ASCO/CAP Guideline Recommendations for IHC Testing of ER and PgR in Breast Cancer Arch Pathol Lab Med, 134:907-22, 2010 Journal of Clinical Oncology, 28:2784-2795, 2010 D. Craig Allred, M.D. Department of Pathology and Immunology Journal of Clinical Oncology, 28:2784-2795, 2010
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ASCO/CAP Guideline Recommendations for IHC
Testing of ER and PgR in Breast Cancer
Arch Pathol Lab Med, 134:907-22, 2010
Journal of Clinical Oncology, 28:2784-2795, 2010
D. Craig Allred, M.D.
Department of Pathology and Immunology
Journal of Clinical Oncology, 28:2784-2795, 2010
ASCO/CAP Guidelines for IHC Testing of
ER and PgR in Breast Cancer
Invasive Breast Cancers (Mandatory)
Ductal Carcinoma In Situ (Optional)
IHC Assays for ER and PgRMust be Comprehensively Validated
Quantitative Scoring of ResultsPercent or Proportion of Positive Cells
Intensity of Positive Cells
≥ 1% Expressing Cells
"Positive"Expect 70-80%
Responsive
to Endocrine Therapy
Interpretation of ResultsCalibrated to Response to Endocrine Therapy
Report ResultsScores
Interpretation
<1% Expressing Cells
"Negative"Expect 20-30%Not Responsive
to Endocrine Therapy
Retest and Confirm If*:
External Control Negative
Internal Control Negative
Low Histological Grade**
Lobular Subtype**
Tubular Subtype**
Mucinous Subtype**
Other…
Comprehensive Ongoing:
Quality Assurance
Proficiency Testing
*Probably single most helpful reccommedation for improving accuracy**Not required if internal control positive
What is Comprehensive Validation?
Technical: The assay should be specific, sensitive, reproducible,
calibrated to clinical outcome, interpreted, and reported in a relativelyuniform manner. There should be comprehensive ongoing qualityassurance.
Clinical: The factor should identify groups of patients with significantly
J Natl Cancer Inst. 1991;83:154.
Cancer. 1993;72:3131.
Arch Pathol Lab Med. 1995;119:1109.
J Clin Oncol. 1996;14:2843.
J Natl Cancer Inst. 1996;88:1456.
different risks of relapse, survival, or treatment response – demonstratedin multiple large studies (ideally randomized clinical trials).
Useful: Actually used by physicians to make important treatment
American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogenand progesterone receptors in breast cancer. Arch Pathol Lab Med. 2010 Jun;134(6):907-22; JCO, 28:2784-2795, 2010
Recommendations for validating estrogen and progesterone receptor immunohistochemistry assays. Arch Pathol Lab Med 134(6):930-5, 2010
Current issues in ER and HER2 testing by IHC in breast cancer. Mod Pathol. 2008 May;21 Suppl 2:S8-S15
Estrogen receptor analysis. Correlation of biochemical and immunohistochemical methods of using monocloncal antireceptor antibodies. Arch Pathol Lab Med. 1985 Aug;109(8):716-21 (first important publication)
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 May;17(5):1474-81
Re-evaluating adjuvant breast cancer trials: assessing hormone receptor status by immunohistochemical versus extraction assays. JNCI. 2006 Nov 1;98(21):1571-81.Nov 1;98(21):1571-81.
Prognostic and predictive value of centrally reviewed expression of estrogen and progesterone receptors in a randomized trial comparing letrozoleand tamoxifen adjuvant therapy for postmenopausal early breast cancer: BIG 1-98. J Clin Oncol. 2007 Sep 1;25(25):3846-52.
Chemoendocrine compared with endocrine adjuvant therapies for node-negative breast cancer: predictive value of centrally reviewed expression of estrogen and progesterone receptors--International Breast Cancer Study Group. J Clin Oncol. 2008 Mar 20;26(9):1404-10.
Immunohistochemical detection using the new rabbit monoclonal antibody SP1 of estrogen receptor in breast cancer is superior to mouse monoclonal antibody 1D5 in predicting survival. J Clin Oncol. 2006 Dec 20;24(36):5637-44. Epub 2006 Nov 20.
Development of standard estrogen and progesterone receptor immunohistochemical assays for selection of patients for antihormonal therapy. Appl Immunohistochem Mol Morphol. 2007 Sep;15(3):325-31.
Relationship between quantitative estrogen and progesterone receptor expression and human epidermal growth factor receptor 2 (HER-2) status with recurrence in the Arimidex, Tamoxifen, Alone or in Combination trial. J Clin Oncol. 2008 Mar 1;26(7):1059-65. Epub 2008 Jan 28.
