P re c i s i o n Me d i c i n e Genomic Profiles to Individualize Therapy Oscar E. Streeter Jr, MDa,*, Phillip J. Beron, MDb, Prashant Natarajan Iyer, BE (Chem), MTPC, SCPMc KEYWORDS Precision medicine Big data Genomic profiling Immunotherapy Checkpoint inhibitors Hyperthermia Radiogenomics Machine learning KEY POINTS Precision medicine is generally understood to be the application of genotypic and Omics biomarkers to determine the most appropriate, outcome-driven treatment or therapy for individual patients. Information technology (IT)-enabled big data management and health care are becoming and will be required tools in the clinical kit to properly manage and leverage the complex data that result from genomic, clinic, financial, and behavioral data to benefit individualize patient care and outcomes by predicting for multiple stratified populations. Immunotherapy in 2017 has been most effective in checkpoint inhibitor medications. One of the novel immunomodulators is hyperthermia (HT) that is most effective in combination with radiation therapy (RT) or chemotherapy.
Precision medicine is an evolving term whose definition is changing as the influence of genomic and population big data biomarkers are becoming well understood. WHAT IS PRECISION MEDICINE?
Precision medicine is generally understood as the application of genotypic and Omics biomarkers to determine the most appropriate, outcome-driven treatment of or therapy for individual patients. The authors agree with this definition but would like to extend it — in line with a more comprehensive and clinically relevant view, that is,
Disclosure Statement: O.E. Streeter and P.J. Beron have no disclosures. P.N. Iyer: Oracle Corporation, employer. a The Center for Thermal Oncology, 2001 Santa Monica Boulevard, Suite 1190, Santa Monica, CA 90404, USA; b Department of Radiation Oncology, UCLA Health System, 200 UCLA Medical Plaza, Suite B265, Los Angeles, CA 90095, USA; c Healthcare Solutions, Oracle Corporation, 5805 Owens Drive, Pleasanton, CA 94588, USA * Corresponding author. E-mail address: [email protected] Otolaryngol Clin N Am 50 (2017) 765–773 http://dx.doi.org/10.1016/j.otc.2017.03.012 0030-6665/17/ª 2017 Elsevier Inc. All rights reserved.
oto.theclinics.com
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Streeter Jr et al
precision medicine is the determination and delivery of the “right therapy to the right patient at the right time.”1 The authors’ view of precision medicine acknowledges a few realities that must be addressed via any multidisciplinary approach that combines people, behaviors, social determinants of health (a patients zip code has as much influence on their health as their genetic code), and their phenotypic data (Fig. 1). An integrated definition, such as the one used in this article, also addresses prevailing concerns about cost, access, and outcomes for individual patients and multidimensional stratified populations. The authors posit that the definition of precision medicine that will enable oncology, chronic/acute care, and prevention/wellness must not only address the availability of genomics sequencing data and biomarkers but also do more on health variables that are constantly being defined. Although genomics at the point of care is fundamental to precision medicine, care at the bedside (in the facility or at home) also requires the acquisition, management, integration, clinician validation, and use of data from disparate sources, such as 1. Clinical care (imaging, electronic medical records [EMRs], computerised physician order entry [CPOE], clinical narratives, and sensor/device data) 2. Research (clinical research, trials, publications, results of data discovery, and secondary use) 3. Financial (cost, charges, affordability, income disparities, and credit scores) 4. External and patient-reported data that encompasses patient self-reported data on the Web and via smartphones; family and disease histories/lore that are not in the history and physical examination; environmental variables; behavior/sentiment data; and, increasingly, income/educational/cognitive disparities. These data sources are varied, voluminous, and processed at high velocities (Fig. 2). There also is a corresponding need in the context of therapy and procedures to examine the veracity and value of data and information in enabling and supporting precision medicine. Supporting the natural evolution of precision medicine requires an
Patient Engagement & Behavior
Imaging & Devices
Things
Research & Clinical Care
Activities
Thoughts
Health Care Big Data: Omics and Much More Fig. 1. Big data collection requires a central repository; processes need to be developed across institutions.
Precision Medicine
Significant reduction in cost of genome sequencing
Aggregation and analysis of all data important
Increase in real-world data
Cost
Will you use secondary health data within the next 2 y?
