The Academy of Oncology Nurse & Patient Navigators (AONN+) is holding its annual meeting November 17-20, 2016, in Las Vegas, NV. In this special breast cancer issue of JONS, we [ Read More ]
Best Practices in Breast Cancer – October 2016 Vol 7
State of Personalized Medicine in Breast Cancer
Sarika Jain,1 Sachin G. Pai,2 Cesar A. Santa-Maria,1 William J. Gradishar1
1Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL 2Northwestern Medicine Developmental Therapeutics Institute, Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL
It was estimated that 231,840 new breast cancers would be diagnosed in 2015, with 40,290 breast cancer–related deaths during the same period in the United States alone.1 With improvements in screening, diagnosis, staging, and surgical and radiation techniques we have been able to cure the majority of patients with early-stage disease and considerably improve the survival in the rest of patients with advanced disease. Better understanding of biology and the molecular mechanisms governing disease progression coupled with rapidly evolving molecular technologies have given access to tools to study breast cancer in the clinic as never before, making personalized medicine a realistic goal. In this review, we will reflect on tools currently available to a clinician involved in the treatment of patients with breast cancer and shed light on emerging technologies that may be utilized in the near future.
Biomarkers in Breast Cancer That Direct Therapy
Breast cancer has been the prototype for personalized medicine for solid cancers ever since it was linked to the estrogen receptor (ER) and progesterone receptor (PR), as ER-rich breast cancers were shown more likely to respond to endocrine manipulation than ER-poor breast cancers.2 The next major breakthrough came with the discovery of the HER2 receptor and the development of an antibody (trastuzumab) directed against the HER2 receptor, which led to further improvement in survival outcomes. To date, ER, PR, and HER2 are the only targetable molecular alterations with confirmed predictive and prognostic value.3 About 10% to 20% of all breast cancers do not express ER or PR and lack overexpression of HER2 and are termed triple-negative breast cancer (TNBC). Other immunohistochemical biomarkers, such as cytokeratin 5/6, epidermal growth factor receptor (EGFR), vimentin, and Ki67, have been used to better understand the biologic behavior of breast cancer but have not provided useful information that influences clinical management. Several serum-based tumor markers, such as carcinoembryonic antigen, cancer antigen (CA) 15-3, and CA 27.29, have all been tested as markers for early detection and surveillance, but none of them provided adequate sensitivity and specificity.4 Current National Comprehensive Cancer Network (NCCN) Guidelines (Version 3.2015) do not recommend the use of tumor markers in routine clinical practice given the lack of evidence demonstrating any improvement in patient outcome. Attention is now being directed to molecular characteristics and genetics of breast tumors in guiding management decisions.
Breast Cancer Genetics
Gene expression–based molecular breast cancer classification was first introduced 15 years ago and has since been validated using several platforms. During the past 15 years, several other groups have studied this extensively and classified breast cancer into at least 5 different intrinsic molecular subtypes: luminal A, luminal B, HER2 enriched, basal-like, and a normal breast-like group.5,6 These studies showed that previously classified breast cancers (based on traditional biomarkers such as ER, PR, and HER2) not only exhibit characteristic biological behavior but also have a distinct genetic makeup as well. For example, gene expression profiling can classify ER-positive tumors into distinctive prognostic groups such as luminal A, luminal B, basal type, etc. The most studied assay is a reverse transcriptase-polymerase chain reaction (RT-PCR)-based 21-gene assay (Oncotype DX).7,8 It is performed on paraffin-embedded tumor tissue and was first validated as a part of National Surgical Adjuvant Breast and Bowel Project B-14 clinical trial over a decade ago. Oncotype DX classifies early-stage breast cancer into low-risk, intermediate-risk, and high-risk groups by calculating 10-year recurrence and can help inform the decision of whether to use adjuvant chemotherapy in patients with node-negative, ER-positive breast cancer.8 In a recently published validation study enrolling 10,253 eligible women, 1626 (15.9%) had a low recurrence score (0-10) on Oncotype DX and were administered endocrine therapy alone. These patients had an excellent outcome with a 5-year invasive disease-free survival of 93.8% (95% CI, 92.4-94.9) and distant disease-free recurrence of 99.3% (95% CI, 98.7-99.6).9 MammaPrint is a validated gene expression profile of 70 genes, although not as frequently used to make treatment decisions; fresh frozen tissue is required for analysis.10 Several other similar gene profiling tests have been clinically validated and are commercially available, including Prosigna and BluePrint.
