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Molecular Oncology and the Neoadjuvant Setting: the Perfect Blend for Treatment Personalization and Clinical Trial Design

Molecular Oncology and the Neoadjuvant Setting: the Perfect Blend for Treatment Personalization... Abstract Breast cancer is a heterogeneous disease. Predictive molecular markers are crucial in patient management, but the only recommended predictive biomarkers are estrogen and progesterone receptors and HER2. There are many new targeted therapies, and although the target pathway expression is readily analyzed on conventional pathology, the dynamic response cannot be assessed and pathway expression is no guarantee it has a major driver role, even if mutated. Selecting therapies requires considering the patient, the molecular characteristics of the tumor, and the microenvironment of the tumor. Thus, the integration of molecular pathology, imaging, and early tumor biological response to therapy may provide evidence of drug activity and allow more rapid changes of therapy. The adaptive response of the tumor is a key resistance mechanism that can be assessed readily in the neoadjuvant setting. Although there are no markers that meet all surrogacy criteria, their use could provide crucial information on mechanisms of drug sensitivity/resistance. Validation of such markers requires a major emphasis on neoadjuvant trials to relate early-biomarker response to outcome. Cancer is a complex and dynamic disease with a variable molecular portrait. To treat cancer more effectively, we need to understand the biological mechanisms driving it. Advances in molecular profiling technologies (including proteomic profiling, metabolomic analysis, and genetic testing), and new imaging methods should allow for a greater degree of personalized medicine. Relating baseline expression profiles and imaging of the microenvironment to therapeutic responses and outcome for each patient may allow optimized drug therapy and more rapid drug development. Serial biopsies to assess the early response on proliferation and adaptive responses should give new insights to guide therapy and develop predictive markers. This review will focus on the analyses available from biopsies. Molecular Oncology Approaches Gene Expression Profile Several studies based on gene expression profiles have provided a molecular classification of breast cancer into clinically relevant subtypes such as basal-like subtype (estrogen receptor [ER]-negative, HER2-negative, basal cytokeratins positive), the ERBB2 subtype (HER2-positive), and luminal subtype (ER-positive) subdivided into luminal A (with high level of expression ER) and luminal B (with lower levels of ER and high expression of genes related to proliferation) (1–4). There are two gene-predictor tests, mainly for prognosis rather than response to a specific therapy, already in use. MammaPrint Test, approved for marketing in United States and European community in 2007, is used in frozen samples of stage I or II breast cancer, lymph node negative and positive. MammaPrint is used as a prognostic test along with the conventional clinicopathologic factors. Test results are reported as low risk (13% chance to develop distant metastases at 10 years without adjuvant treatment) or high risk (56% chance to develop distant metastases at 10 years without adjuvant treatment) (5). Oncotype DX Breast Cancer Assay is used in association with conventional risk assessments to predict the likelihood of distant recurrence in women with stage I or II breast cancer (paraffin-embedded samples), lymph node negative and positive and ER-positive, who will be treated with tamoxifen. The low- (<18), intermediate- (18–30), and high-risk (≥31) categories are stated to correspond to 10-year distant recurrence rates after 5 years of tamoxifen therapy of less than 12%, 12%–21%, and 21%–33%, respectively (6). However, the prognostic roles of MammaPrint and Oncotype DX have been validated retrospectively but not yet prospectively. In the neoadjuvant context, three articles have shown an independent predictive role in pathological complete response (pCR). MINDACT (Microarray in Node-negative Disease may Avoid ChemoTherapy) and TAILORx (Trial Assigning IndividuaLized Options for Treatment) (7,8) are ongoing adjuvant studies, which will require 3–5 years for an outcome assessment. The neoadjuvant context is a more powerful model for the identification or validation of new prognostic molecular markers with many advantages including a requirement of small numbers of patients (hundreds rather than thousands) and an accelerated timeline because tumor response is used as a surrogate of survival. A hindrance of this model is that it is actually based on pCR as a surrogate parameter of efficacy, and additional new potential surrogates are needed. A prospective study–such as the I-SPY 2 (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2)–based on immunohistochemistry (IHC) and gene expression profile, able to give efficient information about each drug’s biomarker signature in relation to each single patient, is ongoing. The I-SPY 2 trial is a clinical trial for women with newly-diagnosed locally advanced breast cancer to test multiple investigational