70%
61%
Provider Pharma
54% Payer
Time
More than half of clinical trials already have a molecular biomarker component
Patient stratification to identify population subsets most likely to respond to a therapy
Enhance collaboration between research and medicine
Fig. 2. The growth of big data.
understanding of the new world of big data technologies, analytics, and machine learning. It also needs recognition that a patient’s data will no longer be created and used inside a facility’s 4 walls or in its EMRs. In this new world of health care datafication, important patient (and other) information will be sourced from secondary use that involves integrated order-results workflows that are driven by 1. Incorporating molecular and clinical annotation by additional collaborators—molecular pathologists, consulting physicians, primary care providers, and other specialists 2. Machine learning–based, large-scale, affordable, and automated analysis of images, speech, video, and large text (clinical narratives, discharge summaries, and progress notes) 3. New data from smart devices, home monitors, telemedicine, medical images, and social networks (life and viral networks) 4. Separation of signal from noise—and incorporating actionable analytics, clinician feedback loops and approval, and annotation into clinical system workflows IT-enabled big data management and health care should be a required tool in the clinical kit to properly manage and leverage the complex data that result from genomic, clinical, financial, and behavioral data that brings the biggest benefit to individual patients, their outcomes, and applying the new clinical guidelines and newly created knowledge by extrapolating or predicting for multiple stratified populations that can be expanded to cover new analytics dimensions beyond the diagnosis or disease, including 1. Demographics—gender, race, ethnicity, zip code, and so forth 2. Real-time, location-specific influences—suspended particulate (smog, forest fires, and disasters) or other carcinogens (eg, asbestos, human activities such as fracking) 3. Social determinants of health—examples are activities of daily living (ADL), physical activity, diet, education, and income 4. Patient outcomes, including the effect of a procedure or therapy on survival, recovery, and health management Addressing key health determinants simultaneously at the population and individual levels and the integration of clinical/Omics/other relevant data are critical in delivering
767
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precision medicine. Relating individuals to both research cohorts and stratified populations by taking advantage of the latest software technologies—prescriptive analytics, big data integration, and machine learning—provide opportunities to create or use knowledge that has not existed before or is undiscovered. Applying precision medicine into the clinical workflow generates preventative and diagnostic solutions to advance human care—bringing new targeted therapies, improved patient outcomes, and cost savings.1 GENOMIC PROFILING
To understand this evolving technology of genomic profiling,2 a few terms need to be defined. Base pairs are 2 nucleotides on opposite complementary DNA or RNA strands that are connected by hydrogen bonds. Sequencing is a method of detecting single bases as they are incorporated into DNA template strands. Whole-exome sequencing is a technique for sequencing all the expressed genes in a genome. Next-generation sequencing is the application of genome sequencers that with a single run of material can analyze more than 1.8 terabases (the amount of genetic sequence data equivalent to 1012 base pairs). The cost of sequencing has fallen approximately 10-fold over the last few years, with improved accuracy and speed, bringing the cost to less than $2000 with targeted, although limited at this point, improvement in care at the bedside. It is now available to most patients covered by insurance. Genomic data analysis is where newly identified sequences are aligned to a reference genome. The first and best example of the success of precision medicine in oncology is imatinib mesylate (Gleevec, Novartis Pharma Services AG, Basel, Switzerland) used to treat chronic myeloid leukemia (CML) with the BCR-ABL translocation. CML is due to a clonal evolution, starting with the acquisition of the 9(9;22) (q34;q11) translocation (Philadelphia chromosome), which creates a fusion between the BCR and ABL1 genes. Imatinib mesylate controls CML because it is an inhibitor of ABL family kinases, including the BCR-ABL fusion gene.3 Most squamous cell carcinomas of the head and neck respond to standard drug therapy. When the clonal composition of the pretreatment biopsy is compared over time, however, after drug treatment, there may be changes in clonal-mutation prevalence.4 Sequencing reveals large changes in the abundance of specific clones that may give clues as to which genotypes may confer resistance and may be sensitive to intervention. Therefore, local recurrence or new metastatic sites should be biopsied and sequenced to determine which drug or other intervention may improve response. Therefore, surgeons play a key role in requesting the pathologist send both the primary tumor and recurrent/metastatic tumor for sequencing. An example of this process used in a multidisciplinary clinic is the Weill Cornell Medical College Institute for Precision Medicine. The process starts with clinical examination and consent, followed by metastatic tumor biopsy and whole-exome sequencing/biobanking of tissue for future reference. Results are discussed in a tumor board, with communication to the patient and referring physicians to guide treatment, and used to fuel translational research and development of new diagnostics and therapeutics.5 A trial currently accruing patients that best demonstrates the application of precision medicine in oncology is the recently opened National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) clinical trial. This precision medicine trial explores treating patients based on the molecular profile of tumors with the inclusion criteria of adult patients, solid tumors (including rare tumors and lymphomas), and tumors that no longer respond to standard treatment, with an accrual goal of approximately 3000 cancer patients screened with a tumor biopsy. Biopsied tumor tissue is submitted for gene