Gene expression profiling also offers potential to stratify new treatment strategies in TNBC, which previously did not have a targetable pathway, and chemotherapy was the only option. Multiple potentially clinically actionable groups of TNBC have been identified: basal-like TNBC with DNA repair deficiency or growth factor pathways, mesenchymal-like TNBC with epithelial to mesenchymal transition and cancer stem cell features, immune-associated TNBC, luminal/apocrine TNBC with androgen-receptor overexpression, and HER2-enriched TNBC. Each of these subtypes has been shown to thrive on a different major biologic pathway, and hence a biology-oriented comprehensive approach is thought to be the way forward in managing these patients.11 Development of therapies targeting these pathways may translate into better treatment outcomes. Numerous clinical trials are exploring this strategy in TNBC.
The advent of high-throughput genomic technologies such as massive parallel sequencing, eg, next-generation sequencing (NGS), has led to rapid advances in our understanding of the genomic changes that underlie breast cancer pathobiology.12 Several genetic alterations have been identified, and a few, such as PIK3CA mutations, FGFR1 amplification, AKT1 mutations, and EGFR amplifications, may be targetable, and agents targeting these signaling pathways are currently being studied in clinical trials.3 It is important to note that these approaches require tissue biopsies, often multiple times, and may not overcome the challenges faced by tumor heterogeneity, both within a given tumor and between different metastatic sites, as well as changes in the tumor over time.
Circulating Tumor Cells and Cell-Free DNA
For cancer cells to metastasize, it is known that they need to traverse the blood before migrating to a new site. Braun and colleagues demonstrated in a large meta-analysis of 4703 breast cancer patients that disseminated tumor cells (DTCs) could be present in bone marrow aspirates of early breast cancer patients. The presence of DTCs was associated with larger tumors and tumors with a higher histologic grade, and patients more often had lymph-node metastases and hormone receptor–negative tumors and a poor overall and breast cancer–specific survival.13 Bone marrow examination is cumbersome and not a standard staging procedure in the workup of metastatic breast cancer. With the advances in technology, it is possible to detect cancer cells called circulating tumor cells (CTCs) in the blood of patients. DNA shed from the tumor cells, called cell-free DNA (cfDNA), can also be detected in peripheral blood. Detection of CTCs and cfDNA are exciting technologic advances, so-called “liquid biopsies,” as they may be minimally invasive surrogates for tumor tissue–based biomarkers. These may circumvent the challenges posed by intratumoral heterogeneity and inaccessible metastatic sites, as these technologies are theorized to pick up the most dominant, clinically relevant and actionable molecular target.
The presence of CTCs has been shown to be an independent prognostic marker for before and after adjuvant chemotherapy in a large prospective trial of patients with primary breast cancer. In this study of 2026 patients, CTCs were detected in 21.5% of patients (435 of 2026) after the complete resection of the primary tumor and before the start of systemic treatment (median, 1.0 cell; range, 0-827 per 30 mL of blood). Lymph node metastasis status was more likely to be associated with the presence of CTCs. At 36 months, patients with 5 or more CTCs had the highest rate of recurrence at 28.1% and death in 14.3%, compared with 7.1% and 3.4% of patients with fewer than 5 CTCs, respectively.14 The SWOG S0500 trial similarly confirmed the prognostic significance of CTCs in patients with metastatic breast cancer receiving first-line chemotherapy in a randomized study of 595 patients. However, this study failed to show an improvement in overall survival (OS), with early switching to alternate chemotherapy regimens in patients who had persistently elevated CTCs.15
The genomic profiling of plasma-derived circulating tumor DNA (ctDNA) and CTCs obtained via liquid biopsies helps to detect and monitor disease progression in real time, and hence may have broad utility in the management of breast cancer.16 In a proof-of-concept analysis, ctDNA was shown to be an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer with greater dynamic range and greater correlation with changes in tumor burden than did CA 15-3 or CTCs. ctDNA levels reflected progressive disease in 17 of the 19 women (89%), while CA 15-3 levels increased in 9 of 18 women (50%) before radiologic progression. ctDNA levels increased by a factor of 505 (range, 2-4457) from the nadir before the establishment of progressive disease.17
Plasma ctDNA can be used to monitor for minimal residual disease in breast cancer. Mutation tracking in serial samples increased sensitivity for the prediction of relapse, with a median lead time of 7.9 months over clinical relapse.18 ESR1 mutations, which are thought to confer resistance to endocrine therapy, may be tracked by monitoring ctDNA in patients with metastatic breast cancer, potentially providing an opportunity to alter therapy prior to frank progression on imaging studies.19 The clinical applications of these technologies are currently under investigation in clinical trials, and, although not considered standards of care by the NCCN or the American Society of Clinical Oncology, hold promise as useful tools in the future.