drugs that are thought to target the biology of each participant’s tumor. The I-SPY 2 trial will test the idea of tailoring treatment by using molecular tests to help identify which patients should be treated with investigational drugs (9). IHC Signature Mammostrat (Applied Genomics, Inc, Huntsville, AL) is based on the expression of five genes (p53, NDRG1, CEACAM5, SLC7A5, and HTF9C), which have been demonstrated to significantly improve prediction of outcome in ER-positive breast cancer patients (10). Protein Expression (IHC) Profile The routine determination by IHC of ER, progesterone receptor, HER2, and the proliferation marker Ki67 can be used to mimic the gene signature subtype classification with several advantages, such as low costs, the relation to cancer cell morphology, the use on paraffinized samples, and the availability and standardization of the methodology. A quality control scheme for Ki67 is currently being developed (M. Dowsett, personal communication). This panel can give both prognostic and predictive information to treatment response/resistance (11). However, to identify new molecular markers that mediate sensitivity and resistance to chemoendocrine therapies, our group used the tissue micro-array (12) approach applied in the neoadjuvant clinical trial setting (13–15). The analyses were based on a multivariate generalized linear regression using a penalized least-square minimization to perform variable selection and regularization. We identified that the activated form of ERα was a robust independent factor for sensitivity to chemoendocrine treatment, whereas hypoxia inducible factor-1α and p42/44 mitogen activated protein kinase were independent factors for resistance (14). Regarding this technique and the role of phosphoprotein in clinic, phosphorylated proteins are frequently used to assess the activity of intracellular signaling pathways and the effects of cancer drugs to inhibit these pathways. It is important to emphasize that phosphorylation of proteins is a posttranslational event modulating the activity or subcellular localization of many key signaling molecules in the cell; thus, it is crucial to have a knowledge of the stability of the protein phosphorylation before the tumor is fixed to prevent dephosphorylation of the marker. The stability of phosphoproteins may depend on the protein being studied and on the tissue or tumor type. However, rapid processing would seem to be essential for any study using phosphoprotein to measure signaling activity or drug inhibition, and postoperative surgical samples may be of limited value for measuring phosphoprotein levels (16,17). Pharmacogenomics and Pharmacodynamics To date, the evidence for the utility of pharmacogenomics in selecting breast cancer chemotherapy is minimal. Recent in vitro findings showed that cell line–derived predictors of response to four commonly used chemotherapy drugs as taxane, anthracyclines, 5-fluorouracil, and cyclophosphamide did not predict response accurately in patients (18). However, validation in large prospective studies to determine if pharmacogenomics would be a useful marker in the clinical setting are warranted. Our group is developing a pharmacogenomics tumor ID card combined with gene expression arrays, immunohistochemical, clinical, and radiological data in letrozole-based therapy in neoadjuvant prospective trial to evaluate its predictive role in selecting responders vs nonresponders. In our ongoing trials, we are also combining the biological data with Ki67 evaluation before and at 2–3 weeks, as it is an ideal integration of proteomics/genomics and pharmacodynamics because an array profile and Ki67 response have already been related to long-term outcome (19). This showed that it was the dynamic response and not baseline expression that was predictive. So, this dynamic response is an important paradigm for future trials. We have used Ki67 expression and fluorine-18 fluorodeoxyglucose positron emission tomography imaging in a neoadjuvant trial of letrozole randomized ± sorafenib (Generali D, unpublished data). This showed concordance of changes in standardized uptake value and changes in Ki67 and provides evidence for this approach to rapidly assess those benefitting from it. Circulating Markers There is currently a major effort to identify plasma and serum biomarkers. The American Society of Clinical Oncology Panel reviewed the literature on the use of several tumor markers in breast cancer, such as CA15-3, CEA, or serum HER2, in prevention, screening, treatment response, and surveillance (20,21). However, it concluded that these markers should now be confirmed in a large prospective trial before using them routinely for monitoring treatment response. Detection of Circulating Tumor Cells Circulating tumor cells may be an indicator for therapeutic efficacy and is licensed to predict patient outcome (22). Their prognostic value is independent of the line of therapy, site of metastasis, and subtype of disease. Moreover, circulating tumor cells have been recently associated with worse outcomes in patients with primary breast cancer after completing neoadjuvant or adjuvant therapy. These findings suggest the need to further investigate the biology of