M E TAB OLI S M CL I NI CAL AN D EX PE RI M EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
Available online at www.sciencedirect.com
Metabolism www.metabolismjournal.com
Biomarkers for personalized oncology: recent advances and future challenges Madhu Kalia⁎ Thomas Jefferson University, Philadelphia PA 19107
A R T I C LE I N FO
AB S T R A C T
Keywords:
Cancer is a group of diseases characterized by the uncontrolled growth and spread of
Genomic biomarkers
abnormal cells and oncology is a branch of medicine that deals with tumors. The last
Protein profiling
decade has seen significant advances in the development of biomarkers in oncology that
Molecular carcinogenesis
play a critical role in understanding molecular and cellular mechanisms which drive tumor
Pharmacogenomics
initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical–pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and gastrointestinal tumors; anaplastic lymphoma kinase (ALK) inhibitors in lung cancer with EML4-ALk fusion; HER2/neu blockage in HER2/neu-positive breast cancer; and epidermal growth factor receptors (EGFR) inhibition in EGFR-mutated lung cancer. This review presents the current state of our knowledge of biomarkers in five selected cancers: chronic myeloid leukemia, colorectal cancer, breast cancer, non-small cell lung cancer and melanoma. © 2015 Elsevier Inc. All rights reserved.
⁎ Thomas Jefferson University, Suite 520, 1020 Walnut Street, Philadelphia PA 19107. Tel.: + 1 215 920 5134. E-mail addresses: [email protected], [email protected]. http://dx.doi.org/10.1016/j.metabol.2014.10.027 0026-0495/© 2015 Elsevier Inc. All rights reserved.
M E TAB OLI S M CL I NI CAL AN D EX PE R IM EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
1.
Introduction
Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells. About 1,665,540 new cancer cases are expected to be diagnosed in 2014 with about 585,720 Americans expected to die of cancer (almost 1600 people per day) [1]. The National Cancer Institute (NCI) has defined personalized medicine …“as a form of medicine that uses information about a person’s genes, proteins and environment to prevent, diagnose and treat disease” [1–3]. Compared with protein biomarkers, cancer genetic markers are more reproducible and less subject to intrinsic and extrinsic stimuli [2]). Personalized medicine has changed the paradigms in oncology (the branch of medicine that deals with tumors), because it is now based on understanding molecular carcinogenesis, pharmacogenomics, and individual genetic differences that determine the response to chemotherapeutics [4,5]. Even though this transition from empiric to mechanism-based, molecular biomarker-driven therapeutic decision process is still evolving, new classes of drugs and companion diagnostics are already beginning to emerge. These are changing the landscape for the management of many advanced-stage cancers [2]. Biomarkers in oncology that provide information use tools of molecular biology to characterize cancer signatures are crucial to personalized treatment and can be divided into: (a) diagnostic, (b) prognostic, (c) treatment and (d) prevention subgroups [5]. (a) Diagnostic biomarkers, also called predictive biomarkers, (targets for diagnostic intervention) are identified by characterizing key mutations and molecular pathways involved in tumor development and proliferation. These predictive biomarkers help optimize therapy decisions by providing information about the likelihood of a response to a chemotherapeutic intervention [5]. (b) Prognostic biomarkers identify somatic germline mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. (c and d) Treatment and prevention biomarkers require more accurate molecular analysis to guide individual therapy by identifying patients with different outcome risks (such as recurrence of the disease) [5]. Information provided concurrently by predictive (diagnostic) and prognostic biomarkers makes possible quicker diagnoses and more accurate treatment choices. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncology for the treatment of following five diseases: chronic myeloid leukemia, colon, breast and lung cancer and melanoma (Table 1). In these diseases, biomarkers are being successfully used to evaluate benefits that can be achieved through targeted therapy and in evaluating the toxic side effects of chemotherapy [5]. Examples of these molecularly targeted therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia (CLM) and gastrointestinal tumors; anaplastic lymphoma kinase (ALK) inhibitors in lung cancer with EML4-ALk fusion; HER2/neu blockage in HER2/neu-positive breast cancer; and epidermal growth factor receptors (EGFR) inhibition in EGFRmutated lung cancer [5]. Predictive biomarkers are also helpful in matching targeted therapies with patients, thereby avoiding the side effects of “one size fits all” standard (systemic) therapies [5]. In oncology biomarkers have been identified for the most common types of tumors: breast, lung and prostate cancers. The poor prognosis of several these metastatic tumors, has
S17
prevented some of these new technological advances from significantly impacting survival rates [5]. The choice of targeted therapies relies on previous genetic analysis which is being used as a basis for detecting abnormalities. This requires that populations of patients carrying genetic abnormalities be first identified so that the given therapy can be used with a positive outcome [6]. New technologies such as microarrays, new generation sequencing methods, and mass spectrometry focused on nucleic acids have the potential of expanding the range of DNA biomarker analyses such as Onco DEEP and Onco TRACE [7].
1.1.
Biomarkers in selected cancers
1.1.1.
Chronic myeloid leukemia
Leukemia is a cancer of the bone marrow which is characterized by the presence of recurring chromosomal and genetic abnormalities that result in losses, amplifications, translocations and inversions of DNA and blood fragments or whole chromosomal aneuploidies [8]. From 2006 to 2010, the overall leukemia incidence rates have increased slightly (by 0.5% per year) with an estimated 52,380 new cases of leukemia expected in 2014. Chronic myeloid leukemia (CML) was the first human malignancy found to be associated with a recurrent chromosomal abnormality [9,10]. CML is genetically characterized by the presence of the reciprocal translocation t(9;22)(q34;q11) resulting in a BCR-A1 gene fusion on the derivative chromosome 22. CML is caused by the fusion protein BCR/ABL1, which exhibits constitutive tyrosine kinase (TK) activity [11–13]. Even though TK inhibitor therapy for CML has been shown to be generally successful [10], the disease is still detectable in spite of treatment [14] and CML leukemic stem cells have been shown to be resistant to TK inhibitor therapy [15]. CML is characterized by three phases: an early or chronic phase (CP), an advanced disease stage consisting of an accelerated phase (AP) and a blast phase (BP) which is rapidly fatal [16]. The National Comprehensive Cancer Network (NCCN) guidelines and the European Leukemia Net (ELN) recommendation specify that the first line therapy for Philadelphia chromosome (Ph+) CML is a BCR-ABL tyrosine kinase inhibitor [16]. Five BCR-ABL inhibitors are approved by the US Food and Drug Administration (FDA) for the treatment of Ph + CML (CP): imatinib, dasatinib, and nilotinib approved for first-line therapy; bosutinib and ponatinib approved for second line therapy. At present in the United Stated no BCRABL inhibitor is available for CML-AP/BP [16]. Ph+ CML patients who have been treated with BCR-ABL inhibitors are presumed to have achieved long-term survival based on surrogate endpoints (biomarkers that are intended to substitute for a clinical end point) [17]. As patients respond to treatment the Ph + clonal population progressively decreases [16]. Surrogate endpoints used in CML have become more sensitive now that they are able to detect residual disease. With the success of imatinib therapy in achieving a complete and long term cytogenic response (CCyR) in CML patients, attention is now being focused on molecular responses (MRs) as measured by reductions (below the threshold of major MRs) in BCL-ABL transcript levels [18]. Molecular monitoring in CML has 2 components: 1) the measurement of BCR-ABL1 mRNA levels to access response to therapy and