Progesterone receptor by immunohistochemistry and clinical outcome in breast cancer: a validation study. Mod Pathol. 2004 Dec;17(12):1545-54.
Resistance to Hormone Therapies in
Breast Cancer: Mechanisms and
Clinical Implications
C. Kent Osborne C. Kent Osborne
Dan L. Duncan Cancer Center
Lester and Sue Smith Breast Center
Baylor College of Medicine, Houston, TX
Estrogen Action in Normal and
Cancer
• Requires binding of E to the estrogen receptor (ER).
• ER is predominantly a nuclear protein that acts as a
transcription factor.
• ER also has a non-nuclear function to activate growth • ER also has a non-nuclear function to activate growth
Acquired Resistance to Tamoxifen is Associated with Increased Levels of
EGFR and HER2
0
200
400
600
800
Days
Tam
MCF7
MCF7/HER2
Tam-S Tam-R
EGFR
HER2
+E2
Massarweh et al., SABCS 2004
(Benz et al., Breast Cancer Res Treat, 1992)
800
1000
1200
1400
Tum
or volum
e E2 TAM
E2+gefitinib
Overcoming Tam Resistance with Gefitinib in HER2-
Positive Tumors
0
200
400
600
800
Days
Tum
or volum
e
E2+gefitinib
TAM+gefitinib
1 30 60 90 120
Shou J, JNCI 2004Massarweh S, ASCO 2002
Hypothesis: GefitinibR is due to incomplete
blockade of the HER signaling pathway
(all HER dimer pairs).
Reversal of Tam Resistance with Gefitinib in
HER2-Negative Tumors
800
1000
1200
1400
Tum
or volum
e
E2E2+ gefitinib
TAM
0
200
400
600
800
Days
Tum
or volum
e
TAM+gefitinib
Massarweh et al., SABCS 2002
Conversion from HER2- to HER2+
Study % Conversion
Gutierrez (tumor) 12
Uhr (CTCs) 37Uhr (CTCs) 37
Lipton (serum) 26
Most converters had intervening endo Rx
tamoxifen 20 mg / day +
gefitinib 250 mg / day
0225 trial: study design
Patients
• Postmenopausal women
• Age ≥ 18 years
• Stratum 1: Newly diagnosed ER- and / or PgR-positive MBC or disease recurring after adjuvant tamoxifen,
Primary• PFS in Stratum 1• CBR (CR + PR + SD for ≥24 weeks using RECIST)in Stratum 2
Response variables
N=290 (206 in Stratum 1 & 84 in Stratum 2)
tamoxifen 20 mg / day + placebo
1:1 randomisationadjuvant tamoxifen, completed ≥1 year before study entry
• Stratum 2: Disease recurring during or after AI therapy or who have failed first-line AI therapy for MBC
• PS 0-2
• No prior chemotherapy for metastatic disease
in Stratum 2
Secondary• CBR in Stratum 1• PFS in Stratum 2• ORR• PFS in patients with HER2-expressing tumours
• Safety and tolerability• PK
Osborne et al, manuscript submitted
Until disease progression or
other event requiring discontinuation
0225 trial: PFS in Stratum 1 patients
PFS similar over first 200 days
and KM curves then diverge
compatible with a delay in the
development of resistance similar
to that in preclinical models
A more substantial difference in
PFS observed for the HER2
positive subset of patients (n=37)
Osborne et al, manuscript submitted
anastrozole 1 mg / day + gefitinib 250 mg / day
0713 trial: study design
Patients
• Postmenopausal women
• Age ≥ 18 years
• Newly diagnosed ER-and / or PgR-positive metastatic breast cancer
Primary• PFS
Secondary
Response variables
N=94
anastrozole 1 mg / day +
placebo
1:1 randomisationcancer
• No prior hormonal therapy, or development of metastatic disease during / after adjuvant tamoxifen
• Measurable or non-measurable disease (via RECIST)
Secondary• ORR• CBR• OS• Safety and tolerability• Expression of biomarkersin tumour tissue sections
Cristofanilli et al, Abstract 1012, oral presentation, ASCO 2008
Until disease progression or
other event requiring discontinuation
Progression-free survival
Probability of PFS
1.0
0.8
0.6
EventsMedian PFS (months)
2214.5
328.2
Gefitinib +anastrozole
(n = 43)
Placebo +anastrozole
(n = 50)
HR (95% CI) = 0.55 (0.32, 0.94)
30
0.4
0.2
0.0
0 3 6 9 12 15 18 21 24 27
5043
3540
2328
1322
913
610
56
33
12 1
PlaceboGefitinib
At risk:Months
Reversal of Tam Resistance with Gefitinib in
HER2- Tumors
800
1000
1200
1400
Tum
or volum
e
E2E2+ gefitinib
TAM
0
200
400
600
800
Days
Tum
or volum
e
TAM+gefitinib
Massarweh et al., SABCS 2002
Other Escape Pathways Identified
1. Oxidative stress/increased AP1 activity
2. Integrin signaling/increased pSrc and pFac
3. PI3K/AKT pathway activation
Oxidative stress and cancer
•Oxidative stress has been defined as “a disturbance in the pro-oxidant-antioxidant balance in favor of the former, leading to potential damage” (Sies, 1991)
• There is increasing evidence that malignant cells are in a pro-oxidant state due to:
• increased formation of reactive oxygen species
• decreased antioxidant defenses (Halliwell B, Biochem J. 2007)
studies have demonstrated that there is intra-tumour genetic heterogeneity within ER-
positive cancers and that some ER-positive tumours harbour fusion genes [75]; however, no
fusion gene in ER-positive disease has been shown to be recurrent as yet.