circulating tumor cells (23), which can also be used for molecular profiling and gene amplification studies. Recent data suggest that microRNA expression may be useful in further dissecting the phenomenon of chemotherapy and endocrine resistance and define new approaches (24). MicroRNA can be detected in peripheral blood associated with exosomes and may be prognostic (25). Requirements for Clinical Trial Design Biopsies to test an individual patient’s tumor for genomic and protein profiles are essential to improving our knowledge of tumor biology and changes in treatment. Mutational profiles would only be the first step; biopsies should then be performed early during therapy to monitor pharmacodynamic markers and predict responsiveness as well as at recurrence or progression to understand mechanisms of resistance. Trials will need to be based on Bayesian designs where data will be used to modify dosing or other parameters. The Bayesian approach allows for faster learning via more effective trial designs and more efficient drugs, while at the same time providing better treatment for patients who participate in the trial. Regimens showing a high Bayesian predictive probability of being effective will be continued with their biomarker signatures. Regimens with low probability of being effective will be dropped. The key points the future trials for personalized molecular medicine in cancer will be based on are: Clinical trial design that attempts to reduce costs and determine efficacy rapidly, such as neoadjuvant phase II trials in women with large primary cancers of the breast (>3.0 cm) Identify improved neoadjuvant treatment regimens for patient subsets on the basis of molecular characteristics (biomarker signatures), repeat early biopsies and imaging Compare efficacy of novel drugs in combination with standard adjuvant chemotherapy. Our unit is promoting a new approach in neoadjuvant trials with the MOON Trial (Molecular Oncology Optimising Neoadjuvant Trial) in breast cancer (Figure 1). Figure 1 View largeDownload slide Schema of the MOON Trial (Molecular Oncology Optimising Neoadjuvant Trial), which is based on molecular marker–driven randomized trial. Patients have a baseline biopsy, molecular profile, and molecular imaging, and are stratified according to breast cancer subtypes. They will receive the standard therapy plus the proper targeted therapy directed to the markers of interest present on the identified molecular subtype. A second biopsy, molecular profile, and molecular imaging will be performed at 14 days for endocrine-based therapy and at 28 days for chemotherapy-based therapy to study pharmacodynamic profile and correlate it to clinical/radiological response to identify resistant or responsive tumors. In case of resistance, according to the new tumor biology, a new therapeutic approach will be introduced. Definitive surgery will be performed at the clinical complete response (as a marker of treatment activity). CTCs = circulating tumor cells; MRI = magnetic resonance imaging; PET-CT = positron emission tomography–computed tomography; SNPs = single-nucleotide polymorphisms; SPECT = single-photon emission computed tomography. Figure 1 View largeDownload slide Schema of the MOON Trial (Molecular Oncology Optimising Neoadjuvant Trial), which is based on molecular marker–driven randomized trial. Patients have a baseline biopsy, molecular profile, and molecular imaging, and are stratified according to breast cancer subtypes. They will receive the standard therapy plus the proper targeted therapy directed to the markers of interest present on the identified molecular subtype. A second biopsy, molecular profile, and molecular imaging will be performed at 14 days for endocrine-based therapy and at 28 days for chemotherapy-based therapy to study pharmacodynamic profile and correlate it to clinical/radiological response to identify resistant or responsive tumors. In case of resistance, according to the new tumor biology, a new therapeutic approach will be introduced. Definitive surgery will be performed at the clinical complete response (as a marker of treatment activity). CTCs = circulating tumor cells; MRI = magnetic resonance imaging; PET-CT = positron emission tomography–computed tomography; SNPs = single-nucleotide polymorphisms; SPECT = single-photon emission computed tomography. Conclusions A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients. In this, light primary systemic therapy is advantageous because it may explore the interaction between tumor biology and treatment, and provides early information of treatment efficacy and also resistance pathways. However, there is also an important need to develop robust biomarkers that are based on standardized good clinical practice-level assays, as the failure to do this even for Ki67 has held back their widespread application for monitoring response to therapy (26). Thus, we need to proceed with: Identification of assays that are quantifiable with consistency in a diagnostic pathology laboratory Availability of testing or identification platforms such as IHC, fluorescence testing, quantitative reverse transcriptase-polymerase chain reaction, sequencing, etc. or other imaging modality Demonstration and further validation of the relation between the marker and the disease mechanism, the pharmacological actions of a medicinal product and relationship of short- and long-term therapy responses. References 1. Perou CM,  Sorlie T,  Eisen MB, et al.  