S18
M E TAB OLI S M CL I NI CAL AN D EX PE RI M EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
Table 1 – Biological roles of oncological predictive biomarkers (modified from Kalia 2013). Malignancy
Predictive biomarker Gene abnormality (mutated gene or receptor)
Chronic myeloid BCR-ABL leukemia (CML)
Translocation. Chromosome abnormality
Colorectal (CRC)
EGFR, K-ras G13D, B-raf V600E DPD UGT 1A1
Breast
No mutated gene, (estrogen receptor) ER/PR gene expression
gene expression gene mutation gene mutation deficiency genotyping None
Breast
BRCA1/2 HER2/neu (Erb-B2)
Mutation, deletion Gene amplification
NSCLC
EGFR ERCC RRM 1 K-ras TS EML4-Alk BRAF
gene expression gene expression, gene expression, gene mutation gene expression gene mutation Mutation
Melanoma
Type of drug therapy
Biological role of biomarker
imatinib dasatinib nilotinib bosutinib ponatinib imatinib cetuximab, panitumumab
BCR-ABL tyrosine kinase inhibitor
tamoxifen. aromatase inhibitor fulvestrant
Primary target Drug metabolism (positive predictive and prognostic biomarkers) Predictive and prognostic biomarkers
Signaling protein downstream of primary target — EGFR. K-ras
olaparib trastuzumab trastuzumab- emtansine gefitinib DNA repair erlotinib Downstream of primary target Platinum Biological roles of oncological biomarkers. agents Vemurafenib, dabrafenib Targets downstream MapK kinases
BCR-ABL, The tyrosine kinase BCR-ABL is the fusion product of a reciprocal chromosome translocation between chromosomes 9 and 22, known as the Philadelphia (Ph) chromosome; BRAF, BRAF is a human gene that makes a protein called B-Raf; BRCA 1/2, BRCA1 and BRCA2 are human genes that produce tumor suppressor proteins; B-raf V600E, B-Raf is a 766-amino acid, regulated signal transduction serine/threonine-specific protein kinase. BRAF V600E is a determinant of sensitivity to proteasome inhibitors; CML, chronic myeloid leukemia; CRC, colorectal cancer; DPD, dihydropyrimidine dehydrogenase; EGFR, epidermal growth factor receptor; EML4-Alk, echinoderm microtubule-associated protein-like 4-anaplasticlymphooma kinase; ER/PR, estrogen receptor/proesterone receptor; ERCC, Excision repair-cross complementation group; ERCC, excision cross complementing; HER2/neu (Erb-B2), human epidermal growth factor receptor type; K-ras G13D, The G13D mutation results in an amino acid substitution at position 13 in KRAS, from a glycine (G) to an aspartic acid (D); MGMT, methylguanine methyl transferase; NSCLC, non-small-cell lung cancer; RRM 1, ribonucleotide reductas; TPMT, thiopurine methyl transferase; TS, thymidylate synthase; UGT 1A1, uridine glucuronyltransferase.
measure minimal residual disease; 2) sequencing of the BCRABL1 domain to detect kinase inhibitor-resistant mutations [19].
1.2.
Colon cancer
Colorectal cancer is the third most common cancer in both men and women with an estimated 96,830 cases of colon cancer and 50,310 deaths from colon cancer expected 2014. There has been a decline in the incidence rate of colon cancer for the past two years because of improvements in early detection by screening tests that allow for the detection and removal of colorectal polyps before they progress to cancer and treatment as a result of several predictive (associated
with treatment response and efficacy) and prognostic (associated with disease outcomes) biomarkers. Five biomarkers of colon cancer are considered to be “emerging biomarkers” [20]: 1) EGFR gene expression, 2) K-ras G13D gene mutation, 3) B-raf V600E gene mutation, 4) dihydropyrimidine dehydrogenase deficiency, and 5) UGT 1A1 I genotyping. 1) EGFR gene expression: Epidermal growth factor receptor (EGFR) is a transmembrane receptor and anti-EGFR monoclonal antibodies such as cetuximab and panitumumab competitively inhibit EGFR by preventing its binding to endogenous ligands. Approximately 70% of human colorectal cancers express EGFR protein. Despite the controversy of the use of EGFR testing in all patients with colorectal cancers [23], it has
M E TAB OLI S M CL I NI CAL AN D EX PE R IM EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
2)
3)
4)
5)
been found that cetuximab prolongs survival — especially when given in combination with chemotherapy. The common side effect of anti-EGFR therapy (rash) has been used as a predictor of a positive response. K-ras G13D gene mutation: Ras genes are among the most frequently activated oncogenes. K-ras is found in adenocarcinomas that transduce extracellular signals from the EGFR to the nucleus. K-ras is the only predictive biomaker established for anti-EGFR monoclonal antibody in colorectal cancer. Approximately 40% of colon cancers are positive for mutations in K-ras in codons 12,13, 61 of colorectal cancer and are resistant to anti-EGFR monoclonal antibodies (cetuximab and panitumumab). A diagnostic kit was recently approved to determine whether or not patients with advanced colorectal cancer have a wild K-ras gene that could indicate whether they would respond to cetuximab or pantiumumab [20]. The current standard for patients with all types of K-ras gene mutations is not to treat with anti-EGFR monoclonal antibodies (cetuximab and panitumumab). In addition to K-ras gene mutation the specific genotype will be important in developing future therapy for these tumors [20]. B-raf V600E gene mutation: This is considered to be an emerging biomarker of negative response to K-ras. Currently B-raf is a mutation found in 35%–45% of colorectal cancers and is considered to be a prognostic biomarker for poor prognosis in patients receiving first-line (initial) colon cancer therapies. Dihydropyrimidine dehydrogenase (DPD) is the major enzyme catabolizing fluorouracil, a widely-used chemotherapeutic agent for colorectal cancers. DPD synthesis is encoded by DPYD. Approximately 3% -5% (or more) of individuals in the general population have at least a partial DPD deficiency and may be subject to severe (even lethal) toxicity if treated with a fluorouracil. It is evident that patients who are candidates for fluorouracil therapy should be tested for DPD deficiency before such treatment is contemplated [20]. UGT1A1 I genotyping. This is the enzyme that metabolizes irinotecan (a colorectal cancer chemotherapeutic agent) to its active metabolite SN-38. The prognostic value of UGT1A1 has not yet been established [20].
1.3.
Breast cancer
Excluding cancers of the skin, breast cancer is the most frequently diagnosed cancer in women [1]. An estimated 232,670 new cases of invasive breast cancer are expected to be diagnosed among women and about 2360 new cases are expected in men. There has been a dramatic decrease in the breast cancer incidence rate (almost 7%) from 2002 to 2003 which has been attributed to reductions in the use of menopausal hormone therapy (MHT), (previously known as hormone replacement therapy), following the publication of results from the Women’s Health Initiative in 2002. This study found that the use of combined estrogen plus progestin MHT was associated with an increased risk of breast cancer, as well as coronary heart disease. From 2006 to 2010, the most recent five years for which data are available, breast cancer incidence rates were stable.
1.3.1.
Biomarkers of breast cancer
1. Estrogen Receptor (ER) and progesterone receptor (PR) gene expression. These are both predictive and prognostic biomarkers.