Single Nucleotide Polymorphism (SNP) and Comparative Genomic Hybridization (CGH)
SNPs and CGH are DNA microarray techniques, until now used only in the research setting as screening tools to identify clinically significant genetic aberrations. Given their easy accessibility and low cost compared with other genomic technologies such as NGS, they may find application in the clinic. For instance, SNP array–based analysis for HER2 copy number variations has been shown to provide additional diagnostic sensitivity and accuracy by supplementing immunohistochemistry/fluorescence in situ hybridization (IHC/FISH) to detect HER2 amplification. This may select more women for targeted treatment with HER2 inhibitors, although it will need to be prospectively tested for efficacy.20 SNPs may be able to predict response to therapy in patients receiving agents targeting the vascular endothelial growth factor pathway as well.21 Cost-effective platforms that assess SNPs in clinically relevant cancer genes such as KRAS, NRAS, BRAF, EGFR, and PIK3CA are being developed. These can potentially overcome the complexities of NGS technologies and data analysis that are slowing their widespread availability.22 A study using CGH combined with focused sequencing to detect changes in PIK3CA and AKT1 in 108 patients with metastatic breast cancer showed that 16% of patients could be identified for targeted therapy on the basis of their genomic profile.23 Hence, these technologies, when used for specific purposes, have the potential to be cost-effective alternative options when performing large whole genome studies is not feasible.
Gene transcription can be modified by several factors, the most important being DNA and histone modifications by processes such as methylation, phosphorylation, and acetylation of certain amino acid residues, termed as epigenetics.24 Epigenetic mechanisms have been implicated in survival and resistance mechanisms of breast cancer, including conversion of endocrine-sensitive to endocrine-resistant tumors, epithelial to mesenchymal transition, and drug resistance.25 One of the important mediators of epigenetic change is through modification of gene expression at a posttranscriptional level by the action of microRNAs (miRNAs). For instance, increased expression of miRNA-21 is associated with advanced stages of breast cancer, and upregulation of miRNA-155 correlates with metastasis and poor prognosis in breast cancer. These epigenetic phenomena, which could be evaluated in peripheral blood, could potentially be used for early detection, predicting biological behavior, mechanisms of resistance, and monitoring for relapse.26
The downstream effect of all genetic and epigenetic change is the production of abnormal proteins, the final effector pathway for all cancer mechanisms. Proteomic research is, however, plagued by procedural weaknesses such as ensuring representativeness of tumor sample due to contamination by nontumor cells and serum proteins. The complexity of biologic structure and physiologic functions of proteins, which still remain to be fully elucidated, add to this conundrum.27 However, the emergence of technologies such as reverse phase protein array have the ability to generate a functional map of known cell signaling networks or pathways for an individual patient that could translate to delivery of clinically relevant information at a reasonable cost and within a reasonable time.28 Commercially available assays such as TheraLink quantifies specific autophosphorylation sites on receptor tyrosine kinases and monitors the levels of key downstream pathways, including the Akt/mTOR, MAPK, and Jak/Stat pathways, many of which have FDA-approved treatment options. Incorporating these technologies into decision pathways for practical application is currently being investigated.
Although traditionally considered to be immunologically inert, recent evidence is emerging to suggest that a subset of breast cancer could be harboring an immunogenic state that could be manipulated for therapeutic utility. In a recent study done on TNBC, the presence of tumor-infiltrating lymphocytes (TILs), a marker of immune activity, was associated with a significantly better disease-free survival and OS.29 In the GeparSixto trial, increased levels of stromal TILs predicted pathologic complete response. Additionally, messenger RNA immune signatures such as PD-1, PD-L1, CTLA4, and FOXP3 showed a positive correlation with proimmune markers, stromal TILs, and treatment response.30 In HER2-positive breast cancer, immune gene enrichment was associated with benefit from adjuvant trastuzumab and increased relapse-free survival in a subpopulation analysis of the adjuvant trastuzumab trial (North Central Cancer Treatment Group N9831).31 Thus, absence of baseline immune response in tumors could be used as a prognostic marker and could be used to direct patients toward clinical trial options aimed at overcoming trastuzumab failure.