Molecular portraits of human breast tumours,  Nature. ,  2000, vol.  406  6797(pg.  747- 752) 2. Sotiriou C,  Pusztai L.  Gene-expression signatures in breast cancer,  N Engl J Med. ,  2009, vol.  360  8(pg.  790- 800) 3. Sorlie 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, vol.  98  19(pg.  10869- 10874) 4. van ’t Veer LJ,  Dai H,  van de Vijver MJ, et al.  Gene expression profiling predicts clinical outcome of breast cancer,  Nature. ,  2002, vol.  415  6871(pg.  530- 536) 5. Slodkowska EA,  Ross JS.  MammaPrint 70-gene signature: another milestone in personalized medical care for breast cancer patients,  Expert Rev Mol Diagn. ,  2009, vol.  9  5(pg.  417- 422) 6. Paik S,  Tang G,  Shak S, et al.  Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer,  J Clin Oncol. ,  2006, vol.  24  23(pg.  3726- 3734) 7. Bogaerts J,  Cardoso F,  Buyse M, et al.  Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial,  Nat Clin Pract Oncol. ,  2006, vol.  3  10(pg.  540- 551) 8. Sparano JA.  TAILORx: trial assigning individualized options for treatment (Rx),  Clin Breast Cancer. ,  2006, vol.  7  4(pg.  347- 350) 9. Barker AD,  Sigman CC,  Kelloff GJ, et al.  I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy,  Clin Pharmacol Ther. ,  2009, vol.  86  1(pg.  97- 100) 10. Ring BZ,  Seitz RS,  Beck R, et al.  Novel prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer,  J Clin Oncol. ,  2006, vol.  24  19(pg.  3039- 3047) 11. Geyer FC,  Marchio C,  Reis-Filho JS.  The role of molecular analysis in breast cancer,  Pathology. ,  2009, vol.  41  1(pg.  77- 88) 12. Jones DN,  McCowage GB,  Sostman HD, et al.  Monitoring of neoadjuvant therapy response of soft-tissue and musculoskeletal sarcoma using fluorine-18-FDG PET,  J Nucl Med. ,  1996, vol.  37  9(pg.  1438- 1444) 13. Generali D,  Bates G,  Berruti A, et al.  Immunomodulation of FOXP3+ regulatory T cells by the aromatase inhibitor letrozole in breast cancer patients,  Clin Cancer Res. ,  2009, vol.  15  3(pg.  1046- 1051) 14. Generali D,  Buffa FM,  Berruti A, et al.  Phosphorylated ERalpha, HIF-1alpha, and MAPK signaling as predictors of primary endocrine treatment response and resistance in patients with breast cancer,  J Clin Oncol. ,  2009, vol.  27  2(pg.  227- 234) 15. Generali D,  Fox SB,  Brizzi MP, et al.  Down-regulation of phosphatidylinositol 3’-kinase/AKT/molecular target of rapamycin metabolic pathway by primary letrozole-based therapy in human breast cancer,  Clin Cancer Res. ,  2008, vol.  14  9(pg.  2673- 2680) 16. Baker AF,  Dragovich T,  Ihle NT, et al.  Stability of phosphoprotein as a biological marker of tumor signaling,  Clin Cancer Res. ,  2005, vol.  11  12(pg.  4338- 4340) 17. Espina V,  Edmiston KH,  Heiby M, et al.  A portrait of tissue phosphoprotein stability in the clinical tissue procurement process,  Mol Cell Proteomics. ,  2008, vol.  7  10(pg.  1998- 2018) 18. Liedtke C,  Wang J,  Tordai A, et al.  Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines,  Breast Cancer Res Treat. ,  2010, vol.  121  2(pg.  301- 309) 19. Jones RL,  Salter J,  A’Hern R, et al.  Relationship between oestrogen receptor status and proliferation in predicting response and long-term outcome to neoadjuvant chemotherapy for breast cancer,  Breast Cancer Res Treat. ,  2010, vol.  119  2(pg.  315- 323) 20. Harris L,  Fritsche H,  Mennel R, et al.  American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer,  J Clin Oncol. ,  2007, vol.  25  33(pg.  5287- 5312) 21. Carney WP,  Neumann R,  Lipton A, et al.  Potential clinical utility of serum HER-2/neu oncoprotein concentrations in patients with breast cancer,  Clin Chem. ,  2003, vol.  49  10(pg.  1579- 1598) 22. Smith BM,  Slade MJ,  English J, et al.  Response of circulating tumor cells to systemic therapy in patients with metastatic breast cancer: comparison of quantitative polymerase chain reaction and immunocytochemical techniques,  J Clin Oncol. ,  2000, vol.  18  7(pg.  1432- 1439) 23. Cristofanilli M,  Braun S.  Circulating tumor cells revisited,  JAMA. ,  2010, vol.  303  11(pg.  1092- 1093) 24. Salter KH,  Acharya CR,  Walters KS, et al.  An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer,  PLoS One. ,  2008, vol.  3  4pg.  e1908  25. Lawrie CH,  Gal S,  Dunlop HM, et al.  Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma,  Br J Haematol. ,  2008, vol.  141  5(pg.  672- 675) 26. Gonzalez-Angulo AM,  Hennessy BT,  Mills GB.  Future of personalized medicine in oncology: a systems biology approach,  J Clin Oncol. ,  2010, vol.  28  16(pg.  2777- 2783) © The Author 2011. Published by Oxford University Press. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JNCI Monographs Oxford University Press

Molecular Oncology and the Neoadjuvant Setting: the Perfect Blend for Treatment Personalization and Clinical Trial Design

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Oxford University Press
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© The Author 2011. Published by Oxford University Press.