S19
2. HER2 (Erb-B2) gene expression or gene amplification in breast cancer is both a predictive and prognostic biomarker: HER2negative tumors do not respond to trastuzumab and, therefore, HER2-positivity is predictive of potential trastuzumab response in a patient of newly diagnosed breast cancer; HER2-positive tumors (over-expression of HER2 genes) are found in 20% of women with breast cancer and these carry a worse survival outcome than patients without them (prognostic). 3. Oncotype Dx (21-gene RT-pcR) expression assay of FFPE samples with recurrence scores as readout. This is a prognostic biomarker in ER-positive, node-negative patients. It has no predictive biomarker. This biomarker is considered to be “emerging” [20]. 4. CYP2D6 genotyping is also considered to be an “emerging” biomarker. There is mixed evidence regarding its use as a predictive biomarker. It has not been recommended as a biomarker by the American Society for Clinical Oncology. 1. Estrogen receptor (ER) and progesterone receptor (PR): The status of a breast cancer tumor is routinely identified by immunohistochemistry (IHC) [20]. ER positive status has the best predictive value for disease-free survival [21]. PR positive status indicates a functionally intact estrogen response pathway [20], but PR gene expression is primarily prognostic and not predictive of benefit from tamoxifen. High expressions of ER and PR are predictive for benefit from hormonal therapy in adjuvant and metastatic (stage VII disease) settings. Current clinical guidelines suggest that hormonal therapy is recommended for all patients with ER-positive disease regardless of their level of ER [20]. Not all ER-positive metastatic breast cancers respond to hormonal therapy. In addition the ER and PR status of breast cancer changes over the course of the disease. Recently there have been reports of cross talk between different signaling pathways and also reports of a genomic index for sensitivity to hormonal therapy based on genes associated with ESR1 (DNA copy of the ER) [22]. 2. HER2 (neu) (Erb-B2): In 15%–25% of invasive breast cancers there is increased expression of HER2 protein (a member of the human epidermal growth factor receptor) that predicts a favorable response to trastuzumab - a monoclonal antibody that targets the HER2 (human EGFR protein) [23]. Blocking of both the intra- and extracellular domains of HER2 receptor by lapatinib and trastuzumab (respectively) could improve progression-free survival and disease control [24]. In addition, there is new evidence that breast cancer patients with HER2-positive tumors often benefit from topoisomerase II (encoded by TOP2A gene) inhibitor-based chemotherapy such as doxorubicin or epirubicin [20]. 3. Oncotype Dx (21-gene RT-pcR): Current gene expression assay such as Oncotype Dx is a genetic test that measures RNA expression in 16 cancer related genes and 5 reference genes have been suggested for determining prognosis in non-negative, ER-positive breast tumors. At present there is not sufficient evidence to recommend its routine use for early breast cancer detection. 4. CYP2D6 genotyping (Cytochrome P450): 2D6 is an emerging biomarker that has been investigated extensively. The hypothesis is that patients with this genotype will receive lesser benefit with tamozifen compared with patients with
S20
M E TAB OLI S M CL I NI CAL AN D EX PE RI M EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
other genotypes. Current guidelines do not recommend routine genetic testing of 2D6 [20].
1.4.
Non-small cell lung cancer (NSCLC)
Lung cancer (of all histologic cell types) is the most common cause of cancer deaths in men and the second leading cause of cancer deaths in females [25,26]. Over the past decade molecular diagnostics have played a major role in understanding the pathogenesis, diagnosis and treatment of lung cancer which has led to the identification of epidermal growth factor receptor (EGFR) and its role in lung cancer. Four non-small cell lung cancer biomarkers are considered “emerging” — EGFR gene expression, ERCC gene expression, RRM 1 gene expression, K-ras gene mutation. The standard of care of care for patients with advanced-stage NSCLC has shifted from selecting treatment empirically based on the patients clinical–pathological profile towards biomarker driven treatment algorithms based on the molecular profile of a patient’s tumor [2]. The molecular analysis of mutations in epidermal growth factor receptor (EGRF) gene, its corresponding downstream signaling cascade and the related mutations have led to the development of novel therapies [25]. Data from this biomarker when combined with analysis of histological material are becoming very important in lung cancer diagnostics as well as in patient stratification for therapy. Mutations in anaplastic lymphoma kinase (ALK) gene arrangements have also been implicated in NSCLC. Epidermal growth factor receptor (EGFR) is a widely used therapeutic target to treat patients with non-small cell lung cancers (NSCLCs). There are mutations that are specific to NSCLCs that activate EGFR — these are deletions in exon 19 and exon 21 point mutation, L585R. These mutations result in ligand-independent activation of EGFR signaling [27]. Two irreversible anti-EGFR tyrosine kinase (TKI) inhibitors are currently approved for the treatment of advanced NSCLC (gefitinib and erlotinib); this established an important milestone and led to the creation of a predictive biomarker assay for NSCLC diagnosis. Recent phase III randomized trials with these EGFR inhibitors, when compared with chemotherapy, have produced significantly longer disease-free survival, higher response rates, less toxicity and a better quality of life [28–31]. The “combination affinity” of increased gefitinib and erlotinib with the mutated form of EGFR is expected to represent an approximately threefold improvement over that likely from chemotherapy alone in unselected NSCLC patients [27,28].
1.5.
Melanoma
Melanoma is the third most common skin cancer which is responsible for almost all of skin cancer related mortalities [32]. An estimated 76,100 new cases of melanoma will be diagnosed in 2014. Melanoma accounts for less than 2% of all skin cancer cases, but for the vast majority of skin cancer deaths. The incidence rates for melanoma have been increasing for the past 30 years, and it is being diagnosed at much younger ages. Genomics, epigenomics and proteomics have led to molecular reclassification of melanoma based on pathological findings [33].
Following the discovery of genetic alterations and biomarkers that drive melanoma progression a number of targeted therapies have been developed: Vemurafenib and Dabrafenib were approved in 2011 and 2013, respectively, for the treatment of melanoma. These drugs target the mutated V600 codon of BRAF signaling molecule that is an important effector of the RAS/RAF/MEK/ERK pathway. Mutations in this gene occur in over 50% of melanoma tumors with BRAF V600E mutations being the most common [33]. Another molecule Trametinib was approved in 2013 which targets downstream MAPK kinases (including MEK1/2 inhibitor). The related companion diagnostic tests for the detection of BRAF V600 mutations were developed and approved soon thereafter. These therapies that selectively target the mutant BRAF driver gene have been shown to induce tumor regression in > 50% of patients with advanced melanoma [28].
2.