Another method in which genomics may help develop predictive biomarkers is through the study of immunopharmacogenomics. A key method by which immune cells identify cancer cells is by detecting the antigens, termed neoantigens, expressed on cancer cells. Higher mutational burden may correlate with a more diverse neoantigen landscape and predict response to immunotherapy.32 Similarly, patients with mismatch repair–deficient cancers have a higher number of somatic mutations compared with those who are mismatch repair proficient and have better responses to immune checkpoint blockade.33 Diverse neoantigen landscapes are more likely to stimulate T cells, which can be measured through T-cell receptor sequencing to identify the clonality of the T-cell receptor repertoire. Preliminary studies in patients with melanoma suggest that patients with oligoclonal T-cell receptor repertoires are more likely to respond to anti–cytotoxic T-lymphocyte antigen 4 therapy.34 Therefore, these immunopharmacogenomic biomarkers may help identify patients with breast cancer more likely to benefit from immunotherapies. The relationship of the neoantigen landscape with the T-cell receptor repertoire in patients with metastatic breast cancer undergoing dual checkpoint immunotherapy has never been described.
Ever since the discovery of BRCA genes, the major genes associated with hereditary breast cancer, several other genetic alterations have been found that are known to confer increased risk of breast cancer. Mutation in one of the well-defined, high-penetrance genes such as BRCA1, BRCA2, PTEN, TP53, CDH1, and STK11 can confer up to an 80% lifetime risk of breast cancer. Other less common mutations in rare, moderate-penetrance genes such as CHEK2, BRIP1, ATM, and PALB2 account for 2% to 3% of cases of hereditary breast cancer. Mutation testing for individual genes in the appropriate clinical setting has been advised. With the advent of NGS platforms, it is possible to assess for several hereditary cancer syndromes with a single test, which is being increasingly adopted. Stringent guidelines have been developed for screening and management of subjects with highly penetrant genes related to breast cancer.35
This rapid increase in the knowledge of human cancer biology is not without its limitations and challenges when it comes to practical applicability, especially when considering feasibility and cost-benefit analysis. As an example, a simple 2-rule model based on routine IHC markers was used to simulate the Oncotype DX score. Low-grade and positive PR tumors were classified as low risk, and high-grade or low ER tumors (ER <20%) were classified as high risk. This simple model had a very low rate of misclassifications in 2 independent data sets in different hospitals. This model was estimated to save over $200,000 in cost avoidance with Oncotype DX testing per 100 invasive early breast cancer patients.36 Another study from MD Anderson Cancer Center enrolled 2000 consecutive patients with advanced cancer who underwent testing on a genomic testing protocol; 789 patients with potentially actionable alterations were detected. However, only 83 patients (11%) with potentially actionable mutations went on genotype-matched trials targeting these alterations, and an additional 54 (7%) went into a genotype-selected trial (requiring a mutation for eligibility). For the other 150 patients (19%) who were enrolled in clinical trials at the institution, genetic testing did not make a significant difference to their treatment.37 Several other issues, such as tumor heterogeneity, clonal evolution, identification of driver mutations, and epigenetics, will need to be addressed before evidence-based clinical decisions can be made on some of these molecular tests. Routine use of expensive molecular studies outside the scope of clinical trials and research is not recommended, especially given their cost and lack of evidence to show substantial benefits.
This is an exciting time for the field of breast oncology, with several evolving diagnostic and therapeutic options that may lead to a paradigm shift in the way breast cancer is managed. With several advances in genetic and molecular biology and better understanding of pathobiology of the cancers, we expect personalized medicine to be a reality in the near future. Some key biologic limitations of this targeted approach, however, include tumor heterogeneity, clonal evolution, and identification of driver versus passenger mutations. It is unlikely that targeting 1 mutational aberration at a time will lead to substantial clinical benefit, but a better understanding of genomic landscapes may facilitate multitargeted approaches. Standardization of tests and better algorithms to incorporate these diagnostics into treatment decisions are sorely needed to translate this plethora of information into a meaningful clinical benefit. Availability and prohibitive costs of some of the genetic and molecular tests could pose challenges, but evolving technology and increasing use could circumvent those.
- National Cancer Institute. SEER Stat Fact Sheets: Female Breast Cancer. http://seer.cancer.gov/statfacts/html/breast.html.
- Jensen EV, Block GE, Smith S, et al. Estrogen receptors and breast cancer response to adrenalectomy. Natl Cancer Inst Monogr. 1971;34:55-70.
- Arnedos M, Vicier C, Loi S, et al. Precision medicine for metastatic breast cancer—limitations and solutions. Nat Rev Clin Oncol. 2015;12:693-704.
- Duffy MJ. Serum tumor markers in breast cancer: are they of clinical value? Clin Chem. 2006;52:345-351.
- Perou CM, Sørlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747-752.
- Prat A, Pineda E, Adamo B, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast. 2015;24(suppl 2):S26-S35.
- Sørlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98:10869-10874.
- Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351:2817-2826.
- Sparano JA, Gray RJ, Makower DF, et al. Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med. 2015;373:2005-2014.
- van de Vijver MJ, He YD, van ’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999-2009.