Precision Medicine
sequencing to identify initially 143 gene mutations that may respond to a specific therapy (the list of gene mutations is expanding with new discoveries). If a patient’s tumor has a genetic abnormality that matches one targeted by a drug used in the trial, the patient becomes eligible to join the treatment portion of NCI-MATCH. This is an important trial found at the Web site, clinicaltrials.gov, and includes a listing of the mutations examined and drugs useful for each mutation. It is an excellent resource for physicians in the clinics helping to individualize therapy and is constantly updated. As of the fall of 2016, more than 1000 clinical sites, across America, are participating in this trial. It is a federally sponsored trial and free to eligible patients with an estimated primary completion date of June 2022.6 Head and neck squamous cell carcinoma (HNSCC) is an immunosuppressive disease that when recurrent or metastatic responds to immunotherapy, such as checkpoint inhibitors that are available currently.7 Ongoing trials are considering combining current immunotherapies with cancer vaccines. An open-access article by Robert Ferris provides a review of immunologic principles related to head and neck cancer, including the concept of cancer immunosurveillance and immune escape.8 The authors recommend this article because it has figures describing immune escape and antigen presentation allowing recognition of tumor cells by immune system. Most importantly are tables listing the mechanisms of immune escape in HNSCC, monoclonal antibodies under investigation in HNSCC, and a detailed table of immunotherapy trials in HNSCC. Most clinicians will be working with checkpoint inhibitors and a brief discussion of this immunotherapy is warranted and illustrated in a free-access JAMA Oncology patient page.9 Immune checkpoint inhibitor drugs can target either tumor cells or T cells. They block normal proteins on cancer cells or the proteins on T cells that respond to those “normal proteins.” The checkpoint inhibitors prevent tumor cells from attaching to T cells, allowing the T cells to stay activated. A response to immune checkpoint inhibitor treatment results in a brief increase in tumor size (pseudoprogression) due to the increase in the number of activated T cells that enter the tumor. To evaluate genetic mutation changes in tumors when there may not be enough tissue for mutation analysis in a primary or metastatic biopsy or to monitor tumor response, there is an increasing use of analyzing cell-free DNA mutations (cfDNAs) in the plasma and circulating tumor cells (CTCs) in the buffy coat of a centrifuged peripheral blood draw (Figs. 3 and 4).10,11 Every month there are more Clinical Laboratory Improvement (CLIA) certified tests for tumors in specific organs. Commercial companies have developed blood sampling techniques to profile and monitor for programmed death ligand-1 (PD-L1) expression, an important biomarker in immuneoncology treatment decision making and will play an increasing role in the treatment of head and neck cancers. HYPERTHERMIA AS A NOVEL IMMUNE MODULATOR
HT is also a form of precision medicine. Because HT over the past 2 decades has been limited in the United States due to a lack of available equipment and trained practitioners who can deliver this modality between the temperature range of 41 C and 43 C, there is limited understanding of its role in stimulating a nontoxic immune response in practically all tumors. An important phase III trial reported long-term results comparing RT alone with RT plus HT to metastatic lymph nodes in stage IV head and neck patients.12 This study was conducted in 1985 to 1986 to improve the outcome of fixed and inoperable (N3) metastatic lymph nodes in HNSCC in 41 patients with 46 metastatic lymph nodes. Because of the striking results of the
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Fig. 3. Illustration of the origin of cfDNAs and CTCs from a milieu of apoptotic and necrotic fragments from tumors and other cellular fragments. (Courtsey of Oscar Streeter, MD, The Center for Thermal Oncology, Santa Monica, California.)
combined modality arm, the study was prematurely closed because of ethical reasons with a 5-year actuarial probability of nodal control of 24.2% for the radiationonly arm versus 68.6% in the RT plus HT arm. The actuarial survival of the 2 groups at 5 year favors the RT plus HT arm of 53.3% versus 0% (P 5 .02). Although metastatic disease developed in 19% of the patients, it was reduced in the RT plus HT arm to 12.5% vs 24% in the RT-alone arm. It is now known that HT not only has a direct cytotoxic effect but stimulates a natural immune response in the fever range of 39 C to 41 C by activation of heat shock proteins, generating important immune
Fig. 4. Liquid biopsies. After centrifugation of a blood sample, cfDNA from shed tumors is detected from the plasma component of the supernatant, whereas CTCs are captured from the buffy coat component.