Conclusions
The central goal of biomarker-based personalized cancer therapy is to make treatment decisions based on tumor genotypes and genetic profiles. Matching targeted therapies against specific genetic aberrations is an important step for personalized cancer therapy. Such an approach holds promise in ultimately improving measurable clinical outcomes: response rates, survival and safety [34,35]. A new molecular classification of many cancers has evolved based on chromosomal aberrations, gene mutations and signaling pathway activation that underlie biologically unique tumors that now need to be managed clinically in several different ways [36]. Early clinical application of these technologies has made possible the rapid and comprehensive molecular annotation of an individual’s cancer. This facilitates the identification of actionable and/or novel drug targets and treatment options, as well as the characterization of underlying pathogenic mechanisms [2]. Understanding molecular carcinogenesis will continue to shape the approach taken towards tumor diseases. Patients will be treated with preferentially targeted substances based on specific molecular profiles found in individual tumor tissues [2]. miRNAs are becoming more relevant in the diagnosis, sub-classification, prognosis and as biomarkers of myeloid malignancies and genomic DNA could be used to monitor residual disease. In the future, mutational testing and the development of serological biomarkers will enable us to stratify patients for targeted therapies. This new paradigm in drug development and clinical care has created both opportunities and challenges. Numerous multiplex genotyping platforms are being evaluated for actionable hotspot oncogene mutations or gene amplifications and arrangements are being evaluated with promising results that are progressing towards use in clinical settings [2]. The biggest challenge in personalized cancer treatment involving translating cancer genomics is to understand how these aberrations are related to the progression of the cancer over time. In spite of this knowledge gap, these recent advances in identifying biomarkers using genomic technologies continue to make great strides — developments which hold enormous promise for advancing cancer treatment.
M E TAB OLI S M CL I NI CAL AN D EX PE R IM EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
REFERENCES
[1] American Cancer Society. Facts and figures. Atlanta: American Cancer Society; 2014. [2] Li T, Kung HJ, Mack PC, et al. J Clin Oncol 2013;31(8):1039–49. [3] National Cancer Institute. Dictionary of cancer terms. http:// www.cancer.gov/dictionary?CdrD=56717. [4] Grullich C, Von Kalle C. Recent developments and future perspectives of personalized oncology. Onkologie 2012;35(1):4–7. [5] Nalejska E, Maczwynska E. Prognostic and predictive biomarkers: tools in personalized oncology. Mol Diagn Ther 2014;18:273–84. [6] Dias-Santagata D, Akhavanfard S, David SS, et al. Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine. EMBO Mol Med 2010;2950:146–58. [7] Poznanski M, Lewandowska MA. Application of highthroughput technologies in cancer molecular diagnostics. Oncol Clin Pract 2013;9(2):70–7. [8] Iacobucci H, Lonetti A, Papayannaidis C, et al. Use of single nucleotide polymorphism array technology to omprove the identification of chromosomal lesions in leukemia. Curr Cancer Drug Targets 2013;13:791–810. [9] Nowell PC, Hungerford DA. Choromosome studies on normal and leukemic human leukocytes. J Natl Cancer Inst 1960;25: 85–109. [10] Gordon JE, Wong JJ, Rasko JE. MicroRNAs in myleoid malignancies. Br J Haematol 2013;162:162–76. [11] McLaughin L, Chianese E, Witte ON. In vitro transformation of immature hematopoetic cells by the P210 BCR/ABL oncogene product of the Philadelphia chromosome. Proc Natl Acad Sci 1987;84:6558–62. [12] Daley GQ, Van Etten RA, Baltimore D. Induction of chronic myelogenous leukemia in mice by the P210bcr/abl gene of the Philadelphia chromosome. Science 1990;247:824–30. [13] Lugo TG, Pendergast AM, Muller AJ, et al. Tyrosine kinase activity and transformation potency of bcr-abl oncogene products. Science 1990;247:1079–82. [14] Ross DM, Branford S, Seymore JF. Patients with chronic myeloid leukemia who maintain a complete response after stopping imatinib treatment have evidence of persistent leukemia by DNA PCR. Leukemia 2010;24:1719–24. [15] Corbin AS, Agarwal A, Loriaux M. Human chronic myeloid leukemia stem cells are insensitive to imatinib despite inhibition of BCR-ABL activity. J Clin Invest 2011;121:396–409. [16] Akwaa F, Liesveld J. Surrogate end points for long-term outcomes in chronic myeloid leukemia. Leuk Lymphoma 2013;54(10):2103–11. [17] Colburn WA. Optimizing the use of biomarkers, surrogate endpoints and clinical end points for more efficient drug development. Clin Pharmacol 2000;40:1419–27. [18] Cross NCP, White HE, Muller MC, et al. Standardized definitions of molecular response in chronic myeloid leukemia. Leukemia 2012;26:2172–5. [19] Branford S. Continued therapeutic response monitoring in optimal responders with CML. Curr Adv Hematol Oncol 2012; 10(12):819–21.
S21
[20] Chung C, Christianson M. Predictive and prognostic biomarkers with therapeutic targets in breast, colorectal and non-small cell lung cancers: a systematic review of current development, evidence, and recommendation. J Oncol Pharm Pract 2014;20(1):11–28. [21] Harvey JM, Clark GM, Osborne CK, et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol 1999;17: 1474–81. [22] Symmans WF, Hatzis C, Sotirtiou C, et al. Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol 2010;28:4111–9. [23] Salomon DS, Brandt R, Ciardiello F, et al. Epidermal growth factor-related peptides and their receptors in human malignancies. Crit Rev Oncol Hematol 1995;19:183–232. [24] Blackwell KI, Burstein HJ, Storniolo AM, et al. Randomized study of lapatinib alone or in combination with trastuzumab in women with ErB-2 positive, trasuzumab-refractory metastatic breast cancer. J Clin Oncol 2010;28:1124–30. [25] Ozretic L, Heukmp L, Odenthal M, et al. The role of molecular diagnostics in cancer diagnosis and treatment. Onkologie 2012;35(Suppl. 1):8–12. [26] Jemal A, Center MM, DeSantis C. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol Biomarkers Prev 2010;19:893–907. [27] Heuckmann JM, Rauh D, Thomas RK. Epidermal growth factor receptor (EGFR) signaling and covalent EGFR inhibition in lung cancer. J Clin Oncol 2012;30:3417–20. [28] Duffy MJ. The war on cancer; are we winning? Tumor Biol 2013;34:1275–84. [29] Rossi A, Pasquale R, Esposito C. Should epidermal growth factor receptor tyrosine kinase inhibitors be considered ideal drugs for the treatment of selected advanced non-small cell lung cancer patients? Cancer Treat Rev 2012. http://dx.doi.org/ 10.1016/jctrv.2012.09.001. [30] Moran T, Sequist LV. Timing of epidermal growth factor receptor tyrosine kinase inhibitor therpy in patients with lung cancer with EGRF mutations. J Clin Oncol 2012;30: 3330–6. [31] Soria JC, Mork TS, Cappuzzo F, et al. EGFR-mutated oncogene-addicted non-small cell lung cancer: current trends and future prospects. Cancer Treat Rev 2012;38: 416–30. [32] Davar D, Ahmad AT, Gogas H, et al. Advances in adjuvant therapy:potential for prognostic and predictive biomarkers. Methods Mol Biol 2014;1102:45–9. [33] Thurin M, Marincola FM. Preface. Molecular diagnostics for melanoma: methods and protocols. Methods Mol Biol 2014; 1102:v–xiv. [34] Gandara DR, Li T, Lara PN, et al. Algorithim for codevelopment of new drug-predictive biomarker combinations: accounting for inter- and intrapatient tumor heterogeneity. Clin Lung Cancer 2012;13:321–5. [35] Haber DA, Gray NC, Baselga J. The evolving war on cancer. Cell 2011;145:19–24. [36] Kalia M. Personalized oncology: recent advances and future challenges. Metabolism 2013;62(Suppl. 1):S11–4.