- Le Du F, Eckhardt BL, Lim B, et al. Is the future of personalized therapy in triple-negative breast cancer based on molecular subtype? Oncotarget. 2015;6:12890-12908.
- Curtis C. Genomic profiling of breast cancers. Curr Opin Obstet Gynecol. 2015;27:34-39.
- Braun S, Vogl FD, Naume B, et al. A pooled analysis of bone marrow micrometastasis in breast cancer. N Engl J Med. 2005;353:793-802.
- Rack B, Schindlbeck C, Jückstock J, et al. Circulating tumor cells predict survival in early average-to-high risk breast cancer patients. J Natl Cancer Inst. 2014;106(5).
- Smerage JB, Barlow WE, Hortobagyi GN, et al. Circulating tumor cells and response to chemotherapy in metastatic breast cancer: SWOG S0500. J Clin Oncol. 2014;32:3483-3489.
- De Mattos-Arruda L, Cortes J, Santarpia L, et al. Circulating tumour cells and cell-free DNA as tools for managing breast cancer. Nat Rev Clin Oncol. 2013;10:377-389.
- Dawson SJ, Tsui DW, Murtaza M, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med. 2013;368:1199-1209.
- Garcia-Murillas I, Schiavon G, Weigelt B, et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med. 2015;7:302ra133.
- Sefrioui D, Perdrix A, Sarafan-Vasseur N, et al. Short report: monitoring ESR1 mutations by circulating tumor DNA in aromatase inhibitor resistant metastatic breast cancer. Int J Cancer. 2015;137:2513-2519.
- Hansen TV, Vikesaa J, Buhl SS, et al. High-density SNP arrays improve detection of HER2 amplification and polyploidy in breast tumors. BMC Cancer. 2015;15:35.
- Koutras A, Kotoula V, Fountzilas G. Prognostic and predictive role of vascular endothelial growth factor polymorphisms in breast cancer. Pharmacogenomics. 2015;16:79-94.
- Magliacane G, Grassini G, Bartocci P, et al. Rapid targeted somatic mutation analysis of solid tumors in routine clinical diagnostics. Oncotarget. 2015;6:30592-30603.
- Arnedos M, Scott V, Job B, et al. Array CGH and PIK3CA/AKT1 mutations to drive patients to specific targeted agents: a clinical experience in 108 patients with metastatic breast cancer. Eur J Cancer. 2012;48:2293-2299.
- Esteller M. Epigenetics in cancer. N Engl J Med. 2008;358:1148-1159.
- Martinez-Galan J, Torres-Torres B, Núñez MI, et al. ESR1 gene promoter region methylation in free circulating DNA and its correlation with estrogen receptor protein expression in tumor tissue in breast cancer patients. BMC Cancer. 2014;14:59.
- Basse C, Arock M. The increasing roles of epigenetics in breast cancer: implications for pathogenicity, biomarkers, prevention and treatment. Int J Cancer. 2015;137:2785-2794.
- Alaiya A, Al-Mohanna M, Linder S. Clinical cancer proteomics: promises and pitfalls. J Proteome Res. 2005;4:1213-1222.
- Creighton CJ, Huang S. Reverse phase protein arrays in signaling pathways: a data integration perspective. Drug Des Devel Ther. 2015;9:3519-3527.
- Pruneri G, Vingiani A, Bagnardi V, et al. Clinical validity of tumor-infiltrating lymphocytes analysis in patients with triple-negative breast cancer. Ann Oncol. 2016;27:249-256.
- Denkert C, von Minckwitz G, Brase JC, et al. Tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy with or without carboplatin in human epidermal growth factor receptor 2-positive and triple-negative primary breast cancers. J Clin Oncol. 2015;33:983-991.
- Perez EA, Thompson EA, Ballman KV, et al. Genomic analysis reveals that immune function genes are strongly linked to clinical outcome in the North Central Cancer Treatment Group N9831 adjuvant trastuzumab trial. J Clin Oncol. 2015;33:701-708.
- Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124-128.
- Le DT, Uram JN, Wang H, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509-2520.
- Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371:2189-2199.
- Shiovitz S, Korde LA. Genetics of breast cancer: a topic in evolution. Ann Oncol. 2015;26:1291-1299.
- Gage MM, Rosman M, Mylander WC, et al. A validated model for identifying patients unlikely to benefit from the 21-gene recurrence score assay. Clin Breast Cancer. 2015;15:467-472.
- Meric-Bernstam F, Brusco L, Shaw K, et al. Feasibility of large-scale genomic testing to facilitate enrollment onto genomically matched clinical trials. J Clin Oncol. 2015;33:2753-2762.