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Abstract

Abstract Breast cancer is a heterogeneous disease. Predictive molecular markers are crucial in patient management, but the only recommended predictive biomarkers are estrogen and progesterone receptors and HER2. There are many new targeted therapies, and although the target pathway expression is readily analyzed on conventional pathology, the dynamic response cannot be assessed and pathway expression is no guarantee it has a major driver role, even if mutated. Selecting therapies requires considering the patient, the molecular characteristics of the tumor, and the microenvironment of the tumor. Thus, the integration of molecular pathology, imaging, and early tumor biological response to therapy may provide evidence of drug activity and allow more rapid changes of therapy. The adaptive response of the tumor is a key resistance mechanism that can be assessed readily in the neoadjuvant setting. Although there are no markers that meet all surrogacy criteria, their use could provide crucial information on mechanisms of drug sensitivity/resistance. Validation of such markers requires a major emphasis on neoadjuvant trials to relate early-biomarker response to outcome. Cancer is a complex and dynamic disease with a variable molecular portrait. To treat cancer more effectively, we need to understand the biological mechanisms driving it. Advances in molecular profiling technologies (including proteomic profiling, metabolomic analysis, and genetic testing), and new imaging methods should allow for a greater degree of personalized medicine. Relating baseline expression profiles and imaging of the microenvironment to therapeutic responses and outcome for each patient may allow optimized drug therapy and more rapid drug development. Serial biopsies to assess the early response on proliferation and adaptive responses should give new insights to guide therapy and develop predictive markers. This review will focus on the analyses available from biopsies. Molecular Oncology Approaches Gene Expression Profile Several studies based on gene expression profiles have provided a molecular classification of breast cancer into clinically relevant subtypes such as basal-like subtype (estrogen receptor [ER]-negative, HER2-negative, basal cytokeratins positive), the ERBB2 subtype (HER2-positive), and luminal subtype (ER-positive) subdivided into luminal A (with high level of expression ER) and luminal B (with lower levels of ER and high expression of genes related to proliferation) (1–4). There are two gene-predictor tests, mainly for prognosis rather than response to a specific therapy, already in use. MammaPrint Test, approved for marketing in United States and European community in 2007, is used in frozen samples of stage I or II breast cancer, lymph node negative and positive. MammaPrint is used as a prognostic test along with the conventional clinicopathologic factors. Test results are reported as low risk (13% chance to develop distant metastases at 10 years without adjuvant treatment) or high risk (56% chance to develop distant metastases at 10 years without adjuvant treatment) (5). Oncotype DX Breast Cancer Assay is used in association with conventional risk assessments to predict the likelihood of distant recurrence in women with stage I or II breast cancer (paraffin-embedded samples), lymph node negative and positive and ER-positive, who will be treated with tamoxifen. The low- (<18), intermediate- (18–30), and high-risk (≥31) categories are stated to correspond to 10-year distant recurrence rates after 5 years of tamoxifen therapy of less than 12%, 12%–21%, and 21%–33%, respectively (6). However, the prognostic roles of MammaPrint and Oncotype DX have been validated retrospectively but not yet prospectively. In the neoadjuvant context, three articles have shown an independent predictive role in pathological complete response (pCR). MINDACT (Microarray in Node-negative Disease may Avoid ChemoTherapy) and TAILORx (Trial Assigning IndividuaLized Options for Treatment) (7,8) are ongoing adjuvant studies, which will require 3–5 years for an outcome assessment. The neoadjuvant context is a more powerful model for the identification or validation of new prognostic molecular markers with many advantages including a requirement of small numbers of patients (hundreds rather than thousands) and an accelerated timeline because tumor response is used as a surrogate of survival. A hindrance of this model is that it is actually based on pCR as a surrogate parameter of efficacy, and additional new potential surrogates are needed. A prospective study–such as the I-SPY 2 (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2)–based on immunohistochemistry (IHC) and gene expression profile, able to give efficient information about each drug’s biomarker signature in relation to each single patient, is ongoing. The I-SPY 2 trial is a clinical trial for women with newly-diagnosed locally advanced breast cancer to test multiple investigational drugs that are thought to target the biology of each participant’s tumor. The I-SPY 2 trial will