigene reverse transcriptase-polymerase chain reaction assay for assessment of recurrence risk in patients with stage II colon cancer. J. Clin. Oncol. 29(35), 4611–4619 (2011). 12 The Cancer Genome Atlas Network.
Comprehensive molecular portraits of human breast tumours. Nature 490(7418), 61–70 (2012). 13 The Cancer Genome Atlas Network.
Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407), 330–337 (2012).
Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513 (7517), 202–209 (2014). Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511), 543–550 (2014). Comprehensive genomic characterization of squamous cell lung cancers. Nature 489(7417), 519–525 (2012). 17 Dawson S-J, Tsui DWY, Murtaza M et al.
Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013). 18 Bettegowda C, Sausen M, Leary RJ et al.
Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl Med. 6(224), 224ra24 (2014). 19 Schwarzenbach H, Nishida N, Calin GA,
Pantel K. Clinical relevance of circulating cell-free microRNAs in cancer. Nat. Rev. Clin. Oncol. 11(3), 145–156 (2014). 20 Hanash SM, Pitteri SJ, Faca VM. Mining the
plasma proteome for cancer biomarkers. Nature 452(7187), 571–579 (2008).
www.futuremedicine.com
555