Precision Medicine
actions and increasing blood flow, effective in primary and metastatic disease.13 The mechanisms of how HT can be used in metastatic disease as immunotherapy is because of the following effect on heated tumor cells: (1) an increase in the surface expression of MICA, a NKG2D ligand, and major histocompatibility class 1 (MHC 1), making the tumor cells more sensitive to lysis by natural killer (NK) cells and CD81 T cells, respectively, and (2) the release of heat shock proteins (HSPs), which activate NK cells and antigen-presenting cells (APCs). HSPs contain potential tumor antigens, and APCs take up the HSP-antigen complex and cross-present the antigen to CD81 T cells; (3) release of exosomes, which contain potential tumor antigens, and APCs take up the antigen and cross present the antigen to CD81 T cells; (4) immune cells, such as NK cells, CD81 T cells and dendritic cells, in the tumor also get heated and become activated; and (5) tumor vasculature becomes more permeable and may increase expression of the intercellular cell adhesion molecule (ICAM)-1, a protein expressed by the cancer cell with one function to facilitate signal transduction immune response. ICAM-1 also facilitates better immune trafficking between tumor cells and draining lymph nodes. RADIOGENOMICS
Although the use of big data in health care research is still in its infancy, the potential of combining big data with information from comparative effectiveness research and EMR data with machine learning in the future will help with decision making on what type of therapy is best for individual patients, including informing individual patients of potential complications and risk of secondary cancers based on their genomic profile.14 The challenges associated with implementing big data analytics in radiation oncology or in medical oncology are (1) validity of the training data set, (2) availability of public health and computer science experts, (3) silo ownership of the data, and (4) and the critical need to shift from deductive to inductive reasoning using new statistical and probabilistic tools from the field of machine learning (Fig. 5).
Fig. 5. Precision medicine applications.
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WHAT IS MACHINE LEARNING?
Machine learning approaches problems as a doctor progressing through a residency would, starting with patient observation, using algorithms that sift through variables, and looking for combinations that reliably predict outcome. Ongoing research at Harvard Medical School and at the Perelman School of Medicine and Wharton School, University of Pennsylvania, have demonstrated that machine learning algorithms can predict death in metastatic cancer patients. This is accomplished by not only looking at known outcomes for a specific stage of cancer, but other variables such as infections during treatment, use of a wheelchair as well as other variables that are not ordinarily considered in determining prognosis. Obermeyer and Emanuel15 predict prognostic algorithms will come into use in the next 5 years with several more years of prospective validation. SUMMARY
Although there has been significant progress in the ability to sequence a person’s genome in 1 sequencing process, the ability to aggregate all these data into a useful instrument to treat a single patient with cancer is a big data problem that will not be solved by any single institution. That type of computing power and transmitting that information across different platforms require a new type of thinking in medicine, the idea that the institution does not own the data, patients do, and must be part of a larger database tool that currently is in its infancy. For head and neck cancer patients, what can be used today is looking for a mutation that has a therapeutic target and understanding that the tumor will undergo clonal evolution and eventually become resistant. Also, a counterintuitive benefit for patients that is just as important is the ability to identify therapies of low value and avoiding adverse reactions. REFERENCES
1. Oracle Health Sciences. Precision medicine: providing the right therapy to the right patient at the right time. 2016. Available at: http://www.oracle.com/us/industries/ health-sciences/precision-medicine-info-2692756.pdf. Accessed October 1, 2016. 2. Green ED, Guyeer MS, National Human Genome Research Institute. Charting a course for genomic medicine from base pairs to bedside. Nature 2011; 470(7333):204–13. 3. Mohamed AN, Pemberton P, Zonder J, et al. The effect of imatinib mesylate on patients with Philadelphia chromosome-positive chronic myeloid leukemia with secondary chromosomal aberrations. Clin Cancer Res 2003;9(4):1333–7. 4. Aparicio S, Caldas C. The implications of clonal genome evolution for cancer medicine. N Engl J Med 2013;368(9):842–51. 5. Beltran H, Eng K, Mosquera JM, et al. Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol 2015;1(4):466–74. 6. National Cancer Institute. NCI-MATCH: targeted therapy directed by genetic testing in treating patients with advanced refractory solid tumors or lymphomas (ClinicalTrials.gov Identifier: NCT02465060). 2015. Available at: https:// clinicaltrials.gov/ct2/results?term5nct02465060. Accessed September 28, 2016. 7. Lalami Y, Awada A. Innovative perspectives of immunotherapy in head and neck cancer. From relevant scientific rationale to effective clinical practice. Cancer Treat Rev 2016;43:113–23.
Precision Medicine
8. Ferris RL. Immunology and immunotherapy of head and neck cancer. J Clin Oncol 2015;33(29):3293–304. 9. West H. Immune checkpoint inhibitors. JAMA Oncol 2015;1(1):115. 10. Diaz LA Jr, Bardelli A. Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol 2014;32(6):579–86. 11. Economopoulo P, Agelaki S, Perisanidis C, et al. The promise of immunotherapy in head and neck squamous cell carcinoma. Ann Oncol 2016;27(9):1675–85. 12. Valdagni R, Amichetti M. Report of long-term follow-up in a randomized trial comparing radiation therapy and radiation therappy plus hyperthermia to metastatic lymph nodes in Stage IV head and neck patients. Int J Radiat Oncol Biol Phys 1994;28(1):163–9. 13. Toraya-Brown S, Fiering S. Local tumor hyperthermia as immunotherapy for metastatic cancer. Int J Hyperthermia 2014;30(8):531–9. 14. Trifiletti DM, Showaalter TN. Big data and comparative effectiveness research in radiation oncology: synergy and accelerated discovery. Front Oncol 2015;5(274): 1–5. 15. Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med 2016;375(13):1216–9.