ER-positive disease has been shown to harbour numerous gene mutations and the
repertoire is quite vast; it should be emphasised, however, that the majority of mutations
identified so far are present in a minority of lesions. One of the most prevalently mutated
genes in ER-positive breast cancers is PIK3CA [76], which is an integral component of the
PI3K-AKT-mTOR pathway. Interestingly, in vitro and clinical studies have suggested that
tumours with PIK3CA mutations may be sensitive to inhibitors of mTOR (e.g. rapalogs) and
small molecule inhibitors that inhibit PIK3CA, TORC1 and TORC2 [77].
CONCLUSION
ER positive disease comprises a spectrum of tumours, with varying degrees of proliferation
and levels of genetic aberrations. Proliferation as defined by microarray-based gene
signatures and OncotypeDxTM has been shown to be one of the main independent prognostic
markers for patients with ER-positive disease, and also to be a predictive marker of benefit
for addition of multi-drug chemotherapy to endocrine therapy. As a group, these tumours
respond to endocrine therapies, however a substantial proportion of cases are either de novo
resistant or develop resistance over time. Active research using high throughput methods is
11
currently being performed to identify the potential mechanisms of resistance to hormone
therapies and ways to circumvent them. Despite the enthusiasm with the use of the
molecular taxonomy for breast cancers and the terminology luminal A and luminal B [26],
recent studies have called into question the reproducibility of these subtypes [18, 30, 38]. In
fact, their clinical utility remains to be determined.
With the advent of massively parallel sequencing and the ability to characterise the entire
genomes of cancers, it is likely that the drivers of ER-positive disease will soon be identified.
It is anticipated that the repertoire of mutations in breast cancer will be vast with few
recurrent genetic lesions. Nevertheless, this deluge of information in conjunction with
functional genomic approaches may expedite the development of predictive classification
systems for ER-positive disease.
REFERENCES
1. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009; 360: 790-800.
2. Weigelt B, Baehner FL, Reis-Filho JS. The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. J Pathol 2010; 220: 263-280.
3. Weigelt B, Geyer FC, Reis-Filho JS. Histological types of breast cancer: how special are they? Mol Oncol 2010; 4: 192-208.
4. Weigelt B, Reis-Filho JS. Histological and molecular types of breast cancer: is there a unifying taxonomy? Nat Rev Clin Oncol 2009; 6: 718-730.
5. Weigel MT, Dowsett M. Current and emerging biomarkers in breast cancer: prognosis and prediction. Endocr Relat Cancer 2010; 17: R245-262.
6. Mook S, Schmidt MK, Rutgers EJ, et al. Calibration and discriminatory accuracy of prognosis calculation for breast cancer with the online Adjuvant! program: a hospital-based retrospective cohort study. Lancet Oncol 2009; 10: 1070-1076.
7. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747-752.
8. 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; 98: 10869-10874.
9. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003; 100: 8418-8423.
10. Hu Z, Fan C, Oh DS, et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006; 7: 96.
11. Parker JS, Mullins M, Cheang MC, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009; 27: 1160-1167.
12. Peppercorn J, Perou CM, Carey LA. Molecular subtypes in breast cancer evaluation and management: divide and conquer. Cancer Invest 2008; 26: 1-10.