Available online at www.sciencedirect.com
Metabolism www.metabolismjournal.com
Biomarkers for personalized oncology: recent advances and future challenges Madhu Kalia⁎ Thomas Jefferson University, Philadelphia PA 19107
A R T I C LE I N FO
AB S T R A C T
Keywords:
Cancer is a group of diseases characterized by the uncontrolled growth and spread of
Genomic biomarkers
abnormal cells and oncology is a branch of medicine that deals with tumors. The last
Protein profiling
decade has seen significant advances in the development of biomarkers in oncology that
Molecular carcinogenesis
play a critical role in understanding molecular and cellular mechanisms which drive tumor
Pharmacogenomics
initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical–pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and gastrointestinal tumors; anaplastic lymphoma kinase (ALK) inhibitors in lung cancer with EML4-ALk fusion; HER2/neu blockage in HER2/neu-positive breast cancer; and epidermal growth factor receptors (EGFR) inhibition in EGFR-mutated lung cancer. This review presents the current state of our knowledge of biomarkers in five selected cancers: chronic myeloid leukemia, colorectal cancer, breast cancer, non-small cell lung cancer and melanoma. © 2015 Elsevier Inc. All rights reserved.
⁎ Thomas Jefferson University, Suite 520, 1020 Walnut Street, Philadelphia PA 19107. Tel.: + 1 215 920 5134. E-mail addresses: [email protected], [email protected]. http://dx.doi.org/10.1016/j.metabol.2014.10.027 0026-0495/© 2015 Elsevier Inc. All rights reserved.
M E TAB OLI S M CL I NI CAL AN D EX PE R IM EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
1.
Introduction
Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells. About 1,665,540 new cancer cases are expected to be diagnosed in 2014 with about 585,720 Americans expected to die of cancer (almost 1600 people per day) [1]. The National Cancer Institute (NCI) has defined personalized medicine …“as a form of medicine that uses information about a person’s genes, proteins and environment to prevent, diagnose and treat disease” [1–3]. Compared with protein biomarkers, cancer genetic markers are more reproducible and less subject to intrinsic and extrinsic stimuli [2]). Personalized medicine has changed the paradigms in oncology (the branch of medicine that deals with tumors), because it is now based on understanding molecular carcinogenesis, pharmacogenomics, and individual genetic differences that determine the response to chemotherapeutics [4,5]. Even though this transition from empiric to mechanism-based, molecular biomarker-driven therapeutic decision process is still evolving, new classes of drugs and companion diagnostics are already beginning to emerge. These are changing the landscape for the management of many advanced-stage cancers [2]. Biomarkers in oncology that provide information use tools of molecular biology to characterize cancer signatures are crucial to personalized treatment and can be divided into: (a) diagnostic, (b) prognostic, (c) treatment and (d) prevention subgroups [5]. (a) Diagnostic biomarkers, also called predictive biomarkers, (targets for diagnostic intervention) are identified by characterizing key mutations and molecular pathways involved in tumor development and proliferation. These predictive biomarkers help optimize therapy decisions by providing information about the likelihood of a response to a chemotherapeutic intervention [5]. (b) Prognostic biomarkers identify somatic germline mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. (c and d) Treatment and prevention biomarkers require more accurate molecular analysis to guide individual therapy by identifying patients with different outcome risks (such as recurrence of the disease) [5]. Information provided concurrently by predictive (diagnostic) and prognostic biomarkers makes possible quicker diagnoses and more accurate treatment choices. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncology for the treatment of following five diseases: chronic myeloid leukemia, colon, breast and lung cancer and melanoma (Table 1). In these diseases, biomarkers are being successfully used to evaluate benefits that can be achieved through targeted therapy and in evaluating the toxic side effects of chemotherapy [5]. Examples of these molecularly targeted therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia (CLM) and gastrointestinal tumors; anaplastic lymphoma kinase (ALK) inhibitors in lung cancer with EML4-ALk fusion; HER2/neu blockage in HER2/neu-positive breast cancer; and epidermal growth factor receptors (EGFR) inhibition in EGFRmutated lung cancer [5]. Predictive biomarkers are also helpful in matching targeted therapies with patients, thereby avoiding the side effects of “one size fits all” standard (systemic) therapies [5]. In oncology biomarkers have been identified for the most common types of tumors: breast, lung and prostate cancers. The poor prognosis of several these metastatic tumors, has
S17
prevented some of these new technological advances from significantly impacting survival rates [5]. The choice of targeted therapies relies on previous genetic analysis which is being used as a basis for detecting abnormalities. This requires that populations of patients carrying genetic abnormalities be first identified so that the given therapy can be used with a positive outcome [6]. New technologies such as microarrays, new generation sequencing methods, and mass spectrometry focused on nucleic acids have the potential of expanding the range of DNA biomarker analyses such as Onco DEEP and Onco TRACE [7].
1.1.
Biomarkers in selected cancers
1.1.1.
Chronic myeloid leukemia
Leukemia is a cancer of the bone marrow which is characterized by the presence of recurring chromosomal and genetic abnormalities that result in losses, amplifications, translocations and inversions of DNA and blood fragments or whole chromosomal aneuploidies [8]. From 2006 to 2010, the overall leukemia incidence rates have increased slightly (by 0.5% per year) with an estimated 52,380 new cases of leukemia expected in 2014. Chronic myeloid leukemia (CML) was the first human malignancy found to be associated with a recurrent chromosomal abnormality [9,10]. CML is genetically characterized by the presence of the reciprocal translocation t(9;22)(q34;q11) resulting in a BCR-A1 gene fusion on the derivative chromosome 22. CML is caused by the fusion protein BCR/ABL1, which exhibits constitutive tyrosine kinase (TK) activity [11–13]. Even though TK inhibitor therapy for CML has been shown to be generally successful [10], the disease is still detectable in spite of treatment [14] and CML leukemic stem cells have been shown to be resistant to TK inhibitor therapy [15]. CML is characterized by three phases: an early or chronic phase (CP), an advanced disease stage consisting of an accelerated phase (AP) and a blast phase (BP) which is rapidly fatal [16]. The National Comprehensive Cancer Network (NCCN) guidelines and the European Leukemia Net (ELN) recommendation specify that the first line therapy for Philadelphia chromosome (Ph+) CML is a BCR-ABL tyrosine kinase inhibitor [16]. Five BCR-ABL inhibitors are approved by the US Food and Drug Administration (FDA) for the treatment of Ph + CML (CP): imatinib, dasatinib, and nilotinib approved for first-line therapy; bosutinib and ponatinib approved for second line therapy. At present in the United Stated no BCRABL inhibitor is available for CML-AP/BP [16]. Ph+ CML patients who have been treated with BCR-ABL inhibitors are presumed to have achieved long-term survival based on surrogate endpoints (biomarkers that are intended to substitute for a clinical end point) [17]. As patients respond to treatment the Ph + clonal population progressively decreases [16]. Surrogate endpoints used in CML have become more sensitive now that they are able to detect residual disease. With the success of imatinib therapy in achieving a complete and long term cytogenic response (CCyR) in CML patients, attention is now being focused on molecular responses (MRs) as measured by reductions (below the threshold of major MRs) in BCL-ABL transcript levels [18]. Molecular monitoring in CML has 2 components: 1) the measurement of BCR-ABL1 mRNA levels to access response to therapy and