test the idea of tailoring treatment by using molecular tests to help identify which patients should be treated with investigational drugs (9). IHC Signature Mammostrat (Applied Genomics, Inc, Huntsville, AL) is based on the expression of five genes (p53, NDRG1, CEACAM5, SLC7A5, and HTF9C), which have been demonstrated to significantly improve prediction of outcome in ER-positive breast cancer patients (10). Protein Expression (IHC) Profile The routine determination by IHC of ER, progesterone receptor, HER2, and the proliferation marker Ki67 can be used to mimic the gene signature subtype classification with several advantages, such as low costs, the relation to cancer cell morphology, the use on paraffinized samples, and the availability and standardization of the methodology. A quality control scheme for Ki67 is currently being developed (M. Dowsett, personal communication). This panel can give both prognostic and predictive information to treatment response/resistance (11). However, to identify new molecular markers that mediate sensitivity and resistance to chemoendocrine therapies, our group used the tissue micro-array (12) approach applied in the neoadjuvant clinical trial setting (13–15). The analyses were based on a multivariate generalized linear regression using a penalized least-square minimization to perform variable selection and regularization. We identified that the activated form of ERα was a robust independent factor for sensitivity to chemoendocrine treatment, whereas hypoxia inducible factor-1α and p42/44 mitogen activated protein kinase were independent factors for resistance (14). Regarding this technique and the role of phosphoprotein in clinic, phosphorylated proteins are frequently used to assess the activity of intracellular signaling pathways and the effects of cancer drugs to inhibit these pathways. It is important to emphasize that phosphorylation of proteins is a posttranslational event modulating the activity or subcellular localization of many key signaling molecules in the cell; thus, it is crucial to have a knowledge of the stability of the protein phosphorylation before the tumor is fixed to prevent dephosphorylation of the marker. The stability of phosphoproteins may depend on the protein being studied and on the tissue or tumor type. However, rapid processing would seem to be essential for any study using phosphoprotein to measure signaling activity or drug inhibition, and postoperative surgical samples may be of limited value for measuring phosphoprotein levels (16,17). Pharmacogenomics and Pharmacodynamics To date, the evidence for the utility of pharmacogenomics in selecting breast cancer chemotherapy is minimal. Recent in vitro findings showed that cell line–derived predictors of response to four commonly used chemotherapy drugs as taxane, anthracyclines, 5-fluorouracil, and cyclophosphamide did not predict response accurately in patients (18). However, validation in large prospective studies to determine if pharmacogenomics would be a useful marker in the clinical setting are warranted. Our group is developing a pharmacogenomics tumor ID card combined with gene expression arrays, immunohistochemical, clinical, and radiological data in letrozole-based therapy in neoadjuvant prospective trial to evaluate its predictive role in selecting responders vs nonresponders. In our ongoing trials, we are also combining the biological data with Ki67 evaluation before and at 2–3 weeks, as it is an ideal integration of proteomics/genomics and pharmacodynamics because an array profile and Ki67 response have already been related to long-term outcome (19). This showed that it was the dynamic response and not baseline expression that was predictive. So, this dynamic response is an important paradigm for future trials. We have used Ki67 expression and fluorine-18 fluorodeoxyglucose positron emission tomography imaging in a neoadjuvant trial of letrozole randomized ± sorafenib (Generali D, unpublished data). This showed concordance of changes in standardized uptake value and changes in Ki67 and provides evidence for this approach to rapidly assess those benefitting from it. Circulating Markers There is currently a major effort to identify plasma and serum biomarkers. The American Society of Clinical Oncology Panel reviewed the literature on the use of several tumor markers in breast cancer, such as CA15-3, CEA, or serum HER2, in prevention, screening, treatment response, and surveillance (20,21). However, it concluded that these markers should now be confirmed in a large prospective trial before using them routinely for monitoring treatment response. Detection of Circulating Tumor Cells Circulating tumor cells may be an indicator for therapeutic efficacy and is licensed to predict patient outcome (22). Their prognostic value is independent of the line of therapy, site of metastasis, and subtype of disease. Moreover, circulating tumor cells have been recently associated with worse outcomes in patients with primary breast cancer after completing neoadjuvant or adjuvant therapy. These findings suggest the need to further investigate the biology