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Precision medicine is an evolving term whose definition is changing as the influence of genomic and population big data biomarkers are becoming well understood. WHAT IS PRECISION MEDICINE?
Precision medicine is generally understood as the application of genotypic and Omics biomarkers to determine the most appropriate, outcome-driven treatment of or therapy for individual patients. The authors agree with this definition but would like to extend it — in line with a more comprehensive and clinically relevant view, that is,
Disclosure Statement: O.E. Streeter and P.J. Beron have no disclosures. P.N. Iyer: Oracle Corporation, employer. a The Center for Thermal Oncology, 2001 Santa Monica Boulevard, Suite 1190, Santa Monica, CA 90404, USA; b Department of Radiation Oncology, UCLA Health System, 200 UCLA Medical Plaza, Suite B265, Los Angeles, CA 90095, USA; c Healthcare Solutions, Oracle Corporation, 5805 Owens Drive, Pleasanton, CA 94588, USA * Corresponding author. E-mail address: [email protected] Otolaryngol Clin N Am 50 (2017) 765–773 http://dx.doi.org/10.1016/j.otc.2017.03.012 0030-6665/17/ª 2017 Elsevier Inc. All rights reserved.
oto.theclinics.com
766
Streeter Jr et al
precision medicine is the determination and delivery of the “right therapy to the right patient at the right time.”1 The authors’ view of precision medicine acknowledges a few realities that must be addressed via any multidisciplinary approach that combines people, behaviors, social determinants of health (a patients zip code has as much influence on their health as their genetic code), and their phenotypic data (Fig. 1). An integrated definition, such as the one used in this article, also addresses prevailing concerns about cost, access, and outcomes for individual patients and multidimensional stratified populations. The authors posit that the definition of precision medicine that will enable oncology, chronic/acute care, and prevention/wellness must not only address the availability of genomics sequencing data and biomarkers but also do more on health variables that are constantly being defined. Although genomics at the point of care is fundamental to precision medicine, care at the bedside (in the facility or at home) also requires the acquisition, management, integration, clinician validation, and use of data from disparate sources, such as 1. Clinical care (imaging, electronic medical records [EMRs], computerised physician order entry [CPOE], clinical narratives, and sensor/device data) 2. Research (clinical research, trials, publications, results of data discovery, and secondary use) 3. Financial (cost, charges, affordability, income disparities, and credit scores) 4. External and patient-reported data that encompasses patient self-reported data on the Web and via smartphones; family and disease histories/lore that are not in the history and physical examination; environmental variables; behavior/sentiment data; and, increasingly, income/educational/cognitive disparities. These data sources are varied, voluminous, and processed at high velocities (Fig. 2). There also is a corresponding need in the context of therapy and procedures to examine the veracity and value of data and information in enabling and supporting precision medicine. Supporting the natural evolution of precision medicine requires an
Patient Engagement & Behavior
Imaging & Devices
Things
Research & Clinical Care
Activities
Thoughts
Health Care Big Data: Omics and Much More Fig. 1. Big data collection requires a central repository; processes need to be developed across institutions.
Precision Medicine
Significant reduction in cost of genome sequencing
Aggregation and analysis of all data important
Increase in real-world data
Cost
Will you use secondary health data within the next 2 y?
70%
61%
Provider Pharma
54% Payer
Time
More than half of clinical trials already have a molecular biomarker component
Patient stratification to identify population subsets most likely to respond to a therapy
Enhance collaboration between research and medicine
Fig. 2. The growth of big data.