S18
M E TAB OLI S M CL I NI CAL AN D EX PE RI M EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
Table 1 – Biological roles of oncological predictive biomarkers (modified from Kalia 2013). Malignancy
Predictive biomarker Gene abnormality (mutated gene or receptor)
Chronic myeloid BCR-ABL leukemia (CML)
Translocation. Chromosome abnormality
Colorectal (CRC)
EGFR, K-ras G13D, B-raf V600E DPD UGT 1A1
Breast
No mutated gene, (estrogen receptor) ER/PR gene expression
gene expression gene mutation gene mutation deficiency genotyping None
Breast
BRCA1/2 HER2/neu (Erb-B2)
Mutation, deletion Gene amplification
NSCLC
EGFR ERCC RRM 1 K-ras TS EML4-Alk BRAF
gene expression gene expression, gene expression, gene mutation gene expression gene mutation Mutation
Melanoma
Type of drug therapy
Biological role of biomarker
imatinib dasatinib nilotinib bosutinib ponatinib imatinib cetuximab, panitumumab
BCR-ABL tyrosine kinase inhibitor
tamoxifen. aromatase inhibitor fulvestrant
Primary target Drug metabolism (positive predictive and prognostic biomarkers) Predictive and prognostic biomarkers
Signaling protein downstream of primary target — EGFR. K-ras
olaparib trastuzumab trastuzumab- emtansine gefitinib DNA repair erlotinib Downstream of primary target Platinum Biological roles of oncological biomarkers. agents Vemurafenib, dabrafenib Targets downstream MapK kinases
BCR-ABL, The tyrosine kinase BCR-ABL is the fusion product of a reciprocal chromosome translocation between chromosomes 9 and 22, known as the Philadelphia (Ph) chromosome; BRAF, BRAF is a human gene that makes a protein called B-Raf; BRCA 1/2, BRCA1 and BRCA2 are human genes that produce tumor suppressor proteins; B-raf V600E, B-Raf is a 766-amino acid, regulated signal transduction serine/threonine-specific protein kinase. BRAF V600E is a determinant of sensitivity to proteasome inhibitors; CML, chronic myeloid leukemia; CRC, colorectal cancer; DPD, dihydropyrimidine dehydrogenase; EGFR, epidermal growth factor receptor; EML4-Alk, echinoderm microtubule-associated protein-like 4-anaplasticlymphooma kinase; ER/PR, estrogen receptor/proesterone receptor; ERCC, Excision repair-cross complementation group; ERCC, excision cross complementing; HER2/neu (Erb-B2), human epidermal growth factor receptor type; K-ras G13D, The G13D mutation results in an amino acid substitution at position 13 in KRAS, from a glycine (G) to an aspartic acid (D); MGMT, methylguanine methyl transferase; NSCLC, non-small-cell lung cancer; RRM 1, ribonucleotide reductas; TPMT, thiopurine methyl transferase; TS, thymidylate synthase; UGT 1A1, uridine glucuronyltransferase.
measure minimal residual disease; 2) sequencing of the BCRABL1 domain to detect kinase inhibitor-resistant mutations [19].
1.2.
Colon cancer
Colorectal cancer is the third most common cancer in both men and women with an estimated 96,830 cases of colon cancer and 50,310 deaths from colon cancer expected 2014. There has been a decline in the incidence rate of colon cancer for the past two years because of improvements in early detection by screening tests that allow for the detection and removal of colorectal polyps before they progress to cancer and treatment as a result of several predictive (associated
with treatment response and efficacy) and prognostic (associated with disease outcomes) biomarkers. Five biomarkers of colon cancer are considered to be “emerging biomarkers” [20]: 1) EGFR gene expression, 2) K-ras G13D gene mutation, 3) B-raf V600E gene mutation, 4) dihydropyrimidine dehydrogenase deficiency, and 5) UGT 1A1 I genotyping. 1) EGFR gene expression: Epidermal growth factor receptor (EGFR) is a transmembrane receptor and anti-EGFR monoclonal antibodies such as cetuximab and panitumumab competitively inhibit EGFR by preventing its binding to endogenous ligands. Approximately 70% of human colorectal cancers express EGFR protein. Despite the controversy of the use of EGFR testing in all patients with colorectal cancers [23], it has
M E TAB OLI S M CL I NI CAL AN D EX PE R IM EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
2)
3)
4)
5)
been found that cetuximab prolongs survival — especially when given in combination with chemotherapy. The common side effect of anti-EGFR therapy (rash) has been used as a predictor of a positive response. K-ras G13D gene mutation: Ras genes are among the most frequently activated oncogenes. K-ras is found in adenocarcinomas that transduce extracellular signals from the EGFR to the nucleus. K-ras is the only predictive biomaker established for anti-EGFR monoclonal antibody in colorectal cancer. Approximately 40% of colon cancers are positive for mutations in K-ras in codons 12,13, 61 of colorectal cancer and are resistant to anti-EGFR monoclonal antibodies (cetuximab and panitumumab). A diagnostic kit was recently approved to determine whether or not patients with advanced colorectal cancer have a wild K-ras gene that could indicate whether they would respond to cetuximab or pantiumumab [20]. The current standard for patients with all types of K-ras gene mutations is not to treat with anti-EGFR monoclonal antibodies (cetuximab and panitumumab). In addition to K-ras gene mutation the specific genotype will be important in developing future therapy for these tumors [20]. B-raf V600E gene mutation: This is considered to be an emerging biomarker of negative response to K-ras. Currently B-raf is a mutation found in 35%–45% of colorectal cancers and is considered to be a prognostic biomarker for poor prognosis in patients receiving first-line (initial) colon cancer therapies. Dihydropyrimidine dehydrogenase (DPD) is the major enzyme catabolizing fluorouracil, a widely-used chemotherapeutic agent for colorectal cancers. DPD synthesis is encoded by DPYD. Approximately 3% -5% (or more) of individuals in the general population have at least a partial DPD deficiency and may be subject to severe (even lethal) toxicity if treated with a fluorouracil. It is evident that patients who are candidates for fluorouracil therapy should be tested for DPD deficiency before such treatment is contemplated [20]. UGT1A1 I genotyping. This is the enzyme that metabolizes irinotecan (a colorectal cancer chemotherapeutic agent) to its active metabolite SN-38. The prognostic value of UGT1A1 has not yet been established [20].
1.3.
Breast cancer
Excluding cancers of the skin, breast cancer is the most frequently diagnosed cancer in women [1]. An estimated 232,670 new cases of invasive breast cancer are expected to be diagnosed among women and about 2360 new cases are expected in men. There has been a dramatic decrease in the breast cancer incidence rate (almost 7%) from 2002 to 2003 which has been attributed to reductions in the use of menopausal hormone therapy (MHT), (previously known as hormone replacement therapy), following the publication of results from the Women’s Health Initiative in 2002. This study found that the use of combined estrogen plus progestin MHT was associated with an increased risk of breast cancer, as well as coronary heart disease. From 2006 to 2010, the most recent five years for which data are available, breast cancer incidence rates were stable.
1.3.1.
Biomarkers of breast cancer
1. Estrogen Receptor (ER) and progesterone receptor (PR) gene expression. These are both predictive and prognostic biomarkers.