of circulating tumor cells (23), which can also be used for molecular profiling and gene amplification studies. Recent data suggest that microRNA expression may be useful in further dissecting the phenomenon of chemotherapy and endocrine resistance and define new approaches (24). MicroRNA can be detected in peripheral blood associated with exosomes and may be prognostic (25). Requirements for Clinical Trial Design Biopsies to test an individual patient’s tumor for genomic and protein profiles are essential to improving our knowledge of tumor biology and changes in treatment. Mutational profiles would only be the first step; biopsies should then be performed early during therapy to monitor pharmacodynamic markers and predict responsiveness as well as at recurrence or progression to understand mechanisms of resistance. Trials will need to be based on Bayesian designs where data will be used to modify dosing or other parameters. The Bayesian approach allows for faster learning via more effective trial designs and more efficient drugs, while at the same time providing better treatment for patients who participate in the trial. Regimens showing a high Bayesian predictive probability of being effective will be continued with their biomarker signatures. Regimens with low probability of being effective will be dropped. The key points the future trials for personalized molecular medicine in cancer will be based on are: Clinical trial design that attempts to reduce costs and determine efficacy rapidly, such as neoadjuvant phase II trials in women with large primary cancers of the breast (>3.0 cm) Identify improved neoadjuvant treatment regimens for patient subsets on the basis of molecular characteristics (biomarker signatures), repeat early biopsies and imaging Compare efficacy of novel drugs in combination with standard adjuvant chemotherapy. Our unit is promoting a new approach in neoadjuvant trials with the MOON Trial (Molecular Oncology Optimising Neoadjuvant Trial) in breast cancer (Figure 1). Figure 1 View largeDownload slide Schema of the MOON Trial (Molecular Oncology Optimising Neoadjuvant Trial), which is based on molecular marker–driven randomized trial. Patients have a baseline biopsy, molecular profile, and molecular imaging, and are stratified according to breast cancer subtypes. They will receive the standard therapy plus the proper targeted therapy directed to the markers of interest present on the identified molecular subtype. A second biopsy, molecular profile, and molecular imaging will be performed at 14 days for endocrine-based therapy and at 28 days for chemotherapy-based therapy to study pharmacodynamic profile and correlate it to clinical/radiological response to identify resistant or responsive tumors. In case of resistance, according to the new tumor biology, a new therapeutic approach will be introduced. Definitive surgery will be performed at the clinical complete response (as a marker of treatment activity). CTCs = circulating tumor cells; MRI = magnetic resonance imaging; PET-CT = positron emission tomography–computed tomography; SNPs = single-nucleotide polymorphisms; SPECT = single-photon emission computed tomography. Figure 1 View largeDownload slide Schema of the MOON Trial (Molecular Oncology Optimising Neoadjuvant Trial), which is based on molecular marker–driven randomized trial. Patients have a baseline biopsy, molecular profile, and molecular imaging, and are stratified according to breast cancer subtypes. They will receive the standard therapy plus the proper targeted therapy directed to the markers of interest present on the identified molecular subtype. A second biopsy, molecular profile, and molecular imaging will be performed at 14 days for endocrine-based therapy and at 28 days for chemotherapy-based therapy to study pharmacodynamic profile and correlate it to clinical/radiological response to identify resistant or responsive tumors. In case of resistance, according to the new tumor biology, a new therapeutic approach will be introduced. Definitive surgery will be performed at the clinical complete response (as a marker of treatment activity). CTCs = circulating tumor cells; MRI = magnetic resonance imaging; PET-CT = positron emission tomography–computed tomography; SNPs = single-nucleotide polymorphisms; SPECT = single-photon emission computed tomography. Conclusions A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients. In this, light primary systemic therapy is advantageous because it may explore the interaction between tumor biology and treatment, and provides early information of treatment efficacy and also resistance pathways. However, there is also an important need to develop robust biomarkers that are based on standardized good clinical practice-level assays, as the failure to do this even for Ki67 has held back their widespread application for monitoring response to therapy (26). Thus, we need to proceed with: Identification of assays that are quantifiable with consistency in a diagnostic pathology laboratory Availability of testing or identification platforms such as IHC, fluorescence testing, quantitative reverse transcriptase-polymerase chain reaction, sequencing, etc. or other imaging modality Demonstration and further validation of the relation between the marker and the disease mechanism, the pharmacological actions of a medicinal product and relationship of short- and long-term therapy responses. References 1. Perou CM,  Sorlie T,  Eisen MB, et al.  