understanding of the new world of big data technologies, analytics, and machine learning. It also needs recognition that a patient’s data will no longer be created and used inside a facility’s 4 walls or in its EMRs. In this new world of health care datafication, important patient (and other) information will be sourced from secondary use that involves integrated order-results workflows that are driven by 1. Incorporating molecular and clinical annotation by additional collaborators—molecular pathologists, consulting physicians, primary care providers, and other specialists 2. Machine learning–based, large-scale, affordable, and automated analysis of images, speech, video, and large text (clinical narratives, discharge summaries, and progress notes) 3. New data from smart devices, home monitors, telemedicine, medical images, and social networks (life and viral networks) 4. Separation of signal from noise—and incorporating actionable analytics, clinician feedback loops and approval, and annotation into clinical system workflows IT-enabled big data management and health care should be a required tool in the clinical kit to properly manage and leverage the complex data that result from genomic, clinical, financial, and behavioral data that brings the biggest benefit to individual patients, their outcomes, and applying the new clinical guidelines and newly created knowledge by extrapolating or predicting for multiple stratified populations that can be expanded to cover new analytics dimensions beyond the diagnosis or disease, including 1. Demographics—gender, race, ethnicity, zip code, and so forth 2. Real-time, location-specific influences—suspended particulate (smog, forest fires, and disasters) or other carcinogens (eg, asbestos, human activities such as fracking) 3. Social determinants of health—examples are activities of daily living (ADL), physical activity, diet, education, and income 4. Patient outcomes, including the effect of a procedure or therapy on survival, recovery, and health management Addressing key health determinants simultaneously at the population and individual levels and the integration of clinical/Omics/other relevant data are critical in delivering
767
768
Streeter Jr et al
precision medicine. Relating individuals to both research cohorts and stratified populations by taking advantage of the latest software technologies—prescriptive analytics, big data integration, and machine learning—provide opportunities to create or use knowledge that has not existed before or is undiscovered. Applying precision medicine into the clinical workflow generates preventative and diagnostic solutions to advance human care—bringing new targeted therapies, improved patient outcomes, and cost savings.1 GENOMIC PROFILING
To understand this evolving technology of genomic profiling,2 a few terms need to be defined. Base pairs are 2 nucleotides on opposite complementary DNA or RNA strands that are connected by hydrogen bonds. Sequencing is a method of detecting single bases as they are incorporated into DNA template strands. Whole-exome sequencing is a technique for sequencing all the expressed genes in a genome. Next-generation sequencing is the application of genome sequencers that with a single run of material can analyze more than 1.8 terabases (the amount of genetic sequence data equivalent to 1012 base pairs). The cost of sequencing has fallen approximately 10-fold over the last few years, with improved accuracy and speed, bringing the cost to less than $2000 with targeted, although limited at this point, improvement in care at the bedside. It is now available to most patients covered by insurance. Genomic data analysis is where newly identified sequences are aligned to a reference genome. The first and best example of the success of precision medicine in oncology is imatinib mesylate (Gleevec, Novartis Pharma Services AG, Basel, Switzerland) used to treat chronic myeloid leukemia (CML) with the BCR-ABL translocation. CML is due to a clonal evolution, starting with the acquisition of the 9(9;22) (q34;q11) translocation (Philadelphia chromosome), which creates a fusion between the BCR and ABL1 genes. Imatinib mesylate controls CML because it is an inhibitor of ABL family kinases, including the BCR-ABL fusion gene.3 Most squamous cell carcinomas of the head and neck respond to standard drug therapy. When the clonal composition of the pretreatment biopsy is compared over time, however, after drug treatment, there may be changes in clonal-mutation prevalence.4 Sequencing reveals large changes in the abundance of specific clones that may give clues as to which genotypes may confer resistance and may be sensitive to intervention. Therefore, local recurrence or new metastatic sites should be biopsied and sequenced to determine which drug or other intervention may improve response. Therefore, surgeons play a key role in requesting the pathologist send both the primary tumor and recurrent/metastatic tumor for sequencing. An example of this process used in a multidisciplinary clinic is the Weill Cornell Medical College Institute for Precision Medicine. The process starts with clinical examination and consent, followed by metastatic tumor biopsy and whole-exome sequencing/biobanking of tissue for future reference. Results are discussed in a tumor board, with communication to the patient and referring physicians to guide treatment, and used to fuel translational research and development of new diagnostics and therapeutics.5 A trial currently accruing patients that best demonstrates the application of precision medicine in oncology is the recently opened National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH) clinical trial. This precision medicine trial explores treating patients based on the molecular profile of tumors with the inclusion criteria of adult patients, solid tumors (including rare tumors and lymphomas), and tumors that no longer respond to standard treatment, with an accrual goal of approximately 3000 cancer patients screened with a tumor biopsy. Biopsied tumor tissue is submitted for gene
Precision Medicine