13. Stingl J, Caldas C. Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis. Nat Rev Cancer 2007; 7: 791-799.
12
14. van 't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-536.
15. Sotiriou C, Neo SY, McShane LM, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 2003; 100: 10393-10398.
16. Correa Geyer F, Reis-Filho JS. Microarray-based gene expression profiling as a clinical tool for breast cancer management: are we there yet? Int J Surg Pathol 2009; 17: 285-302.
17. 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.
18. Weigelt B, Mackay A, A'Hern R, et al. Breast cancer molecular profiling: a retrospective analysis of molecular subtype assignment using single sample predictors. Lancet Oncol 2010; 11: 339-349.
19. Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med 2010; 363: 1938-1948.
20. Fulford LG, Easton DF, Reis-Filho JS, et al. Specific morphological features predictive for the basal phenotype in grade 3 invasive ductal carcinoma of breast. Histopathology 2006; 49: 22-34.
21. Livasy CA, Karaca G, Nanda R, et al. Phenotypic evaluation of the basal-like subtype of invasive breast carcinoma. Mod Pathol 2006; 19: 264-271.
22. Reis-Filho JS, Westbury C, Pierga JY. The impact of expression profiling on prognostic and predictive testing in breast cancer. J Clin Pathol 2006; 59: 225-231.
23. Turner NC, Reis-Filho JS, Russell AM, et al. BRCA1 dysfunction in sporadic basal-like breast cancer. Oncogene 2007; 26: 2126-2132.
24. Rakha EA, Reis-Filho JS, Ellis IO. Basal-like breast cancer: a critical review. J Clin Oncol 2008; 26: 2568-2581.
25. Carey LA, Perou CM, Livasy CA, et al. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 2006; 295: 2492-2502.
26. Nielsen TO, Parker JS, Leung S, et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res 2010; 16: 5222-5232.
27. Reis-Filho JS, Weigelt B, Fumagalli D, et al. Molecular profiling: moving away from tumor philately. Sci Transl Med 2010; 2: 47ps43.
28. Pusztai L, Mazouni C, Anderson K, et al. Molecular classification of breast cancer: limitations and potential. Oncologist 2006; 11: 868-877.
29. Kapp AV, Tibshirani R. Are clusters found in one dataset present in another dataset? Biostatistics 2007; 8: 9-31.
30. Haibe-Kains B, Culhane A, Desmedt C, et al. Robustness of breast cancer molecular subtypes identification. Ann Oncol 2010; 21: iv49-iv59.
31. Chang HY, Nuyten DS, Sneddon JB, et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A 2005; 102: 3738-3743.
32. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006; 355: 560-569.
33. Desmedt C, Haibe-Kains B, Wirapati P, et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res 2008; 14: 5158-5165.
34. Wirapati P, Sotiriou C, Kunkel S, et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 2008; 10: R65.
13
35. de Ronde JJ, Hannemann J, Halfwerk H, et al. Concordance of clinical and molecular breast cancer subtyping in the context of preoperative chemotherapy response. Breast Cancer Res Treat 2010; 119: 119-126.
36. Lim E, Vaillant F, Wu D, et al. Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers. Nat Med 2009; 15: 907-913.
37. Molyneux G, Geyer FC, Magnay FA, et al. BRCA1 basal-like breast cancers originate from luminal epithelial progenitors and not from basal stem cells. Cell Stem Cell 2010; 7: 403-417.
38. Weigelt B, Reis-Filho JS. Molecular profiling currently offers no more than tumour morphology and basic immunohistochemistry. Breast Cancer Res 2010; 12 Suppl 4: S5.
39. Desmedt C, Piette F, Loi S, et al. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res 2007; 13: 3207-3214.
40. Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005; 365: 671-679.
41. Ivshina AV, George J, Senko O, et al. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res 2006; 66: 10292-10301.
42. Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006; 98: 262-272.
43. Ein-Dor L, Zuk O, Domany E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci U S A 2006; 103: 5923-5928.
44. Michiels S, Koscielny S, Hill C. Interpretation of microarray data in cancer. Br J Cancer 2007; 96: 1155-1158.
46. Reyal F, van Vliet MH, Armstrong NJ, et al. A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer. Breast Cancer Res 2008; 10: R93.
47. Weigelt B, Horlings HM, Kreike B, et al. Refinement of breast cancer classification by molecular characterization of histological special types. J Pathol 2008; 216: 141-150.
48. 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.
49. 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; 24: 3726-3734.