Molecular portraits of human breast tumours,  Nature. ,  2000, vol.  406  6797(pg.  747- 752) 2. Sotiriou C,  Pusztai L.  Gene-expression signatures in breast cancer,  N Engl J Med. ,  2009, vol.  360  8(pg.  790- 800) 3. Sorlie 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, vol.  98  19(pg.  10869- 10874) 4. van ’t Veer LJ,  Dai H,  van de Vijver MJ, et al.  Gene expression profiling predicts clinical outcome of breast cancer,  Nature. ,  2002, vol.  415  6871(pg.  530- 536) 5. Slodkowska EA,  Ross JS.  MammaPrint 70-gene signature: another milestone in personalized medical care for breast cancer patients,  Expert Rev Mol Diagn. ,  2009, vol.  9  5(pg.  417- 422) 6. Paik S,  Tang G,  Shak S, et al.  Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer,  J Clin Oncol. ,  2006, vol.  24  23(pg.  3726- 3734) 7. Bogaerts J,  Cardoso F,  Buyse M, et al.  Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial,  Nat Clin Pract Oncol. ,  2006, vol.  3  10(pg.  540- 551) 8. Sparano JA.  TAILORx: trial assigning individualized options for treatment (Rx),  Clin Breast Cancer. ,  2006, vol.  7  4(pg.  347- 350) 9. Barker AD,  Sigman CC,  Kelloff GJ, et al.  I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy,  Clin Pharmacol Ther. ,  2009, vol.  86  1(pg.  97- 100) 10. Ring BZ,  Seitz RS,  Beck R, et al.  Novel prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer,  J Clin Oncol. ,  2006, vol.  24  19(pg.  3039- 3047) 11. Geyer FC,  Marchio C,  Reis-Filho JS.  The role of molecular analysis in breast cancer,  Pathology. ,  2009, vol.  41  1(pg.  77- 88) 12. Jones DN,  McCowage GB,  Sostman HD, et al.  Monitoring of neoadjuvant therapy response of soft-tissue and musculoskeletal sarcoma using fluorine-18-FDG PET,  J Nucl Med. ,  1996, vol.  37  9(pg.  1438- 1444) 13. Generali D,  Bates G,  Berruti A, et al.  Immunomodulation of FOXP3+ regulatory T cells by the aromatase inhibitor letrozole in breast cancer patients,  Clin Cancer Res. ,  2009, vol.  15  3(pg.  1046- 1051) 14. Generali D,  Buffa FM,  Berruti A, et al.  Phosphorylated ERalpha, HIF-1alpha, and MAPK signaling as predictors of primary endocrine treatment response and resistance in patients with breast cancer,  J Clin Oncol. ,  2009, vol.  27  2(pg.  227- 234) 15. Generali D,  Fox SB,  Brizzi MP, et al.  Down-regulation of phosphatidylinositol 3’-kinase/AKT/molecular target of rapamycin metabolic pathway by primary letrozole-based therapy in human breast cancer,  Clin Cancer Res. ,  2008, vol.  14  9(pg.  2673- 2680) 16. Baker AF,  Dragovich T,  Ihle NT, et al.  Stability of phosphoprotein as a biological marker of tumor signaling,  Clin Cancer Res. ,  2005, vol.  11  12(pg.  4338- 4340) 17. Espina V,  Edmiston KH,  Heiby M, et al.  A portrait of tissue phosphoprotein stability in the clinical tissue procurement process,  Mol Cell Proteomics. ,  2008, vol.  7  10(pg.  1998- 2018) 18. Liedtke C,  Wang J,  Tordai A, et al.  Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines,  Breast Cancer Res Treat. ,  2010, vol.  121  2(pg.  301- 309) 19. Jones RL,  Salter J,  A’Hern R, et al.  Relationship between oestrogen receptor status and proliferation in predicting response and long-term outcome to neoadjuvant chemotherapy for breast cancer,  Breast Cancer Res Treat. ,  2010, vol.  119  2(pg.  315- 323) 20. Harris L,  Fritsche H,  Mennel R, et al.  American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer,  J Clin Oncol. ,  2007, vol.  25  33(pg.  5287- 5312) 21. Carney WP,  Neumann R,  Lipton A, et al.  Potential clinical utility of serum HER-2/neu oncoprotein concentrations in patients with breast cancer,  Clin Chem. ,  2003, vol.  49  10(pg.  1579- 1598) 22. Smith BM,  Slade MJ,  English J, et al.  Response of circulating tumor cells to systemic therapy in patients with metastatic breast cancer: comparison of quantitative polymerase chain reaction and immunocytochemical techniques,  J Clin Oncol. ,  2000, vol.  18  7(pg.  1432- 1439) 23. Cristofanilli M,  Braun S.  Circulating tumor cells revisited,  JAMA. ,  2010, vol.  303  11(pg.  1092- 1093) 24. Salter KH,  Acharya CR,  Walters KS, et al.  An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer,  PLoS One. ,  2008, vol.  3  4pg.  e1908  25. Lawrie CH,  Gal S,  Dunlop HM, et al.  Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma,  Br J Haematol. ,  2008, vol.  141  5(pg.  672- 675) 26. Gonzalez-Angulo AM,  Hennessy BT,  Mills GB.  Future of personalized medicine in oncology: a systems biology approach,  J Clin Oncol. ,  2010, vol.  28  16(pg.  2777- 2783) © The Author 2011. Published by Oxford University Press.

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JNCI Monographs – Oxford University Press

Published: Oct 1, 2011

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