sequencing to identify initially 143 gene mutations that may respond to a specific therapy (the list of gene mutations is expanding with new discoveries). If a patient’s tumor has a genetic abnormality that matches one targeted by a drug used in the trial, the patient becomes eligible to join the treatment portion of NCI-MATCH. This is an important trial found at the Web site, clinicaltrials.gov, and includes a listing of the mutations examined and drugs useful for each mutation. It is an excellent resource for physicians in the clinics helping to individualize therapy and is constantly updated. As of the fall of 2016, more than 1000 clinical sites, across America, are participating in this trial. It is a federally sponsored trial and free to eligible patients with an estimated primary completion date of June 2022.6 Head and neck squamous cell carcinoma (HNSCC) is an immunosuppressive disease that when recurrent or metastatic responds to immunotherapy, such as checkpoint inhibitors that are available currently.7 Ongoing trials are considering combining current immunotherapies with cancer vaccines. An open-access article by Robert Ferris provides a review of immunologic principles related to head and neck cancer, including the concept of cancer immunosurveillance and immune escape.8 The authors recommend this article because it has figures describing immune escape and antigen presentation allowing recognition of tumor cells by immune system. Most importantly are tables listing the mechanisms of immune escape in HNSCC, monoclonal antibodies under investigation in HNSCC, and a detailed table of immunotherapy trials in HNSCC. Most clinicians will be working with checkpoint inhibitors and a brief discussion of this immunotherapy is warranted and illustrated in a free-access JAMA Oncology patient page.9 Immune checkpoint inhibitor drugs can target either tumor cells or T cells. They block normal proteins on cancer cells or the proteins on T cells that respond to those “normal proteins.” The checkpoint inhibitors prevent tumor cells from attaching to T cells, allowing the T cells to stay activated. A response to immune checkpoint inhibitor treatment results in a brief increase in tumor size (pseudoprogression) due to the increase in the number of activated T cells that enter the tumor. To evaluate genetic mutation changes in tumors when there may not be enough tissue for mutation analysis in a primary or metastatic biopsy or to monitor tumor response, there is an increasing use of analyzing cell-free DNA mutations (cfDNAs) in the plasma and circulating tumor cells (CTCs) in the buffy coat of a centrifuged peripheral blood draw (Figs. 3 and 4).10,11 Every month there are more Clinical Laboratory Improvement (CLIA) certified tests for tumors in specific organs. Commercial companies have developed blood sampling techniques to profile and monitor for programmed death ligand-1 (PD-L1) expression, an important biomarker in immuneoncology treatment decision making and will play an increasing role in the treatment of head and neck cancers. HYPERTHERMIA AS A NOVEL IMMUNE MODULATOR
HT is also a form of precision medicine. Because HT over the past 2 decades has been limited in the United States due to a lack of available equipment and trained practitioners who can deliver this modality between the temperature range of 41 C and 43 C, there is limited understanding of its role in stimulating a nontoxic immune response in practically all tumors. An important phase III trial reported long-term results comparing RT alone with RT plus HT to metastatic lymph nodes in stage IV head and neck patients.12 This study was conducted in 1985 to 1986 to improve the outcome of fixed and inoperable (N3) metastatic lymph nodes in HNSCC in 41 patients with 46 metastatic lymph nodes. Because of the striking results of the
769
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Streeter Jr et al
Fig. 3. Illustration of the origin of cfDNAs and CTCs from a milieu of apoptotic and necrotic fragments from tumors and other cellular fragments. (Courtsey of Oscar Streeter, MD, The Center for Thermal Oncology, Santa Monica, California.)
combined modality arm, the study was prematurely closed because of ethical reasons with a 5-year actuarial probability of nodal control of 24.2% for the radiationonly arm versus 68.6% in the RT plus HT arm. The actuarial survival of the 2 groups at 5 year favors the RT plus HT arm of 53.3% versus 0% (P 5 .02). Although metastatic disease developed in 19% of the patients, it was reduced in the RT plus HT arm to 12.5% vs 24% in the RT-alone arm. It is now known that HT not only has a direct cytotoxic effect but stimulates a natural immune response in the fever range of 39 C to 41 C by activation of heat shock proteins, generating important immune
Fig. 4. Liquid biopsies. After centrifugation of a blood sample, cfDNA from shed tumors is detected from the plasma component of the supernatant, whereas CTCs are captured from the buffy coat component.
Precision Medicine
actions and increasing blood flow, effective in primary and metastatic disease.13 The mechanisms of how HT can be used in metastatic disease as immunotherapy is because of the following effect on heated tumor cells: (1) an increase in the surface expression of MICA, a NKG2D ligand, and major histocompatibility class 1 (MHC 1), making the tumor cells more sensitive to lysis by natural killer (NK) cells and CD81 T cells, respectively, and (2) the release of heat shock proteins (HSPs), which activate NK cells and antigen-presenting cells (APCs). HSPs contain potential tumor antigens, and APCs take up the HSP-antigen complex and cross-present the antigen to CD81 T cells; (3) release of exosomes, which contain potential tumor antigens, and APCs take up the antigen and cross present the antigen to CD81 T cells; (4) immune cells, such as NK cells, CD81 T cells and dendritic cells, in the tumor also get heated and become activated; and (5) tumor vasculature becomes more permeable and may increase expression of the intercellular cell adhesion molecule (ICAM)-1, a protein expressed by the cancer cell with one function to facilitate signal transduction immune response. ICAM-1 also facilitates better immune trafficking between tumor cells and draining lymph nodes. RADIOGENOMICS
Although the use of big data in health care research is still in its infancy, the potential of combining big data with information from comparative effectiveness research and EMR data with machine learning in the future will help with decision making on what type of therapy is best for individual patients, including informing individual patients of potential complications and risk of secondary cancers based on their genomic profile.14 The challenges associated with implementing big data analytics in radiation oncology or in medical oncology are (1) validity of the training data set, (2) availability of public health and computer science experts, (3) silo ownership of the data, and (4) and the critical need to shift from deductive to inductive reasoning using new statistical and probabilistic tools from the field of machine learning (Fig. 5).