50. Kim C, Paik S. Gene-expression-based prognostic assays for breast cancer. Nat Rev Clin Oncol 2010; 7: 340-347.
51. Paik S. Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with tamoxifen. Oncologist 2007; 12: 631-635.
52. Habel LA, Shak S, Jacobs MK, et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res 2006; 8: R25.
53. Goldstein LJ, Gray R, Badve S, et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol 2008; 26: 4063-4071.
14
54. Dowsett M, Cuzick J, Wale C, et al. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. J Clin Oncol 2010; 28: 1829-1834.
55. Gianni L, Zambetti M, Clark K, et al. Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. J Clin Oncol 2005; 23: 7265-7277.
56. Albain KS, Barlow WE, Shak S, et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol 2010; 11: 55-65.
57. 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; 25: 5287-5312.
58. Cuzick J, Dowsett M, Wale C, et al. Prognostic Value of a Combined ER, PgR, Ki67, HER2 Immunohistochemical (IHC4) Score and Comparison with the GHI Recurrence Score - Results from TransATAC. Cancer Res 2009; 69: 503S-503S (Abstract).
59. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001; 69: 89-95.
60. Knauer M, Mook S, Rutgers EJ, et al. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer. Breast Cancer Res Treat 2010; 120: 655-661.
61. Liedtke C, Hatzis C, Symmans WF, et al. Genomic grade index is associated with response to chemotherapy in patients with breast cancer. J Clin Oncol 2009; 27: 3185-3191.
62. Oakman C, Santarpia L, Di Leo A. Breast cancer assessment tools and optimizing adjuvant therapy. Nat Rev Clin Oncol 2010; 7: 725-732.
63. Symmans WF, Hatzis C, Sotiriou C, et al. Genomic index of sensitivity to endocrine therapy for breast cancer. J Clin Oncol 2010; 28: 4111-4119.
64. Natrajan R, Lambros MB, Rodriguez-Pinilla SM, et al. Tiling path genomic profiling of grade 3 invasive ductal breast cancers. Clin Cancer Res 2009; 15: 2711-2722.
65. Natrajan R, Weigelt B, Mackay A, et al. An integrative genomic and transcriptomic analysis reveals molecular pathways and networks regulated by copy number aberrations in basal-like, HER2 and luminal cancers. Breast Cancer Res Treat 2009: epub ahead of print.
66. Andre F, Job B, Dessen P, et al. Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array. Clin Cancer Res 2009; 15: 441-451.
67. Chin SF, Teschendorff AE, Marioni JC, et al. High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer. Genome Biol 2007; 8: R215.
68. Chin K, DeVries S, Fridlyand J, et al. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 2006; 10: 529-541.
69. Natrajan R, Lambros MB, Geyer FC, et al. Loss of 16q in high grade breast cancer is associated with estrogen receptor status: Evidence for progression in tumors with a luminal phenotype? Genes Chromosomes Cancer 2009; 48: 351-365.
70. Lopez-Garcia MA, Geyer FC, Lacroix-Triki M, et al. Breast cancer precursors revisited: molecular features and progression pathways. Histopathology 2010; 57: 171-192.
71. Flagiello D, Gerbault-Seureau M, Sastre-Garau X, et al. Highly recurrent der(1;16)(q10;p10) and other 16q arm alterations in lobular breast cancer. Genes Chromosomes Cancer 1998; 23: 300-306.
15
72. Tsuda H, Takarabe T, Fukutomi T, et al. der(16)t(1;16)/der(1;16) in breast cancer detected by fluorescence in situ hybridization is an indicator of better patient prognosis. Genes Chromosomes Cancer 1999; 24: 72-77.
73. Simpson PT, Reis-Filho JS, Gale T, et al. Molecular evolution of breast cancer. J Pathol 2005; 205: 248-254.
74. Bergamaschi A, Kim YH, Wang P, et al. Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer. Genes Chromosomes Cancer 2006; 45: 1033-1040.
75. Stephens PJ, McBride DJ, Lin ML, et al. Complex landscapes of somatic rearrangement in human breast cancer genomes. Nature 2009; 462: 1005-1010.
76. Stemke-Hale K, Gonzalez-Angulo AM, Lluch A, et al. An integrative genomic and proteomic analysis of PIK3CA, PTEN, and AKT mutations in breast cancer. Cancer Res 2008; 68: 6084-6091.
77. Janku F, Tsimberidou AM, Garrido-Laguna I, et al. PIK3CA Mutations in Patients with Advanced Cancers Treated with PI3K/AKT/mTOR Axis Inhibitor. Mol Cancer Ther 2011: Epub ahead of print.