S19
2. HER2 (Erb-B2) gene expression or gene amplification in breast cancer is both a predictive and prognostic biomarker: HER2negative tumors do not respond to trastuzumab and, therefore, HER2-positivity is predictive of potential trastuzumab response in a patient of newly diagnosed breast cancer; HER2-positive tumors (over-expression of HER2 genes) are found in 20% of women with breast cancer and these carry a worse survival outcome than patients without them (prognostic). 3. Oncotype Dx (21-gene RT-pcR) expression assay of FFPE samples with recurrence scores as readout. This is a prognostic biomarker in ER-positive, node-negative patients. It has no predictive biomarker. This biomarker is considered to be “emerging” [20]. 4. CYP2D6 genotyping is also considered to be an “emerging” biomarker. There is mixed evidence regarding its use as a predictive biomarker. It has not been recommended as a biomarker by the American Society for Clinical Oncology. 1. Estrogen receptor (ER) and progesterone receptor (PR): The status of a breast cancer tumor is routinely identified by immunohistochemistry (IHC) [20]. ER positive status has the best predictive value for disease-free survival [21]. PR positive status indicates a functionally intact estrogen response pathway [20], but PR gene expression is primarily prognostic and not predictive of benefit from tamoxifen. High expressions of ER and PR are predictive for benefit from hormonal therapy in adjuvant and metastatic (stage VII disease) settings. Current clinical guidelines suggest that hormonal therapy is recommended for all patients with ER-positive disease regardless of their level of ER [20]. Not all ER-positive metastatic breast cancers respond to hormonal therapy. In addition the ER and PR status of breast cancer changes over the course of the disease. Recently there have been reports of cross talk between different signaling pathways and also reports of a genomic index for sensitivity to hormonal therapy based on genes associated with ESR1 (DNA copy of the ER) [22]. 2. HER2 (neu) (Erb-B2): In 15%–25% of invasive breast cancers there is increased expression of HER2 protein (a member of the human epidermal growth factor receptor) that predicts a favorable response to trastuzumab - a monoclonal antibody that targets the HER2 (human EGFR protein) [23]. Blocking of both the intra- and extracellular domains of HER2 receptor by lapatinib and trastuzumab (respectively) could improve progression-free survival and disease control [24]. In addition, there is new evidence that breast cancer patients with HER2-positive tumors often benefit from topoisomerase II (encoded by TOP2A gene) inhibitor-based chemotherapy such as doxorubicin or epirubicin [20]. 3. Oncotype Dx (21-gene RT-pcR): Current gene expression assay such as Oncotype Dx is a genetic test that measures RNA expression in 16 cancer related genes and 5 reference genes have been suggested for determining prognosis in non-negative, ER-positive breast tumors. At present there is not sufficient evidence to recommend its routine use for early breast cancer detection. 4. CYP2D6 genotyping (Cytochrome P450): 2D6 is an emerging biomarker that has been investigated extensively. The hypothesis is that patients with this genotype will receive lesser benefit with tamozifen compared with patients with
S20
M E TAB OLI S M CL I NI CAL AN D EX PE RI M EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
other genotypes. Current guidelines do not recommend routine genetic testing of 2D6 [20].
1.4.
Non-small cell lung cancer (NSCLC)
Lung cancer (of all histologic cell types) is the most common cause of cancer deaths in men and the second leading cause of cancer deaths in females [25,26]. Over the past decade molecular diagnostics have played a major role in understanding the pathogenesis, diagnosis and treatment of lung cancer which has led to the identification of epidermal growth factor receptor (EGFR) and its role in lung cancer. Four non-small cell lung cancer biomarkers are considered “emerging” — EGFR gene expression, ERCC gene expression, RRM 1 gene expression, K-ras gene mutation. The standard of care of care for patients with advanced-stage NSCLC has shifted from selecting treatment empirically based on the patients clinical–pathological profile towards biomarker driven treatment algorithms based on the molecular profile of a patient’s tumor [2]. The molecular analysis of mutations in epidermal growth factor receptor (EGRF) gene, its corresponding downstream signaling cascade and the related mutations have led to the development of novel therapies [25]. Data from this biomarker when combined with analysis of histological material are becoming very important in lung cancer diagnostics as well as in patient stratification for therapy. Mutations in anaplastic lymphoma kinase (ALK) gene arrangements have also been implicated in NSCLC. Epidermal growth factor receptor (EGFR) is a widely used therapeutic target to treat patients with non-small cell lung cancers (NSCLCs). There are mutations that are specific to NSCLCs that activate EGFR — these are deletions in exon 19 and exon 21 point mutation, L585R. These mutations result in ligand-independent activation of EGFR signaling [27]. Two irreversible anti-EGFR tyrosine kinase (TKI) inhibitors are currently approved for the treatment of advanced NSCLC (gefitinib and erlotinib); this established an important milestone and led to the creation of a predictive biomarker assay for NSCLC diagnosis. Recent phase III randomized trials with these EGFR inhibitors, when compared with chemotherapy, have produced significantly longer disease-free survival, higher response rates, less toxicity and a better quality of life [28–31]. The “combination affinity” of increased gefitinib and erlotinib with the mutated form of EGFR is expected to represent an approximately threefold improvement over that likely from chemotherapy alone in unselected NSCLC patients [27,28].
1.5.
Melanoma
Melanoma is the third most common skin cancer which is responsible for almost all of skin cancer related mortalities [32]. An estimated 76,100 new cases of melanoma will be diagnosed in 2014. Melanoma accounts for less than 2% of all skin cancer cases, but for the vast majority of skin cancer deaths. The incidence rates for melanoma have been increasing for the past 30 years, and it is being diagnosed at much younger ages. Genomics, epigenomics and proteomics have led to molecular reclassification of melanoma based on pathological findings [33].
Following the discovery of genetic alterations and biomarkers that drive melanoma progression a number of targeted therapies have been developed: Vemurafenib and Dabrafenib were approved in 2011 and 2013, respectively, for the treatment of melanoma. These drugs target the mutated V600 codon of BRAF signaling molecule that is an important effector of the RAS/RAF/MEK/ERK pathway. Mutations in this gene occur in over 50% of melanoma tumors with BRAF V600E mutations being the most common [33]. Another molecule Trametinib was approved in 2013 which targets downstream MAPK kinases (including MEK1/2 inhibitor). The related companion diagnostic tests for the detection of BRAF V600 mutations were developed and approved soon thereafter. These therapies that selectively target the mutant BRAF driver gene have been shown to induce tumor regression in > 50% of patients with advanced melanoma [28].
2.