Fig. 5. Precision medicine applications.
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WHAT IS MACHINE LEARNING?
Machine learning approaches problems as a doctor progressing through a residency would, starting with patient observation, using algorithms that sift through variables, and looking for combinations that reliably predict outcome. Ongoing research at Harvard Medical School and at the Perelman School of Medicine and Wharton School, University of Pennsylvania, have demonstrated that machine learning algorithms can predict death in metastatic cancer patients. This is accomplished by not only looking at known outcomes for a specific stage of cancer, but other variables such as infections during treatment, use of a wheelchair as well as other variables that are not ordinarily considered in determining prognosis. Obermeyer and Emanuel15 predict prognostic algorithms will come into use in the next 5 years with several more years of prospective validation. SUMMARY
Although there has been significant progress in the ability to sequence a person’s genome in 1 sequencing process, the ability to aggregate all these data into a useful instrument to treat a single patient with cancer is a big data problem that will not be solved by any single institution. That type of computing power and transmitting that information across different platforms require a new type of thinking in medicine, the idea that the institution does not own the data, patients do, and must be part of a larger database tool that currently is in its infancy. For head and neck cancer patients, what can be used today is looking for a mutation that has a therapeutic target and understanding that the tumor will undergo clonal evolution and eventually become resistant. Also, a counterintuitive benefit for patients that is just as important is the ability to identify therapies of low value and avoiding adverse reactions. REFERENCES
1. Oracle Health Sciences. Precision medicine: providing the right therapy to the right patient at the right time. 2016. Available at: http://www.oracle.com/us/industries/ health-sciences/precision-medicine-info-2692756.pdf. Accessed October 1, 2016. 2. Green ED, Guyeer MS, National Human Genome Research Institute. Charting a course for genomic medicine from base pairs to bedside. Nature 2011; 470(7333):204–13. 3. Mohamed AN, Pemberton P, Zonder J, et al. The effect of imatinib mesylate on patients with Philadelphia chromosome-positive chronic myeloid leukemia with secondary chromosomal aberrations. Clin Cancer Res 2003;9(4):1333–7. 4. Aparicio S, Caldas C. The implications of clonal genome evolution for cancer medicine. N Engl J Med 2013;368(9):842–51. 5. Beltran H, Eng K, Mosquera JM, et al. Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol 2015;1(4):466–74. 6. National Cancer Institute. NCI-MATCH: targeted therapy directed by genetic testing in treating patients with advanced refractory solid tumors or lymphomas (ClinicalTrials.gov Identifier: NCT02465060). 2015. Available at: https:// clinicaltrials.gov/ct2/results?term5nct02465060. Accessed September 28, 2016. 7. Lalami Y, Awada A. Innovative perspectives of immunotherapy in head and neck cancer. From relevant scientific rationale to effective clinical practice. Cancer Treat Rev 2016;43:113–23.
Precision Medicine
8. Ferris RL. Immunology and immunotherapy of head and neck cancer. J Clin Oncol 2015;33(29):3293–304. 9. West H. Immune checkpoint inhibitors. JAMA Oncol 2015;1(1):115. 10. Diaz LA Jr, Bardelli A. Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol 2014;32(6):579–86. 11. Economopoulo P, Agelaki S, Perisanidis C, et al. The promise of immunotherapy in head and neck squamous cell carcinoma. Ann Oncol 2016;27(9):1675–85. 12. Valdagni R, Amichetti M. Report of long-term follow-up in a randomized trial comparing radiation therapy and radiation therappy plus hyperthermia to metastatic lymph nodes in Stage IV head and neck patients. Int J Radiat Oncol Biol Phys 1994;28(1):163–9. 13. Toraya-Brown S, Fiering S. Local tumor hyperthermia as immunotherapy for metastatic cancer. Int J Hyperthermia 2014;30(8):531–9. 14. Trifiletti DM, Showaalter TN. Big data and comparative effectiveness research in radiation oncology: synergy and accelerated discovery. Front Oncol 2015;5(274): 1–5. 15. Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med 2016;375(13):1216–9.
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