Conclusions
The central goal of biomarker-based personalized cancer therapy is to make treatment decisions based on tumor genotypes and genetic profiles. Matching targeted therapies against specific genetic aberrations is an important step for personalized cancer therapy. Such an approach holds promise in ultimately improving measurable clinical outcomes: response rates, survival and safety [34,35]. A new molecular classification of many cancers has evolved based on chromosomal aberrations, gene mutations and signaling pathway activation that underlie biologically unique tumors that now need to be managed clinically in several different ways [36]. Early clinical application of these technologies has made possible the rapid and comprehensive molecular annotation of an individual’s cancer. This facilitates the identification of actionable and/or novel drug targets and treatment options, as well as the characterization of underlying pathogenic mechanisms [2]. Understanding molecular carcinogenesis will continue to shape the approach taken towards tumor diseases. Patients will be treated with preferentially targeted substances based on specific molecular profiles found in individual tumor tissues [2]. miRNAs are becoming more relevant in the diagnosis, sub-classification, prognosis and as biomarkers of myeloid malignancies and genomic DNA could be used to monitor residual disease. In the future, mutational testing and the development of serological biomarkers will enable us to stratify patients for targeted therapies. This new paradigm in drug development and clinical care has created both opportunities and challenges. Numerous multiplex genotyping platforms are being evaluated for actionable hotspot oncogene mutations or gene amplifications and arrangements are being evaluated with promising results that are progressing towards use in clinical settings [2]. The biggest challenge in personalized cancer treatment involving translating cancer genomics is to understand how these aberrations are related to the progression of the cancer over time. In spite of this knowledge gap, these recent advances in identifying biomarkers using genomic technologies continue to make great strides — developments which hold enormous promise for advancing cancer treatment.
M E TAB OLI S M CL I NI CAL AN D EX PE R IM EN TA L 6 4 ( 2 0 15 ) S 1 6 – S2 1
REFERENCES
[1] American Cancer Society. Facts and figures. Atlanta: American Cancer Society; 2014. [2] Li T, Kung HJ, Mack PC, et al. J Clin Oncol 2013;31(8):1039–49. [3] National Cancer Institute. Dictionary of cancer terms. http:// www.cancer.gov/dictionary?CdrD=56717. [4] Grullich C, Von Kalle C. Recent developments and future perspectives of personalized oncology. Onkologie 2012;35(1):4–7. [5] Nalejska E, Maczwynska E. Prognostic and predictive biomarkers: tools in personalized oncology. Mol Diagn Ther 2014;18:273–84. [6] Dias-Santagata D, Akhavanfard S, David SS, et al. Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine. EMBO Mol Med 2010;2950:146–58. [7] Poznanski M, Lewandowska MA. Application of highthroughput technologies in cancer molecular diagnostics. Oncol Clin Pract 2013;9(2):70–7. [8] Iacobucci H, Lonetti A, Papayannaidis C, et al. Use of single nucleotide polymorphism array technology to omprove the identification of chromosomal lesions in leukemia. Curr Cancer Drug Targets 2013;13:791–810. [9] Nowell PC, Hungerford DA. Choromosome studies on normal and leukemic human leukocytes. J Natl Cancer Inst 1960;25: 85–109. [10] Gordon JE, Wong JJ, Rasko JE. MicroRNAs in myleoid malignancies. Br J Haematol 2013;162:162–76. [11] McLaughin L, Chianese E, Witte ON. In vitro transformation of immature hematopoetic cells by the P210 BCR/ABL oncogene product of the Philadelphia chromosome. Proc Natl Acad Sci 1987;84:6558–62. [12] Daley GQ, Van Etten RA, Baltimore D. Induction of chronic myelogenous leukemia in mice by the P210bcr/abl gene of the Philadelphia chromosome. Science 1990;247:824–30. [13] Lugo TG, Pendergast AM, Muller AJ, et al. Tyrosine kinase activity and transformation potency of bcr-abl oncogene products. Science 1990;247:1079–82. [14] Ross DM, Branford S, Seymore JF. Patients with chronic myeloid leukemia who maintain a complete response after stopping imatinib treatment have evidence of persistent leukemia by DNA PCR. Leukemia 2010;24:1719–24. [15] Corbin AS, Agarwal A, Loriaux M. Human chronic myeloid leukemia stem cells are insensitive to imatinib despite inhibition of BCR-ABL activity. J Clin Invest 2011;121:396–409. [16] Akwaa F, Liesveld J. Surrogate end points for long-term outcomes in chronic myeloid leukemia. Leuk Lymphoma 2013;54(10):2103–11. [17] Colburn WA. Optimizing the use of biomarkers, surrogate endpoints and clinical end points for more efficient drug development. Clin Pharmacol 2000;40:1419–27. [18] Cross NCP, White HE, Muller MC, et al. Standardized definitions of molecular response in chronic myeloid leukemia. Leukemia 2012;26:2172–5. [19] Branford S. Continued therapeutic response monitoring in optimal responders with CML. Curr Adv Hematol Oncol 2012; 10(12):819–21.
S21
[20] Chung C, Christianson M. Predictive and prognostic biomarkers with therapeutic targets in breast, colorectal and non-small cell lung cancers: a systematic review of current development, evidence, and recommendation. J Oncol Pharm Pract 2014;20(1):11–28. [21] Harvey JM, Clark GM, Osborne CK, et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol 1999;17: 1474–81. [22] Symmans WF, Hatzis C, Sotirtiou C, et al. Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol 2010;28:4111–9. [23] Salomon DS, Brandt R, Ciardiello F, et al. Epidermal growth factor-related peptides and their receptors in human malignancies. Crit Rev Oncol Hematol 1995;19:183–232. [24] Blackwell KI, Burstein HJ, Storniolo AM, et al. Randomized study of lapatinib alone or in combination with trastuzumab in women with ErB-2 positive, trasuzumab-refractory metastatic breast cancer. J Clin Oncol 2010;28:1124–30. [25] Ozretic L, Heukmp L, Odenthal M, et al. The role of molecular diagnostics in cancer diagnosis and treatment. Onkologie 2012;35(Suppl. 1):8–12. [26] Jemal A, Center MM, DeSantis C. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol Biomarkers Prev 2010;19:893–907. [27] Heuckmann JM, Rauh D, Thomas RK. Epidermal growth factor receptor (EGFR) signaling and covalent EGFR inhibition in lung cancer. J Clin Oncol 2012;30:3417–20. [28] Duffy MJ. The war on cancer; are we winning? Tumor Biol 2013;34:1275–84. [29] Rossi A, Pasquale R, Esposito C. Should epidermal growth factor receptor tyrosine kinase inhibitors be considered ideal drugs for the treatment of selected advanced non-small cell lung cancer patients? Cancer Treat Rev 2012. http://dx.doi.org/ 10.1016/jctrv.2012.09.001. [30] Moran T, Sequist LV. Timing of epidermal growth factor receptor tyrosine kinase inhibitor therpy in patients with lung cancer with EGRF mutations. J Clin Oncol 2012;30: 3330–6. [31] Soria JC, Mork TS, Cappuzzo F, et al. EGFR-mutated oncogene-addicted non-small cell lung cancer: current trends and future prospects. Cancer Treat Rev 2012;38: 416–30. [32] Davar D, Ahmad AT, Gogas H, et al. Advances in adjuvant therapy:potential for prognostic and predictive biomarkers. Methods Mol Biol 2014;1102:45–9. [33] Thurin M, Marincola FM. Preface. Molecular diagnostics for melanoma: methods and protocols. Methods Mol Biol 2014; 1102:v–xiv. [34] Gandara DR, Li T, Lara PN, et al. Algorithim for codevelopment of new drug-predictive biomarker combinations: accounting for inter- and intrapatient tumor heterogeneity. Clin Lung Cancer 2012;13:321–5. [35] Haber DA, Gray NC, Baselga J. The evolving war on cancer. Cell 2011;145:19–24. [36] Kalia M. Personalized oncology: recent advances and future challenges. Metabolism 2013;62(Suppl. 1):S11–4.
